src/quant_engine/data_adapter.py

@@ -287,6 +287,8 @@ def prepare_execution_inputs(
     df: pd.DataFrame,
     stock_col: str = "stock_code",
     date_col: str = "trade_date",
+    *,
+    price_col: str = "close",
 ) -> tuple[pd.DataFrame, pd.DataFrame]:
     """长表行情 → execution 输入（prices + volumes 宽表）。
 
@@ -294,10 +296,11 @@ def prepare_execution_inputs(
         df: 长表行情（含 close / volume 列，Tushare rename 后）
         stock_col: 股票代码列名
         date_col: 日期列名
+        price_col: 执行价字段，默认 close；防前视研究可显式选择下一交易日 open
 
     Returns:
         (prices_wide, volumes_wide)：
-        - prices_wide: date × stock_code，值=close
+        - prices_wide: date × stock_code，值=price_col
         - volumes_wide: date × stock_code，值=volume（若无 volume 列则全 1.0）
 
     Examples:
@@ -313,9 +316,9 @@ def prepare_execution_inputs(
     """
     if df.empty:
         return pd.DataFrame(), pd.DataFrame()
-    if "close" not in df.columns:
-        raise ValueError(f"prepare_execution_inputs: 缺 close 列，实际列={list(df.columns)}")
-    prices = long_to_wide(df, value_col="close", date_col=date_col, stock_col=stock_col)
+    if price_col not in df.columns:
+        raise ValueError(f"prepare_execution_inputs: 缺 {price_col} 列，实际列={list(df.columns)}")
+    prices = long_to_wide(df, value_col=price_col, date_col=date_col, stock_col=stock_col)
     if "volume" in df.columns:
         volumes = long_to_wide(df, value_col="volume", date_col=date_col, stock_col=stock_col)
     else:

src/quant_engine/research_pipeline.py

@@ -0,0 +1,217 @@
+"""可信研究链路：因子分数经交易日历滞后后进入执行审计。
+
+本模块只编排现有组合构建与执行组件，不连接账户、券商或实盘订单。
+时间契约借鉴 Qlib 的 prediction/trade time 分离与 Backtrader 的 next-bar
+执行语义：signal_date 上形成的目标权重，默认最早在下一交易时点执行。
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import numpy as np
+import pandas as pd
+from pandas.api.types import is_numeric_dtype
+
+from quant_engine.execution import (
+    ExecutionConfig,
+    ExecutionSimulationResult,
+    simulate_multi_day_with_audit,
+)
+from quant_engine.portfolio_construction import scores_to_weight_table
+
+__all__ = [
+    "TargetWeightSchedule",
+    "FactorExecutionResult",
+    "schedule_target_weights",
+    "run_factor_execution_research",
+]
+
+
+@dataclass(frozen=True, slots=True, eq=False)
+class TargetWeightSchedule:
+    """保留决策时间和执行时间的目标权重调度快照。"""
+
+    decision_weights: pd.DataFrame
+    signal_to_execution: pd.Series
+    execution_weights: pd.DataFrame
+    lag_sessions: int
+
+
+@dataclass(frozen=True, slots=True, eq=False)
+class FactorExecutionResult:
+    """因子到执行审计的一次可复现研究结果。"""
+
+    factor_scores: pd.DataFrame
+    execution_prices: pd.DataFrame
+    schedule: TargetWeightSchedule
+    execution_price_field: str
+    execution: ExecutionSimulationResult
+
+
+def _validate_datetime_index(index: pd.Index, name: str) -> pd.DatetimeIndex:
+    if not isinstance(index, pd.DatetimeIndex):
+        raise TypeError(f"{name} must use a DatetimeIndex")
+    if not index.is_unique:
+        raise ValueError(f"{name} must contain unique sessions")
+    if not index.is_monotonic_increasing:
+        raise ValueError(f"{name} must be in chronological order")
+    return index
+
+
+def _validate_decision_weights(decision_weights: pd.DataFrame) -> None:
+    if not isinstance(decision_weights, pd.DataFrame):
+        raise TypeError(
+            f"decision_weights must be a pandas DataFrame, got {type(decision_weights).__name__}"
+        )
+    _validate_datetime_index(decision_weights.index, "decision_weights index")
+    if not decision_weights.columns.is_unique:
+        raise ValueError("decision_weights must contain unique asset labels")
+    if not all(is_numeric_dtype(dtype) for dtype in decision_weights.dtypes):
+        raise TypeError("decision_weights must contain numeric values")
+    values = decision_weights.to_numpy(dtype=float)
+    if not np.isfinite(values).all() or (values < 0).any():
+        raise ValueError("decision_weights must be finite and non-negative")
+    if (decision_weights.sum(axis=1) > 1.0 + 1e-12).any():
+        raise ValueError("decision_weights rows must sum to at most 1.0")
+
+
+def _validate_execution_prices(execution_prices: pd.DataFrame) -> pd.DatetimeIndex:
+    if not isinstance(execution_prices, pd.DataFrame):
+        raise TypeError(
+            f"execution_prices must be a pandas DataFrame, got {type(execution_prices).__name__}"
+        )
+    calendar = _validate_datetime_index(execution_prices.index, "execution_prices index")
+    if not execution_prices.columns.is_unique:
+        raise ValueError("execution_prices must contain unique asset labels")
+    if not all(is_numeric_dtype(dtype) for dtype in execution_prices.dtypes):
+        raise TypeError("execution_prices must contain numeric values")
+    return calendar
+
+
+def schedule_target_weights(
+    decision_weights: pd.DataFrame,
+    trading_calendar: pd.DatetimeIndex,
+    *,
+    lag_sessions: int = 1,
+) -> TargetWeightSchedule:
+    """将信号日目标权重映射到后续真实交易日，不做整数行盲移位。
+
+    所有信号日必须属于 ``trading_calendar``，且日历必须包含每个信号对应的
+    未来执行日；无法执行的末尾信号会显式失败，避免被静默丢弃。
+    """
+    _validate_decision_weights(decision_weights)
+    calendar = _validate_datetime_index(trading_calendar, "trading_calendar")
+    if isinstance(lag_sessions, bool) or not isinstance(lag_sessions, int) or lag_sessions <= 0:
+        raise ValueError("lag_sessions must be a positive integer")
+
+    decision_snapshot = decision_weights.copy(deep=True)
+    if decision_snapshot.empty:
+        execution_weights = decision_snapshot.copy(deep=True)
+        execution_weights.index = pd.DatetimeIndex([], name="execution_date")
+        mapping = pd.Series(
+            calendar[:0],
+            index=decision_snapshot.index.copy(),
+            name="execution_date",
+        )
+        return TargetWeightSchedule(
+            decision_weights=decision_snapshot,
+            signal_to_execution=mapping,
+            execution_weights=execution_weights,
+            lag_sessions=lag_sessions,
+        )
+
+    signal_positions = calendar.get_indexer(decision_snapshot.index)
+    if (signal_positions < 0).any():
+        missing = decision_snapshot.index[signal_positions < 0]
+        raise ValueError(
+            "signal dates must be trading sessions; missing="
+            + ", ".join(str(date) for date in missing)
+        )
+
+    execution_positions = signal_positions + lag_sessions
+    if (execution_positions >= len(calendar)).any():
+        unavailable = decision_snapshot.index[execution_positions >= len(calendar)]
+        raise ValueError(
+            "trading_calendar lacks a future execution session for signal dates: "
+            + ", ".join(str(date) for date in unavailable)
+        )
+
+    execution_dates = calendar.take(execution_positions)
+    signal_to_execution = pd.Series(
+        execution_dates,
+        index=decision_snapshot.index.copy(),
+        name="execution_date",
+    )
+    execution_weights = decision_snapshot.copy(deep=True)
+    execution_weights.index = pd.DatetimeIndex(execution_dates, name="execution_date")
+    return TargetWeightSchedule(
+        decision_weights=decision_snapshot,
+        signal_to_execution=signal_to_execution,
+        execution_weights=execution_weights,
+        lag_sessions=lag_sessions,
+    )
+
+
+def run_factor_execution_research(
+    factor_scores: pd.DataFrame,
+    execution_prices: pd.DataFrame,
+    *,
+    top_k: int,
+    execution_price_field: str,
+    lag_sessions: int = 1,
+    gross_exposure: float = 1.0,
+    largest: bool = True,
+    initial_cash: float = 1_000_000.0,
+    config: ExecutionConfig | None = None,
+) -> FactorExecutionResult:
+    """运行因子分数 → 目标权重 → 下一交易时点 → 执行审计链路。
+
+    ``execution_prices`` 必须代表实际拟执行时点的价格矩阵，例如日频研究中
+    signal 日收盘生成分数后使用下一交易日 ``open``。价格字段名称被保存在
+    结果元数据中，但函数不会猜测或重写价格语义。
+    """
+    price_field = execution_price_field.strip()
+    if not price_field:
+        raise ValueError("execution_price_field must be non-empty")
+    calendar = _validate_execution_prices(execution_prices)
+
+    factor_snapshot = factor_scores.copy(deep=True)
+    decision_weights = scores_to_weight_table(
+        factor_snapshot,
+        top_k,
+        gross_exposure=gross_exposure,
+        largest=largest,
+    )
+    schedule = schedule_target_weights(
+        decision_weights,
+        calendar,
+        lag_sessions=lag_sessions,
+    )
+    price_snapshot = execution_prices.copy(deep=True)
+
+    target_history: list[tuple[str, dict[str, float]]] = []
+    price_history: list[tuple[str, dict[str, float]]] = []
+    for execution_date, weights in schedule.execution_weights.iterrows():
+        date_label = str(pd.Timestamp(execution_date))
+        target_history.append(
+            (date_label, {asset: float(weight) for asset, weight in weights.items()})
+        )
+        prices = price_snapshot.loc[execution_date]
+        price_history.append(
+            (date_label, {asset: float(price) for asset, price in prices.items()})
+        )
+
+    execution = simulate_multi_day_with_audit(
+        target_history,
+        price_history,
+        initial_cash,
+        config,
+    )
+    return FactorExecutionResult(
+        factor_scores=factor_snapshot,
+        execution_prices=price_snapshot,
+        schedule=schedule,
+        execution_price_field=price_field,
+        execution=execution,
+    )