Files
mt5_python_ea_suite/strategies/mean_reversion.py
T
silencesdg 4cb4f4a15e 重构项目架构,新增 MT5 代理服务
- 重构核心模块:DataProvider 依赖注入、RiskController 门面、信号注册表
- 新增 FastAPI 代理服务 (run/server.py),支持局域网远程调用 MT5
- 新增 RemoteDataProvider + AttrDict,远端无缝替代 LiveDataProvider
- 新增序列化模块,MT5 对象转 JSON 兼容格式
- 重构入口点至 run/ 包,支持 python -m run.realtime/server/backtest/optimize
- 更新 CLAUDE.md 文档

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Co-Authored-By: Claude <noreply@anthropic.com>
Co-Authored-By: Happy <yesreply@happy.engineering>
2026-05-11 12:00:45 +08:00

53 lines
2.4 KiB
Python

import pandas as pd
from .base_strategy import BaseStrategy
from config import STRATEGY_CONFIG
class MeanReversionStrategy(BaseStrategy):
"""均值回归策略 — 价格突破布林带后等待回归确认再入场(与 BollingerStrategy 的即时入场区分)"""
def __init__(self, data_provider, symbol, timeframe, period=None, std_dev=None):
super().__init__(data_provider, symbol, timeframe)
config = STRATEGY_CONFIG.get('mean_reversion', {})
self.period = period if period is not None else config.get('period', 20)
self.std_dev = std_dev if std_dev is not None else config.get('std_dev', 2.0)
def _calculate_indicators(self, df):
mean = df['close'].rolling(self.period).mean()
std = df['close'].rolling(self.period).std()
df['upper_band'] = mean + self.std_dev * std
df['lower_band'] = mean - self.std_dev * std
return df
def generate_signal(self):
rates = self.data_provider.get_historical_data(self.symbol, self.timeframe, self.period + 5)
if rates is None or len(rates) < self.period + 1:
return 0
df = pd.DataFrame(rates)
df = self._calculate_indicators(df)
# 回归确认:价格曾突破边界,现已回归内侧
prev_close = df['close'].iloc[-2]
prev_lower = df['lower_band'].iloc[-2]
prev_upper = df['upper_band'].iloc[-2]
curr_close = df['close'].iloc[-1]
curr_lower = df['lower_band'].iloc[-1]
curr_upper = df['upper_band'].iloc[-1]
# 买入:上一根K线跌破下轨,当前回升至下轨上方(回归确认)
if prev_close < prev_lower and curr_close >= curr_lower:
return 1
# 卖出:上一根K线突破上轨,当前回落至上轨下方(回归确认)
elif prev_close > prev_upper and curr_close <= curr_upper:
return -1
return 0
def run_backtest(self, df):
df = df.copy()
df = self._calculate_indicators(df)
signals = pd.Series(0, index=df.index)
# 前一根在轨外 + 当前回归轨内 = 买入
signals[(df['close'].shift(1) < df['lower_band'].shift(1)) & (df['close'] >= df['lower_band'])] = 1
# 前一根在轨外 + 当前回归轨内 = 卖出
signals[(df['close'].shift(1) > df['upper_band'].shift(1)) & (df['close'] <= df['upper_band'])] = -1
return signals