docs: distinguish signal execution and holding times

README.md

@@ -24,6 +24,7 @@
 - `data_adapter` — 桥接 qtdb_pro 长表与新模块（rename / long-wide / 复权 / vwap 代理）
 - `backtest` — weight-based 多日仿真（rebalance_table / compute_nav / compare_to_benchmark）
 - `portfolio_construction` — 多期因子分数 → Top-K → 等权目标权重表
+- `research_pipeline` — 因子日 → 下一真实交易日 → 显式执行价 → 执行审计（防前视编排）
 - `metrics` — 绩效（年化收益 / 波动率 / Sharpe / 最大回撤 / Calmar）
 - `factor_library` — 通用方法（turnover / winsorize / IC / OLS / jb_test）
 - `portfolio_decomp` — 组合分解（risk_parity / mean_variance / 因子归因）
@@ -61,8 +62,8 @@ from quant_engine.alpha_factors import alpha_001, alpha_005, ALPHA158_REGISTRY
 from quant_engine.execution import (
     ExecutionConfig, simulate_multi_day_with_audit, simulate_with_daily_data,
 )
+from quant_engine.research_pipeline import run_factor_execution_research
 from quant_engine.backtest import run_weight_backtest
-from quant_engine.portfolio_construction import scores_to_weight_table
 from quant_engine.indicators import macd, bollinger, kdj
 from quant_engine.data_adapter import (
     long_to_wide, wide_to_long, rename_tushare_columns,
@@ -73,8 +74,9 @@ from quant_engine.data_adapter import (
 
 # 端到端：qtdb_pro 长表 → 适配 → alpha158 → execution
 df = load_qtdb_daily(["000001.SZ"], "2024-01-01", with_adj=True)
-prices, volumes = prepare_execution_inputs(df)
-result = simulate_with_daily_data(prices, initial_cash=1_000_000.0)
+close_prices, volumes = prepare_execution_inputs(df)
+open_prices, _ = prepare_execution_inputs(df, price_col="open")
+result = simulate_with_daily_data(close_prices, initial_cash=1_000_000.0)
 
 # 已正确滞后的目标权重 → 现金约束执行 → 唯一来源的成交/拒绝/日末持仓/NAV
 execution = simulate_multi_day_with_audit(
@@ -92,18 +94,25 @@ execution = simulate_multi_day_with_audit(
 print(execution.nav_series)
 print(execution.daily_executions)
 
-# 多期因子分数 → Top-K 等权组合 → 稳定回测结果
-rebalance_weights = scores_to_weight_table(
+# 多期因子分数（必须是 point-in-time 数据）→ Top-K → 下一交易日 open 执行
+factor_execution = run_factor_execution_research(
     factor_scores,
     top_k=20,
-    gross_exposure=1.0,
+    execution_prices=open_prices,
+    execution_price_field="open",
+    initial_cash=1_000_000.0,
 )
+
+# run_weight_backtest 是低层算子：只接受收益区间开始前已经生效的持仓权重。
+# 不要把 signal-date 的 factor_scores/decision_weights 直接传给它。
 backtest = run_weight_backtest(
-    weights=rebalance_weights,
+    weights=effective_holding_weights,
     stock_returns=daily_returns,
     initial_capital=1_000_000.0,
     benchmark_nav=benchmark_nav,
 )
+print(factor_execution.schedule.signal_to_execution)
+print(factor_execution.execution.daily_executions)
 print(backtest.stats())
 print(backtest.benchmark_report())
 ```

src/quant_engine/backtest.py

@@ -76,8 +76,9 @@ def compute_nav_from_weights(
 ) -> pd.Series:
     """从调仓表（日期 × 股票权重）+ 个股日收益 → 净值曲线。
 
-    假设：在调仓日之间权重不变（**前向填充**）。
-    调仓日的权重 = `weights.loc[rebalance_date]`。
+    假设：输入是该收益测量区间开始前已经生效的持仓权重，并在调仓日之间
+    保持不变（**前向填充**）。本函数不会把信号日自动解释为执行日；因子分数
+    应先经交易日历调度和实际执行时点处理，避免把同一时点未知的收益计入。
 
     Args:
         weights: 调仓日 × 股票代码 的权重 DataFrame（**0~1**，行和 ≤ 1）

tests/test_portfolio_construction.py

@@ -91,10 +91,10 @@ def test_scores_to_weight_table_constructs_each_rebalance_independently() -> Non
     pd.testing.assert_series_equal(result.iloc[0], changed_result.iloc[0])
 
 
-def test_factor_scores_flow_directly_into_weight_backtest() -> None:
+def test_effective_holding_weights_flow_into_weight_backtest() -> None:
     dates = pd.date_range("2026-01-05", periods=3, freq="B")
-    scores = pd.DataFrame(
-        {"A": [2.0, 0.0], "B": [1.0, 3.0]},
+    effective_weights = pd.DataFrame(
+        {"A": [1.0, 0.0], "B": [0.0, 1.0]},
         index=dates[[0, 2]],
     )
     stock_returns = pd.DataFrame(
@@ -102,11 +102,10 @@ def test_factor_scores_flow_directly_into_weight_backtest() -> None:
         index=dates,
     )
 
-    weights = scores_to_weight_table(scores, top_k=1)
-    result = run_weight_backtest(weights, stock_returns)
+    result = run_weight_backtest(effective_weights, stock_returns)
 
     pd.testing.assert_series_equal(result.nav, pd.Series([1.1, 1.1, 1.32], index=dates))
-    pd.testing.assert_frame_equal(result.weights, weights)
+    pd.testing.assert_frame_equal(result.weights, effective_weights)
 
 
 @pytest.mark.parametrize("top_k", [0, -1])