mabStra Strategy

This strategy try to find best buy/sell position by deviding Simple Moving Averages *You can use other indicators and check the result*
2021-04-08 22:11:30 +04:30
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+# author: Masoud Azizi @mablue
+
+# --- Do not remove these libs ---
+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
+
+FTF, STF = 5, 10
+
+
+class mabStra(IStrategy):
+
+    # 100/100:    727 trades. 486/191/50 Wins/Draws/Losses. Avg profit   3.53 % . Median profit   5.97 % . Total profit  1502.52014358 USDT (2566.80Σ %). Avg duration 1396.1 min. Objective: -15.62092
+
+    # Buy hyperspace params:
+    buy_params = {
+        'buy-div-max': 0.96451, 'buy-div-min': 0.22313
+    }
+
+    # Sell hyperspace params:
+    sell_params = {
+        'sell-div-max': 0.75476, 'sell-div-min': 0.16599
+    }
+
+    # ROI table:
+    minimal_roi = {
+        "0": 0.45574,
+        "307": 0.21971,
+        "428": 0.06762,
+        "1387": 0
+    }
+
+    # Stoploss:
+    stoploss = -0.34773
+
+    # Trailing stop:
+    trailing_stop = True
+    trailing_stop_positive = 0.01573
+    trailing_stop_positive_offset = 0.06651
+    trailing_only_offset_is_reached = True
+    # Optimal timeframe use it in your config
+    timeframe = '1h'
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # SMA - ex Moving Average
+        dataframe['buy-fastMA'] = ta.SMA(dataframe, timeperiod=FTF)
+        dataframe['buy-slowMA'] = ta.SMA(dataframe, timeperiod=STF)
+        dataframe['sell-fastMA'] = ta.SMA(dataframe, timeperiod=FTF)
+        dataframe['sell-slowMA'] = ta.SMA(dataframe, timeperiod=STF)
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+
+        dataframe.loc[
+            (
+                (dataframe['buy-fastMA'].div(dataframe['buy-slowMA'])
+                    > self.buy_params['buy-div-min']) &
+                (dataframe['buy-fastMA'].div(dataframe['buy-slowMA'])
+                    < self.buy_params['buy-div-max'])
+            ),
+            'buy'] = 1
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe.loc[
+            (
+                (dataframe['sell-slowMA'].div(dataframe['sell-fastMA'])
+                    > self.sell_params['sell-div-min']) &
+                (dataframe['sell-slowMA'].div(dataframe['sell-fastMA'])
+                    < self.sell_params['sell-div-max'])
+            ),
+            'sell'] = 1
+        return dataframe