diff --git a/user_data/strategies/AfghanWoman.py b/user_data/strategies/AfghanWoman.py
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+++ b/user_data/strategies/AfghanWoman.py
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+# It is AfghanWoman Strategy.
+# That takes her own rights like Afghanstan women
+# Those who still proud and hopeful.
+# Those who the most beautiful creatures in the depths of the darkest.
+# Those who shine like diamonds buried in the heart of the desert ...
+# Why not help when we can?
+# If we believe there is no man left with them
+# (Which is probably the product of the thought of painless corpses)
+# Where has our humanity gone?
+# Where has humanity gone?
+# Why not help when we can?
+
+# Author: @Mablue (Masoud Azizi)
+# github: https://github.com/mablue/
+# (First Hyperopt it.A hyperopt file is available)
+# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --spaces buy sell --strategy AfghanWoman -j 2
+# freqtrade backtesting --strategy AfghanWoman
+
+# --- 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
+from functools import reduce
+
+##### SETINGS #####
+# It hyperopt just one set of params for all buy and sell strategies if true.
+DUALFIT = False
+# how much Candles to check trand
+TCC = 1
+### END SETINGS ###
+
+
+class AfghanWoman(IStrategy):
+    # ###################### RESULT PLACE ######################
+    # *    4/100:     80 trades. 28/0/52 Wins/Draws/Losses. Avg profit   2.64%. Median profit  -0.19%. Total profit  3366.29721754 USDT ( 336.63Σ%). Avg duration 9:09:00 min. Objective: -8.35746
+
+    # Buy hyperspace params:
+    buy_params = {
+        "buy_count": 2,
+        "buy_gap": 15,
+        "buy_shift": 14,
+    }
+
+    # Sell hyperspace params:
+    sell_params = {
+        "sell_count": 5,
+        "sell_gap": 13,
+        "sell_shift": 4,
+    }
+    # ROI table:
+    minimal_roi = {
+        "0": 1,
+        "13": 1,
+        "64": 1,
+        "178": 1
+    }
+    # Stoploss:
+    stoploss = -0.256
+
+    # Buy hypers
+    timeframe = '5m'
+    # #################### END OF RESULT PLACE ####################
+    buy_count = IntParameter(2, 25, default=6, space='buy')
+    buy_gap = IntParameter(2, 25, default=13, space='buy')
+    buy_shift = IntParameter(0, 25, default=14, space='buy')
+    if not DUALFIT:
+        sell_count = IntParameter(2, 25, default=20, space='sell')
+        sell_gap = IntParameter(2, 25, default=3, space='sell')
+        sell_shift = IntParameter(0, 25, default=0, space='sell')
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe['PLUS_DM'] = ta.PLUS_DM(dataframe, timeperiod=14)
+        dataframe['MINUS_DM'] = ta.MINUS_DM(dataframe, timeperiod=14)
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        conditions = []
+        conditions.append(dataframe['PLUS_DM'] > dataframe['MINUS_DM'])
+        count = gap = shift = None
+
+        count = self.buy_count.value
+        gap = self.buy_gap.value
+        shift = self.buy_shift.value
+
+        for i in range(1, count+1):
+            dataframe[f'buy-ma-{i}'] = ta.EMA(dataframe,
+                                              timeperiod=int(i * gap))
+            for s in range(1, shift+1):
+                if i > 1:
+                    conditions.append(
+                        dataframe[f'buy-ma-{i}'].shift(s) >
+                        dataframe[f'buy-ma-{i-1}'].shift(s)
+                    )
+        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:
+        conditions = []
+        conditions.append(dataframe['PLUS_DM'] <= dataframe['MINUS_DM'])
+
+        count = gap = shift = None
+
+        if DUALFIT:
+            count = self.buy_count.value
+            gap = self.buy_gap.value
+            shift = self.buy_shift.value
+        else:
+            count = self.sell_count.value
+            gap = self.sell_gap.value
+            shift = self.sell_shift.value
+        for i in range(1, count+1):
+            dataframe[f'buy-ma-{i}'] = ta.EMA(dataframe,
+                                              timeperiod=int(i * gap))
+            for s in range(1, shift+1):
+                if i > 1:
+                    conditions.append(
+                        dataframe[f'buy-ma-{i}'].shift(s) <
+                        dataframe[f'buy-ma-{i-1}'].shift(s)
+                    )
+
+        if conditions:
+            dataframe.loc[
+                reduce(lambda x, y: x & y, conditions),
+                'sell']=1
+        return dataframe