Includes Supertrend strategy

2021-06-20 22:22:18 -05:00
parent 0bdb606f00
commit c7ebb2c42f
1 changed files with 115 additions and 0 deletions
@@ -0,0 +1,115 @@
+"""
+Supertrend strategy:
+* Description: Generate a 3 supertrend indicators based on different pairs for [period, multiplier] -> [period: 10, multiplier: 1], [period: 11, multiplier: 2], [period: 13, multiplier: 3]
+               Buys if the 3 indicators are 'up'
+               Sells if the 3 indicators are 'down'
+* Author: @juankysoriano (Juan Carlos Soriano)
+* github: https://github.com/juankysoriano/
+"""
+
+# IMPORTANT: INSTALL TA BEFOUR RUN(pip install ta)
+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
+
+class Supertrend(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 = 10
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe['supertrend_1'] = self.supertrend(dataframe, 1, 10)['STX']
+        dataframe['supertrend_2'] = self.supertrend(dataframe, 2, 11)['STX']
+        dataframe['supertrend_3'] = self.supertrend(dataframe, 3, 12)['STX']
+
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe.loc[
+            (
+               (dataframe['supertrend_1'] == 'up') &
+               (dataframe['supertrend_2'] == 'up') &
+               (dataframe['supertrend_3'] == 'up') & # The three indicators are 'up' for the current candle
+               (dataframe['volume'] > 0) # There is at least some trading volume
+	    ),
+            'buy'] = 1
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe.loc[
+            (
+               (dataframe['supertrend_1'] == 'down') &
+               (dataframe['supertrend_2'] == 'down') &
+               (dataframe['supertrend_3'] == 'down') & # The three indicators are 'down' for the current candle
+               (dataframe['volume'] > 0) # There is at least some trading volume
+            ),
+            '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]
+        })