120 lines
3.7 KiB
Python
120 lines
3.7 KiB
Python
"""
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SOL5mStrategyV6 - 趋势跟随策略 V6（基于V5改进）
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核心改进：
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- 去掉trend_reversal退出（V5中54笔全亏 -611 USDT）
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- 完全依赖 ROI + trailing_stop + stoploss 管理退出
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- 更激进的trailing：1.5%盈利后激活，0.6%回撤
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- 保持V5的入场逻辑（EMA排列 + 回调入场 + RSI + MACD + ADX）
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"""
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from datetime import datetime
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from typing import Optional
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import talib.abstract as ta
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from pandas import DataFrame
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from freqtrade.persistence import Trade
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from freqtrade.strategy import IStrategy
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class SOL5mStrategyV6(IStrategy):
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INTERFACE_VERSION: int = 3
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timeframe = "15m"
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can_short = True
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startup_candle_count: int = 200
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# 止损
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stoploss = -0.025
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use_custom_stoploss = False
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# 更激进的trailing stop
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trailing_stop = True
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trailing_stop_positive = 0.006 # 0.6% 回撤止盈
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trailing_stop_positive_offset = 0.015 # 1.5% 盈利后激活
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trailing_only_offset_is_reached = True
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# ROI
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minimal_roi = {
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"0": 0.04, # 4%直接止盈
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"60": 0.03, # 1小时后 3%
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"180": 0.02, # 3小时后 2%
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"360": 0.01, # 6小时后 1%
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"720": 0.005, # 12小时后 0.5%
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"1440": 0, # 24小时后保本
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}
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order_types = {
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"entry": "market",
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"exit": "market",
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"stoploss": "market",
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"stoploss_on_exchange": False,
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}
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# EMA趋势
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dataframe["ema20"] = ta.EMA(dataframe, timeperiod=20)
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dataframe["ema50"] = ta.EMA(dataframe, timeperiod=50)
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dataframe["ema100"] = ta.EMA(dataframe, timeperiod=100)
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# RSI
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dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
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# MACD
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macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)
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dataframe["macd"] = macd["macd"]
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dataframe["macdsignal"] = macd["macdsignal"]
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dataframe["macdhist"] = macd["macdhist"]
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# ADX (趋势强度)
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dataframe["adx"] = ta.ADX(dataframe, timeperiod=14)
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return dataframe
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# 做多条件（与V5相同）
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dataframe.loc[
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(dataframe["ema20"] > dataframe["ema50"])
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& (dataframe["ema50"] > dataframe["ema100"])
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& (dataframe["close"] <= dataframe["ema20"] * 1.005)
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& (dataframe["close"] >= dataframe["ema50"])
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& (dataframe["rsi"] > 40)
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& (dataframe["rsi"] < 65)
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& (dataframe["macdhist"] > 0)
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& (dataframe["adx"] > 20),
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["enter_long", "enter_tag"],
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] = (1, "trend_pullback_long")
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# 做空条件（与V5相同）
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dataframe.loc[
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(dataframe["ema20"] < dataframe["ema50"])
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& (dataframe["ema50"] < dataframe["ema100"])
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& (dataframe["close"] >= dataframe["ema20"] * 0.995)
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& (dataframe["close"] <= dataframe["ema50"])
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& (dataframe["rsi"] < 60)
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& (dataframe["rsi"] > 35)
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& (dataframe["macdhist"] < 0)
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& (dataframe["adx"] > 20),
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["enter_short", "enter_tag"],
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] = (1, "trend_pullback_short")
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return dataframe
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def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# 不使用信号退出，完全依赖 ROI + trailing + stoploss
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return dataframe
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def leverage(
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self,
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pair: str,
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current_time: datetime,
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current_rate: float,
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proposed_leverage: float,
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max_leverage: float,
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entry_tag: Optional[str],
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side: str,
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**kwargs,
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) -> float:
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return 1.0
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