mirror of
https://ghfast.top/https://github.com/aeroxw/easy_tdx_max.git
synced 2026-09-12 15:44:18 +08:00
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。
286 lines
12 KiB
Python
286 lines
12 KiB
Python
"""QFQ 对拍校验（公式法 vs 跳空检测法）的单元测试。
|
||
|
||
覆盖 ``easy_tdx.mac.qfq_check``：
|
||
|
||
- 已知除权案例回归（合成数据复现两类历史上被下游反馈过的场景）：
|
||
* 「茅台式」——长期多重现金分红叠加深层历史，本地重算后应全正且事件处连续；
|
||
* 「浦发式」——送转股事件，前复权方向应为「旧价向下缩放」。
|
||
- 反例检测：复权方向算反 → ``wrong_direction``；漏事件 → ``residual_gap``；
|
||
NONE 跳空但 XDXR 缺记录 → ``unexplained_gap``；负价 → ``bad_price``。
|
||
- 涨跌停幅度推断（主板/双创/北交所）与跳空检测阈值。
|
||
"""
|
||
|
||
from __future__ import annotations
|
||
|
||
import numpy as np
|
||
import pandas as pd
|
||
|
||
from easy_tdx.mac.adjust import apply_forward_adjust, has_bad_prices
|
||
from easy_tdx.mac.qfq_check import (
|
||
board_limit_ratio,
|
||
crosscheck_qfq,
|
||
detect_ex_dividend_gaps,
|
||
)
|
||
|
||
|
||
def _kline(
|
||
closes: list[float],
|
||
start: str = "2010-01-01",
|
||
opens: list[float] | None = None,
|
||
) -> pd.DataFrame:
|
||
"""构造最小 NONE K 线。默认 open=close；可显式给 opens 制造除权跳空。"""
|
||
n = len(closes)
|
||
dates = pd.date_range(start, periods=n, freq="D")
|
||
arr = np.array(closes, dtype=float)
|
||
o = np.array(opens, dtype=float) if opens is not None else arr.copy()
|
||
return pd.DataFrame(
|
||
{
|
||
"datetime": dates,
|
||
"open": o,
|
||
"high": np.maximum(o, arr) * 1.01,
|
||
"low": np.minimum(o, arr) * 0.99,
|
||
"close": arr,
|
||
"vol": [100.0] * n,
|
||
}
|
||
)
|
||
|
||
|
||
def _xdxr(
|
||
events: list[tuple[str, float, float, float, float]],
|
||
) -> pd.DataFrame:
|
||
"""构造 XDXR 记录：list of (date, fenhong, peigujia, songzhuangu, peigu)。"""
|
||
return pd.DataFrame(
|
||
[
|
||
{
|
||
"date": d,
|
||
"category": 1,
|
||
"fenhong": fh,
|
||
"peigujia": pj,
|
||
"songzhuangu": sz,
|
||
"peigu": pg,
|
||
}
|
||
for d, fh, pj, sz, pg in events
|
||
]
|
||
)
|
||
|
||
|
||
# --------------------------------------------------------------------------- #
|
||
# 涨跌停幅度推断
|
||
# --------------------------------------------------------------------------- #
|
||
|
||
|
||
def test_board_limit_ratio_by_code() -> None:
|
||
"""主板 10%、双创 20%、北交所 30%。"""
|
||
assert board_limit_ratio("600519") == 0.10 # 沪主板（茅台）
|
||
assert board_limit_ratio("600000") == 0.10 # 沪主板（浦发）
|
||
assert board_limit_ratio("000001") == 0.10 # 深主板
|
||
assert board_limit_ratio("300750") == 0.20 # 创业板
|
||
assert board_limit_ratio("688981") == 0.20 # 科创板
|
||
assert board_limit_ratio("832000") == 0.30 # 北交所
|
||
assert board_limit_ratio("430047") == 0.30 # 北交所
|
||
assert board_limit_ratio("600000", market=2) == 0.30 # market 显式指定优先
|
||
|
||
|
||
# --------------------------------------------------------------------------- #
|
||
# 跳空检测（detect_ex_dividend_gaps）
|
||
# --------------------------------------------------------------------------- #
|
||
|
||
|
||
def test_detect_gap_finds_ex_dividend_drop() -> None:
|
||
"""主板股票开盘相对昨收跌超 10.5% → 判定为除权跳空。"""
|
||
# 10 根平稳 + 除权日开盘腰斩（-50%）
|
||
closes = [10.0] * 5 + [5.0] * 5
|
||
opens = [10.0] * 5 + [2.5] + [5.0] * 4
|
||
df = _kline(closes, opens=opens)
|
||
gaps = detect_ex_dividend_gaps(df, "600000")
|
||
assert gaps == ["2010-01-06"] # 第 6 根（index 5）为除权日
|
||
|
||
|
||
def test_detect_gap_ignores_normal_limit_down() -> None:
|
||
"""恰好跌停（-10.0%）不算除权跳空（被 0.5% 余量排除）。"""
|
||
closes = [10.0] * 5 + [9.0] * 5
|
||
opens = [10.0] * 5 + [9.0] + [9.0] * 4 # ex 开盘恰好 -10%
|
||
df = _kline(closes, opens=opens)
|
||
assert detect_ex_dividend_gaps(df, "600000") == []
|
||
|
||
|
||
def test_detect_gap_uses_chinext_threshold() -> None:
|
||
"""创业板（20% 涨跌停）：-15% 的跳空不报警，-25% 报警。"""
|
||
closes = [10.0] * 5 + [7.5] * 5
|
||
opens = [10.0] * 5 + [8.5] + [7.5] * 4 # -15% → 不报
|
||
df = _kline(closes, opens=opens)
|
||
assert detect_ex_dividend_gaps(df, "300750") == []
|
||
|
||
opens2 = [10.0] * 5 + [7.0] + [7.5] * 4 # -30% → 报
|
||
df2 = _kline(closes, opens=opens2)
|
||
assert detect_ex_dividend_gaps(df2, "300750") == ["2010-01-06"]
|
||
|
||
|
||
def test_detect_gap_ignores_nonfinite_ratio() -> None:
|
||
"""前收缺失（NaN）导致的非有限比率不计为除权跳空（首根前收缺失常见）。"""
|
||
df = _kline([float("nan")] + [10.0] * 5, opens=[5.0] + [10.0] * 5)
|
||
# 旧实现把 ratio=NaN 也当跳空 → 误报 ["2010-01-02"]
|
||
assert detect_ex_dividend_gaps(df, "600000") == []
|
||
|
||
|
||
# --------------------------------------------------------------------------- #
|
||
# 已知案例回归（合成）
|
||
# --------------------------------------------------------------------------- #
|
||
|
||
|
||
def test_maotai_style_multi_dividend_case() -> None:
|
||
"""「茅台式」：多笔大额现金分红叠加深层历史。
|
||
|
||
场景：高价股（1700 元）历经 3 次每笔 40~60 元分红，NONE 价格在除权日
|
||
出现 -3% 左右的真实跳空（小额，低于跌停阈值），深层历史经公式法
|
||
前复权后应全正、除权日前后连续、对拍通过。
|
||
"""
|
||
# 构造 300 根：价格在 1700 附近随机游走，3 个除权日各扣一次分红
|
||
rng = np.random.default_rng(42)
|
||
n = 300
|
||
prices = 1700.0 + np.cumsum(rng.normal(0, 8, n))
|
||
events = [(50, "2010-04-20"), (60, "2010-07-20"), (40, "2010-09-20")]
|
||
closes = prices.copy()
|
||
opens = prices.copy()
|
||
dates = pd.date_range("2010-01-01", periods=n, freq="D")
|
||
for fh, ex in events:
|
||
ex_ts = pd.Timestamp(ex)
|
||
idx = int(np.searchsorted(dates.to_numpy(), np.datetime64(ex_ts)))
|
||
opens[idx] = closes[idx - 1] - fh # 除权日开盘 = 昨收 - 分红
|
||
closes[idx:] -= fh # 之后价格整体降一档（简化）
|
||
none_df = _kline(list(closes), opens=list(opens))
|
||
xd = _xdxr([(ex, fh, 0.0, 0.0, 0.0) for fh, ex in events])
|
||
|
||
qfq = apply_forward_adjust(none_df, xd)
|
||
# 1. 全正（茅台负价问题的回归断言）
|
||
assert not has_bad_prices(qfq)
|
||
# 2. 对拍通过：无 bad_price / residual_gap / wrong_direction
|
||
report = crosscheck_qfq(none_df, qfq, xd, "600519", 1)
|
||
assert report.ok, [i.to_dict() for i in report.issues]
|
||
assert report.events_checked == 3
|
||
|
||
|
||
def test_pufa_style_songzhuangu_direction() -> None:
|
||
"""「浦发式」：送转股事件的前复权方向。
|
||
|
||
10 送 3（songzhuangu=0.3）：除权日理论价格 = 昨收 / 1.3。正确的前复权
|
||
应把除权日**之前**的价格向下缩放（factor = 1/1.3），而不是抬升之后的价格。
|
||
"""
|
||
# 20 根 10 元平稳，除权日后理论价 10/1.3 ≈ 7.69
|
||
closes = [10.0] * 10 + [7.69] * 10
|
||
opens = [10.0] * 10 + [7.69] + [7.69] * 9
|
||
none_df = _kline(closes, opens=opens)
|
||
xd = _xdxr([("2010-01-11", 0.0, 0.0, 0.3, 0.0)])
|
||
|
||
qfq = apply_forward_adjust(none_df, xd)
|
||
# 旧价被向下缩放：前 10 根 ≈ 10/1.3 ≈ 7.69，与除权后持平（连续）；
|
||
# 最新价锚定不动
|
||
assert abs(qfq["close"].iloc[0] - 7.69) <= 0.02
|
||
assert abs(qfq["close"].iloc[-1] - 7.69) <= 1e-9
|
||
# 方向正确 → 对拍通过（除权日 open=7.69 ≈ 复权后昨收 7.69）
|
||
report = crosscheck_qfq(none_df, qfq, xd, "600000", 1)
|
||
assert report.ok, [i.to_dict() for i in report.issues]
|
||
|
||
|
||
# --------------------------------------------------------------------------- #
|
||
# 反例检测
|
||
# --------------------------------------------------------------------------- #
|
||
|
||
|
||
def test_wrong_direction_adjustment_detected() -> None:
|
||
"""复权方向算反 → 除权日残留大幅跳空。
|
||
|
||
方向反演（因子取倒数）：把除权日**之前**的价格放大 ×1.3 而非缩放
|
||
÷1.3，除权日 open=7.69 对上「复权后」昨收 13.0 → 残差 -41% →
|
||
``residual_gap``。
|
||
"""
|
||
# NONE：10 送 3 场景，除权日开盘 7.69（-23%，超主板阈值 → 会被跳空检测捕获）
|
||
closes = [10.0] * 10 + [7.69] * 10
|
||
opens = [10.0] * 10 + [7.69] + [7.69] * 9
|
||
none_df = _kline(closes, opens=opens)
|
||
xd = _xdxr([("2010-01-11", 0.0, 0.0, 0.3, 0.0)])
|
||
|
||
reversed_df = none_df.copy()
|
||
reversed_df.loc[reversed_df.index <= 9, ["open", "high", "low", "close"]] *= 1.3
|
||
report = crosscheck_qfq(none_df, reversed_df, xd, "600000", 1)
|
||
assert not report.ok
|
||
assert any(i.kind == "residual_gap" for i in report.issues)
|
||
|
||
|
||
def test_over_adjustment_detected() -> None:
|
||
"""过度复权（旧价缩得过低）→ 除权日向上跳空 → wrong_direction。"""
|
||
closes = [10.0] * 10 + [7.69] * 10
|
||
opens = [10.0] * 10 + [7.69] + [7.69] * 9
|
||
none_df = _kline(closes, opens=opens)
|
||
xd = _xdxr([("2010-01-11", 0.0, 0.0, 0.3, 0.0)])
|
||
|
||
over = none_df.copy()
|
||
over.loc[over.index <= 9, ["open", "high", "low", "close"]] *= 0.5 # 应 ÷1.3 却 ×0.5
|
||
report = crosscheck_qfq(none_df, over, xd, "600000", 1)
|
||
assert not report.ok
|
||
assert any(i.kind == "wrong_direction" for i in report.issues)
|
||
|
||
|
||
def test_missed_event_residual_gap_detected() -> None:
|
||
"""漏算事件（复权结果等于 NONE 原始序列）→ residual_gap。"""
|
||
closes = [10.0] * 10 + [7.69] * 10
|
||
opens = [10.0] * 10 + [7.69] + [7.69] * 9
|
||
none_df = _kline(closes, opens=opens)
|
||
xd = _xdxr([("2010-01-11", 0.0, 0.0, 0.3, 0.0)])
|
||
|
||
# 「复权结果」其实是未复权的 NONE（公式法漏调）→ 除权日残留 -23% 跳空
|
||
report = crosscheck_qfq(none_df, none_df.copy(), xd, "600000", 1)
|
||
assert not report.ok
|
||
assert any(i.kind == "residual_gap" for i in report.issues)
|
||
|
||
|
||
def test_unexplained_gap_without_xdxr_record() -> None:
|
||
"""NONE 存在除权跳空但 XDXR 无对应记录 → unexplained_gap（不影响 ok）。"""
|
||
closes = [10.0] * 10 + [7.69] * 10
|
||
opens = [10.0] * 10 + [7.69] + [7.69] * 9
|
||
none_df = _kline(closes, opens=opens)
|
||
|
||
# XDXR 为空（数据源缺记录），公式法无从调整 → 序列本身「连续性」检查通过，
|
||
# 但跳空检测应报 unexplained_gap 提示人工核查
|
||
report = crosscheck_qfq(none_df, none_df.copy(), None, "600000", 1)
|
||
assert report.gaps_detected == 1
|
||
assert any(i.kind == "unexplained_gap" for i in report.issues)
|
||
# unexplained_gap 属于「证据链不一致」而非「复权结果错误」，ok 保持 True
|
||
assert report.ok
|
||
|
||
|
||
def test_bad_price_reported() -> None:
|
||
"""复权结果含负价 → bad_price（ok=False）。"""
|
||
closes = [10.0] * 5
|
||
none_df = _kline(closes)
|
||
bad = none_df.copy()
|
||
bad.loc[0, ["open", "high", "low", "close"]] = -1.0
|
||
report = crosscheck_qfq(none_df, bad, None, "600000", 1)
|
||
assert not report.ok
|
||
assert any(i.kind == "bad_price" for i in report.issues)
|
||
|
||
|
||
def test_clean_series_passes() -> None:
|
||
"""无事件、无跳空的干净序列 → ok=True、零问题。"""
|
||
closes = [10.0 + 0.1 * i for i in range(20)]
|
||
none_df = _kline(closes)
|
||
report = crosscheck_qfq(none_df, none_df.copy(), None, "600000", 1)
|
||
assert report.ok
|
||
assert report.issues == []
|
||
assert report.events_checked == 0
|
||
assert report.gaps_detected == 0
|
||
|
||
|
||
def test_report_to_dict_roundtrip() -> None:
|
||
"""报告可序列化为 JSON 兼容字典。"""
|
||
closes = [10.0] * 10 + [7.69] * 10
|
||
opens = [10.0] * 10 + [7.69] + [7.69] * 9
|
||
none_df = _kline(closes, opens=opens)
|
||
xd = _xdxr([("2010-01-11", 0.0, 0.0, 0.3, 0.0)])
|
||
report = crosscheck_qfq(none_df, none_df.copy(), xd, "600000", 1)
|
||
d = report.to_dict()
|
||
assert d["symbol"] == "1:600000"
|
||
assert d["ok"] is False
|
||
assert isinstance(d["issues"], list) and d["issues"]
|
||
assert {"kind", "date", "detail"} == set(d["issues"][0].keys())
|