125 lines
3.8 KiB
Python
125 lines
3.8 KiB
Python
"""
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SOL15mStrategy - 基于15m时间框架的SOL/USDT期货策略
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核心逻辑：
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- 15m EMA26/EMA52 交叉做多做空
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- RSI过滤：做多要求RSI<65，做空要求RSI>35（避免超买超卖区入场）
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- 使用 trailing_stop 让利润奔跑
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- 宽止损（2%），给交易足够呼吸空间
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使用命令：
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freqtrade backtesting -c ./user_data/Chan/config/Local_Test.json --strategy SOL15mStrategy --strategy-path ./user_data/Chan/strategies --timerange=20250301-
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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 SOL15mStrategy(IStrategy):
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INTERFACE_VERSION: int = 3
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# === 基础配置 ===
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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 2%
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stoploss = -0.02
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use_custom_stoploss = False
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# trailing stop: 利润达到1.5%后开始追踪，回撤0.5%止盈
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trailing_stop = True
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trailing_stop_positive = 0.005
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trailing_stop_positive_offset = 0.015
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trailing_only_offset_is_reached = True
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# ROI: 阶梯式止盈
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minimal_roi = {
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"0": 0.04, # 4%直接止盈
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"60": 0.025, # 60分钟后 2.5%
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"180": 0.015, # 3小时后 1.5%
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"480": 0.005, # 8小时后 0.5%
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"720": 0, # 12小时后保本退出
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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
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dataframe["ema26"] = ta.EMA(dataframe, timeperiod=26)
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dataframe["ema52"] = ta.EMA(dataframe, timeperiod=52)
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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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# EMA26上穿EMA52
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dataframe["ema26_cross_up_52"] = (
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(dataframe["ema26"] > dataframe["ema52"])
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& (dataframe["ema26"].shift(1) <= dataframe["ema52"].shift(1))
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)
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# EMA26下穿EMA52
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dataframe["ema26_cross_dn_52"] = (
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(dataframe["ema26"] < dataframe["ema52"])
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& (dataframe["ema26"].shift(1) >= dataframe["ema52"].shift(1))
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)
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# MACD死叉
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dataframe["macd_cross_dn"] = (
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(dataframe["macd"] < dataframe["macdsignal"])
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& (dataframe["macd"].shift(1) >= dataframe["macdsignal"].shift(1))
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)
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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: EMA26上穿EMA52 + RSI < 65 (不在超买区)
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dataframe.loc[
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(dataframe["ema26_cross_up_52"])
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& (dataframe["rsi"] < 65),
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["enter_long", "enter_tag"],
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] = (1, "ema26x52_long")
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# 做空: EMA26下穿EMA52 + RSI > 35 (不在超卖区)
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dataframe.loc[
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(dataframe["ema26_cross_dn_52"])
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& (dataframe["rsi"] > 35),
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["enter_short", "enter_tag"],
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] = (1, "ema26x52_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，完全由 trailing_stop + ROI + stoploss 控制
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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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