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())
 ```