mirror of
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。
345 lines
13 KiB
Python
345 lines
13 KiB
Python
"""涨停生态计算（本地 vipdoc .day 文件，离线快速回算连板/炸板/跌停）。
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设计要点：
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- **数据源**：``vipdoc/{sh,sz}/lday/*.day``（与 strength 扫描器同款读取器
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:func:`easy_tdx.offline.daily_bar.read_daily_bars`），不依赖网络；数据新鲜度
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取决于本机通达信客户端的数据日期，因此结果必须携带 ``data_date`` 供前端明示。
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- **涨停判定**：收盘价 == 涨停价（前收 × 涨幅上限，四舍五入到分）。
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涨幅上限按代码段近似：主板(60/00) 10%、创业板(30)/科创板(68) 20%。
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.day 文件无证券名称，无法识别 ST——对主板额外按 5% 判定并标记 ``st=True``
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（常规股票恰收在 +5.00% 整的误报率极低，前端展示名称后可自辨）。
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- **炸板**：当日 high 触及涨停价但收盘未封住（close < 涨停价）。
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- **连板高度（streak）**：截至最新一根 bar 的连续涨停天数（按 bar 连续计，
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停牌跳日不中断，与通行口径一致）。
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- 纯函数 + 文件遍历分离，便于用合成 .day 文件做单测。
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from pathlib import Path
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from easy_tdx.offline.daily_bar import _detect_security_type, read_daily_bars
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from easy_tdx.offline.paths import resolve_vipdoc
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_A_STOCK_TYPES = frozenset({"SH_A_STOCK", "SZ_A_STOCK"})
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__all__ = [
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"LimitUpEntry",
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"LimitUpEcology",
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"compute_limitup_ecology",
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"compute_limitup_history",
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]
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def _to_cents(price: float) -> int:
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"""元 → 分。.day 价格本身按 ×100 存 uint，round 消除读回的浮点表示误差。"""
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return int(round(price * 100))
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def _limit_price_cents(prev_cents: int, pct: int) -> int:
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"""交易所涨跌停价（分）：前收 × (1 ± pct%)，四舍五入到分（半进位）。
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纯整数运算 ``(prev_cents * (100 + pct) + 50) // 100``，与交易所逐价位
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对账零差异。不能用 float 乘完再 ``floor(x*100+0.5)``：乘法在半分边界
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受浮点表示误差影响，±10% 档 67/318 个、±5% 档 90/884 个价位会算低
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1 分（如 33.05×1.1 → 误算 36.35，交易所 36.36），导致真实涨跌停被漏判。
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"""
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return (prev_cents * (100 + pct) + 50) // 100
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def _limit_price(prev: float, pct: int) -> float:
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"""交易所涨跌停价（元）：pct 为整数百分数（正=涨停档，负=跌停档）。"""
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return _limit_price_cents(_to_cents(prev), pct) / 100.0
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def _limit_ratio(code: str) -> float:
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"""涨幅上限：创业板/科创板 20%，其余主板 10%（ST 由调用侧按 5% 二次判定）。"""
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if code.startswith(("30", "68")):
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return 0.20
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return 0.10
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@dataclass
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class LimitUpEntry:
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"""单只涨停/跌停/炸板股票的回算结果。"""
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code: str
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market: str # SH / SZ
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pct: float # 最新日涨跌幅（%，按 close/prev_close-1）
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streak: int = 0 # 连续涨停/跌停天数（截至最新 bar）
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st: bool = False # 主板 5% 判定（疑似 ST）
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blown: bool = False # 炸板（曾触及涨停未封住）
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@dataclass
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class LimitUpEcology:
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"""全市场涨停生态快照。"""
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data_date: int # 全市场最新 bar 日期 YYYYMMDD（vipdoc 新鲜度）
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total: int # 参与统计的股票数
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limit_up: list[LimitUpEntry] = field(default_factory=list)
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limit_down: list[LimitUpEntry] = field(default_factory=list)
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blown: list[LimitUpEntry] = field(default_factory=list) # 炸板（曾涨停未封住）
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def summary(self) -> dict[str, object]:
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heights = [e.streak for e in self.limit_up]
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touched = len(self.limit_up) + len(self.blown)
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return {
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"data_date": self.data_date,
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"total": self.total,
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"limit_up_count": len(self.limit_up),
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"limit_down_count": len(self.limit_down),
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"blown_count": len(self.blown),
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# 炸板率 = 炸板 / (封住 + 炸板)，无分母时为 None
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"blown_rate": round(len(self.blown) / touched * 100, 1) if touched else None,
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"max_streak": max(heights) if heights else 0,
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"first_board": sum(1 for h in heights if h == 1),
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"second_board": sum(1 for h in heights if h == 2),
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"plus3": sum(1 for h in heights if h >= 3),
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}
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def _entry_from_closes(
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closes: list[float],
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last_high: float,
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market: str,
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code: str,
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) -> LimitUpEntry | None:
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"""从收盘价序列判定最新交易日的涨停/跌停/炸板与连板高度。
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Args:
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closes: 最近若干根 bar 的收盘价（时间升序，最后一根 = 数据日）。
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last_high: 数据日的最高价（炸板判定用）。
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market: SH / SZ。
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code: 6 位代码。
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"""
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if len(closes) < 2:
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return None
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prev = closes[-2]
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if prev <= 0:
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return None
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pct = (closes[-1] / prev - 1.0) * 100.0
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entry = LimitUpEntry(code=code, market=market, pct=round(pct, 2))
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up_pct = 20 if _limit_ratio(code) == 0.20 else 10
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# 全程分币整数比较，杜绝浮点舍入在半分边界错 1 分（漏判涨跌停）
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cents = [_to_cents(c) for c in closes]
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high_cents = _to_cents(last_high)
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prev_c = cents[-2]
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limit_up_c = _limit_price_cents(prev_c, up_pct)
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limit_down_c = _limit_price_cents(prev_c, -up_pct)
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# 主板 5%：疑似 ST 涨停。低价股（< 3 元）最小报价单位 0.01 占比过大，
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# +5% 整的巧合概率骤增，跳过 ST 判定（宁可漏报不误报）。
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st_applicable = up_pct == 10 and prev >= 3.0
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st_price_c = _limit_price_cents(prev_c, 5) if st_applicable else None
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st_down_price_c = _limit_price_cents(prev_c, -5) if st_applicable else None
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def _is_up(i: int) -> bool:
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"""第 i 根是否涨停（用第 i-1 根收盘作前收；ST/3 元门槛逐 bar 判定，
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避免「按最新前收定性整段历史」在价格穿越 3 元时漏计/多计）。"""
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if i < 1:
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return False
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p_c = cents[i - 1]
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c_c = cents[i]
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if c_c == _limit_price_cents(p_c, up_pct):
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return True
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return up_pct == 10 and p_c >= 300 and c_c == _limit_price_cents(p_c, 5)
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# 连板高度（截至最后一根）
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streak = 0
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i = len(closes) - 1
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while i >= 1 and _is_up(i):
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streak += 1
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i -= 1
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entry.streak = streak
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entry.st = bool(streak > 0 and st_price_c is not None and cents[-1] == st_price_c)
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if streak > 0:
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entry.blown = False
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return entry
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# 未封住的场合：炸板（high 触及涨停价）或跌停。
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# 口径说明：炸板仅按 10%/20% 档判定——.day 文件无法识别 ST，若对主板
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# 额外按 5% 判炸板，非 ST 股恰好摸到 +5.00% 的会误报，故维持漏报方向。
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if high_cents == limit_up_c:
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entry.blown = True
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return entry
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if cents[-1] == limit_down_c or (st_down_price_c is not None and cents[-1] == st_down_price_c):
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down_streak = 0
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j = len(closes) - 1
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while j >= 1:
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p_c = cents[j - 1]
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c_c = cents[j]
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hit = c_c == _limit_price_cents(p_c, -up_pct) or (
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up_pct == 10 and p_c >= 300 and c_c == _limit_price_cents(p_c, -5)
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)
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if not hit:
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break
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down_streak += 1
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j -= 1
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entry.streak = down_streak
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return entry
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return None
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def compute_limitup_ecology(
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vipdoc_path: str | Path | None = None,
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*,
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max_files: int = 20000,
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) -> LimitUpEcology:
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"""扫描全市场 .day 文件，回算最新交易日的涨停生态。
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Args:
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vipdoc_path: vipdoc 目录，None 则自动检测。
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max_files: 文件数上限（防意外巨量文件拖死扫描）。
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Returns:
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:class:`LimitUpEcology`；vipdoc 不可用时 total=0。
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"""
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eco = LimitUpEcology(data_date=0, total=0)
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try:
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vipdoc = resolve_vipdoc(vipdoc_path)
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except Exception: # noqa: BLE001 — 路径不存在/自动检测失败：按空数据处理
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return eco
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if not vipdoc.is_dir():
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return eco
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files: list[tuple[Path, str, str]] = []
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for exchange in ("sz", "sh"):
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lday_dir = vipdoc / exchange / "lday"
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if not lday_dir.is_dir():
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continue
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for filepath in sorted(lday_dir.glob("*.day")):
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if _detect_security_type(filepath.name) not in _A_STOCK_TYPES:
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continue
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code = filepath.name.lower()[2:8]
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files.append((filepath, exchange.upper(), code))
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if len(files) >= max_files:
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break
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if len(files) >= max_files:
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break
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eco.total = len(files)
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# 一遍读取，仅保留尾部收盘/最高价；随后按"最后一根日期 == 全市场最新交易日"
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# 过滤——vipdoc 里大量文件因停牌/退市/未下载而停在历史日期，若不过滤会把
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# 多年前的"涨停"当成今天的（真实教训：退市前仙股文件冒出 5 连板）。
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_TAIL = 13 # 连板判定最多回看 12 根 + 判定用前收
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scanned: list[tuple[int, str, str, list[float], list[float]]] = []
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for filepath, market, code in files:
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try:
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bars = read_daily_bars(filepath)
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except Exception: # noqa: BLE001 — 单文件损坏不阻塞整体
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continue
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if len(bars) < 2:
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continue
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tail = bars[-_TAIL:]
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last_date = bars[-1].year * 10000 + bars[-1].month * 100 + bars[-1].day
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scanned.append(
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(
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last_date,
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market,
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code,
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[b.close for b in tail],
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[b.high for b in tail],
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)
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)
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if last_date > eco.data_date:
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eco.data_date = last_date
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for last_date, market, code, closes, highs in scanned:
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if last_date != eco.data_date:
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continue # 数据不新鲜（停牌/退市/未下载），不参与今日生态
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entry = _entry_from_closes(closes, highs[-1], market, code)
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if entry is None:
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continue
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if entry.blown:
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eco.blown.append(entry)
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elif entry.pct > 0 and entry.streak > 0:
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eco.limit_up.append(entry)
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elif entry.pct < 0 and entry.streak > 0:
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eco.limit_down.append(entry)
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eco.limit_up.sort(key=lambda e: (-e.streak, -e.pct))
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eco.limit_down.sort(key=lambda e: (-e.streak, e.pct))
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eco.blown.sort(key=lambda e: -e.pct)
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return eco
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def compute_limitup_history(
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vipdoc_path: str | Path | None = None,
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*,
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days: int = 60,
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max_files: int = 20000,
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) -> list[dict[str, int]]:
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"""逐日统计最近 ``days`` 个交易日的涨停/跌停家数（离线回补，无需采样积累）。
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与 :func:`compute_limitup_ecology` 的"只看最新交易日"不同，本函数把每只股票
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窗口内的每一根 bar 都按同一涨停判定规则计数——历史日期上它就是当时真实的
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涨停家数（陈旧文件在此是合法的历史数据，无污染问题）。
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Returns:
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按 date 升序的 ``[{"date": YYYYMMDD, "limit_up": n, "limit_down": m}]``；
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vipdoc 不可用时返回空列表。
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"""
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try:
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vipdoc = resolve_vipdoc(vipdoc_path)
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except Exception: # noqa: BLE001 — 路径不存在/自动检测失败：按空数据处理
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return []
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counts: dict[int, dict[str, int]] = {}
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if not vipdoc.is_dir():
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return []
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n_files = 0
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for exchange in ("sz", "sh"):
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lday_dir = vipdoc / exchange / "lday"
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if not lday_dir.is_dir():
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continue
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for filepath in sorted(lday_dir.glob("*.day")):
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if _detect_security_type(filepath.name) not in _A_STOCK_TYPES:
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continue
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code = filepath.name.lower()[2:8]
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try:
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bars = read_daily_bars(filepath)
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except Exception: # noqa: BLE001 — 单文件损坏不阻塞整体
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continue
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tail = bars[-(days + 13) :]
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if len(tail) < 2:
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continue
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n_files += 1
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if n_files >= max_files:
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break
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up_pct = 20 if _limit_ratio(code) == 0.20 else 10
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cents = [_to_cents(b.close) for b in tail]
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date_ints = [b.year * 10000 + b.month * 100 + b.day for b in tail]
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for i in range(1, len(tail)):
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p_c, c_c = cents[i - 1], cents[i]
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if p_c <= 0:
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continue
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d = date_ints[i]
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bucket = counts.setdefault(d, {"limit_up": 0, "limit_down": 0})
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if c_c == _limit_price_cents(p_c, up_pct) or (
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up_pct == 10 and p_c >= 300 and c_c == _limit_price_cents(p_c, 5)
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):
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bucket["limit_up"] += 1
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elif c_c == _limit_price_cents(p_c, -up_pct) or (
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up_pct == 10 and p_c >= 300 and c_c == _limit_price_cents(p_c, -5)
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):
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bucket["limit_down"] += 1
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if n_files >= max_files:
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break
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recent = sorted(counts)[-days:] if days > 0 else []
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return [
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{
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"date": d,
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"limit_up": counts[d]["limit_up"],
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"limit_down": counts[d]["limit_down"],
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}
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for d in recent
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]
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