claude_dev
8d55e414fa
审计发现包装层系统性失真(2 CRITICAL+7 HIGH)，vnpy底座可信但A股场景未适配： - C1 定寸: engine.size=N(满仓手数)，策略volume=1手=N股，开平对称(pos归零) - C2 做空拦截: SHORT+OPEN拒单，long-only，SHORT+CLOSE平多允许 - H3 A股费用: AShareDailyResult重算(佣金保底5元/印花税卖方/过户费沪市) - H4 收益口径: simple return从balance算(不再用vnpy log return喂empyrical) - H5+口径: benchmark ffill对齐不缩样本; sizing_shares_per_lot暴露 - H7 退化检测: 零成交/空数据标degenerate不静默done - H8 task_id: optimize/factor用uuid4(原id()内存地址) - 静默except改warning 验证: 容器内真实vnpy DoubleMa 600000 2022-2024, total_return 1e-6→42.3%, end_balance 100万→142万, SHORT+OPEN成交0笔, N=7800股/手. 22 backtest测试全绿(含集成测试), API健康200.
97 lines
4.3 KiB
Python
97 lines
4.3 KiB
Python
"""回测相对/绝对指标计算（empyrical，聚宽同源口径）。纯函数。"""
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from dataclasses import dataclass, field
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from typing import Dict, Literal
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import math
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import numpy as np
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import pandas as pd
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import empyrical
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BenchmarkCode = Literal["hs300", "zz500"]
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BENCHMARK_SYMBOL: Dict[str, str] = {"hs300": "sh000300", "zz500": "sz000905"}
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@dataclass
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class MetricsResult:
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scalars: Dict[str, float] = field(default_factory=dict)
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series: Dict[str, pd.Series] = field(default_factory=dict)
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def compute_metrics(
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daily_df: pd.DataFrame,
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benchmark_returns: pd.Series,
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period: int = 252,
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) -> MetricsResult:
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"""对 vnpy daily_df + 基准日收益计算聚宽级指标。
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H4: 从 daily_df["balance"] 自算 simple return（vnpy df["return"] 是 log return，
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empyrical 期望 simple return，直接用会失真）。
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H5: period='daily' → empyrical 内部 252 交易日年化，口径统一。
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benchmark 对齐：reindex 到策略交易日 + ffill，不丢策略日期（原 dropna 会丢停牌日）。
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daily_df: vnpy calculate_result() 产出，须含 "balance" 列（账户余额），index 为日期。
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benchmark_returns: 基准日收益率 Series，index 为日期（不必与 daily_df 对齐）。
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period: 年化周期（整数，默认252交易日），empyrical 内部使用 'daily'
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"""
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# H4: simple return from balance（pct_change 首项 NaN → 0）
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if "balance" not in daily_df.columns:
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raise ValueError("daily_df 缺少 balance 列，无法计算 simple return")
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strat = daily_df["balance"].astype(float).pct_change().fillna(0)
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# benchmark ffill 对齐：reindex 到策略交易日，前向填充，不丢策略日期
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b = benchmark_returns.reindex(daily_df.index).ffill().fillna(0)
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aligned = pd.concat([strat.rename("s"), b.rename("b")], axis=1)
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s, b = aligned["s"], aligned["b"]
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scalars = {
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"total_return": float(empyrical.cum_returns_final(s)),
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"annual_return": float(empyrical.annual_return(s, period='daily')),
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"alpha": float(empyrical.alpha(s, b, period='daily')),
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"beta": float(empyrical.beta(s, b)),
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"sharpe_ratio": float(empyrical.sharpe_ratio(s, period='daily')),
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"sortino_ratio": float(empyrical.sortino_ratio(s, period='daily')),
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"information_ratio": float(empyrical.excess_sharpe(s, b)),
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"annual_volatility": float(empyrical.annual_volatility(s, period='daily')),
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"max_drawdown": float(empyrical.max_drawdown(s)),
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"benchmark_return": float(empyrical.cum_returns_final(b)),
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"benchmark_volatility": float(empyrical.annual_volatility(b, period='daily')),
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}
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# Sanitize non-finite floats (NaN/Inf from degenerate inputs) → None for JSON safety
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scalars = {k: (None if isinstance(v, float) and not math.isfinite(v) else v) for k, v in scalars.items()}
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equity = empyrical.cum_returns(s)
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bench_curve = empyrical.cum_returns(b)
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# rolling alpha/beta (expanding window 用于画图，口径由 scalars 保证)
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roll_beta = pd.Series(index=s.index, dtype=float)
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roll_alpha = pd.Series(index=s.index, dtype=float)
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for i in range(len(s)):
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sub = aligned.iloc[: i + 1]
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if len(sub) >= 2 and sub["b"].var() > 0:
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beta = sub["s"].cov(sub["b"]) / sub["b"].var()
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alpha = sub["s"].mean() - beta * sub["b"].mean()
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roll_beta.iloc[i] = beta
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roll_alpha.iloc[i] = alpha * period
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# drawdown: 从峰值回落 (值 <= 0)
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cummax = equity.cummax()
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drawdown = (equity - cummax) / cummax
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# Rolling annualized volatility (quarterly window) for the volatility chart
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_vol_window = min(63, len(s))
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if _vol_window >= 2:
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vol_strategy = s.rolling(_vol_window, min_periods=2).std() * np.sqrt(period)
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vol_benchmark = b.rolling(_vol_window, min_periods=2).std() * np.sqrt(period)
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else:
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vol_strategy = pd.Series([np.nan] * len(s), index=s.index)
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vol_benchmark = pd.Series([np.nan] * len(s), index=s.index)
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series = {
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"equity_curve": equity,
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"benchmark_curve": bench_curve,
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"alpha": roll_alpha,
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"beta": roll_beta,
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"drawdown": drawdown,
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"volatility_strategy": vol_strategy,
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"volatility_benchmark": vol_benchmark,
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}
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return MetricsResult(scalars=scalars, series=series)
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