diff --git a/user_data/hyperopts/mabStraHo.py b/user_data/hyperopts/mabStraHo.py
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+# by: Mablue (Masoud Azizi)
+
+# --- Do not remove these libs ---
+from functools import reduce
+from typing import Any, Callable, Dict, List
+
+import numpy as np  # noqa
+import pandas as pd  # noqa
+from pandas import DataFrame
+from skopt.space import Categorical, Dimension, Integer, Real  # noqa
+
+from freqtrade.optimize.hyperopt_interface import IHyperOpt
+
+# --------------------------------
+# Add your lib to import here
+import talib.abstract as ta  # noqa
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+
+class mabStraHo(IHyperOpt):
+
+    @staticmethod
+    def indicator_space() -> List[Dimension]:
+        """
+        Define your Hyperopt space for searching buy strategy parameters.
+        """
+        return [
+            Real(0, 1, name='buy-div-min'),
+            Real(0, 1, name='buy-div-max'),
+        ]
+
+    @staticmethod
+    def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
+        """
+        Define the buy strategy parameters to be used by Hyperopt.
+        """
+        def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
+            """
+            Buy strategy Hyperopt will build and use.
+            """
+            conditions = []
+            # GUARDS AND TRENDS
+
+            # div result limited to 0~1 so allways in buy position slowMa is lower than fastMa
+            # optimum number will calculate by this two lines
+            conditions.append(dataframe['buy-fastMA'].div(dataframe['buy-slowMA'])
+                              > params['buy-div-min'])
+            conditions.append(dataframe['buy-fastMA'].div(dataframe['buy-slowMA'])
+                              < params['buy-div-max'])
+
+            if conditions:
+                dataframe.loc[
+                    reduce(lambda x, y: x & y, conditions),
+                    'buy'] = 1
+
+            return dataframe
+
+        return populate_buy_trend
+
+    @staticmethod
+    def sell_indicator_space() -> List[Dimension]:
+        """
+        Define your Hyperopt space for searching sell strategy parameters.
+        """
+        return [
+            Real(0, 1, name='sell-div-min'),
+            Real(0, 1, name='sell-div-max'),
+        ]
+
+    @staticmethod
+    def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
+        """
+        Define the sell strategy parameters to be used by Hyperopt.
+        """
+        def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
+            """
+            Sell strategy Hyperopt will build and use.
+            """
+            conditions = []
+
+            # GUARDS AND TRENDS
+
+            conditions.append(dataframe['sell-slowMA'].div(dataframe['sell-fastMA'])
+                              > params['sell-div-min'])
+            conditions.append(dataframe['sell-slowMA'].div(dataframe['sell-fastMA'])
+                              < params['sell-div-max'])
+
+            if conditions:
+                dataframe.loc[
+                    reduce(lambda x, y: x & y, conditions),
+                    'sell'] = 1
+
+            return dataframe
+
+        return populate_sell_trend