mirror of
https://ghfast.top/https://github.com/aeroxw/easy_tdx_max.git
synced 2026-09-12 15:44:18 +08:00
Justin Gu
06b2617ebc
- Add --cov and --cov-fail-under=50 to CI pytest command - Replace hardcoded avg_holding_days=5.0 with FIFO-based calculation from actual trade datetime pairs (handles int and Timestamp types) - Vectorize _datetime_to_int using pd.to_datetime().strftime() instead of Python for-loop (~100-200x faster on large arrays) - Add 3 new test cases: weighted holding days, no datetime fallback, only-buys edge case
320 lines
10 KiB
Python
320 lines
10 KiB
Python
"""回测绩效分析器。
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计算资金曲线和交易记录的各项绩效指标。
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"""
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from __future__ import annotations
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from collections import deque
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from typing import TYPE_CHECKING
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import numpy as np
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import numpy.typing as npt
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import pandas as pd
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if TYPE_CHECKING:
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NDArray = npt.NDArray[np.float64]
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else:
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NDArray = np.ndarray
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class PerformanceAnalyzer:
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"""绩效分析器。
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从资金曲线和交易记录计算 19 项绩效指标。
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Attributes:
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ANNUAL_DAYS: 年化交易日数（默认 252）
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RISK_FREE_RATE: 无风险利率（默认 3%）
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"""
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ANNUAL_DAYS = 252
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RISK_FREE_RATE = 0.03
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def __init__(
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self,
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equity_curve: pd.DataFrame,
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trades: pd.DataFrame,
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risk_free_rate: float = 0.03,
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) -> None:
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"""初始化分析器。
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Args:
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equity_curve: 资金曲线 DataFrame，必须包含 total 和 drawdown 列
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trades: 交易记录 DataFrame，必须包含 direction, pnl, rejected 列
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risk_free_rate: 无风险利率（默认 3%）
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"""
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self._equity_curve = equity_curve
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self._trades = trades
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self._risk_free_rate = risk_free_rate
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def compute(self) -> dict[str, float]:
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"""计算绩效指标。
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Returns:
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包含 19 项指标的字典：
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- total_return: 总收益率
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- annual_return: 年化收益率
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- max_drawdown: 最大回撤
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- max_dd_duration: 最大回撤持续时间（bar 数）
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- sharpe: 夏普比率
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- sortino: 索提诺比率
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- calmar: 卡玛比率
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- total_trades: 总交易次数
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- win_trades: 盈利交易次数
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- lose_trades: 亏损交易次数
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- rejected_trades: 被拒绝的交易次数
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- win_rate: 胜率
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- profit_factor: 盈亏比
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- avg_win: 平均盈利
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- avg_loss: 平均亏损
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- max_win: 最大盈利
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- max_loss: 最大亏损
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- avg_holding_days: 平均持仓天数（简化为固定值 5.0）
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- volatility: 年化波动率
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"""
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# 边界检查
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if len(self._equity_curve) < 2:
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return self._empty_metrics()
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total = self._equity_curve["total"].to_numpy()
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drawdown = self._equity_curve["drawdown"].to_numpy()
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# 计算日收益率
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daily_ret = np.diff(total) / total[:-1]
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daily_ret = daily_ret[~np.isnan(daily_ret)]
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# 日收益率数量太少时返回空指标
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if len(daily_ret) < 2:
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return self._empty_metrics()
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# 1. 总收益率
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total_return = (total[-1] / total[0]) - 1
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# 2. 年化收益率
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n = len(daily_ret)
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annual_return = (1 + total_return) ** (self.ANNUAL_DAYS / n) - 1
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# 3. 最大回撤（从峰值的最大跌幅百分比，0~1 之间）
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drawdown_pct = self._equity_curve["drawdown_pct"].to_numpy()
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max_drawdown = float(np.max(drawdown_pct))
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# 4. 最大回撤持续时间
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max_dd_duration = self._compute_max_dd_duration(total, drawdown)
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# 5. 夏普比率
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rf_daily = self._risk_free_rate / self.ANNUAL_DAYS
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excess_ret = daily_ret - rf_daily
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sharpe = (
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np.mean(excess_ret) / np.std(daily_ret) * np.sqrt(self.ANNUAL_DAYS)
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if np.std(daily_ret) != 0
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else 0
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)
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# 6. 索提诺比率（分母只用负收益标准差）
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neg_ret = excess_ret[excess_ret < 0]
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if len(neg_ret) > 0 and np.std(neg_ret) != 0:
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sortino = np.mean(excess_ret) / np.std(neg_ret) * np.sqrt(self.ANNUAL_DAYS)
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elif len(neg_ret) == 0 and np.mean(excess_ret) > 0:
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# 没有负收益时，返回一个很大的值表示表现优异
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sortino = 999.0
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else:
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sortino = 0.0
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# 7. 卡玛比率
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# 使用小阈值避免除以极小值
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if max_drawdown > 1e-10:
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calmar = annual_return / max_drawdown
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elif annual_return > 0:
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# 无回撤且有正收益时，返回一个很大的值
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calmar = 999.0
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else:
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calmar = 0.0
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# 交易统计
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sell_trades = self._trades[self._trades["direction"] == "SELL"]
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win_trades_mask = sell_trades["pnl"] > 0
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lose_trades_mask = sell_trades["pnl"] <= 0
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# 8. 总交易次数
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total_trades = len(sell_trades)
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# 9. 盈利交易次数
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win_count = np.sum(win_trades_mask)
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# 10. 亏损交易次数
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lose_count = np.sum(lose_trades_mask)
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# 11. 被拒绝的交易次数
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rejected_trades = self._trades["rejected"].sum()
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# 12. 胜率
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win_rate = win_count / (win_count + lose_count) if (win_count + lose_count) > 0 else 0
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# 13. 盈亏比
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win_pnl = sell_trades.loc[win_trades_mask, "pnl"]
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lose_pnl = sell_trades.loc[lose_trades_mask, "pnl"]
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if len(win_pnl) > 0 and len(lose_pnl) > 0:
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profit_factor = win_pnl.sum() / abs(lose_pnl.sum())
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# 限制 inf
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if np.isinf(profit_factor):
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profit_factor = 999.0
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else:
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profit_factor = 0.0
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# 14. 平均盈利
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avg_win = win_pnl.mean() if len(win_pnl) > 0 else 0.0
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# 15. 平均亏损
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avg_loss = lose_pnl.mean() if len(lose_pnl) > 0 else 0.0
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# 16. 最大盈利
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max_win = win_pnl.max() if len(win_pnl) > 0 else 0.0
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# 17. 最大亏损
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max_loss = lose_pnl.min() if len(lose_pnl) > 0 else 0.0
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# 18. 平均持仓天数（FIFO 配对计算）
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avg_holding_days = self._compute_avg_holding_days()
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# 19. 年化波动率
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volatility = np.std(daily_ret) * np.sqrt(self.ANNUAL_DAYS)
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return {
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"total_return": total_return,
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"annual_return": annual_return,
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"max_drawdown": max_drawdown,
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"max_dd_duration": max_dd_duration,
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"sharpe": sharpe,
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"sortino": sortino,
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"calmar": calmar,
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"total_trades": total_trades,
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"win_trades": win_count,
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"lose_trades": lose_count,
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"rejected_trades": rejected_trades,
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"win_rate": win_rate,
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"profit_factor": profit_factor,
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"avg_win": avg_win,
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"avg_loss": avg_loss,
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"max_win": max_win,
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"max_loss": max_loss,
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"avg_holding_days": avg_holding_days,
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"volatility": volatility,
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}
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def _compute_avg_holding_days(self) -> float:
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"""计算平均持仓天数（FIFO 配对）。
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遍历非 rejected 的交易记录，使用 FIFO 队列配对买入和卖出，
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按 size 加权计算平均持仓天数。
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Returns:
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加权平均持仓天数，无完整配对时返回 0.0
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"""
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if "datetime" not in self._trades.columns:
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return 0.0
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# 只处理非 rejected 的交易
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valid = self._trades[~self._trades["rejected"]]
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if len(valid) == 0:
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return 0.0
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buy_queue: deque[tuple[int, float]] = deque() # (datetime, size)
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total_days = 0.0
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total_size = 0.0
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for _, row in valid.iterrows():
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raw_dt = row["datetime"]
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# datetime 可能是 int (YYYYMMDD) 或 pd.Timestamp
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dt = (
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int(raw_dt)
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if not isinstance(raw_dt, pd.Timestamp)
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else int(raw_dt.strftime("%Y%m%d"))
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)
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direction = row["direction"]
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size = float(row["size"]) if "size" in valid.columns else 100.0
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if direction == "BUY":
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buy_queue.append((dt, size))
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elif direction == "SELL" and buy_queue:
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remaining = size
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while remaining > 0 and buy_queue:
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buy_dt, buy_size = buy_queue[0]
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# 消费该笔 BUY 的部分或全部
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consumed = min(remaining, buy_size)
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holding_days = dt - buy_dt
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total_days += holding_days * consumed
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total_size += consumed
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remaining -= consumed
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buy_size -= consumed
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if buy_size <= 0:
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buy_queue.popleft()
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else:
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buy_queue[0] = (buy_dt, buy_size)
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if total_size == 0:
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return 0.0
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return total_days / total_size
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def _compute_max_dd_duration(self, total: NDArray, drawdown: NDArray) -> int:
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"""计算最大回撤持续时间。
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找到最大回撤点，然后计算从回撤前的高点到该点的 bar 数。
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Args:
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total: 总权益数组
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drawdown: 回撤数组
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Returns:
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最大回撤持续时间（bar 数）
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"""
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if len(drawdown) == 0:
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return 0
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max_dd_idx: int = int(np.argmax(drawdown))
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max_dd_value = drawdown[max_dd_idx]
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# 如果没有回撤，返回 0
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if max_dd_value == 0:
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return 0
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# 找到回撤前的高点
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peak_idx: int = max_dd_idx
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for i in range(max_dd_idx - 1, -1, -1):
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if total[i] > total[max_dd_idx]:
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peak_idx = i
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break
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return int(max_dd_idx - peak_idx)
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def _empty_metrics(self) -> dict[str, float]:
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"""返回全零指标字典。
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用于数据不足时的默认返回值。
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Returns:
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全零的绩效指标字典
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"""
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return {
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"total_return": 0.0,
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"annual_return": 0.0,
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"max_drawdown": 0.0,
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"max_dd_duration": 0,
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"sharpe": 0.0,
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"sortino": 0.0,
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"calmar": 0.0,
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"total_trades": 0,
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"win_trades": 0,
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"lose_trades": 0,
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"rejected_trades": 0,
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"win_rate": 0.0,
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"profit_factor": 0.0,
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"avg_win": 0.0,
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"avg_loss": 0.0,
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"max_win": 0.0,
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"max_loss": 0.0,
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"avg_holding_days": 0.0,
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"volatility": 0.0,
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}
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