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silencesdg
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>
59 lines
2.3 KiB
Python
59 lines
2.3 KiB
Python
import pandas as pd
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from datetime import datetime
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from .base_strategy import BaseStrategy
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from config import STRATEGY_CONFIG
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class DailyBreakoutStrategy(BaseStrategy):
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def __init__(self, data_provider, symbol, timeframe, bars_count=None):
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super().__init__(data_provider, symbol, timeframe)
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# 从配置中获取参数，如果传入参数则使用传入的参数
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config = STRATEGY_CONFIG.get('daily_breakout', {})
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self.bars_count = bars_count if bars_count is not None else config.get('bars_count', 1440)
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def _calculate_indicators(self, df):
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df['time'] = pd.to_datetime(df['time'], unit='s')
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today = datetime.now().date()
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day_data = df[df['time'].dt.date == today]
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if day_data.empty:
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return df, None, None
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day_high = day_data['high'].max()
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day_low = day_data['low'].min()
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return df, day_high, day_low
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def generate_signal(self):
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rates = self.data_provider.get_historical_data(self.symbol, self.timeframe, self.bars_count)
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if rates is None or len(rates) < 2:
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return 0
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df = pd.DataFrame(rates)
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df, day_high, day_low = self._calculate_indicators(df)
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if day_high is None or day_low is None:
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return 0
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if df['close'].iloc[-1] > day_high:
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return 1
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elif df['close'].iloc[-1] < day_low:
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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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# 'time' 可能是列（来自MT5原始数据）或索引（来自MultiTimeframeDataStore）
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if 'time' in df.columns:
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df['time'] = pd.to_datetime(df['time'], unit='s')
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elif isinstance(df.index, pd.DatetimeIndex):
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df['time'] = df.index
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else:
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df['time'] = pd.to_datetime(df.index, unit='s')
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df['date'] = df['time'].dt.date
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# 用前一日的最高/最低价作为突破基准，避免未来数据泄露
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daily_high = df.groupby('date')['high'].max()
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daily_low = df.groupby('date')['low'].min()
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prev_high = df['date'].map(daily_high.shift(1))
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prev_low = df['date'].map(daily_low.shift(1))
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signals = pd.Series(0, index=df.index)
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signals[df['close'] > prev_high] = 1
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signals[df['close'] < prev_low] = -1
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return signals |