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https://ghfast.top/https://github.com/aeroxw/tick-stock-panel.git
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shy3130
fb7785ceb9
- abnormal_moves 新增 build_intraday: enriched 七类当日信号聚合, 优先级(涨停>炸板>翘板>跌停>新高>新低>放量)+涨跌幅排序, GET /api/abnormal/intraday - 维度排名项携带 group_source 与 source_field(configId.field), 前端可精确判定概念/行业而非字符串包含 - ext_data 新增 dimension-intraday: 成分股×当日分钟分区等权聚合, prev_close 优先/首根退化基准、成分网格化 ffill、全市场对照线, 小数制涨跌幅契约、60s 进程内缓存、点击触发不预计算 - 板块分时 7 测 + 盘中异动 4 测
76 lines
2.7 KiB
Python
76 lines
2.7 KiB
Python
"""盘中异动聚合测试 (build_intraday, 不依赖真实网络/enriched)。
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覆盖: 信号命中过滤、counts 计数、优先级排序 (涨停 > 炸板 > …)、
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多信号行、limit 截断、空快照与缺信号列的降级。
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"""
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from __future__ import annotations
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from datetime import date
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import polars as pl
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from app.services.abnormal_moves import build_intraday
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class _FakeRepo:
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def __init__(self, df: pl.DataFrame):
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self._df = df
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def get_enriched_latest(self):
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return self._df, date(2026, 8, 28)
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def _df(rows: list[dict]) -> pl.DataFrame:
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cols = ["symbol", "name", "close", "change_pct", "amplitude", "vol_ratio_5d",
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"turnover_rate", "consecutive_limit_ups",
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"signal_limit_up", "signal_broken_limit_up", "signal_limit_down_recovery",
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"signal_limit_down", "signal_n_day_high", "signal_n_day_low",
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"signal_volume_surge"]
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base = {c: [] for c in cols}
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for r in rows:
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for c in cols:
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base[c].append(r.get(c))
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return pl.DataFrame(base)
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def test_counts_filter_and_priority():
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repo = _FakeRepo(_df([
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{"symbol": "A1", "name": "甲", "close": 10.0, "change_pct": 0.1,
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"signal_limit_up": True, "signal_n_day_high": True},
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{"symbol": "B1", "name": "乙", "close": 5.0, "change_pct": -0.05,
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"signal_limit_down": True},
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{"symbol": "C1", "name": "丙", "close": 8.0, "change_pct": 0.02,
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"signal_volume_surge": True},
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{"symbol": "D1", "name": "丁", "close": 7.0, "change_pct": None}, # 无信号 → 不出现
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]))
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out = build_intraday(repo)
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assert out["cache_date"] == "2026-08-28"
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assert out["counts"] == {"limit_up": 1, "broken": 0, "recovery": 0,
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"limit_down": 1, "new_high": 1, "new_low": 0,
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"volume_surge": 1}
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syms = [r["symbol"] for r in out["rows"]]
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assert syms == ["A1", "B1", "C1"] # 优先级: 涨停 > 跌停 > 放量; 无信号被过滤
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assert out["rows"][0]["signals"] == ["limit_up", "new_high"] # 多信号按优先级序
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def test_limit_truncates():
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repo = _FakeRepo(_df([
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{"symbol": f"S{i}", "signal_volume_surge": True, "change_pct": 0.01} for i in range(10)
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]))
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out = build_intraday(repo, limit=3)
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assert len(out["rows"]) == 3
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assert out["counts"]["volume_surge"] == 10 # counts 不受 limit 影响
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def test_empty_snapshot():
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repo = _FakeRepo(pl.DataFrame({"symbol": [], "name": []}))
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out = build_intraday(repo)
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assert out["rows"] == [] and out["counts"] == {}
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def test_missing_signal_columns_degrades():
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repo = _FakeRepo(pl.DataFrame({"symbol": ["A1"], "name": ["甲"]}))
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out = build_intraday(repo)
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assert out["rows"] == [] and out["counts"] == {}
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