diff --git a/user_data/strategies/MultiMa.py b/user_data/strategies/MultiMa.py
index 55e5273..8909b65 100644
--- a/user_data/strategies/MultiMa.py
+++ b/user_data/strategies/MultiMa.py
@@ -1,8 +1,7 @@
-# MultiMa Strategy
+# MultiMa Strategy V2
 # 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
@@ -17,72 +16,84 @@ from functools import reduce
 
 
 class MultiMa(IStrategy):
+    # 111/2000:     18 trades. 12/4/2 Wins/Draws/Losses. Avg profit   9.72%. Median profit   3.01%. Total profit  733.01234143 USDT (  73.30%). Avg duration 2 days, 18:40:00 min. Objective: 1.67048
 
-    buy_ma_count = IntParameter(0, 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')
+    # Buy hyperspace params:
+    buy_params = {
+        "buy_ma_count": 4,
+        "buy_ma_gap": 15,
+    }
 
-    sell_ma_count = IntParameter(0, 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')
+    # Sell hyperspace params:
+    sell_params = {
+        "sell_ma_count": 12,
+        "sell_ma_gap": 68,
+    }
 
     # ROI table:
-    minimal_roi = {"0": 0.30873, "569": 0.16689, "3211": 0.06473, "7617": 0}
+    minimal_roi = {
+        "0": 0.523,
+        "1553": 0.123,
+        "2332": 0.076,
+        "3169": 0
+    }
 
     # Stoploss:
-    stoploss = -0.1
+    stoploss = -0.345
 
-    # Buy hypers
+    # Trailing stop:
+    trailing_stop = False  # value loaded from strategy
+    trailing_stop_positive = None  # value loaded from strategy
+    trailing_stop_positive_offset = 0.0  # value loaded from strategy
+    trailing_only_offset_is_reached = False  # value loaded from strategy
+
+    # Opimal Timeframe
     timeframe = "4h"
 
-    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    count_max = 20
+    gap_max = 100
 
-        # 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
+    buy_ma_count = IntParameter(1, count_max, default=7, space="buy")
+    buy_ma_gap = IntParameter(1, gap_max, default=7, space="buy")
+
+    sell_ma_count = IntParameter(1, count_max, default=7, space="sell")
+    sell_ma_gap = IntParameter(1, gap_max, default=94, space="sell")
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        for count in range(self.count_max):
+            for gap in range(self.gap_max):
+                if count*gap > 1 and count*gap not in dataframe.keys():
+                    dataframe[count*gap] = ta.TEMA(
+                        dataframe, timeperiod=int(count*gap)
+                    )
+        print(" ", metadata['pair'], end="\t\r")
 
         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 = []
+        # I used range(self.buy_ma_count.value) instade of self.buy_ma_count.range
+        # Cuz it returns range(7,8) but we need range(8) for all modes hyperopt, backtest and etc
+
+        for ma_count in range(self.buy_ma_count.value):
+            key = ma_count*self.buy_ma_gap.value
+            past_key = (ma_count-1)*self.buy_ma_gap.value
+            if past_key > 1 and key in dataframe.keys() and past_key in dataframe.keys():
+                conditions.append(dataframe[key] < dataframe[past_key])
 
-        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)
-                    )
+        for ma_count in range(self.sell_ma_count.value):
+            key = ma_count*self.sell_ma_gap.value
+            past_key = (ma_count-1)*self.sell_ma_gap.value
+            if past_key > 1 and key in dataframe.keys() and past_key in dataframe.keys():
+                conditions.append(dataframe[key] > dataframe[past_key])
+
         if conditions:
-            dataframe.loc[reduce(lambda x, y: x & y, conditions), "sell"] = 1
+            dataframe.loc[reduce(lambda x, y: x | y, conditions), "sell"] = 1
         return dataframe