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shy3130
9883208847
用户反馈市场环境点重算偶发 'Series' object has no attribute 'group_by': 若 _load_concept_map_df 返回裸 DataFrame, 元组解包会把两列拆成 Series (Series.is_empty 可通过, group_by 不存在)。改为按 tuple/DataFrame 实际 形态取值, market_mainline 与 build_rps_rotation 两处解包点同修, 附回归测试(裸 DataFrame 假实现端到端不炸)。
252 lines
12 KiB
Python
252 lines
12 KiB
Python
"""市场主线(market_mainline)与过滤配置单元测试。"""
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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 import market_mainline, preferences
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def _write_enriched(root, rows: list[dict]) -> None:
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enriched = root / "kline_daily_enriched"
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by_date: dict[date, list[dict]] = {}
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for r in rows:
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by_date.setdefault(r["date"], []).append(r)
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for d, day_rows in by_date.items():
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part = enriched / f"date={d.isoformat()}" / "part.parquet"
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part.parent.mkdir(parents=True, exist_ok=True)
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pl.DataFrame(day_rows).write_parquet(part)
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def _fake_repo(tmp_path):
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import types
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return types.SimpleNamespace(store=types.SimpleNamespace(data_dir=tmp_path))
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def _patch_map(monkeypatch, mapping: dict[str, list[str]], kind: str = "concept") -> None:
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map_df = pl.DataFrame(
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{"_sym_up": [s for s, ms in mapping.items() for _ in ms],
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kind: [m for _, ms in mapping.items() for m in ms]},
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schema={"_sym_up": pl.Utf8, kind: pl.Utf8},
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).unique()
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def fake_load(repo, k="concept"):
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return (map_df, map_df[kind].n_unique()) if k == kind else (pl.DataFrame(), 0)
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monkeypatch.setattr(market_mainline, "_load_concept_map_df", fake_load)
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def _mk_rows(d: date, spec: list[tuple[str, int, float]]) -> list[dict]:
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return [
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{"symbol": sym, "date": d, "consecutive_limit_ups": consec, "amount": amt}
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for sym, consec, amt in spec
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]
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class TestComputeMainline:
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def _setup(self, tmp_path, monkeypatch):
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d1, d2 = date(2024, 1, 2), date(2024, 1, 3)
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# 概念 X: d1 三个涨停(2,1,1), d2 三个涨停(3,2,1); 概念 Y: 单股 2 板
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# S5 无概念映射; 大概念 BIG 成员 700 家但只有 5 家涨停(数据里只写 5 行)
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rows = _mk_rows(d1, [("S1.SH", 2, 5e8), ("S2.SH", 1, 1e8), ("S3.SH", 1, 2e8),
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("S4.SH", 2, 3e8), ("S5.SH", 1, 1e8),
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("B1.SH", 1, 1e8), ("B2.SH", 1, 1e8)])
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rows += _mk_rows(d2, [("S1.SH", 3, 6e8), ("S2.SH", 2, 2e8), ("S3.SH", 0, 1e8),
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("S4.SH", 3, 4e8), ("S5.SH", 1, 1e8),
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("B1.SH", 2, 1e8), ("B2.SH", 0, 1e8)])
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_write_enriched(tmp_path, rows)
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mapping = {
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"S1.SH": ["X"], "S2.SH": ["X"], "S3.SH": ["X"],
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"S4.SH": ["X", "Y"], "S5.SH": [],
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"B1.SH": ["BIG"], "B2.SH": ["BIG"],
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**{f"F{i}.SH": ["BIG"] for i in range(700)}, # BIG 成员 702 → 超 600 上限
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}
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_patch_map(monkeypatch, mapping)
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return _fake_repo(tmp_path), d1, d2
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def test_aggregation_and_big_concept_filter(self, tmp_path, monkeypatch):
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repo, d1, d2 = self._setup(tmp_path, monkeypatch)
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out = market_mainline.compute_mainline_range(
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repo, tmp_path, d1, d2, kind="concept",
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filter_cfg={"min_members": 4, "max_members": 600, "blacklist": []},
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)
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members = set(out["member"].to_list())
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assert "BIG" not in members # 成员数超上限被过滤
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assert "X" in members
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x_d2 = out.filter((pl.col("date") == d2) & (pl.col("member") == "X")).to_dicts()[0]
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assert x_d2["limit_up_count"] == 3 # S1,S2,S4
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assert x_d2["ge2_count"] == 3
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assert x_d2["max_boards"] == 3
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assert x_d2["rungs_filled"] == 2 # 档位 {2,3}
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assert x_d2["leader_symbol"] == "S1.SH" # 最高板且成交额大
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assert x_d2["rank"] == 1
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def test_bare_dataframe_map_return_compat(self, tmp_path, monkeypatch):
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"""回归: _load_concept_map_df 若返回裸 DataFrame(旧版/被改动实现),
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元组解包会把两列拆成两个 Series, Series.is_empty() 能通过但后续
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group_by 报 'Series' object has no attribute 'group_by'
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(用户反馈: 市场环境点重算偶发报错)。compute 应兼容不炸。"""
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repo, d1, d2 = self._setup(tmp_path, monkeypatch)
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bare = pl.DataFrame(
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{"_sym_up": ["S1.SH", "S2.SH", "S3.SH", "S4.SH", "B1.SH", "B2.SH"],
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"concept": ["X", "X", "X", "X", "BIG", "BIG"]},
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schema={"_sym_up": pl.Utf8, "concept": pl.Utf8},
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)
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monkeypatch.setattr(
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market_mainline, "_load_concept_map_df",
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lambda r, k="concept": bare if k == "concept" else
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pl.DataFrame(schema={"_sym_up": pl.Utf8, "industry": pl.Utf8}),
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)
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out = market_mainline.compute_mainline_range(
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repo, tmp_path, d1, d2, kind="concept",
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filter_cfg={"min_members": 4, "max_members": 600, "blacklist": []},
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)
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assert "X" in set(out["member"].to_list())
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assert out.filter((pl.col("date") == d2) & (pl.col("member") == "X")).height == 1
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def test_blacklist_and_min_limit_up(self, tmp_path, monkeypatch):
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repo, d1, d2 = self._setup(tmp_path, monkeypatch)
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out = market_mainline.compute_mainline_range(
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repo, tmp_path, d1, d2, kind="concept",
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filter_cfg={"min_members": 1, "max_members": 5000, "blacklist": ["X"]},
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)
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# X 被黑名单; BIG 只有 2-3 家涨停 < _MIN_LIMIT_UP=3 也不参与 → 只剩空/无 X
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assert "X" not in set(out["member"].to_list())
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def test_upsert_replaces_same_day_kind(self, tmp_path, monkeypatch):
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repo, d1, d2 = self._setup(tmp_path, monkeypatch)
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cfg = {"min_members": 4, "max_members": 600, "blacklist": []}
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first = market_mainline.compute_mainline_range(repo, tmp_path, d1, d1, kind="concept", filter_cfg=cfg)
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market_mainline.upsert_mainline_history(tmp_path, first)
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both = market_mainline.compute_mainline_range(repo, tmp_path, d1, d2, kind="concept", filter_cfg=cfg)
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market_mainline.upsert_mainline_history(tmp_path, both)
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stored = pl.read_parquet(market_mainline.mainline_path(tmp_path))
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assert set(stored["date"].to_list()) == {d1, d2}
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# 同日重算不产生重复行
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assert stored.filter(pl.col("date") == d1).height == first.height
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def test_incremental_fills_missing_days(self, tmp_path, monkeypatch):
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repo, d1, d2 = self._setup(tmp_path, monkeypatch)
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cfg = {"min_members": 4, "max_members": 600, "blacklist": []}
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first = market_mainline.compute_mainline_range(repo, tmp_path, d1, d1, kind="concept", filter_cfg=cfg)
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market_mainline.upsert_mainline_history(tmp_path, first)
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new = market_mainline.compute_mainline_incremental(repo, tmp_path, kind="concept")
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assert not new.is_empty()
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assert set(new["date"].to_list()) == {d2}
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def test_industry_level_truncation(self, tmp_path, monkeypatch):
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d1 = date(2024, 1, 2)
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rows = _mk_rows(d1, [("S1.SH", 2, 5e8), ("S2.SH", 1, 1e8),
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("S3.SH", 1, 2e8), ("S4.SH", 3, 4e8)])
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_write_enriched(tmp_path, rows)
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_patch_map(
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monkeypatch,
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{"S1.SH": ["计算机-软件开发-垂直应用软件"],
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"S2.SH": ["计算机-软件开发-垂直应用软件"],
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"S3.SH": ["计算机-IT服务-IT服务Ⅲ"],
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"S4.SH": ["计算机-软件开发-垂直应用软件"]},
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kind="industry",
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)
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out = market_mainline.compute_mainline_range(
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_fake_repo(tmp_path), tmp_path, d1, d1, kind="industry",
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filter_cfg={"min_members": 1, "max_members": 5000, "blacklist": []},
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)
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members = set(out["member"].to_list())
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assert "计算机-软件开发" in members
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assert all(m.count("-") <= 1 for m in members)
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sw = out.filter(pl.col("member") == "计算机-软件开发").to_dicts()[0]
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assert sw["limit_up_count"] == 3
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assert sw["max_boards"] == 3
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class TestMainlineFilterPreferences:
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def test_blacklist_string_parsing_and_clamp(self, tmp_path, monkeypatch):
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path = tmp_path / "preferences.json"
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monkeypatch.setattr(preferences, "_path", lambda: path)
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got = preferences.set_mainline_filter_config({
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"max_members": 99999, # 超上限被夹到 5000
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"min_members": 0, # 低于下限被夹到 1
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"blacklist": "融资融券, 沪股通；深股通", # noqa: RUF001
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})
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assert got["max_members"] == 5000
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assert got["min_members"] == 1
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assert set(got["blacklist"]) == {"融资融券", "沪股通", "深股通"}
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# 部分更新: 只改黑名单, 其他保持
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got2 = preferences.set_mainline_filter_config({"blacklist": ["ST板块"]})
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assert got2["blacklist"] == ["ST板块"]
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assert got2["max_members"] == 5000
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def test_defaults(self, tmp_path, monkeypatch):
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path = tmp_path / "preferences.json"
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monkeypatch.setattr(preferences, "_path", lambda: path)
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cfg = preferences.get_mainline_filter_config()
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assert cfg == {"min_members": 4, "max_members": 600, "blacklist": [], "exclude_st": True}
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def test_sentiment_exclude_st_roundtrip(self, tmp_path, monkeypatch):
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path = tmp_path / "preferences.json"
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monkeypatch.setattr(preferences, "_path", lambda: path)
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assert preferences.get_sentiment_exclude_st() is True # 默认剔除
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assert preferences.set_sentiment_exclude_st(False) is False
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assert preferences.get_sentiment_exclude_st() is False
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# 经主线过滤配置部分更新同样生效
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got = preferences.set_mainline_filter_config({"exclude_st": True})
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assert got["exclude_st"] is True
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class TestExcludeST:
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"""风险警示股剔除: 维表名称含 ST → 主线聚合前过滤。"""
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@staticmethod
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def _write_instruments(tmp_path, names: dict[str, str]) -> None:
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part = tmp_path / "instruments" / "part.parquet"
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part.parent.mkdir(parents=True, exist_ok=True)
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pl.DataFrame({
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"symbol": list(names),
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"name": list(names.values()),
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}).write_parquet(part)
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def _reset_cache(self, monkeypatch):
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monkeypatch.setattr(market_mainline, "_ST_SYMBOLS_CACHE", None)
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def test_load_risk_warning_symbols(self, tmp_path, monkeypatch):
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self._reset_cache(monkeypatch)
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self._write_instruments(tmp_path, {
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"s1.SH": "*ST环保", "S2.SH": "ST万邦", "S3.SZ": "正常股",
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"s4.BJ": "S*ST京", "S5.SH": "斯太尔", # 中文名含"斯"不含 ST 标记
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})
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got = market_mainline.load_risk_warning_symbols(tmp_path)
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assert got == frozenset({"S1.SH", "S2.SH", "S4.BJ"}) # 大写归一
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# 缓存命中: 再次读取不重扫磁盘
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self._write_instruments(tmp_path, {"S9.SH": "ST新增"})
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assert market_mainline.load_risk_warning_symbols(tmp_path) == got
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def test_load_risk_warning_symbols_empty_dir(self, tmp_path, monkeypatch):
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self._reset_cache(monkeypatch)
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assert market_mainline.load_risk_warning_symbols(tmp_path) == frozenset()
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def test_compute_mainline_excludes_st(self, tmp_path, monkeypatch):
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"""S1(ST) 涨停被剔除 → 概念 X 计数/高度/龙头随之变化; 关闭开关恢复。"""
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self._reset_cache(monkeypatch)
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self._write_instruments(tmp_path, {"S1.SH": "*ST一", "S2.SH": "正常一"})
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repo, d1, d2 = TestComputeMainline()._setup(tmp_path, monkeypatch)
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cfg = {"min_members": 4, "max_members": 600, "blacklist": []}
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out = market_mainline.compute_mainline_range(
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repo, tmp_path, d1, d2, kind="concept", filter_cfg=cfg, exclude_st=True,
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)
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x_d1 = out.filter((pl.col("date") == d1) & (pl.col("member") == "X")).to_dicts()[0]
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assert x_d1["limit_up_count"] == 3 # S1(ST) 被剔除, 剩 S2,S3,S4
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assert x_d1["ge2_count"] == 1 # 仅 S4=2板
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assert x_d1["max_boards"] == 2
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assert x_d1["leader_symbol"] == "S4.SH"
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out_keep = market_mainline.compute_mainline_range(
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repo, tmp_path, d1, d2, kind="concept", filter_cfg=cfg, exclude_st=False,
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)
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x_d1_keep = out_keep.filter((pl.col("date") == d1) & (pl.col("member") == "X")).to_dicts()[0]
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assert x_d1_keep["limit_up_count"] == 4
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assert x_d1_keep["ge2_count"] == 2 # S1=2板, S4=2板
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assert x_d1_keep["leader_symbol"] == "S1.SH"
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