fix(regime): aggregate fallback batches incrementally

2026-09-12 16:44:15 +08:00 · 2026-09-04 12:19:35 +08:00
parent 8c28132361
commit 885e693118
2 changed files with 140 additions and 26 deletions
@@ -356,17 +356,30 @@ def _compute_batch(repo, enriched_dir, instruments, historical_shares,
    return df.filter((pl.col("date") >= batch_start) & (pl.col("date") <= batch_end))


-def _scan_enriched_fallback(repo, start: date, end: date) -> pl.DataFrame | None:
-    """缓存不覆盖时的慢路径: scan enriched parquet + 重算所需指标列。
+def _filter_excluded_symbols(df: pl.DataFrame, excluded_symbols: list[str]) -> pl.DataFrame:
+    if excluded_symbols and "symbol" in df.columns:
+        return df.filter(~pl.col("symbol").str.to_uppercase().is_in(excluded_symbols))
+    return df

-    仅在 regime 首次全量回填或缓存未预热时触发。返回含信号列的多日 DataFrame。
+
+def _scan_enriched_fallback(
+    repo,
+    start: date,
+    end: date,
+    *,
+    index_pct_map: dict | None = None,
+    excluded_symbols: list[str] | None = None,
+) -> pl.DataFrame | None:
+    """缓存不覆盖时的慢路径: 分批扫描并直接返回日级环境聚合。
+
+    仅在 regime 首次全量回填或缓存未预热时触发。

    内存控制(关键, 两层优化):
    1. needed 白名单: regime 只需 change_pct/ma20/涨跌停信号等少数列, 不用 compute_all
       算 72 列全套指标(那会让全量峰值达 6.8GB)。
-    2. 分批: 范围超过 batch_days 个交易日时按批切片, 每批带 warmup 前缀算完后 concat。
+    2. 分批: 每批带 warmup 前缀算完后立即聚合为日级行, 不保留跨批个股明细。
       batch_days / warmup_days 由用户偏好控制(数据页「市场环境」卡片设置),
-       实测默认值(60/40)全量(515万行)峰值约 1.9GB, 4GB 内存机器可稳跑。
+       峰值随单批大小受控, 不再随完整历史长度线性增长。
    必须传入 instruments(涨跌停价表), 否则 compute_limit_signals 会跳过涨跌停信号。
    """
    try:
@@ -381,6 +394,7 @@ def _scan_enriched_fallback(repo, start: date, end: date) -> pl.DataFrame | None
        enriched_dir = repo.store.data_dir / "kline_daily_enriched"
        if not enriched_dir.exists():
            return None
+        excluded_symbols = excluded_symbols or []
        instruments = repo.get_instruments()
        historical_shares = repo.get_historical_shares()

@@ -394,23 +408,31 @@ def _scan_enriched_fallback(repo, start: date, end: date) -> pl.DataFrame | None
        if len(target_dates) <= batch_days:
            df = _compute_batch(repo, enriched_dir, instruments, historical_shares,
                                target_dates[0], target_dates[-1], warmup_days)
-            return df if not df.is_empty() else None
+            if df.is_empty():
+                return None
+            df = _filter_excluded_symbols(df, excluded_symbols)
+            result = _aggregate_daily(df, index_pct_map)
+            return result if not result.is_empty() else None

-        # 大范围: 按交易日分批, 逐批算 + concat
+        # 大范围: 每批个股明细立即压缩为日级行, 只保留小型聚合结果。
        batches = [
            (target_dates[i], target_dates[min(i + batch_days - 1, len(target_dates) - 1)])
            for i in range(0, len(target_dates), batch_days)
        ]
-        logger.info("regime fallback: %d 天分 %d 批 (每批≤%d天 + %d天warmup)",
+        logger.info("regime fallback: %d 天分 %d 批逐批聚合 (每批≤%d天 + %d天warmup)",
                    len(target_dates), len(batches), batch_days, warmup_days)
-        parts: list[pl.DataFrame] = []
+        daily_parts: list[pl.DataFrame] = []
        for bs, be in batches:
            df = _compute_batch(repo, enriched_dir, instruments, historical_shares, bs, be, warmup_days)
-            if not df.is_empty():
-                parts.append(df)
-        if not parts:
+            if df.is_empty():
+                continue
+            df = _filter_excluded_symbols(df, excluded_symbols)
+            daily = _aggregate_daily(df, index_pct_map)
+            if not daily.is_empty():
+                daily_parts.append(daily)
+        if not daily_parts:
            return None
-        return pl.concat(parts, how="vertical_relaxed")
+        return pl.concat(daily_parts, how="vertical_relaxed")
    except Exception as e:  # noqa: BLE001
        logger.warning("regime scan_enriched_fallback failed: %s", e)
        return None
@@ -440,15 +462,6 @@ def run_regime_batch(repo, start: date, end: date) -> pl.DataFrame:
    # 指数涨幅(主力指数)
    index_pct_map = _load_index_pct(repo, start, end)

-    # enriched 多日数据(优先缓存)
-    df = repo.get_enriched_range(start, end)
-    if df is None or df.is_empty():
-        logger.info("regime batch: enriched cache miss [%s~%s], fallback to scan", start, end)
-        df = _scan_enriched_fallback(repo, start, end)
-    if df is None or df.is_empty():
-        logger.info("regime batch: no enriched data for [%s~%s]", start, end)
-        return pl.DataFrame()
-
    # 口径: 默认剔除风险警示(ST)股(与主线统计同一开关) — 主板 ST 在 2026-07 前
    # 享 5% 涨跌幅且是跨行业状态桶, 混入会系统性抬高涨停宽度/高度(弱市炒 ST 尤甚)。
    # 涨跌家数/MA20 占比等宽度指标几乎不受影响。切换口径需全量重算 regime。
@@ -457,14 +470,33 @@ def run_regime_batch(repo, start: date, end: date) -> pl.DataFrame:
        exclude_st = _prefs_st.get_sentiment_exclude_st()
    except Exception:
        exclude_st = True
+    excluded_symbols: list[str] = []
    if exclude_st:
        from app.services.market_mainline import load_risk_warning_symbols

        st_syms = load_risk_warning_symbols(repo.store.data_dir)
-        if st_syms and "symbol" in df.columns:
-            df = df.filter(
-                ~pl.col("symbol").str.to_uppercase().is_in(sorted(st_syms))
-            )
+        excluded_symbols = sorted(st_syms)
+
+    # enriched 多日数据(优先缓存)。慢路径在每批内部完成过滤和日级聚合,
+    # 避免把所有批次的个股明细同时保留到最终 group_by。
+    df = repo.get_enriched_range(start, end)
+    if df is None or df.is_empty():
+        logger.info("regime batch: enriched cache miss [%s~%s], fallback to scan", start, end)
+        result = _scan_enriched_fallback(
+            repo,
+            start,
+            end,
+            index_pct_map=index_pct_map,
+            excluded_symbols=excluded_symbols,
+        )
+        if result is None or result.is_empty():
+            logger.info("regime batch: no enriched data for [%s~%s]", start, end)
+            return pl.DataFrame()
+        return result
+
+    df = _filter_excluded_symbols(df, excluded_symbols)
+    if df.is_empty():
+        return pl.DataFrame()

    return _aggregate_daily(df, index_pct_map)

@@ -14,6 +14,7 @@ from datetime import date

 import polars as pl
 import pytest
+from polars.testing import assert_frame_equal

 from app.services import regime_builder

@@ -183,6 +184,87 @@ def test_run_regime_batch_excludes_st(tmp_path, monkeypatch):
    assert r1_all["up_count"] == 2      # A,C 上涨


+def test_run_regime_batch_aggregates_fallback_per_batch(tmp_path, monkeypatch):
+    """回归 #241: fallback 不得保留所有批次的个股明细后再统一聚合。"""
+    from app.services import market_mainline, preferences
+
+    target_dates = [date(2026, 1, day) for day in range(2, 6)]
+    enriched_dir = tmp_path / "kline_daily_enriched"
+    for trade_date in target_dates:
+        partition = enriched_dir / f"date={trade_date.isoformat()}"
+        partition.mkdir(parents=True)
+        (partition / "part.parquet").write_bytes(b"")
+
+    class _Store:
+        data_dir = tmp_path
+
+    class _FakeRepo:
+        store = _Store
+
+        def get_enriched_range(self, start, end):
+            return None
+
+        def get_instruments(self):
+            return pl.DataFrame()
+
+        def get_historical_shares(self):
+            return pl.DataFrame()
+
+    def _batch_frame(batch_start, batch_end):
+        dates = [d for d in target_dates if batch_start <= d <= batch_end]
+        rows = []
+        for trade_date in dates:
+            for symbol, change_pct in (("A", 0.05), ("B", -0.03)):
+                rows.append({
+                    "date": trade_date,
+                    "symbol": symbol,
+                    "close": 10.0,
+                    "change_pct": change_pct,
+                    "amount": 1e8,
+                    "ma20": 9.0,
+                    "signal_limit_up": symbol == "A",
+                    "signal_limit_down": False,
+                    "signal_broken_limit_up": False,
+                    "consecutive_limit_ups": 1 if symbol == "A" else 0,
+                    "_prev_consec": 0,
+                })
+        return pl.DataFrame(rows)
+
+    monkeypatch.setattr(preferences, "get_regime_batch_days", lambda: 2)
+    monkeypatch.setattr(preferences, "get_regime_warmup_days", lambda: 40)
+    monkeypatch.setattr(preferences, "get_sentiment_exclude_st", lambda: True)
+    monkeypatch.setattr(market_mainline, "load_risk_warning_symbols", lambda *a, **k: {"A"})
+    monkeypatch.setattr(regime_builder, "_load_index_pct", lambda *a, **k: {})
+    monkeypatch.setattr(
+        regime_builder,
+        "_compute_batch",
+        lambda repo, enriched, instruments, shares, start, end, warmup: _batch_frame(start, end),
+    )
+
+    aggregate_input_heights = []
+    original_aggregate = regime_builder._aggregate_daily
+
+    def _recording_aggregate(df, index_pct_map=None):
+        aggregate_input_heights.append(df.height)
+        return original_aggregate(df, index_pct_map)
+
+    monkeypatch.setattr(regime_builder, "_aggregate_daily", _recording_aggregate)
+
+    result = regime_builder.run_regime_batch(_FakeRepo(), target_dates[0], target_dates[-1])
+    expected_detail = pl.concat([
+        _batch_frame(target_dates[0], target_dates[1]),
+        _batch_frame(target_dates[2], target_dates[3]),
+    ]).filter(pl.col("symbol") == "B")
+    expected = original_aggregate(expected_detail, {})
+
+    assert result.height == len(target_dates)
+    assert aggregate_input_heights == [2, 2]
+    assert result["up_count"].to_list() == [0, 0, 0, 0]
+    assert result["down_count"].to_list() == [1, 1, 1, 1]
+    assert result["limit_up"].sum() == 0
+    assert_frame_equal(result.sort("date"), expected.sort("date"))
+
+
 # ───────────────────────── 持久化(upsert) ─────────────────────────