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- 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>
48 lines
2.1 KiB
Python
48 lines
2.1 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 MomentumBreakoutStrategy(BaseStrategy):
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"""动量突破策略 — 在通道突破基础上增加动量方向确认(与 TurtleStrategy 的纯突破区分)"""
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def __init__(self, data_provider, symbol, timeframe, period=None, momentum_period=None):
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super().__init__(data_provider, symbol, timeframe)
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config = STRATEGY_CONFIG.get('momentum_breakout', {})
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self.period = period if period is not None else config.get('period', 20)
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self.momentum_period = momentum_period if momentum_period is not None else config.get('momentum_period', 10)
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def _calculate_indicators(self, df):
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df['high_period'] = df['high'].rolling(self.period).max()
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df['low_period'] = df['low'].rolling(self.period).min()
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# 计算动量:当前收盘价相对于 N 根前的涨跌幅
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df['momentum'] = df['close'] - df['close'].shift(self.momentum_period)
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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, max(self.period, self.momentum_period) + 5)
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if rates is None or len(rates) < max(self.period, self.momentum_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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close = df['close'].iloc[-1]
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breakout_high = df['high_period'].iloc[-2]
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breakout_low = df['low_period'].iloc[-2]
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momentum = df['momentum'].iloc[-1]
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# 突破上轨 + 正动量确认
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if close > breakout_high and momentum > 0:
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return 1
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# 跌破下轨 + 负动量确认
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elif close < breakout_low and momentum < 0:
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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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signals[(df['close'] > df['high_period'].shift(1)) & (df['momentum'] > 0)] = 1
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signals[(df['close'] < df['low_period'].shift(1)) & (df['momentum'] < 0)] = -1
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return signals
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