mirror of
https://ghfast.top/https://github.com/aeroxw/easy_tdx_max.git
synced 2026-09-12 19:14:19 +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。
545 lines
19 KiB
Python
545 lines
19 KiB
Python
"""本地 K 线仓库测试（DuckDB store + 增量 sync + provisional 状态机 + 健康自检）。"""
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from __future__ import annotations
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import datetime as _dt
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import logging
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import numpy as np
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import pandas as pd
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import pytest
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pytest.importorskip("duckdb")
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from easy_tdx.warehouse.store import KlineWarehouse # noqa: E402
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from easy_tdx.warehouse.sync import WarehouseSyncer # noqa: E402
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@pytest.fixture()
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def wh(tmp_path):
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warehouse = KlineWarehouse(tmp_path / "test.duckdb")
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yield warehouse
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warehouse.close()
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def _bars(n: int = 10, start: str = "2024-01-01", base: float = 10.0) -> pd.DataFrame:
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dates = pd.date_range(start, periods=n, freq="B")
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close = base + np.linspace(0, 1, n)
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return pd.DataFrame(
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{
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"datetime": dates,
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"open": close,
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"high": close * 1.01,
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"low": close * 0.99,
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"close": close,
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"vol": 1000.0,
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"amount": close * 1000,
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}
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)
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class _FakeClient:
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"""返回预置 K 线的假客户端（duck-typed get_stock_kline）。"""
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def __init__(self, df: pd.DataFrame) -> None:
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self._df = df
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self.calls: list[dict] = []
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def get_stock_kline(self, market, code, period="DAILY", start=0, count=800, adjust="NONE"):
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self.calls.append({"market": market, "count": count, "adjust": adjust})
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return self._df.iloc[max(0, len(self._df) - count) :].reset_index(drop=True)
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# ── store：写入 / 查询 ───────────────────────────────────────────────────────
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def test_upsert_and_query_roundtrip(wh):
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df = _bars(10)
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added, updated = wh.upsert_bars("SH", "600519", df)
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assert (added, updated) == (10, 0)
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out = wh.query("SH", "600519")
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assert len(out) == 10
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assert list(out.columns)[:5] == ["market", "code", "period", "datetime", "open"]
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assert out["market"].iloc[0] == "SH"
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# 升序
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dts = pd.to_datetime(out["datetime"])
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assert dts.is_monotonic_increasing
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def test_upsert_same_bars_updates_not_duplicates(wh):
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df = _bars(10)
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wh.upsert_bars("SH", "600519", df)
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# 同一批再写 → 全部 update，无重复行
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added, updated = wh.upsert_bars("SH", "600519", df)
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assert (added, updated) == (0, 10)
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assert len(wh.query("SH", "600519")) == 10
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def test_query_count_takes_latest(wh):
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full = _bars(50)
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wh.upsert_bars("SZ", "000001", full)
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out = wh.query("SZ", "000001", count=10)
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assert len(out) == 10
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# 是最近 10 根（时间仍升序，且末根 = 全量末根）
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last_full = pd.Timestamp(full["datetime"].iloc[-1]).normalize()
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assert pd.Timestamp(out["datetime"].iloc[-1]) == last_full
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def test_query_date_range_filter(wh):
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wh.upsert_bars("SZ", "000001", _bars(50))
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out = wh.query("SZ", "000001", start="2024-01-15", end="2024-01-25")
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dts = pd.to_datetime(out["datetime"]).dt.date.astype(str)
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assert (dts >= "2024-01-15").all() and (dts <= "2024-01-25").all()
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def test_last_datetime_and_symbols(wh):
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assert wh.last_datetime("SH", "600519") is None
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wh.upsert_bars("SH", "600519", _bars(10))
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wh.upsert_bars("SZ", "000001", _bars(5))
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assert wh.last_datetime("SH", "600519") == pd.Timestamp("2024-01-12")
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syms = wh.symbols()
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assert len(syms) == 2
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assert set(syms["code"]) == {"600519", "000001"}
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def test_delete_symbol(wh):
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wh.upsert_bars("SH", "600519", _bars(10))
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assert wh.delete_symbol("SH", "600519") == 10
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assert len(wh.query("SH", "600519")) == 0
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def test_missing_optional_columns_filled(wh):
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df = _bars(5).drop(columns=["amount"])
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wh.upsert_bars("SH", "600519", df)
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out = wh.query("SH", "600519")
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assert out["amount"].isna().all()
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# ── provisional 状态机 ───────────────────────────────────────────────────────
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def test_today_bars_before_close_marked_provisional(wh, monkeypatch):
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"""当日 bar 在 15:05 前落盘 → provisional（逐行判定），默认查询忽略。"""
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import datetime as _dt
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import easy_tdx.warehouse.store as store_mod
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today = pd.Timestamp.today().normalize()
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dates = pd.date_range(today - pd.Timedelta(days=10), periods=11, freq="D")
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df = pd.DataFrame(
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{
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"datetime": dates,
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"open": 10.0,
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"high": 10.1,
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"low": 9.9,
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"close": 10.0,
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"vol": 100.0,
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"amount": 1000.0,
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}
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)
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class _FixedDT(_dt.datetime):
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@classmethod
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def now(cls, tz=None): # 固定在当日 10:00（盘中）
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return _dt.datetime(today.year, today.month, today.day, 10, 0)
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monkeypatch.setattr(store_mod, "datetime", _FixedDT)
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added, _ = wh.upsert_bars("SH", "600519", df)
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assert added == 11
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all_rows = wh.query("SH", "600519", include_provisional=True)
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completed = wh.query("SH", "600519")
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assert len(all_rows) == 11
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assert len(completed) == 10 # 仅当日 bar 是 provisional
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# 显式 include_provisional 时当日可见且标记正确
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today_rows = all_rows[all_rows["status"] == "provisional"]
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assert len(today_rows) == 1
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assert pd.Timestamp(today_rows["datetime"].iloc[0]).date() == today.date()
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def test_promote_provisional(wh):
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"""过期的 provisional 行（日期 < 今天）转正。"""
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old = pd.DataFrame(
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{
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"datetime": pd.date_range("2024-01-01", periods=3),
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"open": 10.0,
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"high": 10.1,
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"low": 9.9,
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"close": 10.0,
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"vol": 100.0,
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"amount": 1000.0,
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}
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)
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wh.upsert_bars("SH", "600519", old, status="provisional")
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assert len(wh.query("SH", "600519")) == 0 # provisional 默认不可见
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n = wh.promote_provisional()
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assert n >= 3
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assert len(wh.query("SH", "600519")) == 3 # 转正后可见
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def test_promote_provisional_scoped_to_market_code_and_before(wh):
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"""scoped 转正：只转正指定标的且 datetime <= before 的 provisional 行。"""
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def _one(d: str) -> pd.DataFrame:
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return pd.DataFrame(
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{
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"datetime": pd.date_range(d, periods=1),
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"open": 10.0,
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"high": 10.1,
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"low": 9.9,
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"close": 10.0,
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"vol": 100.0,
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"amount": 1000.0,
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}
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)
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wh.upsert_bars("SH", "600519", _one("2024-01-05"), status="provisional")
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wh.upsert_bars("SH", "600519", _one("2024-06-01"), status="provisional")
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wh.upsert_bars("SZ", "000001", _one("2024-01-05"), status="provisional")
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n = wh.promote_provisional(market="SH", code="600519", before=pd.Timestamp("2024-03-01"))
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assert n == 1
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out = wh.query("SH", "600519") # 默认查询只含 completed
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assert len(out) == 1
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assert pd.Timestamp(out["datetime"].iloc[0]) == pd.Timestamp("2024-01-05")
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all_rows = wh.query("SH", "600519", include_provisional=True)
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assert len(all_rows) == 2 # 2024-06-01 行超出 before，保持 provisional
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def _fake_clock(
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store_mod, # noqa: ANN001 — monkeypatch 目标模块（未用）
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*,
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shanghai: tuple[int, int, int, int],
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local: tuple[int, int, int, int],
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):
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"""伪造 store 模块时钟：now(tz)=沪时区正确墙钟；now()=本地误判墙钟。
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模拟「UTC 主机」：沪市已 18:00（当日 bar 应为 completed），本地 naive
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时钟却还是 10:00（旧实现会误标 provisional）。
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"""
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class _FixedDT(_dt.datetime):
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@classmethod
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def now(cls, tz=None): # type: ignore[override]
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if tz is not None:
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y, m, d, hh = shanghai
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return _dt.datetime(y, m, d, hh, 0, tzinfo=tz)
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y, m, d, hh = local
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return _dt.datetime(y, m, d, hh, 0)
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return _FixedDT
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def test_provisional_uses_shanghai_clock_not_local(wh, monkeypatch):
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"""provisional 判定按沪市墙钟：沪市 18:00（收盘后）当日 bar 必须 completed。
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回归：旧实现用系统本地 now()——UTC 主机上沪市收盘时本地才 10:00，
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当日 bar 被误标 provisional，默认查询隐藏当天数据。
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"""
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import easy_tdx.warehouse.store as store_mod
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monkeypatch.setattr(
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store_mod,
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"datetime",
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_fake_clock(store_mod, shanghai=(2026, 9, 7, 18), local=(2026, 9, 7, 10)),
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)
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dates = pd.date_range(
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pd.Timestamp("2026-09-07") - pd.Timedelta(days=10), periods=11, freq="D"
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).tolist() # 2026-08-28 .. 2026-09-07（末根 = 沪市「当日」）
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df = pd.DataFrame(
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{
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"datetime": dates,
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"open": 10.0,
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"high": 10.1,
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"low": 9.9,
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"close": 10.0,
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"vol": 100.0,
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"amount": 1000.0,
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}
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)
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wh.upsert_bars("SH", "600519", df)
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# 沪市已收盘：全部 11 根都应为 completed（旧实现：当日根 provisional）
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assert len(wh.query("SH", "600519")) == 11
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def test_promote_provisional_uses_shanghai_date(wh, monkeypatch):
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"""无参转正的「今日」边界按沪市日期：沪市已过 0 点即转正昨日临时行。"""
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import easy_tdx.warehouse.store as store_mod
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monkeypatch.setattr(
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store_mod,
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"datetime",
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_fake_clock(store_mod, shanghai=(2026, 9, 8, 0), local=(2026, 9, 7, 16)),
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)
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old = pd.DataFrame(
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{
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"datetime": pd.date_range("2026-09-07", periods=1),
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"open": 10.0,
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"high": 10.1,
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"low": 9.9,
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"close": 10.0,
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"vol": 100.0,
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"amount": 1000.0,
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}
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)
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wh.upsert_bars("SH", "600519", old, status="provisional")
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# 沪市日期已是 9/8 → 9/7 的临时行应转正（旧实现按本地 9/7 → n=0）
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assert wh.promote_provisional() == 1
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assert len(wh.query("SH", "600519")) == 1
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def test_open_conflict_clear_error(tmp_path, monkeypatch):
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"""仓库文件被其他进程占用：给可操作的中文错误而非裸 duckdb 异常。"""
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import duckdb as duckdb_mod
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def _raise(*args, **kwargs): # type: ignore[no-untyped-def]
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raise duckdb_mod.IOException("Could not set lock on file")
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monkeypatch.setattr(duckdb_mod, "connect", _raise)
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with pytest.raises(RuntimeError, match="占用"):
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KlineWarehouse(tmp_path / "lock.duckdb")
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# ── 健康自检 ─────────────────────────────────────────────────────────────────
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def test_health_check_detects_gap_and_stale(wh):
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# 构造缺口：跳过 2 周
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df1 = _bars(5, start="2024-01-01")
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df2 = _bars(5, start="2024-03-01")
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wh.upsert_bars("SH", "600519", pd.concat([df1, df2], ignore_index=True))
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report = wh.health_check()
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assert report["symbols_checked"] == 1
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kinds = [i["kind"] for i in report["issues"]]
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assert "gap" in kinds # 1 月→3 月的缺口
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assert report["summary"]["stale_symbols"] # 2024 年数据 → 明显过期
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def test_health_check_price_jump(wh):
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"""除权式跳空被检出（kind=price_jump）。"""
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closes = [10.0] * 10 + [7.0] * 10
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dates = pd.date_range("2024-01-01", periods=20, freq="B")
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df = pd.DataFrame(
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{
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"datetime": dates,
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"open": closes,
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"high": [c * 1.01 for c in closes],
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"low": [c * 0.99 for c in closes],
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"close": closes,
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"vol": 100.0,
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"amount": 1000.0,
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}
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)
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wh.upsert_bars("SH", "600519", df)
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report = wh.health_check(market="SH", code="600519")
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assert any(i["kind"] == "price_jump" for i in report["issues"])
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def test_health_check_clean_series_no_issues(wh):
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"""连续无跳空数据（工作日）→ 无 gap/price_jump 问题。"""
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wh.upsert_bars("SZ", "000001", _bars(30))
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report = wh.health_check(market="SZ", code="000001")
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assert report["issues"] == []
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# ── 增量同步 ─────────────────────────────────────────────────────────────────
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def test_sync_initial_full_then_incremental(tmp_path):
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warehouse = KlineWarehouse(tmp_path / "s.duckdb")
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try:
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full = _bars(100)
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client = _FakeClient(full)
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syncer = WarehouseSyncer(client, warehouse, max_bars=800, tail_bars=15)
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s1 = syncer.sync(["SH:600519"])
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assert s1["added"] == 100 and s1["failed"] == 0
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# 首同步请求了全量（count=800）
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assert client.calls[-1]["count"] == 800
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s2 = syncer.sync([("SH", "600519")])
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assert s2["added"] == 0 and s2["updated"] == 15 # 增量只补尾部 15 根
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assert client.calls[-1]["count"] == 15
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assert len(warehouse.query("SH", "600519")) == 100 # 无重复
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finally:
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warehouse.close()
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def test_sync_new_bars_appended(tmp_path):
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warehouse = KlineWarehouse(tmp_path / "s2.duckdb")
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try:
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client = _FakeClient(_bars(50))
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syncer = WarehouseSyncer(client, warehouse, tail_bars=20)
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syncer.sync(["SZ:000001"])
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# 行情前滚 5 根：新 bar 接在原末根之后
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end = pd.Timestamp(client._df["datetime"].iloc[-1])
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client._df = pd.concat(
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[client._df, _bars(5, start=str(end + pd.Timedelta(days=1)))], ignore_index=True
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)
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s2 = syncer.sync(["SZ:000001"])
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assert s2["added"] == 5
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assert len(warehouse.query("SZ", "000001")) == 55
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finally:
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warehouse.close()
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def test_sync_failure_does_not_break_batch(tmp_path):
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warehouse = KlineWarehouse(tmp_path / "s3.duckdb")
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try:
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class _BadClient:
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def get_stock_kline(self, *a, **kw):
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||
raise ConnectionError("网络故障")
|
||
|
||
syncer = WarehouseSyncer(_BadClient(), warehouse)
|
||
s = syncer.sync(["SH:600519", "SZ:000001"])
|
||
assert s["failed"] == 2
|
||
assert all(d["error"] for d in s["details"])
|
||
finally:
|
||
warehouse.close()
|
||
|
||
|
||
def test_sync_progress_callback(tmp_path):
|
||
warehouse = KlineWarehouse(tmp_path / "s4.duckdb")
|
||
try:
|
||
client = _FakeClient(_bars(20))
|
||
seen: list[tuple[int, int, str]] = []
|
||
|
||
def progress(done, total, sym):
|
||
seen.append((done, total, sym))
|
||
|
||
WarehouseSyncer(client, warehouse).sync(["SH:600519", "SZ:000001"], progress=progress)
|
||
assert seen == [(1, 2, "SH:600519"), (2, 2, "SZ:000001")]
|
||
finally:
|
||
warehouse.close()
|
||
|
||
|
||
class _ScriptedClient:
|
||
"""按调用序返回预置 DataFrame 的假客户端（末帧可重复）。"""
|
||
|
||
def __init__(self, frames: list[pd.DataFrame]) -> None:
|
||
self._frames = frames
|
||
self.calls: list[int] = []
|
||
|
||
def get_stock_kline(self, market, code, period="DAILY", start=0, count=800, adjust="NONE"):
|
||
self.calls.append(count)
|
||
idx = min(len(self.calls) - 1, len(self._frames) - 1)
|
||
return self._frames[idx].copy()
|
||
|
||
|
||
def test_sync_refetch_full_when_tail_gap(tmp_path, caplog):
|
||
"""增量尾部覆盖不到上次同步点（首 bar 晚于 existing_last）→ 全量重拉补缺。
|
||
|
||
回归：旧实现固定只拉 tail_bars 根——超过 15 个交易日未同步的标的，
|
||
中间日期永不补齐且无任何告警。
|
||
"""
|
||
warehouse = KlineWarehouse(tmp_path / "gap.duckdb")
|
||
try:
|
||
source_full = _bars(130) # 2024-01-01 起 130 个工作日
|
||
initial = source_full.iloc[:100] # 首同步窗口（末根 idx99）
|
||
stale_tail = source_full.iloc[115:] # 增量窗口：首根 idx115 > idx99 → 有缺口
|
||
client = _ScriptedClient([initial, stale_tail, source_full])
|
||
syncer = WarehouseSyncer(client, warehouse, max_bars=800, tail_bars=15)
|
||
|
||
with caplog.at_level(logging.WARNING, logger="easy_tdx.warehouse.sync"):
|
||
syncer.sync(["SH:600519"])
|
||
syncer.sync(["SH:600519"])
|
||
|
||
assert client.calls == [800, 15, 800] # 第二次 sync 触发了全量重拉
|
||
rows = warehouse.query("SH", "600519")
|
||
assert len(rows) == 130 # 无缺口
|
||
bridge = pd.Timestamp(source_full["datetime"].iloc[100])
|
||
dts = pd.to_datetime(rows["datetime"])
|
||
assert (dts == bridge).any() # 缺口桥接 bar 已补上
|
||
assert "缺口" in caplog.text
|
||
finally:
|
||
warehouse.close()
|
||
|
||
|
||
def test_sync_failure_keeps_provisional(tmp_path):
|
||
"""拉取失败：不转正 provisional，盘中临时值不会被洗成 completed。"""
|
||
warehouse = KlineWarehouse(tmp_path / "keep.duckdb")
|
||
try:
|
||
old = pd.DataFrame(
|
||
{
|
||
"datetime": pd.date_range("2024-01-01", periods=3),
|
||
"open": 10.0,
|
||
"high": 10.1,
|
||
"low": 9.9,
|
||
"close": 10.0,
|
||
"vol": 100.0,
|
||
"amount": 1000.0,
|
||
}
|
||
)
|
||
warehouse.upsert_bars("SH", "600519", old, status="provisional")
|
||
|
||
class _BadClient:
|
||
def get_stock_kline(self, *a, **kw):
|
||
raise ConnectionError("断网")
|
||
|
||
s = WarehouseSyncer(_BadClient(), warehouse).sync(["SH:600519"])
|
||
assert s["failed"] == 1
|
||
# 仍为 provisional：默认查询不可见（旧实现 sync 前盲转正 → 可见）
|
||
assert len(warehouse.query("SH", "600519")) == 0
|
||
assert len(warehouse.query("SH", "600519", include_provisional=True)) == 3
|
||
finally:
|
||
warehouse.close()
|
||
|
||
|
||
def test_sync_promotes_only_up_to_fetched_max(tmp_path):
|
||
"""转正上界 = 本次成功拉到的最大 datetime：未覆盖到的行保持 provisional。"""
|
||
warehouse = KlineWarehouse(tmp_path / "bound.duckdb")
|
||
try:
|
||
|
||
def _one(d: str) -> pd.DataFrame:
|
||
return pd.DataFrame(
|
||
{
|
||
"datetime": pd.date_range(d, periods=1),
|
||
"open": 10.0,
|
||
"high": 10.1,
|
||
"low": 9.9,
|
||
"close": 10.0,
|
||
"vol": 100.0,
|
||
"amount": 1000.0,
|
||
}
|
||
)
|
||
|
||
warehouse.upsert_bars("SH", "600519", _one("2024-01-05"), status="provisional")
|
||
warehouse.upsert_bars("SH", "600519", _one("2024-06-01"), status="provisional")
|
||
|
||
fetched = _bars(11, start="2024-01-10") # 最大 datetime 2024-01-24
|
||
client = _ScriptedClient([fetched])
|
||
WarehouseSyncer(client, warehouse, tail_bars=15).sync(["SH:600519"])
|
||
|
||
completed = warehouse.query("SH", "600519")
|
||
# 01-05 行 <= 拉取上界 → 已转正；06-01 行超出上界 → 保持 provisional
|
||
assert len(completed) == 12
|
||
all_rows = warehouse.query("SH", "600519", include_provisional=True)
|
||
assert len(all_rows) == 13
|
||
stale = all_rows[all_rows["status"] == "provisional"]
|
||
assert len(stale) == 1
|
||
assert pd.Timestamp(stale["datetime"].iloc[0]) == pd.Timestamp("2024-06-01")
|
||
finally:
|
||
warehouse.close()
|
||
|
||
|
||
def test_missing_duckdb_helpful_error(tmp_path, monkeypatch):
|
||
"""duckdb 未安装时给出安装指引（模拟 ImportError）。"""
|
||
import builtins
|
||
|
||
real_import = builtins.__import__
|
||
|
||
def _no_duckdb(name, *args, **kwargs):
|
||
if name == "duckdb":
|
||
raise ImportError("No module named 'duckdb'")
|
||
return real_import(name, *args, **kwargs)
|
||
|
||
monkeypatch.setattr(builtins, "__import__", _no_duckdb)
|
||
with pytest.raises(ImportError, match=r"easy-tdx\[warehouse\]"):
|
||
KlineWarehouse(tmp_path / "x.duckdb")
|