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SupertrendFuturesStrategyV4

🏆 League #1437 / 1937
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jinzheng8115/freqtrade-crypto-system/user_data/strategies/SupertrendFuturesStrategyV4.py · first seen 2026-07-16 · repo updated 2026-02-23 · ⬇ 1 download

Basics mode: futures timeframe: 30m interface version: 3
Settings stoploss: -0.03 has minimal roi trailing startup candle count: 200 hyperopt hyperopt params: 6
Indicators ADX ATR EMA RSI Supertrend talib
Concepts trailing trend_following
Methods leverage populate_entry_trend_short populate_exit_trend_short supertrend
15 related strategies (⧉ identical code, ≈ similar name)

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# SupertrendFuturesStrategyV4 - 15m 优化版
# 更新时间: 2026-02-21 20:31
# 优化数据: 173 天 15m 数据

import numpy as np
import pandas as pd
from pandas import DataFrame
from datetime import datetime
from typing import Optional
from functools import reduce

from freqtrade.strategy import IStrategy, DecimalParameter, IntParameter
import talib.abstract as ta


class SupertrendFuturesStrategyV4(IStrategy):
    INTERFACE_VERSION = 3

    # 参数 - 15m 优化后（173天数据）
    atr_period = IntParameter(5, 30, default=29, space="buy")
    atr_multiplier = DecimalParameter(2.0, 5.0, default=3.476, space="buy")
    ema_fast = IntParameter(5, 50, default=37, space="buy")
    ema_slow = IntParameter(20, 200, default=174, space="buy")

    # ADX 参数 - 15m 优化后
    adx_threshold_long = IntParameter(20, 35, default=35, space="buy")
    adx_threshold_short = IntParameter(15, 30, default=21, space="buy")

    minimal_roi = {"0": 0.06}
    stoploss = -0.03  # 3% (最优止损)

    timeframe = '30m'  # 2026-02-22: 15m -> 30m (回测显示30m表现更优)

    trailing_stop = True
    trailing_stop_positive = 0.02
    trailing_stop_positive_offset = 0.03
    trailing_only_offset_is_reached = True

    startup_candle_count = 200

    order_types = {
        'entry': 'limit',
        'exit': 'limit',
        'stoploss': 'market',
        'stoploss_on_exchange': False
    }

    can_short: bool = True
    leverage_default = 2

    def supertrend(self, dataframe, period=14, multiplier=3):
        df = dataframe.copy()
        hl2 = (df['high'] + df['low']) / 2
        atr = ta.ATR(df, timeperiod=period)
        upperband = hl2 + (multiplier * atr)
        lowerband = hl2 - (multiplier * atr)
        supertrend = [0] * len(df)
        direction = [1] * len(df)
        for i in range(1, len(df)):
            if df['close'].iloc[i] > upperband.iloc[i-1]:
                direction[i] = 1
            elif df['close'].iloc[i] < lowerband.iloc[i-1]:
                direction[i] = -1
            else:
                direction[i] = direction[i-1]
            supertrend[i] = lowerband.iloc[i] if direction[i] == 1 else upperband.iloc[i]
        return pd.Series(supertrend, index=df.index), pd.Series(direction, index=df.index)

    def leverage(self, pair, current_time, current_rate, proposed_leverage, max_leverage, entry_tag, side, **kwargs):
        return min(self.leverage_default, max_leverage)

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['ema_fast'] = ta.EMA(dataframe, timeperiod=self.ema_fast.value)
        dataframe['ema_slow'] = ta.EMA(dataframe, timeperiod=self.ema_slow.value)
        dataframe['supertrend'], dataframe['st_dir'] = self.supertrend(
            dataframe, period=self.atr_period.value, multiplier=self.atr_multiplier.value
        )
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)
        dataframe['adx_pos'] = ta.PLUS_DI(dataframe, timeperiod=14)
        dataframe['adx_neg'] = ta.MINUS_DI(dataframe, timeperiod=14)
        dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
        dataframe['volume_ma'] = dataframe['volume'].rolling(20).mean()

        # 趋势判断：EMA 200 判断大趋势
        dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)
        dataframe['is_uptrend'] = dataframe['close'] > dataframe['ema_200']
        dataframe['is_downtrend'] = dataframe['close'] < dataframe['ema_200']

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """做多 - 优化版"""
        dataframe.loc[:, 'enter_long'] = 0

        conditions = [
            dataframe['st_dir'] == 1,
            dataframe['ema_fast'] > dataframe['ema_slow'],
            dataframe['adx'] > self.adx_threshold_long.value,
            dataframe['adx_pos'] > dataframe['adx_neg'],
            dataframe['rsi'] < 70,
            dataframe['volume'] > dataframe['volume_ma'],
            dataframe['close'] > dataframe['supertrend'],
            dataframe['is_uptrend'],
        ]

        if conditions:
            dataframe.loc[reduce(lambda x, y: x & y, conditions), 'enter_long'] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[:, 'exit_long'] = 0
        conditions = [dataframe['st_dir'] == -1]
        if conditions:
            dataframe.loc[reduce(lambda x, y: x & y, conditions), 'exit_long'] = 1
        return dataframe

    def populate_entry_trend_short(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """做空 - 优化版"""
        dataframe.loc[:, 'enter_short'] = 0

        conditions = [
            dataframe['st_dir'] == -1,
            dataframe['ema_fast'] < dataframe['ema_slow'],
            dataframe['adx'] > self.adx_threshold_short.value,
            dataframe['adx_neg'] > dataframe['adx_pos'],
            dataframe['rsi'] > 30,
            dataframe['close'] < dataframe['supertrend'],
            dataframe['is_downtrend'],
        ]

        if conditions:
            dataframe.loc[reduce(lambda x, y: x & y, conditions), 'enter_short'] = 1

        return dataframe

    def populate_exit_trend_short(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[:, 'exit_short'] = 0
        conditions = [dataframe['st_dir'] == 1]
        if conditions:
            dataframe.loc[reduce(lambda x, y: x & y, conditions), 'exit_short'] = 1
        return dataframe