Rekey
4da520c14b
- backtest/engine.py: 事件驱动回测引擎核心，支持 K 线推进/订单撮合/权益曲线 - backtest/models.py: 回测数据模型（订单/成交/持仓/账户快照） - backtest/README.md: 回测模块使用说明 - backtest/STRATEGY.md: 策略开发指南与最佳实践 - backtest/TIMEFRAME_COMPARISON*.md: 多周期回测对比分析报告
147 lines
4.5 KiB
Python
147 lines
4.5 KiB
Python
"""
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回测引擎数据模型 — 配置、交易记录、绩效指标和结果
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"""
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from dataclasses import dataclass, field
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from datetime import datetime
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from typing import Optional
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@dataclass
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class BacktestConfig:
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"""回测配置
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Attributes:
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symbol: 交易对（如 BTCUSDT）
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exchange: 交易所标识
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interval: K 线周期
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start_time: 回测起始时间（None 表示从最早可用数据开始）
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end_time: 回测结束时间（None 表示到最新可用数据结束）
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commission_pct: 手续费率（0.001 = 0.1%）
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slippage_pct: 滑点率（0.0005 = 0.05%）
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min_order_qty: 最小下单量
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initial_capital: 初始资金（Quote 币种，如 USDT）
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warmup_bars: 预热 K 线条数（策略初始化指标所需最少数据量）
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"""
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symbol: str
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exchange: str = "binance"
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interval: str = "1h"
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start_time: Optional[datetime] = None
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end_time: Optional[datetime] = None
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# 交易成本
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commission_pct: float = 0.001
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slippage_pct: float = 0.0005
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min_order_qty: float = 0.001
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# 资金
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initial_capital: float = 10_000.0
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# 数据
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warmup_bars: int = 100
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@dataclass
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class BacktestTrade:
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"""单笔回测交易记录"""
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timestamp: float
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"""成交时间（Unix 毫秒）"""
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symbol: str
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"""交易对"""
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side: str
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"""方向：BUY / SELL"""
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price: float
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"""成交价格（含滑点）"""
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quantity: float
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"""成交数量（Base 币种）"""
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notional: float
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"""成交额（Quote 币种 = price × quantity）"""
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commission: float
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"""手续费"""
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slippage: float
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"""滑点成本"""
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pnl: Optional[float] = None
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"""平仓盈亏（BUY 时为 None，SELL 时有效）"""
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reason: str = ""
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"""交易原因（来自 Signal.reason）"""
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@dataclass
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class BacktestMetrics:
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"""回测绩效指标"""
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total_return_pct: float = 0.0
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"""总收益率（%）"""
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annual_return_pct: float = 0.0
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"""年化收益率（%）"""
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sharpe_ratio: float = 0.0
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"""夏普比率（无风险利率假定为 0）"""
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max_drawdown_pct: float = 0.0
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"""最大回撤（%），以负值表示"""
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max_drawdown_duration_days: int = 0
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"""最大回撤持续天数"""
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win_rate: float = 0.0
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"""胜率（0-1）"""
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profit_factor: float = 0.0
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"""盈亏比（总盈利 / 总亏损绝对值）"""
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total_trades: int = 0
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"""总交易次数"""
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avg_trade_pnl: float = 0.0
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"""平均每笔盈亏"""
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best_trade_pnl: float = 0.0
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"""最佳单笔盈亏"""
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worst_trade_pnl: float = 0.0
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"""最差单笔盈亏"""
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calmar_ratio: float = 0.0
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"""卡尔玛比率（年化收益 / 最大回撤绝对值）"""
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final_equity: float = 0.0
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"""最终权益"""
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@dataclass
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class BacktestResult:
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"""完整回测结果"""
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config: BacktestConfig
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"""回测配置"""
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strategy_config: dict
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"""策略配置（转为 dict 便于序列化）"""
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metrics: BacktestMetrics
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"""绩效指标"""
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trades: list[BacktestTrade] = field(default_factory=list)
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"""交易记录"""
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equity_curve: list[dict] = field(default_factory=list)
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"""资金曲线 [{"timestamp": float, "equity": float, "drawdown": float}, ...]"""
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@property
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def total_bars(self) -> int:
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"""回测 K 线总数"""
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return len(self.equity_curve)
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def summary(self) -> str:
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"""生成人类可读的摘要"""
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m = self.metrics
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lines = [
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"=" * 60,
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f" 回测结果摘要 — {self.config.symbol} {self.config.interval}",
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"=" * 60,
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f" 初始资金: {self.config.initial_capital:>12.2f} USDT",
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f" 最终权益: {m.final_equity:>12.2f} USDT",
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f" 总收益率: {m.total_return_pct:>11.2f}%",
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f" 年化收益率: {m.annual_return_pct:>11.2f}%",
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f" 夏普比率: {m.sharpe_ratio:>12.2f}",
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f" 卡尔玛比率: {m.calmar_ratio:>12.2f}",
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f" 最大回撤: {m.max_drawdown_pct:>11.2f}%",
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f" 回撤持续: {m.max_drawdown_duration_days:>9} 天",
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f" 总交易次数: {m.total_trades:>11}",
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f" 胜率: {m.win_rate:>11.1%}",
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f" 盈亏比: {m.profit_factor:>12.2f}",
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f" 平均盈亏: {m.avg_trade_pnl:>12.4f} USDT",
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f" 最佳盈亏: {m.best_trade_pnl:>12.4f} USDT",
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f" 最差盈亏: {m.worst_trade_pnl:>12.4f} USDT",
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"=" * 60,
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]
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return "\n".join(lines)
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