refactor(quant): M7.2a Composite Engine 模块化（quant/composite.py）

- cross_sectional_zscore / composite_score / build_factor_panels 从 local_engine 迁入
  quant/composite.py；新增统一入口 build_score_panel(daily, factor_specs)
- local_engine re-export 保持旧引用兼容；selection/engine 的评分面板构建均指向
  composite —— 选股与回测的复合分实现收敛于一处
- 回归：quant/eval/research/selection 一致性/qlib 引擎测试全过；全量 pytest 通过

backend/app/quant/composite.py

@@ -0,0 +1,73 @@
+"""Composite Factor Engine（M7.2，ARCHITECTURE_v2 §13）。
+
+把「多因子 → 加权复合分面板」独立成模块，供：
+- 选股（SelectionEngine.run_score_selection）
+- 回测（LocalEngine.run_backtest）
+共用同一实现（v2 §25 一致性）。
+method 先落地 fixed（截面 zscore × 方向 × 权重 求和）；Rank/Z/IC 加权留接口。
+"""
+
+from __future__ import annotations
+
+import math
+
+import pandas as pd
+
+from app.quant.factors import FactorDef, compute_factor
+
+
+def cross_sectional_zscore(panel: pd.DataFrame) -> pd.DataFrame:
+    """截面 z-score。
+
+    候选不足 2 只（如单股票池）时退化为 0：无比较基准，但保留为可候选值；
+    全列缺失才为 NaN（该日不可选股）。
+    """
+
+    def _row_z(row: pd.Series) -> pd.Series:
+        valid = row.dropna()
+        if len(valid) == 0:
+            return pd.Series(float("nan"), index=row.index)
+        if len(valid) == 1:
+            return pd.Series(0.0, index=row.index)
+        mu, sd = valid.mean(), valid.std()
+        if sd == 0 or math.isnan(sd):
+            return pd.Series(0.0, index=row.index)
+        return (row - mu) / sd
+
+    return panel.apply(_row_z, axis=1)
+
+
+def composite_score(panels: list[tuple[str, pd.DataFrame, float, str]]) -> pd.DataFrame:
+    """按 (name, panel, weight, direction) 计算加权复合 zscore。
+
+    direction="lower_is_better" 的因子取负号后相加（统一为「得分高者优先」）。
+    """
+    total = None
+    for _name, panel, weight, direction in panels:
+        z = cross_sectional_zscore(panel)
+        if direction == "lower_is_better":
+            z = -z
+        contribution = z * weight
+        total = contribution if total is None else total.add(contribution, fill_value=0)
+    assert total is not None
+    return total
+
+
+def build_factor_panels(
+    daily: pd.DataFrame, factor_specs
+) -> list[tuple[str, pd.DataFrame, float, str]]:
+    """按 spec.factors 计算面板与权重（因子不存在即报错）。"""
+    panels: list[tuple[str, pd.DataFrame, float, str]] = []
+    for fs in factor_specs:
+        defn: FactorDef
+        defn, panel = compute_factor(fs.name, daily)
+        panels.append((fs.name, panel, fs.weight, defn.direction))
+    return panels
+
+
+def build_score_panel(daily: pd.DataFrame, factor_specs) -> pd.DataFrame:
+    """因子加权复合分面板（index=trade_date, columns=symbol）。
+
+    回测与选股共用的统一入口 —— 保证 v2 §25/§27 一致性。
+    """
+    return composite_score(build_factor_panels(daily, factor_specs))

backend/app/quant/local_engine.py

@@ -26,8 +26,12 @@ from app.domain.entities.research import (
     Trade,
     YearlyReturn,
 )
+from app.quant.composite import (  # noqa: F401 —— re-export（模块化后旧引用仍可用）
+    build_factor_panels,
+    composite_score,
+    cross_sectional_zscore,
+)
 from app.quant.evaluation import run_factor_test
-from app.quant.factors import FactorDef, compute_factor
 
 TRADING_DAYS = 252
 _DEFAULT_UNIMPLEMENTED = [
@@ -46,45 +50,6 @@ def _limit_up_ratio(symbol: str) -> float:
     return 1.099
 
 
-def cross_sectional_zscore(panel: pd.DataFrame) -> pd.DataFrame:
-    """截面 z-score。
-
-    候选不足 2 只（如单股票池）时退化为 0：无比较基准，但保留为可候选值；
-    全列缺失才为 NaN（该日不可选股）。
-    """
-
-    def _row_z(row: pd.Series) -> pd.Series:
-        valid = row.dropna()
-        if len(valid) == 0:
-            return pd.Series(float("nan"), index=row.index)
-        if len(valid) == 1:
-            return pd.Series(0.0, index=row.index)
-        mu, sd = valid.mean(), valid.std()
-        if sd == 0 or math.isnan(sd):
-            return pd.Series(0.0, index=row.index)
-        return (row - mu) / sd
-
-    return panel.apply(_row_z, axis=1)
-
-
-def composite_score(
-    panels: list[tuple[str, pd.DataFrame, float, str]],
-) -> pd.DataFrame:
-    """按 (name, panel, weight, direction) 计算加权复合 zscore。
-
-    direction="lower_is_better" 的因子取负号后相加（统一为「得分高者优先」）。
-    """
-    total = None
-    for _name, panel, weight, direction in panels:
-        z = cross_sectional_zscore(panel)
-        if direction == "lower_is_better":
-            z = -z
-        contribution = z * weight
-        total = contribution if total is None else total.add(contribution, fill_value=0)
-    assert total is not None
-    return total
-
-
 def rebalance_dates(index: pd.Index, rebalance: str, start: date) -> list[pd.Timestamp]:
     """按频率取首个交易日（>= start）。"""
     periods = index.to_period("M" if rebalance == "monthly" else "W")
@@ -303,18 +268,6 @@ class TopKBacktestRunner:
         )
 
 
-def build_factor_panels(
-    daily: pd.DataFrame, factor_specs
-) -> list[tuple[str, pd.DataFrame, float, str]]:
-    """按 spec.factors 计算面板与权重（因子不存在即报错）。"""
-    panels: list[tuple[str, pd.DataFrame, float, str]] = []
-    for fs in factor_specs:
-        defn: FactorDef
-        defn, panel = compute_factor(fs.name, daily)
-        panels.append((fs.name, panel, fs.weight, defn.direction))
-    return panels
-
-
 def run_spec_factor_test(
     daily: pd.DataFrame,
     spec: ResearchSpec,

backend/app/quant/selection.py

@@ -21,8 +21,8 @@ from app.domain.entities.selection import (
     SelectionResult,
     SelectionStatistics,
 )
+from app.quant.composite import build_score_panel
 from app.quant.factors import FactorError, compute_factor, get_factor
-from app.quant.local_engine import build_factor_panels, composite_score
 
 _UNIMPLEMENTED_DEFAULT = [
     "exclude_suspended 依赖停牌数据，当前未建模（结果可能包含停牌股）",
@@ -35,8 +35,7 @@ def score_panel_for_factors(daily: pd.DataFrame, factor_specs) -> pd.DataFrame:
     回测（LocalEngine）与选股（run_score_selection）共用同一构建 ——
     保证 v2 §25/§27「历史回测与当前选股使用同一套引擎」的一致性。
     """
-    panels = build_factor_panels(daily, factor_specs)  # 未知因子在此抛 FactorError
-    return composite_score(panels)
+    return build_score_panel(daily, factor_specs)  # 未知因子在此抛 FactorError
 
 
 def resolve_observation_date(daily: pd.DataFrame, as_of: date | None) -> pd.Timestamp | None: