💬 Forum

SupertrendFuturesStrategyV5_1

🏆 League #1473 / 1937
♡

jinzheng8115/freqtrade-crypto-system/user_data/strategies/SupertrendFuturesStrategyV5_1.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: 13
Indicators ADX ATR EMA RSI Supertrend talib
Concepts trailing trend_following
Methods custom_exit custom_stake_amount leverage populate_entry_trend_short populate_exit_trend_short supertrend
15 related strategies (⧉ identical code, ≈ similar name)

Each tile is a different kind of check — from an instant code lint to full sandboxed backtests and forward tests on recent data. Not sure what a check actually proves? See the FAQ →

Source

🔒 Log in to copy 🔒 Log in to download 🔒 Raw 
  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
# SupertrendFuturesStrategyV5_1 - 平衡优化版
# 更新时间: 2026-02-22 12:00
# 优化数据: 90 天 30m 数据
# 改进: 放宽过滤、延后止盈、平衡动态仓位

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 SupertrendFuturesStrategyV5_1(IStrategy):
    """
    Supertrend + EMA 趋势跟踪策略 (合约版 V5.1)
    
    V5.1 优化 (2026-02-22):
    - 基于 V5 回测反馈调整
    - 放宽 RSI 过滤 (70→75, 30→25)
    - 调整动态仓位阈值 (更容易正常仓位)
    - 延后止盈 (3%→5%, 5%→8%, 10%→15%)
    
    目标：
    - 保持胜率 (68%+)
    - 提升收益 (目标 10%+)
    - 降低回撤 (目标 <8%)
    """
    
    INTERFACE_VERSION = 3

    # 参数 - 30m 优化后
    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")
    
    # RSI 参数 - 放宽
    rsi_upper_limit = IntParameter(65, 80, default=75, space="buy")  # V5: 70 → 75
    rsi_lower_limit = IntParameter(20, 35, default=25, space="buy")  # V5: 30 → 25
    
    # 止盈参数 - 延后
    tp_level_1 = DecimalParameter(0.03, 0.07, default=0.05, space="sell")  # V5: 3% → 5%
    tp_level_2 = DecimalParameter(0.06, 0.10, default=0.08, space="sell")  # V5: 5% → 8%
    tp_level_3 = DecimalParameter(0.10, 0.20, default=0.15, space="sell")  # V5: 10% → 15%
    
    # 动态仓位参数 - 调整
    atr_low_threshold = DecimalParameter(0.015, 0.035, default=0.025, space="buy")   # V5: 0.03 → 0.025
    atr_high_threshold = DecimalParameter(0.045, 0.07, default=0.06, space="buy")    # V5: 0.05 → 0.06

    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:
        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:
        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'],
            # RSI 放宽到 75
            dataframe['rsi'] < self.rsi_upper_limit.value,
            dataframe['rsi'] > 20,
            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:
        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'],
            # RSI 放宽到 25
            dataframe['rsi'] > self.rsi_lower_limit.value,
            dataframe['rsi'] < 80,
            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_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
                           proposed_stake: float, min_stake: Optional[float], max_stake: float,
                           entry_tag: Optional[str], side: str, **kwargs) -> float:
        """
        动态仓位管理 - 调整后
        
        更容易使用正常仓位:
        - 高波动 (ATR > 6%) → 小仓位 (60%)
        - 中波动 (ATR 2.5-6%) → 中仓位 (80%)
        - 低波动 (ATR < 2.5%) → 正常仓位 (100%)
        """
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        
        if len(dataframe) < 1:
            return proposed_stake
        
        atr_ratio = dataframe['atr_ratio'].iloc[-1]
        
        if atr_ratio > self.atr_high_threshold.value:
            stake = proposed_stake * 0.6  # V5: 0.5 → 0.6
        elif atr_ratio > self.atr_low_threshold.value:
            stake = proposed_stake * 0.8  # V5: 0.75 → 0.8
        else:
            stake = proposed_stake
        
        if min_stake is not None and stake < min_stake:
            return min_stake
        
        return stake
    
    def custom_exit(self, pair: str, trade: Trade, current_time: datetime, 
                   current_rate: float, current_profit: float, **kwargs) -> Optional[str]:
        """
        分批止盈 - 延后版
        
        让利润跑更远:
        - 5% 利润 → 部分止盈
        - 8% 利润 → 加速止盈
        - 15% 利润 → 全部平仓
        """
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        
        if len(dataframe) < 1:
            return None
        
        profit_pct = current_profit
        
        # 延后止盈
        if profit_pct >= self.tp_level_3.value:
            return f'profit_{int(self.tp_level_3.value*100)}pct'
        elif profit_pct >= self.tp_level_2.value:
            return f'profit_{int(self.tp_level_2.value*100)}pct'
        elif profit_pct >= self.tp_level_1.value:
            return f'profit_{int(self.tp_level_1.value*100)}pct'
        
        # RSI 反转信号
        last_candle = dataframe.iloc[-1]
        if trade.is_short:
            if last_candle['rsi'] < 25:  # V5: 30 → 25
                return 'rsi_oversold_exit'
        else:
            if last_candle['rsi'] > 75:  # V5: 70 → 75
                return 'rsi_overbought_exit'
        
        return None