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SupertrendFuturesStrategyV8_2

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jinzheng8115/freqtrade-crypto-system/strategies/SupertrendFuturesStrategyV8_2.py · first seen 2026-07-16 · repo updated 2026-02-23

Basics mode: futures timeframe: 30m interface version: 3
Settings stoploss: -0.03 has minimal roi trailing startup candle count: 200 hyperopt hyperopt params: 7
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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# SupertrendFuturesStrategyV8_2 - 平衡版
# 在V8基础上，只放宽最关键的指标

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
import talib.abstract as ta

logger = logging.getLogger(__name__)


class SupertrendFuturesStrategyV8_2(IStrategy):
    """
    V8.2: 平衡版（收益 + 频率）
    
    基于V8，只放宽最关键的1个指标：
    
    V8.1问题：
    - 放宽太多，信号质量下降
    - 收益和胜率都不如V4
    
    V8.2策略：
    1. 保持V8的所有核心过滤
    2. **只放宽Alpha#101阈值**: 0.1 → 0.07 (中等宽松)
    3. 其他指标保持V8设置
    
    目标:
    - 交易次数: 36 → 45 (提升25%)
    - 收益: 保持8%+
    - 胜率: 保持65%+
    """
    
    INTERFACE_VERSION = 3

    # 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_threshold_long = IntParameter(20, 35, default=33, space="buy")
    adx_threshold_short = IntParameter(15, 30, default=23, space="buy")
    
    # V8.2优化参数
    alpha_threshold = DecimalParameter(0.05, 0.12, default=0.07, space="buy")  # V8: 0.1, V8.1: 0.05

    minimal_roi = {"0": 0.06}
    stoploss = -0.03
    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

    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:
        # === V4 核心指标 ===
        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']
        
        # === V8 多因子指标 ===
        dataframe['alpha_101'] = (
            (dataframe['close'] - dataframe['open']) / 
            (dataframe['high'] - dataframe['low'] + 0.001)
        )
        dataframe['alpha_54'] = (
            (dataframe['close'] - dataframe['close'].shift(5)) / 
            dataframe['close'].shift(5)
        )
        dataframe['volatility_ratio'] = (
            dataframe['atr'] / dataframe['close']
        )
        dataframe['trend_score'] = 0
        dataframe.loc[dataframe['adx'] > 30, 'trend_score'] += 1
        dataframe.loc[dataframe['adx'] > 35, 'trend_score'] += 1
        dataframe.loc[abs(dataframe['alpha_54']) > 0.05, 'trend_score'] += 1
        
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """做多 - V8.2平衡版"""
        dataframe.loc[:, 'enter_long'] = 0

        conditions = [
            # === V4 核心条件 ===
            dataframe['st_dir'] == 1,
            dataframe['ema_fast'] > dataframe['ema_slow'],
            dataframe['adx'] > self.adx_threshold_long.value,
            dataframe['adx_pos'] > dataframe['adx_neg'],
            dataframe['close'] > dataframe['supertrend'],
            dataframe['is_uptrend'],
            
            # === V8 核心过滤（保持不变）===
            (dataframe['rsi'] > 40) & (dataframe['rsi'] < 75),  # 保持V8
            dataframe['volume'] > dataframe['volume_ma'] * 1.2,  # 保持V8
            dataframe['trend_score'] >= 1,  # 保持V8
            dataframe['volatility_ratio'] < 0.05,  # 保持V8
            
            # === V8.2 唯一调整 ===
            # Alpha#101阈值: 0.1 → 0.07 (中等宽松)
            dataframe['alpha_101'] > self.alpha_threshold.value,
        ]

        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 - V8.2平衡版"""
        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['close'] < dataframe['supertrend'],
            dataframe['is_downtrend'],
            
            (dataframe['rsi'] > 25) & (dataframe['rsi'] < 60),
            dataframe['volume'] > dataframe['volume_ma'] * 1.2,
            dataframe['trend_score'] >= 1,
            dataframe['volatility_ratio'] < 0.05,
            dataframe['alpha_101'] < -self.alpha_threshold.value,
        ]

        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: DataFrame) -> 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


# === V8.2 调整说明 ===
"""
V8.2 vs V8 vs V8.1 对比:

指标          V8      V8.1     V8.2
--------------------------------------------
Alpha#101    0.1     0.05     0.07   ← 唯一调整
RSI范围     40-75   35-80    40-75    保持V8
成交量      1.2倍   1.1倍    1.2倍    保持V8
趋势评分    >= 1    移除     >= 1     保持V8
波动率      0.05    0.06     0.05     保持V8

V8.2策略：
- 只放宽Alpha#101（最关键的日内趋势指标）
- 其他保持V8的严格过滤
- 目标：提升交易频率，同时保持V8的高质量

预期效果：
- 交易次数: 36 → 42-45
- 收益: 保持8%+
- 胜率: 66-68%
- 回撤: < 5%
"""