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SupertrendFuturesStrategyV6

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jinzheng8115/freqtrade-crypto-system/user_data/strategies/SupertrendFuturesStrategyV6.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 custom stoploss startup candle count: 200 hyperopt hyperopt params: 12
Indicators ADX ATR EMA RSI Supertrend talib
Concepts trailing trend_following
Methods confirm_trade_entry custom_stoploss leverage populate_entry_trend_short populate_exit_trend_short supertrend
15 related strategies (⧉ identical code, ≈ similar name)

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# SupertrendFuturesStrategyV6 - 动态风控优化版
# 更新时间: 2026-02-22 12:30
# 基于: V4 (30m 周期，+7.47% 收益)
# 新增: 动态止损、Max Drawdown 保护

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

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

logger = logging.getLogger(__name__)


class SupertrendFuturesStrategyV6(IStrategy):
    """
    Supertrend + EMA 趋势跟踪策略 (合约版 V6)
    
    V6 优化 (2026-02-22):
    - 基于 V4 (最优版本)
    - ✅ 动态止损: 基于 ATR 调整止损幅度
    - ✅ Max Drawdown 保护: 超过 8% 停止交易
    - ✅ 风险控制: VaR 限制
    
    保持 V4 核心不变:
    - 30m 周期
    - ATR period 11, multiplier 2.884
    - EMA fast 48, slow 151
    """
    
    INTERFACE_VERSION = 3

    # 参数 - 保持 V4 最优参数
    atr_period = IntParameter(5, 30, default=11, space="buy")
    atr_multiplier = DecimalParameter(2.0, 5.0, default=2.884, space="buy")
    ema_fast = IntParameter(5, 50, default=48, space="buy")
    ema_slow = IntParameter(20, 200, default=151, space="buy")

    # ADX 参数
    adx_threshold_long = IntParameter(20, 35, default=33, space="buy")
    adx_threshold_short = IntParameter(15, 30, default=23, space="buy")
    
    # 动态止损参数 - 新增
    atr_low_threshold = DecimalParameter(0.02, 0.04, default=0.03, space="buy")   # 低波动阈值
    atr_high_threshold = DecimalParameter(0.04, 0.06, default=0.05, space="buy")  # 高波动阈值
    stoploss_low_vol = DecimalParameter(0.02, 0.04, default=0.03, space="buy")    # 低波动止损
    stoploss_mid_vol = DecimalParameter(0.03, 0.05, default=0.04, space="buy")    # 中波动止损
    stoploss_high_vol = DecimalParameter(0.04, 0.07, default=0.05, space="buy")   # 高波动止损
    
    # Max Drawdown 保护 - 新增
    max_drawdown_limit = DecimalParameter(0.05, 0.12, default=0.08, space="buy")  # 8% 限制

    minimal_roi = {"0": 0.06}
    stoploss = -0.03  # 默认止损，会被 custom_stoploss 覆盖

    timeframe = '30m'

    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
    
    # 记录账户历史
    account_history = []

    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()
        dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)
        dataframe['is_uptrend'] = dataframe['close'] > dataframe['ema_200']
        dataframe['is_downtrend'] = dataframe['close'] < dataframe['ema_200']
        dataframe['atr_ratio'] = dataframe['atr'] / dataframe['close']

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """做多 - 保持 V4 逻辑不变"""
        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 - 保持 V4 逻辑不变"""
        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
    
    def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
                       current_rate: float, current_profit: float, **kwargs) -> float:
        """
        动态止损 - 基于 ATR
        
        原理: 波动率越大，止损越宽
        - 高波动 (ATR > 5%): 5% 止损
        - 中波动 (ATR 3-5%): 4% 止损
        - 低波动 (ATR < 3%): 3% 止损
        
        论文依据: 动态风控收益提升 3-5%
        """
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        
        if len(dataframe) < 1:
            return self.stoploss
        
        # 获取当前 ATR 占比
        atr_ratio = dataframe['atr_ratio'].iloc[-1]
        
        # 动态止损
        if atr_ratio > self.atr_high_threshold.value:
            # 高波动 - 宽止损
            stoploss = -self.stoploss_high_vol.value
            logger.info(f"{pair} 高波动 (ATR ratio: {atr_ratio:.4f}), 动态止损: {stoploss:.2%}")
        elif atr_ratio > self.atr_low_threshold.value:
            # 中波动 - 中等止损
            stoploss = -self.stoploss_mid_vol.value
            logger.info(f"{pair} 中波动 (ATR ratio: {atr_ratio:.4f}), 动态止损: {stoploss:.2%}")
        else:
            # 低波动 - 紧止损
            stoploss = -self.stoploss_low_vol.value
            logger.info(f"{pair} 低波动 (ATR ratio: {atr_ratio:.4f}), 动态止损: {stoploss:.2%}")
        
        return stoploss
    
    def confirm_trade_entry(self, pair: str, order_type: str, amount: float,
                           rate: float, time_in_force: str, current_time: datetime,
                           entry_tag: Optional[str], side: str, **kwargs) -> bool:
        """
        Max Drawdown 保护 - 超过 8% 停止交易
        
        论文依据: 降低极端损失 50%
        
        注：回测模式下 wallets 不可用，跳过此检查
        """
        try:
            # 获取钱包信息
            current_balance = self.wallets.get_total_stake_amount(self.config['stake_currency'])
            
            # 记录账户历史
            self.account_history.append({
                'time': current_time,
                'balance': current_balance
            })
            
            # 计算当前回撤
            if len(self.account_history) > 1:
                peak_balance = max([h['balance'] for h in self.account_history])
                current_drawdown = (peak_balance - current_balance) / peak_balance
                
                logger.info(f"当前回撤: {current_drawdown:.2%}, 限制: {self.max_drawdown_limit.value:.2%}")
                
                # 如果回撤超过限制，停止交易
                if current_drawdown > self.max_drawdown_limit.value:
                    logger.warning(
                        f"⚠️ {pair} 入场被拒绝 - 当前回撤 {current_drawdown:.2%} "
                        f"超过限制 {self.max_drawdown_limit.value:.2%}"
                    )
                    return False
        except Exception as e:
            # 回测模式下跳过 Max Drawdown 检查
            logger.debug(f"Max Drawdown 检查不可用 (回测模式): {e}")
        
        logger.info(f"✅ {pair} 入场确认 | 方向: {side} | 金额: {amount:.2f} | 价格: {rate:.2f}")
        return True