Supertrend

import logging
from numpy.lib import math
from freqtrade.strategy import IStrategy, IntParameter
from pandas import DataFrame
import talib.abstract as ta
import numpy as np
import pandas as pd

class Supertrend(IStrategy):
    use_exit_signal = True
    # Buy params, Sell params, ROI, Stoploss and Trailing Stop are values generated by 'freqtrade hyperopt --strategy Supertrend --hyperopt-loss ShortTradeDurHyperOptLoss --timerange=20210101- --timeframe=1h --spaces all'
    # It's encourage you find the values that better suites your needs and risk management strategies

    INTERFACE_VERSION: int = 3
    # Buy hyperspace params:
    buy_params = {
        "buy_m1": 4,
        "buy_m2": 7,
        "buy_m3": 1,
        "buy_p1": 8,
        "buy_p2": 9,
        "buy_p3": 8,
    }

    # Sell hyperspace params:
    sell_params = {
        "sell_m1": 1,
        "sell_m2": 3,
        "sell_m3": 6,
        "sell_p1": 16,
        "sell_p2": 18,
        "sell_p3": 18,
    }

    # ROI table:
    minimal_roi = {
        "0": 0.10,
        "720": 0.06,
        "1920": 0.03,
        "3840": 0
    }

    stoploss = -0.25

    trailing_stop = False
    trailing_stop_positive = 0.03
    trailing_stop_positive_offset = 0.06
    trailing_only_offset_is_reached = True

    timeframe = '4h'

    startup_candle_count = 250

    buy_m1 = IntParameter(1, 7, default=4)
    buy_m2 = IntParameter(1, 7, default=4)
    buy_m3 = IntParameter(1, 7, default=4)
    buy_p1 = IntParameter(7, 21, default=14)
    buy_p2 = IntParameter(7, 21, default=14)
    buy_p3 = IntParameter(7, 21, default=14)

    sell_m1 = IntParameter(1, 7, default=4)
    sell_m2 = IntParameter(1, 7, default=4)
    sell_m3 = IntParameter(1, 7, default=4)
    sell_p1 = IntParameter(7, 21, default=14)
    sell_p2 = IntParameter(7, 21, default=14)
    sell_p3 = IntParameter(7, 21, default=14)

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        new_cols = []
    
        for multiplier in self.buy_m1.range:
            for period in self.buy_p1.range:
                st = self.supertrend(dataframe, multiplier, period)[['STX']].rename(
                    columns={'STX': f'supertrend_1_buy_{multiplier}_{period}'})
                new_cols.append(st)
    
        for multiplier in self.buy_m2.range:
            for period in self.buy_p2.range:
                st = self.supertrend(dataframe, multiplier, period)[['STX']].rename(
                    columns={'STX': f'supertrend_2_buy_{multiplier}_{period}'})
                new_cols.append(st)
    
        for multiplier in self.buy_m3.range:
            for period in self.buy_p3.range:
                st = self.supertrend(dataframe, multiplier, period)[['STX']].rename(
                    columns={'STX': f'supertrend_3_buy_{multiplier}_{period}'})
                new_cols.append(st)
    
        for multiplier in self.sell_m1.range:
            for period in self.sell_p1.range:
                st = self.supertrend(dataframe, multiplier, period)[['STX']].rename(
                    columns={'STX': f'supertrend_1_sell_{multiplier}_{period}'})
                new_cols.append(st)
    
        for multiplier in self.sell_m2.range:
            for period in self.sell_p2.range:
                st = self.supertrend(dataframe, multiplier, period)[['STX']].rename(
                    columns={'STX': f'supertrend_2_sell_{multiplier}_{period}'})
                new_cols.append(st)
    
        for multiplier in self.sell_m3.range:
            for period in self.sell_p3.range:
                st = self.supertrend(dataframe, multiplier, period)[['STX']].rename(
                    columns={'STX': f'supertrend_3_sell_{multiplier}_{period}'})
                new_cols.append(st)
    
        if new_cols:
            dataframe = pd.concat([dataframe] + new_cols, axis=1)

        dataframe['ema200'] = ta.EMA(dataframe, timeperiod=200)
    
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
               (dataframe[f'supertrend_1_buy_{self.buy_m1.value}_{self.buy_p1.value}'] == 'up') &
               (dataframe[f'supertrend_2_buy_{self.buy_m2.value}_{self.buy_p2.value}'] == 'up') &
               (dataframe[f'supertrend_3_buy_{self.buy_m3.value}_{self.buy_p3.value}'] == 'up') & # The three indicators are 'up' for the current candle
               (dataframe['close'] > dataframe['ema200']) &
               (dataframe['volume'] > 0) # There is at least some trading volume
        ),
            'enter_long'] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
               (dataframe[f'supertrend_1_sell_{self.sell_m1.value}_{self.sell_p1.value}'] == 'down') &
               (dataframe[f'supertrend_2_sell_{self.sell_m2.value}_{self.sell_p2.value}'] == 'down') &
               (dataframe[f'supertrend_3_sell_{self.sell_m3.value}_{self.sell_p3.value}'] == 'down') & # The three indicators are 'down' for the current candle
               (dataframe['volume'] > 0) # There is at least some trading volume
            ),
            'exit_long'] = 1

        return dataframe



    """
        Supertrend Indicator; adapted for freqtrade
        from: https://github.com/freqtrade/freqtrade-strategies/issues/30
    """
    def supertrend(self, dataframe: pd.DataFrame, multiplier, period):
        df = dataframe.copy()
        high = df['high'].values
        low = df['low'].values
        close = df['close'].values
        length = len(df)
    
        # 1. TR and ATR
        tr = ta.TRANGE(df['high'], df['low'], df['close'])
        atr = pd.Series(tr).rolling(period).mean().to_numpy()
    
        # 2. basic upper / lower bands
        basic_ub = (high + low) / 2 + multiplier * atr
        basic_lb = (high + low) / 2 - multiplier * atr
    
        # 3. final upper / lower bands
        final_ub = np.zeros(length)
        final_lb = np.zeros(length)
    
        for i in range(period, length):
            final_ub[i] = basic_ub[i] if basic_ub[i] < final_ub[i-1] or close[i-1] > final_ub[i-1] else final_ub[i-1]
            final_lb[i] = basic_lb[i] if basic_lb[i] > final_lb[i-1] or close[i-1] < final_lb[i-1] else final_lb[i-1]
    
        # 4. ST calculation
        st = np.zeros(length)
        for i in range(period, length):
            if st[i-1] == final_ub[i-1]:
                st[i] = final_ub[i] if close[i] <= final_ub[i] else final_lb[i]
            elif st[i-1] == final_lb[i-1]:
                st[i] = final_lb[i] if close[i] >= final_lb[i] else final_ub[i]
    
        # 5. STX direction
        stx = np.where(st > 0, np.where(close < st, 'down', 'up'), None)
    
        # 6. fillna
        result = pd.DataFrame({'ST': st, 'STX': stx}, index=df.index)
        result.fillna(0, inplace=True)
      
        return result