fix(screener): 涨停梯队的时序扩展列只取最新分区, 不再放大行数

ext_{config_id} 视图对 timeseries 模式覆盖 timeseries/**/*.parquet 全部分区
(app/api/ext_data._refresh_views), 一只票在 N 天快照里就有 N 行。梯队直接
LEFT JOIN 该视图, 同一只涨停股被复制 N 份, 各档 count 一并放大 N 倍。

改为与自选股列表 (app/api/watchlist) 同口径: 有配置时走 _read_ext_dataframe
取最新分区, 再按 symbol 去重后 JOIN; 无配置时保留视图查询兜底。

backend/app/api/screener.py

@@ -933,32 +933,48 @@ def limit_ladder(
     if ext_specs:
         db = repo.store.db
         data_dir = repo.store.data_dir
+        from app.api.ext_data import _read_ext_dataframe
         from app.services.ext_data import ExtConfigStore
 
         ext_store = ExtConfigStore(data_dir)
         configs = {c.id: c for c in ext_store.load_all()}
 
+        def _dedup_ext(frame: pl.DataFrame, field: str, out_col: str) -> pl.DataFrame | None:
+            """(symbol, 字段) 两列并按 symbol 去重; 缺列时返回 None。"""
+            if frame.is_empty() or "symbol" not in frame.columns or field not in frame.columns:
+                return None
+            return (
+                frame
+                .select(["symbol", field])
+                .unique(subset=["symbol"], keep="last")
+                .rename({field: out_col})
+            )
+
         for config_id, field_name in ext_specs:
             view_name = f"ext_{config_id}"
             ext_col_name = f"{config_id}__{field_name}"
             try:
+                # 扩展时序数据必须只取最新分区; 否则一个 symbol 会按历史分区数被 JOIN 放大
+                # (ext_{id} 视图覆盖 timeseries/**), 与自选股列表同口径。
+                cfg = configs.get(config_id)
+                if cfg:
+                    ext_df, _ = _read_ext_dataframe(cfg, data_dir)
+                else:
                     ext_df = pl.from_arrow(db.query(
                         f"SELECT symbol, {quote_ident(field_name)} FROM {view_name}"
                     ).arrow())
-                if not ext_df.is_empty() and "symbol" in ext_df.columns:
-                    ext_df = ext_df.rename({field_name: ext_col_name})
-                    df = df.join(ext_df.select(["symbol", ext_col_name]), on="symbol", how="left")
+                joined = _dedup_ext(ext_df, field_name, ext_col_name)
+                if joined is not None:
+                    df = df.join(joined, on="symbol", how="left")
                     ext_col_names.append(ext_col_name)
             except Exception:
                 cfg = configs.get(config_id)
                 if cfg:
                     try:
-                        from app.api.ext_data import _parquet_glob
-                        glob = _parquet_glob(cfg, data_dir)
-                        ext_df = pl.read_parquet(glob)
-                        if not ext_df.is_empty() and "symbol" in ext_df.columns and field_name in ext_df.columns:
-                            ext_df = ext_df.select(["symbol", field_name]).rename({field_name: ext_col_name})
-                            df = df.join(ext_df, on="symbol", how="left")
+                        ext_df, _ = _read_ext_dataframe(cfg, data_dir)
+                        joined = _dedup_ext(ext_df, field_name, ext_col_name)
+                        if joined is not None:
+                            df = df.join(joined, on="symbol", how="left")
                             ext_col_names.append(ext_col_name)
                     except Exception:
                         pass

backend/tests/test_limit_ladder_ext_timeseries.py

@@ -0,0 +1,151 @@
+"""涨停梯队挂时序扩展列时, 一只票不能按历史分区数被 JOIN 放大。
+
+ext_{config_id} DuckDB 视图对 timeseries 模式覆盖 timeseries/**/*.parquet 全部
+分区 (见 app/api/ext_data._refresh_views), 一只票在 N 天快照里就有 N 行。
+自选股列表 (app/api/watchlist) 对同一场景先走 _read_ext_dataframe 取最新分区
+再按 symbol 去重, 梯队这边直接查视图, 于是同一只涨停股在梯队里出现 N 次、
+档位 count 被放大 N 倍。
+"""
+from __future__ import annotations
+
+import json
+from datetime import date
+from types import SimpleNamespace
+
+import polars as pl
+import pytest
+
+from app.api import screener as screener_api
+from app.services.screener import ScreenerService
+
+_AS_OF = date(2026, 9, 10)
+
+
+class _FakeQuery:
+    def __init__(self, df: pl.DataFrame) -> None:
+        self._df = df
+
+    def arrow(self):
+        return self._df.to_arrow()
+
+
+class _FakeDB:
+    """模拟 ext_{id} 视图: timeseries 模式下返回全部分区的行。"""
+
+    def __init__(self, df: pl.DataFrame) -> None:
+        self._df = df
+
+    def query(self, sql: str) -> _FakeQuery:
+        return _FakeQuery(self._df)
+
+
+class _NoDepth:
+    def get_sealed_map(self, _as_of, is_down: bool = False) -> dict:
+        return {}
+
+    def is_sealed_ready(self, _as_of) -> bool:
+        return False
+
+    def get_sealed_age(self, _as_of):
+        return None
+
+
+def _write_timeseries_ext(data_dir, partitions: dict[str, pl.DataFrame]) -> pl.DataFrame:
+    """写 timeseries 扩展表; 返回视图口径 (全部分区拼接) 的 DataFrame。"""
+    cfg_dir = data_dir / "ext_data" / "concept_ts"
+    cfg_dir.mkdir(parents=True, exist_ok=True)
+    cfg_dir.joinpath("config.json").write_text(
+        json.dumps({
+            "id": "concept_ts",
+            "label": "概念时序",
+            "mode": "timeseries",
+            "fields": [{"name": "concept", "dtype": "string", "label": "概念"}],
+        }, ensure_ascii=False),
+        encoding="utf-8",
+    )
+    for day, frame in partitions.items():
+        part = cfg_dir / "timeseries" / f"date={day}"
+        part.mkdir(parents=True, exist_ok=True)
+        frame.write_parquet(part / "part.parquet")
+    return pl.concat([partitions[d] for d in sorted(partitions)])
+
+
+def _request(data_dir, view_df: pl.DataFrame):
+    repo = SimpleNamespace(
+        store=SimpleNamespace(data_dir=data_dir, db=_FakeDB(view_df)),
+    )
+    state = SimpleNamespace(repo=repo, depth_service=_NoDepth())
+    return SimpleNamespace(app=SimpleNamespace(state=state))
+
+
+@pytest.fixture
+def _stub_enriched(monkeypatch):
+    """单只涨停股的 enriched 快照 + 空的昨日连板表。"""
+    snapshot = pl.DataFrame({
+        "symbol": ["600000.SH"],
+        "name": ["示例"],
+        "close": [11.0],
+        "change_pct": [0.1],
+        "signal_limit_up": [True],
+        "signal_limit_down": [False],
+        "signal_broken_limit_up": [False],
+        "consecutive_limit_ups": [2],
+    })
+    monkeypatch.setattr(
+        ScreenerService, "_load_enriched_for_date", lambda self, d: snapshot
+    )
+    monkeypatch.setattr(
+        ScreenerService, "load_prior_consecutive", lambda self, d, c: pl.DataFrame()
+    )
+    return snapshot
+
+
+def test_timeseries_ext_column_does_not_duplicate_ladder_rows(tmp_path, _stub_enriched):
+    view_df = _write_timeseries_ext(tmp_path, {
+        "2026-09-08": pl.DataFrame({"symbol": ["600000.SH"], "concept": ["旧概念"]}),
+        "2026-09-09": pl.DataFrame({"symbol": ["600000.SH"], "concept": ["次新概念"]}),
+        "2026-09-10": pl.DataFrame({"symbol": ["600000.SH"], "concept": ["最新概念"]}),
+    })
+
+    payload = screener_api.limit_ladder(
+        _request(tmp_path, view_df),
+        as_of=_AS_OF,
+        direction="up",
+        ext_columns="concept_ts.concept",
+    )
+
+    tiers = payload["tiers"]
+    assert len(tiers) == 1
+    stocks = tiers[0]["stocks"]
+    assert [s["symbol"] for s in stocks] == ["600000.SH"], "同一只票被历史分区放大了"
+    assert tiers[0]["count"] == 1
+    # 取最新分区的值, 不是任意一期的历史值
+    assert stocks[0]["concept_ts__concept"] == "最新概念"
+
+
+def test_snapshot_ext_column_still_joins(tmp_path, _stub_enriched):
+    """snapshot 模式扩展表照常挂列 (回归保护)。"""
+    cfg_dir = tmp_path / "ext_data" / "concept_snap"
+    cfg_dir.mkdir(parents=True, exist_ok=True)
+    cfg_dir.joinpath("config.json").write_text(
+        json.dumps({
+            "id": "concept_snap",
+            "label": "概念快照",
+            "mode": "snapshot",
+            "fields": [{"name": "concept", "dtype": "string", "label": "概念"}],
+        }, ensure_ascii=False),
+        encoding="utf-8",
+    )
+    snap = pl.DataFrame({"symbol": ["600000.SH"], "concept": ["人工智能"]})
+    snap.write_parquet(cfg_dir / "part.parquet")
+
+    payload = screener_api.limit_ladder(
+        _request(tmp_path, snap),
+        as_of=_AS_OF,
+        direction="up",
+        ext_columns="concept_snap.concept",
+    )
+
+    stocks = payload["tiers"][0]["stocks"]
+    assert len(stocks) == 1
+    assert stocks[0]["concept_snap__concept"] == "人工智能"