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经三轮代码审计后的综合质量加固版本，覆盖协议核心层、数据正确性、 错误处理、测试真实度与可维护性。761 单测全绿（+58），ruff/mypy 全过。 主要修复： - 离线 .day 写入原子化（fsync + _repair_tail + 读取校验，CQS 守住） - 回测止损前视偏差（延迟下一根开盘 + 跳空保护） - VWAP 权重索引 / bar_time fail-fast / 绩效除零保护 - 闭包绑定 / 路径穿越 / naive datetime 跨时区 / ruff UP038 重构： - 抽 AsyncHeartbeatMixin 收敛 4 处心跳副本（12→1） - 统一 _RETRY_DELAYS 退避序列 / scanner 失败可观测性 新增 5 个测试文件 + 公共 API 类型契约，CI 加 Windows 矩阵 + trusted publishing 签名 + 锁文件。 详见 CHANGELOG.md
563 lines
21 KiB
Python
563 lines
21 KiB
Python
"""run-all 命令 — 批量运行 strategies/ 目录下所有策略并比较结果。
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用法::
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easy-tdx run-all SZ 300308 --count 2000 --cash 1000000 --adjust QFQ
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输出每个策略的绩效指标，并按总收益率排名。
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"""
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from __future__ import annotations
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import importlib.util
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import logging
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import time
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from pathlib import Path
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from typing import Any
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import click
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logger = logging.getLogger(__name__)
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# ── 辅助函数 ──────────────────────────────────────────────────────────────────
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def _load_strategy_class(file_path: Path) -> type | None:
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"""从 Python 文件加载 Strategy 子类，失败返回 None。"""
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from ..backtest.strategy import Strategy
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spec = importlib.util.spec_from_file_location("strategy_module", file_path)
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if spec is None or spec.loader is None:
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return None
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module = importlib.util.module_from_spec(spec)
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try:
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spec.loader.exec_module(module)
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except Exception:
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# 策略文件可能有语法错误 / ImportError / 运行期异常，记录完整 traceback
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# 而非静默吞掉，否则用户只看到"加载失败"却不知根因（审计 #6）。
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logger.exception("策略文件加载失败: %s", file_path)
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return None
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for attr_name in dir(module):
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obj = getattr(module, attr_name)
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try:
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if isinstance(obj, type) and issubclass(obj, Strategy) and obj is not Strategy:
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return obj
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except TypeError:
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pass
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return None
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def _setup_chinese_font() -> None:
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"""配置 matplotlib 中文字体，按平台自动选择。"""
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import platform
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import matplotlib
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system = platform.system()
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if system == "Windows":
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candidates = ["Microsoft YaHei", "SimHei", "KaiTi", "FangSong"]
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elif system == "Darwin":
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candidates = ["PingFang SC", "Heiti SC", "STHeiti"]
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else:
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candidates = ["WenQuanYi Micro Hei", "Noto Sans CJK SC", "Droid Sans Fallback"]
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import matplotlib.font_manager as fm
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available = {f.name for f in fm.fontManager.ttflist}
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for font in candidates:
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if font in available:
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matplotlib.rcParams["font.sans-serif"] = [font, "DejaVu Sans"]
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break
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matplotlib.rcParams["axes.unicode_minus"] = False
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def _map_trade_values(trades_df: Any, equity: Any, initial_cash: float) -> list[float]:
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"""将交易的 datetime 映射到 equity_curve 对应的归一化值。"""
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eq_dt = equity["datetime"].values
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eq_norm = equity["total"].values / initial_cash
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result_vals: list[float] = []
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for dt in trades_df["datetime"].values:
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idx = eq_dt.searchsorted(dt, side="right") - 1
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if idx < 0:
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idx = 0
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if idx >= len(eq_norm):
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idx = len(eq_norm) - 1
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result_vals.append(float(eq_norm[idx]))
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return result_vals
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def _print_ranking(
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results: list[dict[str, Any]],
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backtest_results: dict[str, Any],
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) -> bool:
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"""输出策略绩效排名、综合评分和最佳策略明细。
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Returns:
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True 表示有有效结果，False 表示全部失败。
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"""
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valid = [r for r in results if "error" not in r]
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errored = [r for r in results if "error" in r]
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if not valid:
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click.echo("所有策略均运行失败！")
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for r in errored:
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click.echo(f" {r['strategy']}: {r['error']}")
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return False
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valid.sort(key=lambda x: x["total_return"], reverse=True)
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# ── 绩效排名 ──────────────────────────────────────────────────────────────
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click.echo("\n" + "=" * 80)
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click.echo("[*] 策略绩效排名 (按总收益率降序)")
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click.echo("=" * 80)
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click.echo(
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f"{'排名':>4} {'策略':<22} {'总收益率':>10} {'年化收益':>10} "
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f"{'最大回撤':>10} {'夏普':>8} {'胜率':>8} {'交易次数':>8} {'盈亏比':>8}"
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)
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click.echo("-" * 100)
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for i, r in enumerate(valid, 1):
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medal = " *1*" if i == 1 else " *2*" if i == 2 else " *3*" if i == 3 else " "
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click.echo(
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f"{medal}{i:>2} {r['strategy']:<22} "
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f"{r['total_return']:>9.2%} "
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f"{r['annual_return']:>9.2%} "
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f"{r['max_drawdown']:>9.2%} "
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f"{r['sharpe']:>8.2f} "
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f"{r['win_rate']:>7.1%} "
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f"{r['total_trades']:>8} "
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f"{r['profit_factor']:>8.2f}"
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)
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# ── 最佳策略详细报告 ──────────────────────────────────────────────────────
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best = valid[0]
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click.echo("\n" + "=" * 80)
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click.echo(f"[BEST] 最佳策略: {best['strategy']}")
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click.echo("=" * 80)
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click.echo(f" 总收益率: {best['total_return']:.2%}")
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click.echo(f" 年化收益: {best['annual_return']:.2%}")
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click.echo(f" 最大回撤: {best['max_drawdown']:.2%}")
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click.echo(f" 夏普比率: {best['sharpe']:.2f}")
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click.echo(f" 索提诺: {best['sortino']:.2f}")
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click.echo(f" 卡玛比率: {best['calmar']:.2f}")
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click.echo(f" 胜率: {best['win_rate']:.1%}")
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click.echo(f" 交易次数: {best['total_trades']}")
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click.echo(f" 盈亏比: {best['profit_factor']:.2f}")
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click.echo(f" 年化波动: {best['volatility']:.4f}")
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# ── 综合评分 ──────────────────────────────────────────────────────────────
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click.echo("\n" + "=" * 80)
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click.echo("[*] 综合评分排名 (Sharpe*0.4 + Ret/DD*0.3 + WinRate*0.3)")
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click.echo("=" * 80)
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scored: list[tuple[dict[str, Any], float]] = []
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for r in valid:
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ret_dd_ratio = r["annual_return"] / r["max_drawdown"] if r["max_drawdown"] > 1e-6 else 999.0
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score = r["sharpe"] * 0.4 + ret_dd_ratio * 0.3 + r["win_rate"] * 100 * 0.3
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scored.append((r, score))
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scored.sort(key=lambda x: x[1], reverse=True)
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click.echo(
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f"{'排名':>4} {'策略':<22} {'综合评分':>10} {'夏普':>8} {'收益/回撤':>10} {'胜率':>8}"
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)
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click.echo("-" * 70)
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for i, (r, score) in enumerate(scored, 1):
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ret_dd_ratio = r["annual_return"] / r["max_drawdown"] if r["max_drawdown"] > 1e-6 else 999.0
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medal = " *1*" if i == 1 else " *2*" if i == 2 else " *3*" if i == 3 else " "
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click.echo(
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f"{medal}{i:>2} {r['strategy']:<22} {score:>10.2f} "
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f"{r['sharpe']:>8.2f} {ret_dd_ratio:>10.2f} {r['win_rate']:>7.1%}"
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)
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# ── 最佳策略交易明细 ──────────────────────────────────────────────────────
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best_name = valid[0]["strategy"]
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if best_name in backtest_results:
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bt = backtest_results[best_name]
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bp = bt.performance
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bc = bt.config
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click.echo("\n" + "=" * 80)
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click.echo(f"[DETAIL] 最佳策略交易明细: {best_name}")
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click.echo("=" * 80)
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click.echo("=== 回测绩效概要 ===")
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click.echo(f"总收益率: {bp.get('total_return', 0):.2%}")
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click.echo(f"年化收益: {bp.get('annual_return', 0):.2%}")
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click.echo(f"最大回撤: {bp.get('max_drawdown', 0):.2%}")
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click.echo(f"夏普比率: {bp.get('sharpe', 0):.2f}")
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click.echo(f"胜率: {bp.get('win_rate', 0):.2%}")
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click.echo(f"交易次数: {bp.get('total_trades', 0)}")
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click.echo()
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click.echo("=== 配置参数 ===")
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click.echo(f"初始资金: {bc.get('cash', 0):.2f}")
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click.echo(f"佣金率: {bc.get('commission', 0):.4f}")
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click.echo(f"成交规则: {bc.get('execution', 'next_open')}")
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click.echo()
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if not bt.trades.empty:
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click.echo("=== 最近交易记录 ===")
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recent_trades = bt.trades.tail(10)
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for _, trade in recent_trades.iterrows():
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direction = "买入" if trade["direction"] == "BUY" else "卖出"
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status = "拒绝" if trade["rejected"] else "成交"
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click.echo(
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f" [{trade['datetime']}] {direction} "
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f"数量={trade['size']:.0f} 价格={trade['price']:.2f} "
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f"盈亏={trade['pnl']:.2f} [{status}]"
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)
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else:
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click.echo("无交易记录")
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# ── 报告错误 ──────────────────────────────────────────────────────────────
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if errored:
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click.echo("\n[!] 以下策略运行失败:")
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for r in errored:
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click.echo(f" {r['strategy']}: {r['error']}")
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return True
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def _run_combo_screen(
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strategy_classes: dict[str, type],
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df: Any,
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cash: float,
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commission: float,
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combo_sizes: tuple[int, ...],
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combo_mode: str,
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) -> None:
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"""运行多因子组合回测并输出排名。"""
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from math import comb
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from ..backtest.combo import CombinationRunner
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classes_list = list(strategy_classes.values())
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if len(classes_list) < 2:
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click.echo("[!] 策略数量不足 2 个，跳过组合回测")
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return
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# 单个 Runner 跨 size 复用信号缓存，避免重复提取
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runner = CombinationRunner(
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strategy_classes=classes_list,
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df=df,
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cash=cash,
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commission=commission,
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)
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for size in combo_sizes:
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total = comb(len(classes_list), size)
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click.echo("\n" + "=" * 80)
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click.echo(f"[*] {size}因子组合回测 (共{total}组, 模式={combo_mode})")
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click.echo("=" * 80)
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results = runner.screen(combo_sizes=(size,), mode=combo_mode.upper())
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if not results:
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click.echo(" 无有效交易组合（所有组合均为零交易）")
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continue
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click.echo(
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f"{'排名':>4} {'因子组合':<50} {'总收益率':>10} {'年化收益':>10} "
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f"{'最大回撤':>10} {'夏普':>8} {'胜率':>8} {'交易':>6}"
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)
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click.echo("-" * 120)
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for i, r in enumerate(results[:20], 1):
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medal = " *1*" if i == 1 else " *2*" if i == 2 else " *3*" if i == 3 else " "
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perf = r.result.performance
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click.echo(
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f"{medal}{i:>2} {r.name:<50} "
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f"{perf.get('total_return', 0):>9.2%} "
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f"{perf.get('annual_return', 0):>9.2%} "
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f"{perf.get('max_drawdown', 0):>9.2%} "
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f"{perf.get('sharpe', 0):>8.2f} "
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f"{perf.get('win_rate', 0):>7.1%} "
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f"{perf.get('total_trades', 0):>6}"
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)
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if len(results) > 20:
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click.echo(f" ... 共 {len(results)} 个有效组合，仅显示前 20")
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# 最佳组合详细报告
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best = results[0]
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bp = best.result.performance
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click.echo(f"\n[BEST {size}因子] {best.name}")
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click.echo(f" 总收益率: {bp.get('total_return', 0):.2%}")
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click.echo(f" 年化收益: {bp.get('annual_return', 0):.2%}")
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click.echo(f" 最大回撤: {bp.get('max_drawdown', 0):.2%}")
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click.echo(f" 夏普比率: {bp.get('sharpe', 0):.2f}")
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click.echo(f" 胜率: {bp.get('win_rate', 0):.1%}")
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def _show_best_chart(
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df: Any,
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result: Any,
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strategy_name: str,
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stock_label: str,
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stock_name: str,
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initial_cash: float,
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) -> None:
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"""展示最佳策略资金曲线与股价归一化对比图。"""
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try:
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import matplotlib.pyplot as plt
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except ImportError:
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click.echo("[!] 需要 matplotlib 才能展示图表: pip install matplotlib")
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return
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_setup_chinese_font()
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equity = result.equity_curve
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if equity.empty:
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click.echo("[!] 最佳策略无资金曲线数据，跳过绘图")
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return
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fig, ax1 = plt.subplots(figsize=(14, 7))
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# 归一化股价（以第一天收盘价为基准）
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close_prices = df["close"].values
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norm_price = close_prices / close_prices[0]
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dates = df["datetime"] if "datetime" in df.columns else df.index
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ax1.plot(dates, norm_price, color="steelblue", linewidth=1.2, label="股价 (归一化)")
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ax1.set_ylabel("股价归一化", color="steelblue", fontsize=11)
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ax1.tick_params(axis="y", labelcolor="steelblue")
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# 归一化资金曲线（以初始资金为基准）
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eq_dates = equity["datetime"]
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eq_values = equity["total"].values / initial_cash
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ax2 = ax1.twinx()
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ax2.plot(eq_dates, eq_values, color="crimson", linewidth=1.5, label=f"策略: {strategy_name}")
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ax2.set_ylabel("资金曲线 (归一化)", color="crimson", fontsize=11)
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ax2.tick_params(axis="y", labelcolor="crimson")
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# 标记买卖点
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trades = result.trades
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if not trades.empty:
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buy_trades = trades[trades["direction"] == "BUY"]
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sell_trades = trades[trades["direction"] == "SELL"]
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if not buy_trades.empty:
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ax2.scatter(
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buy_trades["datetime"].values,
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_map_trade_values(buy_trades, equity, initial_cash),
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marker="^",
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color="green",
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s=30,
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alpha=0.7,
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zorder=5,
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label="买入",
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)
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if not sell_trades.empty:
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ax2.scatter(
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sell_trades["datetime"].values,
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_map_trade_values(sell_trades, equity, initial_cash),
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marker="v",
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color="orange",
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s=30,
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alpha=0.7,
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zorder=5,
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label="卖出",
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)
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# 标题：股票代码 + 名称 + 策略绩效
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title = f"{stock_label}"
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if stock_name:
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title += f" {stock_name}"
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perf = result.performance
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ret_str = f"{perf.get('total_return', 0):.1%}"
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dd_str = f"{perf.get('max_drawdown', 0):.1%}"
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sharpe_str = f"{perf.get('sharpe', 0):.2f}"
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title += f" | 最佳策略: {strategy_name} | 收益 {ret_str} 回撤 {dd_str} 夏普 {sharpe_str}"
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ax1.set_title(title, fontsize=12, pad=15)
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ax1.set_xlabel("日期", fontsize=11)
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# 合并两个轴的图例
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lines1, labels1 = ax1.get_legend_handles_labels()
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lines2, labels2 = ax2.get_legend_handles_labels()
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ax1.legend(lines1 + lines2, labels1 + labels2, loc="upper left", fontsize=9)
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fig.autofmt_xdate()
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plt.tight_layout()
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click.echo("\n正在显示图表，关闭窗口后继续...")
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plt.show()
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# ── 主命令 ──────────────────────────────────────────────────────────────────
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||
@click.command("run-all")
|
||
@click.argument("market")
|
||
@click.argument("code")
|
||
@click.option("--count", default=2000, type=int, help="K线数量")
|
||
@click.option("--cash", default=1000000.0, type=float, help="初始资金")
|
||
@click.option("--commission", default=0.0003, type=float, help="佣金率")
|
||
@click.option("--adjust", default="QFQ", help="复权: NONE/QFQ/HFQ")
|
||
@click.option("--period", default="DAILY", help="K线周期")
|
||
@click.option(
|
||
"--combo",
|
||
"combo_sizes",
|
||
multiple=True,
|
||
type=int,
|
||
help="多因子组合回测（可多次指定，如 --combo 2 --combo 3）",
|
||
)
|
||
@click.option(
|
||
"--combo-mode",
|
||
"combo_mode",
|
||
default="MAJORITY",
|
||
type=click.Choice(["AND", "OR", "MAJORITY"], case_sensitive=False),
|
||
help="多因子信号合并模式（默认 MAJORITY）",
|
||
)
|
||
@click.option("--show", "show_chart", is_flag=True, help="显示最佳策略资金曲线 vs 股价对比图")
|
||
@click.option(
|
||
"--strategies-dir",
|
||
"strategies_dir",
|
||
default="strategies",
|
||
help="策略文件目录（默认 strategies/）",
|
||
)
|
||
def run_all(
|
||
market: str,
|
||
code: str,
|
||
count: int,
|
||
cash: float,
|
||
commission: float,
|
||
adjust: str,
|
||
period: str,
|
||
combo_sizes: tuple[int, ...],
|
||
combo_mode: str,
|
||
show_chart: bool,
|
||
strategies_dir: str,
|
||
) -> None:
|
||
"""批量运行 strategies/ 目录下所有策略并比较结果。
|
||
|
||
依次运行指定目录下所有策略文件，输出绩效排名和综合评分。
|
||
|
||
示例：
|
||
|
||
easy-tdx run-all SZ 300308 --count 2000 --cash 1000000 --adjust QFQ
|
||
|
||
easy-tdx run-all SZ 300308 --combo 2 --combo-mode MAJORITY
|
||
|
||
easy-tdx run-all SZ 300308 --show
|
||
"""
|
||
from ..backtest.engine import BacktestEngine
|
||
from ..cli.conn import get_mac_client
|
||
from ..cli.parsers import parse_adjust, parse_market, parse_period
|
||
|
||
# 1. 发现策略文件
|
||
sdir = Path(strategies_dir)
|
||
strategy_files = sorted(sdir.glob("*.py"))
|
||
if not strategy_files:
|
||
click.echo(f"未找到策略文件 ({strategies_dir}/*.py)", err=True)
|
||
raise SystemExit(1)
|
||
|
||
click.echo(f"发现 {len(strategy_files)} 个策略文件")
|
||
click.echo(f"标的: {market} {code} | K线: {count} | 资金: {cash:,.0f} | 复权: {adjust}")
|
||
click.echo("=" * 80)
|
||
|
||
# 2. 获取数据（所有策略共享同一份数据）
|
||
mkt = parse_market(market)
|
||
click.echo("正在获取行情数据...")
|
||
stock_name = ""
|
||
with get_mac_client() as client:
|
||
df = client.get_stock_kline(
|
||
mkt,
|
||
code,
|
||
period=parse_period(period),
|
||
start=0,
|
||
count=count,
|
||
adjust=parse_adjust(adjust),
|
||
)
|
||
# 获取股票名称（仅图表模式需要）
|
||
if show_chart:
|
||
try:
|
||
quotes_df = client.get_stock_quotes([(mkt, code)])
|
||
if not quotes_df.empty and "name" in quotes_df.columns:
|
||
stock_name = str(quotes_df.iloc[0]["name"])
|
||
except Exception:
|
||
pass
|
||
click.echo(f"获取到 {len(df)} 条K线数据")
|
||
click.echo("=" * 80)
|
||
|
||
# 3. 逐个运行策略
|
||
results: list[dict[str, Any]] = []
|
||
backtest_results: dict[str, Any] = {}
|
||
strategy_classes: dict[str, type] = {}
|
||
|
||
for sf in strategy_files:
|
||
strategy_name = sf.stem
|
||
click.echo(f"\n>> 运行策略: {strategy_name} ...", nl=False)
|
||
|
||
strategy_cls = _load_strategy_class(sf)
|
||
if strategy_cls is None:
|
||
click.echo(" [加载失败/无 Strategy 子类]")
|
||
continue
|
||
|
||
strategy_classes[strategy_name] = strategy_cls
|
||
t0 = time.perf_counter()
|
||
try:
|
||
engine = BacktestEngine(
|
||
strategy=strategy_cls,
|
||
cash=cash,
|
||
commission=commission,
|
||
)
|
||
result = engine.run(df)
|
||
elapsed = time.perf_counter() - t0
|
||
perf = result.performance
|
||
click.echo(f" 完成 ({elapsed:.1f}s)")
|
||
|
||
results.append(
|
||
{
|
||
"strategy": strategy_name,
|
||
"total_return": perf.get("total_return", 0),
|
||
"annual_return": perf.get("annual_return", 0),
|
||
"max_drawdown": perf.get("max_drawdown", 0),
|
||
"sharpe": perf.get("sharpe", 0),
|
||
"sortino": perf.get("sortino", 0),
|
||
"calmar": perf.get("calmar", 0),
|
||
"win_rate": perf.get("win_rate", 0),
|
||
"total_trades": perf.get("total_trades", 0),
|
||
"profit_factor": perf.get("profit_factor", 0),
|
||
"volatility": perf.get("volatility", 0),
|
||
}
|
||
)
|
||
backtest_results[strategy_name] = result
|
||
except Exception as e:
|
||
elapsed = time.perf_counter() - t0
|
||
click.echo(f" 错误 ({elapsed:.1f}s): {e}")
|
||
results.append({"strategy": strategy_name, "error": str(e)})
|
||
|
||
# 4. 输出排名
|
||
has_valid = _print_ranking(results, backtest_results)
|
||
if not has_valid:
|
||
raise SystemExit(1)
|
||
|
||
# 5. 多因子组合回测
|
||
if combo_sizes:
|
||
_run_combo_screen(
|
||
strategy_classes=strategy_classes,
|
||
df=df,
|
||
cash=cash,
|
||
commission=commission,
|
||
combo_sizes=combo_sizes,
|
||
combo_mode=combo_mode,
|
||
)
|
||
|
||
# 6. 展示最佳策略曲线图
|
||
if show_chart:
|
||
valid = [r for r in results if "error" not in r]
|
||
if valid:
|
||
valid.sort(key=lambda x: x["total_return"], reverse=True)
|
||
best_name = valid[0]["strategy"]
|
||
if best_name in backtest_results:
|
||
_show_best_chart(
|
||
df=df,
|
||
result=backtest_results[best_name],
|
||
strategy_name=best_name,
|
||
stock_label=f"{market}{code}",
|
||
stock_name=stock_name,
|
||
initial_cash=cash,
|
||
)
|