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https://ghfast.top/https://github.com/aeroxw/easy_tdx_max.git
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Justin Gu
e374a0da28
对 v1.21→v1.32.5 的 249 文件 4.2 万行改动做六路专项审查，本轮落地全部发现： 回测正确性：组合收益 fillna(0) 虚增、轮动停牌日过期价成交、单标的 WF 逐窗指标 被预热区稀释（三件套均带先红后绿回归）；worst_drawdown 方向、grading 容错、 组合体检品种费率、寻优端点费率透传。 安全：LLM api_url 仅 http/https 且禁 userinfo（封死 file:// 读取与 Key 外送链）、 错误响应不回显原始 body、响应体 2MB 上限、配置原子写、坏配置字段级防御。 数据：涨跌停价整数分币舍入（67/318/90 个价位错 1 分漏判清零）、交易时段/采样/ provisional 统一沪时区、warehouse 增量缺口自动全量重拉、provisional 定点转正、 baostock 真故障抛错 + W/M 去 tradestatus（实测服务端报错，周月兜底此前从未工作） + 指数 vol 股→手（实测锚定）、ccpm 结构变更抛错。 Web API：缓存键补 count/vipdoc、NaN 清洗先于缓存、count>800 分页取全量、 submit 透传真实状态、pending 不再被淘汰成幽灵、watchlist/server 入参约束。 公式：FILTER 去副作用、0-1 值域误判收严、递归深度上限、REF 负移位显式禁止。 前端：4 处请求竞态序号守卫、Sparkline viewBox、北交所 market=2 映射、 空数据缓存死角、AI 弹窗卸载中止轮询、量能/资金日历口径修正。 CLI/CI：warehouse sync 失败 exit 1、参数校验干净报错、release 真实发布 SHA256、 CI 超时与缓存、spec 补 baostock 前提。 约 60 条回归测试先红后绿；pytest 1820 全过，ruff/mypy/vue-tsc/node --test 全绿。
642 lines
21 KiB
Python
642 lines
21 KiB
Python
"""数据评级系统（S/A/B/C/D）的 Python 后端实现（v1.25 新增）。
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移植自 ``web-ui/src/grading/``（engine.ts / thresholds.ts / index.ts /
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combinedMetrics.ts）——此前评级只存在于前端 TS，CLI 与 REST API 无法输出。
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本模块与其保持口径一致（对拍单测保证），三通道均可获得评级。
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评级哲学（同前端）：**不看收益率**。收益维度只通过卡玛/夏普间接体现，
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「哪怕近期收益率高，长期风险大也该低评」。六维加权（单标的场景）+
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一票否决（亏损系统 / 深度套牢 / 极低胜率 / 高回撤 / 微利）。
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场景：
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- :func:`grade_performance`：单标的回测（完整 19 项绩效，6 维度）
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- :func:`grade_grid_point`：参数寻优网格点（4 字段子集，4 维度降级版）
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- :func:`grade_portfolio_equity`：组合回测（净值曲线重算，5 维度）
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阈值锚点集中在本文件 ``THRESHOLDS``，与前端 thresholds.ts 一一对应；
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两处需同步修改时以对拍单测（``test_grading_backend.py``）为门禁。
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"""
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from __future__ import annotations
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import math
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from collections.abc import Iterable
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from dataclasses import dataclass, field
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from typing import Any, Literal
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__all__ = [
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"Grade",
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"Anchor",
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"DimensionScore",
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"VetoHit",
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"GradeResult",
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"CombinedMetrics",
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"compute_combined_metrics",
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"interpolate",
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"score_to_grade",
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"grade_performance",
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"grade_grid_point",
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"grade_portfolio_equity",
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]
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Grade = Literal["S", "A", "B", "C", "D"]
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_TRADING_DAYS_PER_YEAR = 252
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# 档位 → 最低分（score_to_grade）
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_GRADE_THRESHOLDS: list[tuple[Grade, float]] = [
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("S", 90.0),
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("A", 80.0),
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("B", 65.0),
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("C", 50.0),
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("D", 0.0),
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]
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_GRADE_ORDER: list[Grade] = ["D", "C", "B", "A", "S"]
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@dataclass(frozen=True)
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class Anchor:
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"""(指标原始值, 对应分数) 锚点，线性插值用。"""
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threshold: float
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score: float
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# 阈值锚点表（与前端 thresholds.ts 一一对应，勿单侧修改）
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THRESHOLDS: dict[str, tuple[str, tuple[Anchor, ...]]] = {
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"calmar": (
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"卡玛比率",
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(
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Anchor(0.0, 0),
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Anchor(0.3, 20),
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Anchor(0.5, 35),
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Anchor(0.8, 50),
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Anchor(1.0, 65),
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Anchor(1.5, 80),
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Anchor(2.0, 90),
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Anchor(3.0, 100),
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),
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),
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"sharpe": (
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"夏普比率",
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(
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Anchor(0.0, 10),
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Anchor(0.3, 25),
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Anchor(0.5, 40),
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Anchor(0.8, 55),
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Anchor(1.0, 68),
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Anchor(1.5, 82),
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Anchor(2.0, 92),
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Anchor(3.0, 100),
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),
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),
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"sortino": (
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"索提诺比率",
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(
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Anchor(0.0, 10),
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Anchor(0.5, 30),
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Anchor(1.0, 50),
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Anchor(1.5, 65),
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Anchor(2.0, 78),
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Anchor(2.5, 88),
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Anchor(4.0, 100),
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),
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),
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"max_drawdown": (
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"最大回撤",
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(
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Anchor(0.0, 100),
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Anchor(0.1, 88),
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Anchor(0.15, 78),
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Anchor(0.2, 68),
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Anchor(0.25, 58),
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Anchor(0.3, 48),
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Anchor(0.4, 30),
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Anchor(0.5, 15),
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Anchor(0.6, 0),
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),
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),
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"volatility": (
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"波动率",
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(
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Anchor(0.0, 100),
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Anchor(0.1, 85),
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Anchor(0.15, 75),
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Anchor(0.2, 62),
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Anchor(0.25, 50),
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Anchor(0.3, 38),
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Anchor(0.4, 22),
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Anchor(0.6, 0),
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),
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),
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# 回撤持续：该维度输入的单位是「bar 数」（performance.max_dd_duration 输出
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# 水下期的 bar 数），下方天数锚点按日线（1 bar ≈ 1 交易日）校准。分钟级
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# 周期下 bar 数远大于天数，得分会系统性偏低（偏保守）。注意：评分维度
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# （grade_performance / grade_portfolio_equity）当前均未使用该表项，
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# 锚点仅供展示与前端对照。
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"max_dd_duration": (
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"回撤持续",
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(
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Anchor(0, 100),
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Anchor(30, 80),
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Anchor(90, 62),
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Anchor(180, 45),
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Anchor(365, 28),
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Anchor(730, 10),
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Anchor(1095, 0),
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),
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),
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"win_rate": (
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"胜率",
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(
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Anchor(0.0, 0),
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Anchor(0.25, 12),
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Anchor(0.3, 22),
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Anchor(0.35, 32),
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Anchor(0.4, 45),
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Anchor(0.45, 58),
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Anchor(0.5, 70),
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Anchor(0.55, 82),
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Anchor(0.6, 92),
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Anchor(0.7, 100),
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),
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),
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"profit_factor": (
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"利润因子",
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(
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Anchor(0.0, 0),
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Anchor(0.8, 10),
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Anchor(1.0, 25),
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Anchor(1.2, 42),
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Anchor(1.5, 60),
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Anchor(1.8, 75),
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Anchor(2.0, 84),
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Anchor(2.5, 92),
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Anchor(3.0, 100),
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),
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),
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}
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@dataclass
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class DimensionScore:
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"""单个评分维度。"""
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key: str
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label: str
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raw: float
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score: float
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weight: float
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def to_dict(self) -> dict[str, Any]:
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return {
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"key": self.key,
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"label": self.label,
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"raw": self.raw,
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"score": round(self.score, 1),
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"weight": round(self.weight, 4),
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}
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@dataclass
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class VetoHit:
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"""触发的一票否决规则。"""
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key: str
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reason: str
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cap: Grade
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def to_dict(self) -> dict[str, str]:
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return {"key": self.key, "reason": self.reason, "cap": self.cap}
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@dataclass
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class GradeResult:
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"""评级结果。"""
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grade: Grade
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score: float # 0-100，保留 1 位小数的加权原始分
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dimensions: list[DimensionScore] = field(default_factory=list)
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vetoes: list[VetoHit] = field(default_factory=list)
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insufficient_sample: bool = False
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is_losing: bool = False
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scenario: str = "single"
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def to_dict(self) -> dict[str, Any]:
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return {
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"grade": self.grade,
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"score": self.score,
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"dimensions": [d.to_dict() for d in self.dimensions],
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"vetoes": [v.to_dict() for v in self.vetoes],
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"insufficient_sample": self.insufficient_sample,
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"is_losing": self.is_losing,
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"scenario": self.scenario,
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}
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# ── 插值与基础函数（对应 engine.ts）──────────────────────────────────────────
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def interpolate(anchors: Iterable[Anchor], value: float) -> float:
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"""按锚点列表做线性插值，返回 0-100 分。值越界取端点。"""
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arr = list(anchors)
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if not arr or not math.isfinite(value):
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return 0.0
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if value <= arr[0].threshold:
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return float(arr[0].score)
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if value >= arr[-1].threshold:
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return float(arr[-1].score)
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for a, b in zip(arr, arr[1:], strict=False):
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if a.threshold <= value <= b.threshold:
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if a.threshold == b.threshold:
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return float(a.score)
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ratio = (value - a.threshold) / (b.threshold - a.threshold)
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return float(a.score + ratio * (b.score - a.score))
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return float(arr[-1].score)
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def score_dimension(key: str, raw: float, weight: float) -> DimensionScore:
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"""构造维度评分对象。"""
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label, anchors = THRESHOLDS[key]
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return DimensionScore(
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key=key, label=label, raw=float(raw), score=interpolate(anchors, raw), weight=weight
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)
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def weighted_total(dimensions: list[DimensionScore]) -> float:
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"""加权求和（权重在调用方归一化）。"""
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total_weight = sum(d.weight for d in dimensions)
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if total_weight <= 0:
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return 0.0
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return sum(d.score * d.weight for d in dimensions) / total_weight
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def score_to_grade(score: float) -> Grade:
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"""分数 → 档位（不考虑否决）。"""
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for grade, min_score in _GRADE_THRESHOLDS:
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if score >= min_score:
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return grade
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return "D"
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def _worse_grade(a: Grade, b: Grade) -> Grade:
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"""取两个档位中更差者。"""
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return a if _GRADE_ORDER.index(a) <= _GRADE_ORDER.index(b) else b
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def _build_result(
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scenario: str,
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dimensions: list[DimensionScore],
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vetoes: list[VetoHit],
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insufficient_sample: bool,
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is_losing: bool,
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) -> GradeResult:
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raw_score = weighted_total(dimensions)
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grade = score_to_grade(raw_score)
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for v in vetoes:
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grade = _worse_grade(grade, v.cap)
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return GradeResult(
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grade=grade,
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score=round(raw_score * 10) / 10,
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dimensions=dimensions,
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vetoes=vetoes,
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insufficient_sample=insufficient_sample,
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is_losing=is_losing,
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scenario=scenario,
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)
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# ── 一票否决（对应 index.ts applyVetoes）─────────────────────────────────────
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def _apply_vetoes(
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profit_factor: float | None = None,
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total_trades: int | None = None,
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max_drawdown: float | None = None,
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win_rate: float | None = None,
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) -> tuple[list[VetoHit], bool, bool]:
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"""应用一票否决规则。返回 (否决列表, 样本不足, 系统亏损)。"""
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vetoes: list[VetoHit] = []
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insufficient_sample = False
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is_losing = False
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if profit_factor is not None and profit_factor < 1:
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vetoes.append(
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VetoHit(
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key="losing_system",
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reason=f"利润因子 {profit_factor:.2f} < 1，系统实际亏损",
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cap="D",
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)
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)
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is_losing = True
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if total_trades is not None and total_trades < 10:
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insufficient_sample = True
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if max_drawdown is not None and max_drawdown > 0.6:
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vetoes.append(
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VetoHit(
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key="deep_drawdown",
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reason=f"最大回撤 {max_drawdown * 100:.1f}% > 60%，深度套牢几乎无法回本",
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cap="D",
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)
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)
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enough_trades = total_trades is None or total_trades >= 10
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if enough_trades and win_rate is not None and win_rate < 0.25:
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vetoes.append(
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VetoHit(
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key="very_low_winrate",
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reason=f"胜率 {win_rate * 100:.1f}% < 25% 且样本充足，几乎一直亏",
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cap="D",
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)
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)
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if max_drawdown is not None and max_drawdown > 0.5:
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vetoes.append(
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VetoHit(
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key="high_drawdown",
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reason=f"最大回撤 {max_drawdown * 100:.1f}% > 50%，套牢难回本",
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cap="B",
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)
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)
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if enough_trades and win_rate is not None and 0.25 <= win_rate < 0.3:
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vetoes.append(
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VetoHit(
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key="low_winrate",
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reason=f"胜率 {win_rate * 100:.1f}% < 30% 且样本充足，普通人拿不住",
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cap="C",
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)
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)
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if profit_factor is not None and 1 <= profit_factor < 1.2:
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vetoes.append(
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VetoHit(
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key="thin_edge",
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reason=f"利润因子 {profit_factor:.2f} 接近 1，仅勉强盈亏平衡",
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cap="B",
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)
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)
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return vetoes, insufficient_sample, is_losing
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def _downweight_unreliable(dimensions: list[DimensionScore]) -> bool:
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"""样本不足时把 win_rate / profit_factor 权重降 0，按比例重分配。"""
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keys = {"win_rate", "profit_factor"}
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to_down = [d for d in dimensions if d.key in keys]
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if not to_down:
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return False
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released = sum(d.weight for d in to_down)
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if released <= 0:
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return False
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for d in to_down:
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d.weight = 0.0
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receivers = [d for d in dimensions if d.weight > 0]
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if not receivers:
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return False
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receiver_total = sum(d.weight for d in receivers)
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for d in receivers:
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d.weight += released * (d.weight / receiver_total)
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return True
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# ── 组合净值指标重算（对应 combinedMetrics.ts）────────────────────────────────
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@dataclass
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class CombinedMetrics:
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"""从净值序列重算的组合级指标（净值可推导的字段子集）。
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``max_dd_duration`` 的单位是 **bar 数**（一根 K 线计 1），与
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``performance.max_dd_duration`` 同口径；THRESHOLDS 里对应锚点的天数
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（30/90/365…）按日线（1 bar ≈ 1 交易日）校准，分钟级周期下该值按 bar
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直读会显著大于天数（评级维度未使用，仅展示）。
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"""
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total_return: float = 0.0
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annual_return: float = 0.0
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max_drawdown: float = 0.0
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max_dd_duration: int = 0
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sharpe: float = 0.0
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sortino: float = 0.0
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calmar: float = 0.0
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volatility: float = 0.0
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n_points: int = 0
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years: float = 0.0
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def compute_combined_metrics(equity: list[dict[str, Any]]) -> CombinedMetrics:
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"""从组合净值曲线重算绩效指标（与前端 computeCombinedMetrics 同口径）。
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Args:
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equity: 净值点列表（按时间升序），每点含 ``total``（= cash +
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position_value），可选 ``datetime`` / ``drawdown_pct``。
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||
|
||
Returns:
|
||
:class:`CombinedMetrics`。数据不足（<2 点）时字段全 0。
|
||
"""
|
||
import numpy as np
|
||
|
||
n = len(equity)
|
||
if n < 2:
|
||
return CombinedMetrics(n_points=n)
|
||
|
||
totals = [float(e["total"]) for e in equity]
|
||
start_v, end_v = totals[0], totals[-1]
|
||
total_return = end_v / start_v - 1 if start_v > 0 else 0.0
|
||
|
||
years = n / _TRADING_DAYS_PER_YEAR
|
||
try:
|
||
import pandas as pd
|
||
|
||
first = pd.Timestamp(str(equity[0].get("datetime", "")))
|
||
last = pd.Timestamp(str(equity[-1].get("datetime", "")))
|
||
span_days = (last - first).total_seconds() / 86400.0
|
||
if span_days > 0:
|
||
years = span_days / 365.25
|
||
except (ValueError, TypeError):
|
||
pass
|
||
annual_return = (end_v / start_v) ** (1.0 / years) - 1 if years > 0 and start_v > 0 else 0.0
|
||
|
||
arr = np.array(totals, dtype=float)
|
||
prev = arr[:-1]
|
||
cur = arr[1:]
|
||
mask = prev > 0
|
||
rets = cur[mask] / prev[mask] - 1.0
|
||
mean_r = float(np.mean(rets)) if len(rets) else 0.0
|
||
std_r = float(np.std(rets, ddof=1)) if len(rets) >= 2 else 0.0
|
||
volatility = std_r * math.sqrt(_TRADING_DAYS_PER_YEAR)
|
||
sharpe = mean_r / std_r * math.sqrt(_TRADING_DAYS_PER_YEAR) if std_r > 0 else 0.0
|
||
downside = rets[rets < 0]
|
||
downside_std = float(np.sqrt(np.mean(downside**2))) if len(downside) else 0.0
|
||
sortino = mean_r / downside_std * math.sqrt(_TRADING_DAYS_PER_YEAR) if downside_std > 0 else 0.0
|
||
|
||
# 最大回撤：优先用 drawdown_pct（与前端一致），缺则从 totals 反推。
|
||
# 持续 = 最长水下期（峰值 → 重新创新高；末日未修复则计到最后一点），
|
||
# 与 performance.py / combinedMetrics.ts / max_dd_duration 锚点量纲同口径。
|
||
# 缺行 / None / NaN 的 drawdown_pct 按「状态延续」处理：沿用上一根的
|
||
# 水下/峰值状态——此前缺行被当 0（创新高）截断水下期、NaN 永久脱离
|
||
# 峰值判定，两者都会扭曲 max_dd_duration。
|
||
max_dd = 0.0
|
||
max_dd_dur = 0
|
||
if equity[0].get("drawdown_pct") is not None:
|
||
last_peak = 0
|
||
prev_dd = 0.0 # 上一根的有效回撤（首根之前视作峰值状态）
|
||
for i, e in enumerate(equity):
|
||
raw = e.get("drawdown_pct")
|
||
dd: float | None
|
||
try:
|
||
dd = None if raw is None else float(raw)
|
||
except (TypeError, ValueError):
|
||
dd = None
|
||
if dd is None or not math.isfinite(dd):
|
||
dd = prev_dd
|
||
prev_dd = dd
|
||
if dd > max_dd:
|
||
max_dd = dd
|
||
if dd == 0:
|
||
# 间隔 ≥2 点才夹着真实的水下段（相邻新高不算回撤）
|
||
if i - last_peak > 1:
|
||
max_dd_dur = max(max_dd_dur, i - last_peak)
|
||
last_peak = i
|
||
max_dd_dur = max(max_dd_dur, n - 1 - last_peak)
|
||
else:
|
||
running_peak = totals[0]
|
||
last_peak = 0
|
||
for i, v in enumerate(totals):
|
||
if v > running_peak:
|
||
running_peak = v
|
||
if running_peak > 0:
|
||
dd_pct = (running_peak - v) / running_peak
|
||
if dd_pct > max_dd:
|
||
max_dd = dd_pct
|
||
if dd_pct == 0:
|
||
if i - last_peak > 1:
|
||
max_dd_dur = max(max_dd_dur, i - last_peak)
|
||
last_peak = i
|
||
max_dd_dur = max(max_dd_dur, n - 1 - last_peak)
|
||
|
||
if max_dd > 0:
|
||
calmar = annual_return / max_dd
|
||
else:
|
||
calmar = 999.0 if annual_return > 0 else 0.0
|
||
|
||
return CombinedMetrics(
|
||
total_return=total_return,
|
||
annual_return=annual_return,
|
||
max_drawdown=max_dd,
|
||
max_dd_duration=max_dd_dur,
|
||
sharpe=sharpe,
|
||
sortino=sortino,
|
||
calmar=calmar,
|
||
volatility=volatility,
|
||
n_points=n,
|
||
years=years,
|
||
)
|
||
|
||
|
||
# ── 场景函数（对应 index.ts）─────────────────────────────────────────────────
|
||
|
||
|
||
def grade_performance(perf: dict[str, Any]) -> GradeResult:
|
||
"""评级单标的回测（完整绩效字典，6 维度，不含收益率维度）。
|
||
|
||
Args:
|
||
perf: ``PerformanceAnalyzer.compute()`` 产出的绩效字典（键同
|
||
``total_return`` / ``sharpe`` / ``max_drawdown`` / ``win_rate`` /
|
||
``profit_factor`` / ``calmar`` / ``volatility`` / ``total_trades``）。
|
||
"""
|
||
dimensions = [
|
||
score_dimension("calmar", perf.get("calmar", 0.0), 0.18),
|
||
score_dimension("max_drawdown", perf.get("max_drawdown", 0.0), 0.17),
|
||
score_dimension("win_rate", perf.get("win_rate", 0.0), 0.17),
|
||
score_dimension("profit_factor", perf.get("profit_factor", 0.0), 0.18),
|
||
score_dimension("sharpe", perf.get("sharpe", 0.0), 0.15),
|
||
score_dimension("volatility", perf.get("volatility", 0.0), 0.15),
|
||
]
|
||
vetoes, insufficient, is_losing = _apply_vetoes(
|
||
profit_factor=_num_opt(perf.get("profit_factor")),
|
||
total_trades=int(perf.get("total_trades") or 0),
|
||
max_drawdown=_num_opt(perf.get("max_drawdown")),
|
||
win_rate=_num_opt(perf.get("win_rate")),
|
||
)
|
||
if insufficient:
|
||
_downweight_unreliable(dimensions)
|
||
return _build_result("single", dimensions, vetoes, insufficient, is_losing)
|
||
|
||
|
||
def grade_grid_point(
|
||
point: dict[str, Any],
|
||
total_trades_override: int | None = None,
|
||
) -> GradeResult:
|
||
"""评级寻优网格点（4 字段子集，4 维度降级版）。
|
||
|
||
Args:
|
||
point: 网格点结果（total_return/sharpe/max_drawdown/total_trades/
|
||
win_rate/profit_factor）。
|
||
total_trades_override: 覆盖交易笔数（排名表统一基准）。
|
||
"""
|
||
total_trades = (
|
||
total_trades_override
|
||
if total_trades_override is not None
|
||
else int(point.get("total_trades") or 0)
|
||
)
|
||
dimensions = [
|
||
score_dimension("sharpe", _num_or(point.get("sharpe")), 0.3),
|
||
score_dimension("max_drawdown", _num_or(point.get("max_drawdown"), 1.0), 0.28),
|
||
score_dimension("win_rate", _num_or(point.get("win_rate")), 0.22),
|
||
score_dimension("profit_factor", _num_or(point.get("profit_factor")), 0.2),
|
||
]
|
||
vetoes, insufficient, is_losing = _apply_vetoes(
|
||
profit_factor=_num_opt(point.get("profit_factor")),
|
||
total_trades=total_trades,
|
||
max_drawdown=_num_opt(point.get("max_drawdown")),
|
||
win_rate=_num_opt(point.get("win_rate")),
|
||
)
|
||
if insufficient:
|
||
_downweight_unreliable(dimensions)
|
||
return _build_result("optimize", dimensions, vetoes, insufficient, is_losing)
|
||
|
||
|
||
def grade_portfolio_equity(equity: list[dict[str, Any]]) -> GradeResult:
|
||
"""评级组合回测（净值曲线重算，5 维度）。
|
||
|
||
Args:
|
||
equity: 组合净值点列表（combined_equity，含 total / datetime /
|
||
drawdown_pct）。
|
||
"""
|
||
m = compute_combined_metrics(equity)
|
||
dimensions = [
|
||
score_dimension("calmar", m.calmar, 0.25),
|
||
score_dimension("max_drawdown", m.max_drawdown, 0.22),
|
||
score_dimension("sharpe", m.sharpe, 0.22),
|
||
score_dimension("sortino", m.sortino, 0.15),
|
||
score_dimension("volatility", m.volatility, 0.16),
|
||
]
|
||
# 样本充足性：≥60 个净值点视为统计有效（与前端同口径）
|
||
sample_proxy = 30 if m.n_points >= 60 else 5
|
||
vetoes, insufficient, is_losing = _apply_vetoes(
|
||
max_drawdown=m.max_drawdown,
|
||
total_trades=sample_proxy,
|
||
)
|
||
return _build_result("portfolio", dimensions, vetoes, insufficient, is_losing)
|
||
|
||
|
||
def _num_opt(v: Any) -> float | None:
|
||
"""安全取数（否决规则用）：None/NaN/非法 → None（规则跳过）。"""
|
||
if v is None:
|
||
return None
|
||
try:
|
||
f = float(v)
|
||
except (TypeError, ValueError):
|
||
return None
|
||
if not math.isfinite(f):
|
||
return None
|
||
return f
|
||
|
||
|
||
def _num_or(v: Any, default: float = 0.0) -> float:
|
||
"""安全取数（评分维度用）：None/NaN/非法 → default（前端 ``?? default`` 语义）。"""
|
||
f = _num_opt(v)
|
||
return default if f is None else f
|