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4cb4f4a15e
- 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 Generated with [Claude Code](https://claude.ai/code) via [Happy](https://happy.engineering) Co-Authored-By: Claude <noreply@anthropic.com> Co-Authored-By: Happy <yesreply@happy.engineering>
53 lines
2.4 KiB
Python
53 lines
2.4 KiB
Python
import pandas as pd
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from .base_strategy import BaseStrategy
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from config import STRATEGY_CONFIG
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class MeanReversionStrategy(BaseStrategy):
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"""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)"""
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def __init__(self, data_provider, symbol, timeframe, period=None, std_dev=None):
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super().__init__(data_provider, symbol, timeframe)
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config = STRATEGY_CONFIG.get('mean_reversion', {})
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self.period = period if period is not None else config.get('period', 20)
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self.std_dev = std_dev if std_dev is not None else config.get('std_dev', 2.0)
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def _calculate_indicators(self, df):
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mean = df['close'].rolling(self.period).mean()
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std = df['close'].rolling(self.period).std()
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df['upper_band'] = mean + self.std_dev * std
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df['lower_band'] = mean - self.std_dev * std
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return df
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def generate_signal(self):
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rates = self.data_provider.get_historical_data(self.symbol, self.timeframe, self.period + 5)
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if rates is None or len(rates) < self.period + 1:
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return 0
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df = pd.DataFrame(rates)
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df = self._calculate_indicators(df)
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# 回归确认:价格曾突破边界,现已回归内侧
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prev_close = df['close'].iloc[-2]
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prev_lower = df['lower_band'].iloc[-2]
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prev_upper = df['upper_band'].iloc[-2]
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curr_close = df['close'].iloc[-1]
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curr_lower = df['lower_band'].iloc[-1]
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curr_upper = df['upper_band'].iloc[-1]
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# 买入:上一根K线跌破下轨,当前回升至下轨上方(回归确认)
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if prev_close < prev_lower and curr_close >= curr_lower:
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return 1
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# 卖出:上一根K线突破上轨,当前回落至上轨下方(回归确认)
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elif prev_close > prev_upper and curr_close <= curr_upper:
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return -1
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return 0
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def run_backtest(self, df):
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df = df.copy()
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df = self._calculate_indicators(df)
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signals = pd.Series(0, index=df.index)
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# 前一根在轨外 + 当前回归轨内 = 买入
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signals[(df['close'].shift(1) < df['lower_band'].shift(1)) & (df['close'] >= df['lower_band'])] = 1
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# 前一根在轨外 + 当前回归轨内 = 卖出
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signals[(df['close'].shift(1) > df['upper_band'].shift(1)) & (df['close'] <= df['upper_band'])] = -1
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return signals
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