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Author: jilv220

SuperTrendPure.py

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+import logging
+from numpy.lib import math
+from freqtrade.strategy.interface import IStrategy
+from freqtrade.strategy.hyper import IntParameter
+from pandas import DataFrame
+import talib.abstract as ta
+import numpy as np
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+
+class SuperTrendPure(IStrategy):
+   
+    # ROI table:
+    minimal_roi = {
+        "0": 0.087,
+        "372": 0.058,
+        "861": 0.029,
+        "2221": 0
+    }
+    # Stoploss:
+    stoploss = -0.265
+
+    # Trailing stop:
+    trailing_stop = True
+    trailing_stop_positive = 0.05
+    trailing_stop_positive_offset = 0.144
+    trailing_only_offset_is_reached = False
+
+    timeframe = '1h'
+
+    startup_candle_count = 50
+    
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+       
+        supertrend = self.supertrend(dataframe, 2, 8)
+        dataframe['st'] = supertrend['ST']
+        dataframe['stx'] = supertrend['STX']
+        
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe.loc[
+            (
+               (qtpylib.crossed_above(dataframe['close'], dataframe['st'])) &
+               (dataframe['volume'].gt(0))
+            ),
+            'buy'] = 1
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe.loc[
+            (
+               (qtpylib.crossed_below(dataframe['close'], dataframe['st'])) &
+               (dataframe['volume'].gt(0))
+            ),
+            'sell'] = 1
+
+        return dataframe
+
+    """
+        Supertrend Indicator; adapted for freqtrade
+        from: https://github.com/freqtrade/freqtrade-strategies/issues/30
+    """
+    def supertrend(self, dataframe: DataFrame, multiplier, period):
+        df = dataframe.copy()
+
+        df['TR'] = ta.TRANGE(df)
+        df['ATR'] = ta.SMA(df['TR'], period)
+
+        st = 'ST_' + str(period) + '_' + str(multiplier)
+        stx = 'STX_' + str(period) + '_' + str(multiplier)
+
+        # Compute basic upper and lower bands
+        df['basic_ub'] = (df['high'] + df['low']) / 2 + multiplier * df['ATR']
+        df['basic_lb'] = (df['high'] + df['low']) / 2 - multiplier * df['ATR']
+
+        # Compute final upper and lower bands
+        df['final_ub'] = 0.00
+        df['final_lb'] = 0.00
+        for i in range(period, len(df)):
+            df['final_ub'].iat[i] = df['basic_ub'].iat[i] if df['basic_ub'].iat[i] < df['final_ub'].iat[i - 1] or df['close'].iat[i - 1] > df['final_ub'].iat[i - 1] else df['final_ub'].iat[i - 1]
+            df['final_lb'].iat[i] = df['basic_lb'].iat[i] if df['basic_lb'].iat[i] > df['final_lb'].iat[i - 1] or df['close'].iat[i - 1] < df['final_lb'].iat[i - 1] else df['final_lb'].iat[i - 1]
+
+        # Set the Supertrend value
+        df[st] = 0.00
+        for i in range(period, len(df)):
+            df[st].iat[i] = df['final_ub'].iat[i] if df[st].iat[i - 1] == df['final_ub'].iat[i - 1] and df['close'].iat[i] <= df['final_ub'].iat[i] else \
+                            df['final_lb'].iat[i] if df[st].iat[i - 1] == df['final_ub'].iat[i - 1] and df['close'].iat[i] >  df['final_ub'].iat[i] else \
+                            df['final_lb'].iat[i] if df[st].iat[i - 1] == df['final_lb'].iat[i - 1] and df['close'].iat[i] >= df['final_lb'].iat[i] else \
+                            df['final_ub'].iat[i] if df[st].iat[i - 1] == df['final_lb'].iat[i - 1] and df['close'].iat[i] <  df['final_lb'].iat[i] else 0.00
+        # Mark the trend direction up/down
+        df[stx] = np.where((df[st] > 0.00), np.where((df['close'] < df[st]), 'down',  'up'), np.NaN)
+
+        # Remove basic and final bands from the columns
+        df.drop(['basic_ub', 'basic_lb', 'final_ub', 'final_lb'], inplace=True, axis=1)
+
+        df.fillna(0, inplace=True)
+
+        return DataFrame(index=df.index, data={
+            'ST' : df[st],
+            'STX' : df[stx]
+        })