diff --git a/user_data/strategies/MultiMa.py b/user_data/strategies/MultiMa.py
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+# MultiMa Strategy
+# Author: @Mablue (Masoud Azizi)
+# github: https://github.com/mablue/
+# (First Hyperopt it.A hyperopt file is available)
+#
+# --- Do not remove these libs ---
+from freqtrade.strategy.hyper import IntParameter
+from freqtrade.strategy.interface import IStrategy
+from pandas import DataFrame
+# --------------------------------
+
+# Add your lib to import here
+import talib.abstract as ta
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+from functools import reduce
+
+
+class MultiMa(IStrategy):
+
+    buy_ma_count = IntParameter(2, 10, default=10, space='buy')
+    buy_ma_gap = IntParameter(2, 10, default=2, space='buy')
+    buy_ma_shift = IntParameter(0, 10, default=0, space='buy')
+    # buy_ma_rolling = IntParameter(0, 10, default=0, space='buy')
+
+    sell_ma_count = IntParameter(2, 10, default=10, space='sell')
+    sell_ma_gap = IntParameter(2, 10, default=2, space='sell')
+    sell_ma_shift = IntParameter(0, 10, default=0, space='sell')
+    # sell_ma_rolling = IntParameter(0, 10, default=0, space='sell')
+
+    # ROI table:
+    minimal_roi = {
+        "0": 0.30873,
+        "569": 0.16689,
+        "3211": 0.06473,
+        "7617": 0
+    }
+
+    # Stoploss:
+    stoploss = -0.1
+
+    # Buy hypers
+    timeframe = '4h'
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+       
+        # We will dinamicly generate the indicators
+        # cuz this method just run one time in hyperopts
+        # if you have static timeframes you can move first loop of buy and sell trends populators inside this method
+
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        
+        for i in self.buy_ma_count.range:
+            dataframe[f'buy-ma-{i+1}'] = ta.SMA(dataframe,
+                                              timeperiod=int((i+1) * self.buy_ma_gap.value))
+        
+        conditions = []
+
+        for i in self.buy_ma_count.range:
+            if i > 1:
+                shift = self.buy_ma_shift.value
+                for shift in self.buy_ma_shift.range:
+                    conditions.append(
+                        dataframe[f'buy-ma-{i}'].shift(shift) >
+                        dataframe[f'buy-ma-{i-1}'].shift(shift)
+                    )
+        if conditions:
+            dataframe.loc[
+                reduce(lambda x, y: x & y, conditions),
+                'buy']=1
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        for i in self.sell_ma_count.range:
+            dataframe[f'sell-ma-{i+1}'] = ta.SMA(dataframe,
+                                               timeperiod=int((i+1) * self.sell_ma_gap.value))
+
+        conditions = []
+
+        for i in self.sell_ma_count.range:
+            if i > 1:
+                shift = self.sell_ma_shift.value
+                for shift in self.sell_ma_shift.range:
+                    conditions.append(
+                        dataframe[f'sell-ma-{i}'].shift(shift) <
+                        dataframe[f'sell-ma-{i-1}'].shift(shift)
+                    )
+        if conditions:
+            dataframe.loc[
+                reduce(lambda x, y: x & y, conditions),
+                'sell']=1
+        return dataframe