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https://ghfast.top/https://github.com/aeroxw/tick-stock-panel.git
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shy3130
697c27bb02
- 市场环境: 新增情绪周期6阶段(冰点/启动/主升/高潮/退潮/修复, 连板梯队驱动, EMA平滑+2日确认+弱档否决, 平均段长9.7天)与概念/行业主线排名(涨停梯队聚合, 可配置宽基/风格标签过滤); 市场环境页重构, regime 透明加列, 与5档state并存 - 挖掘: 因子与策略挖掘全链路(API/worker/进程锁/候选库/前端工作台/文档), 周度调度默认关闭且永不自动发布 - 回测: 财务快照因子(点时口径), 批量回测预计算共享下期收益, 信号路径矩阵列依赖展开修复(consecutive_limit_ups 缺列报错) - 数据/性能: enriched 生成与预热治理, 重任务限流, 行情/K线缓存复用, 时区修复 - 测试: 后端全量 914 通过; GUI 黑盒验证截图存证 gui-test-screenshots/
257 lines
8.2 KiB
Python
257 lines
8.2 KiB
Python
from __future__ import annotations
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from datetime import date
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import numpy as np
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import polars as pl
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import pytest
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from app.api import kline
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from app.backtest.matrix import load_market_data_matrix_from_parquet
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from app.indicators import pipeline
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from app.price_limits import (
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numpy_limit_price,
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numpy_price_limit_matrix,
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polars_is_risk_warning_name,
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polars_limit_price,
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polars_price_limit_pct,
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price_limit_pct,
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)
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@pytest.mark.parametrize(
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("symbol", "trade_date", "is_st", "expected"),
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[
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("600001.SH", date(2026, 7, 3), True, 0.05),
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("600001.SH", date(2026, 7, 6), True, 0.10),
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("000001.SZ", date(2026, 7, 3), False, 0.10),
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("300001.SZ", date(2026, 7, 3), True, 0.20),
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("688001.SH", date(2026, 7, 3), True, 0.20),
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("689001.SH", date(2026, 7, 3), True, 0.20),
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("830001.BJ", date(2026, 7, 3), True, 0.30),
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],
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)
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def test_scalar_price_limit_rules(symbol, trade_date, is_st, expected):
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assert price_limit_pct(
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symbol,
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trade_date,
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is_risk_warning=is_st,
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) == pytest.approx(expected)
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def test_polars_and_numpy_price_limit_rules_match():
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dates = [date(2026, 7, 3), date(2026, 7, 6)]
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symbols = ["600001.SH", "300001.SZ", "689001.SH", "830001.BJ"]
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names = ["*st主板", "*ST创业", "科创ST", "北交ST"]
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panel = pl.DataFrame({
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"date": [value for value in dates for _ in symbols],
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"symbol": symbols * len(dates),
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"name": names * len(dates),
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}).with_columns(
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polars_is_risk_warning_name(pl.col("name")).alias("is_st")
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).with_columns(
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polars_price_limit_pct(
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pl.col("symbol"), pl.col("date"), pl.col("is_st"),
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).alias("limit_pct")
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)
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polars_values = panel["limit_pct"].to_numpy().reshape(len(dates), len(symbols))
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numpy_values = numpy_price_limit_matrix(dates, symbols, names)
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np.testing.assert_allclose(polars_values, numpy_values)
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def test_polars_and_numpy_limit_prices_use_identical_half_up_rounding():
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previous = np.array([18.90, 10.00], dtype=np.float64)
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limits = np.array([0.05, 0.10], dtype=np.float64)
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frame = pl.DataFrame({"previous": previous, "limit": limits})
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for up in (True, False):
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polars_values = frame.select(
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polars_limit_price(
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pl.col("previous"), pl.col("limit"), up=up,
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).alias("price")
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)["price"].to_numpy()
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numpy_values = numpy_limit_price(previous, limits, up=up)
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np.testing.assert_allclose(polars_values, numpy_values)
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assert numpy_limit_price(previous, limits, up=False)[0] == pytest.approx(17.96)
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def test_matrix_uses_date_specific_st_limits_across_change(tmp_path):
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root = tmp_path / "market"
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rows = [
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(date(2026, 7, 2), 10.0),
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(date(2026, 7, 3), 10.5),
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(date(2026, 7, 6), 11.03),
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]
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for trade_date, close in rows:
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partition = root / f"date={trade_date.isoformat()}"
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partition.mkdir(parents=True)
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pl.DataFrame({
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"symbol": ["600001.SH"],
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"date": [trade_date],
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"open": [close],
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"high": [close],
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"low": [close],
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"close": [close],
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"raw_close": [close],
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"volume": [1000.0],
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}).write_parquet(partition / "part.parquet")
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market = load_market_data_matrix_from_parquet(
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root,
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rows[0][0],
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rows[-1][0],
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field_columns={"raw_close", "price_limit_pct"},
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instruments=pl.DataFrame({
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"symbol": ["600001.SH"],
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"name": ["*ST主板"],
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}),
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cache_root=tmp_path / "cache",
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)
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np.testing.assert_allclose(
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market.field("price_limit_pct")[:, 0],
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np.array([0.05, 0.05, 0.10], dtype=np.float32),
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)
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assert market.limit_up_locked[:, 0].tolist() == [0, 1, 0]
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class _InstrumentRepo:
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def get_instruments_asset(self, asset_type: str) -> pl.DataFrame:
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assert asset_type == "stock"
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return pl.DataFrame({
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"symbol": ["600001.SH"],
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"limit_up": [10.88],
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"limit_down": [8.90],
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})
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def test_minute_price_limit_prefers_authoritative_prices_only_today(monkeypatch):
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today = date(2026, 7, 18)
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monkeypatch.setattr(kline, "cn_today", lambda: today)
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current = kline._get_price_limit_info(
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_InstrumentRepo(), "600001.SH", today, "stock", "*ST主板",
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)
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historical = kline._get_price_limit_info(
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_InstrumentRepo(), "600001.SH", date(2026, 7, 3), "stock", "*ST主板",
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)
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assert current == {
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"rate": 0.10,
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"limit_up": 10.88,
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"limit_down": 8.90,
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"source": "instrument",
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}
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assert historical == {
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"rate": 0.05,
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"limit_up": None,
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"limit_down": None,
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"source": "rule",
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}
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def _daily_limit_rows(current_close: float) -> pl.DataFrame:
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return pl.DataFrame({
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"symbol": ["600001.SH", "600001.SH"],
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"date": [date(2026, 7, 17), date(2026, 7, 20)],
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"open": [10.0, current_close],
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"high": [10.0, current_close],
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"low": [10.0, current_close],
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"close": [10.0, current_close],
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"raw_close": [10.0, current_close],
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"raw_high": [10.0, current_close],
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})
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@pytest.mark.parametrize(
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("instrument_as_of", "expected"),
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[
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(date(2026, 7, 17), False),
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(date(2026, 7, 20), True),
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(None, True),
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],
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)
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def test_daily_limit_prices_require_matching_instrument_date(instrument_as_of, expected):
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instrument_data = {
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"symbol": ["600001.SH"],
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"name": ["普通股"],
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"limit_up": [10.90],
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"limit_down": [9.10],
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}
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if instrument_as_of is not None:
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instrument_data["as_of"] = [instrument_as_of]
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result = pipeline.compute_limit_signals(
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_daily_limit_rows(9.10),
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pl.DataFrame(instrument_data),
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needed={"signal_limit_down"},
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)
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assert result["signal_limit_down"][-1] is expected
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assert "_instrument_as_of" not in result.columns
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def test_realtime_limit_prices_ignore_stale_instrument_date():
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today = date(2026, 7, 20)
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rows = pl.DataFrame({
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"symbol": ["600001.SH"],
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"date": [today],
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"open": [9.10],
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"high": [9.10],
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"low": [9.10],
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"close": [9.10],
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"raw_close": [9.10],
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"raw_high": [9.10],
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"raw_low": [9.10],
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"_prev_close_raw": [10.0],
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"volume": [1000.0],
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})
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instruments = pl.DataFrame({
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"symbol": ["600001.SH"],
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"name": ["普通股"],
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"limit_up": [10.90],
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"limit_down": [9.10],
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"as_of": [date(2026, 7, 17)],
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})
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result = pipeline._compute_limit_signals_today(rows, instruments)
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assert result["signal_limit_down"][0] is False
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assert "_instrument_as_of" not in result.columns
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def test_limit_down_recovery_uses_raw_low_under_later_ex_div():
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"""除权事件之后重算历史时, 跌停翘板"曾触及跌停"必须用原始价 low 判断。
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day2 (历史日): 原始 low 9.30 未触及跌停价 9.00, 不应触发翘板;
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但 day3 除权 (ex_factor=2) 使 day2 前复权 low 变为 4.65,
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若误用复权 low 对比原始口径跌停价会误报翘板。
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day3 (除权日, 最新日不复权): 涨跌停基准切换为前复权昨收 4.825 → 跌停价 4.34,
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原始 low 4.34 触及且收阳未封死 → 真翘板。
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"""
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raw = pl.DataFrame({
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"symbol": ["600001.SH"] * 3,
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"date": [date(2024, 1, 2), date(2024, 1, 3), date(2024, 1, 4)],
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"open": [10.00, 9.60, 4.30],
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"high": [10.10, 9.70, 4.45],
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"low": [9.90, 9.30, 4.34],
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"close": [10.00, 9.65, 4.42],
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"volume": [10000.0, 10000.0, 10000.0],
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"amount": [1.0e7, 1.0e7, 1.0e7],
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})
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factors = pl.DataFrame({
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"symbol": ["600001.SH"],
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"trade_date": [date(2024, 1, 4)],
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"ex_factor": [2.0],
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})
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instruments = pl.DataFrame({
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"symbol": ["600001.SH"],
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"name": ["普通股"],
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"float_shares": [1.0e8],
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})
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df = pipeline.compute_enriched(raw, factors=factors, instruments=instruments)
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day2 = df.filter(pl.col("date") == date(2024, 1, 3))
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assert day2["signal_limit_down_recovery"][0] is False
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day3 = df.filter(pl.col("date") == date(2024, 1, 4))
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assert day3["signal_limit_down_recovery"][0] is True
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