feat: add post-execution daily ledger

src/quant_engine/execution.py

@@ -22,7 +22,7 @@ from __future__ import annotations
 
 import math
 from collections.abc import Mapping
-from dataclasses import dataclass
+from dataclasses import dataclass, replace
 from typing import Any
 
 import pandas as pd
@@ -103,6 +103,9 @@ class ExecutionResult:
     net_cash_flow: float  # 净现金流（买入为负，卖出为正）
     partial_fill_pct: float = 1.0  # 实际成交占目标的比例（1.0 = 全部成交）
     blocked_reason: str = ""  # 阻塞原因（如涨跌停停牌）
+    side: str = ""  # buy / sell；未成交记录也保留目标方向
+    quantity: float = 0.0  # 实际成交股数
+    price: float = 0.0  # 未含滑点的参考执行价
 
 
 def _apply_costs(
@@ -369,6 +372,56 @@ class ExecutionSimulationResult:
             dtype=float,
         )
 
+    @property
+    def normalized_nav_series(self) -> pd.Series:
+        """返回以初始资金为 1 的净值曲线副本。"""
+        nav = self.nav_series
+        if self.initial_cash == 0:
+            return pd.Series(0.0, index=nav.index, dtype=float)
+        return nav / self.initial_cash
+
+    @property
+    def daily_returns(self) -> pd.Series:
+        """返回逐日收益；首日相对初始资金计算，保留首日交易成本。"""
+        nav = self.nav_series
+        if nav.empty:
+            return nav
+        returns = nav.pct_change()
+        returns.iloc[0] = (
+            nav.iloc[0] / self.initial_cash - 1.0 if self.initial_cash != 0 else 0.0
+        )
+        return returns.fillna(0.0)
+
+    @property
+    def trades_frame(self) -> pd.DataFrame:
+        """返回可投影到平台成交明细的实际成交表，不包含纯拒绝记录。"""
+        columns = [
+            "trade_date",
+            "ts_code",
+            "side",
+            "qty",
+            "price",
+            "amount",
+            "fee",
+            "slippage",
+        ]
+        rows = [
+            {
+                "trade_date": daily.date,
+                "ts_code": execution.stock_code,
+                "side": execution.side,
+                "qty": execution.quantity,
+                "price": execution.price,
+                "amount": execution.executed_value,
+                "fee": execution.commission + execution.stamp_tax,
+                "slippage": execution.slippage_cost,
+            }
+            for daily in self.daily_executions
+            for execution in daily.executions
+            if execution.quantity > 0
+        ]
+        return pd.DataFrame(rows, columns=columns)
+
     @property
     def total_costs(self) -> float:
         """汇总实际成交产生的成本。"""
@@ -420,6 +473,7 @@ def _blocked_execution(stock_code: str, target_value: float, reason: str) -> Exe
         net_cash_flow=0.0,
         partial_fill_pct=0.0,
         blocked_reason=reason,
+        side="buy" if target_value > 0 else "sell" if target_value < 0 else "",
     )
 
 
@@ -466,6 +520,236 @@ def _partially_fill_buy(
     )
 
 
+def _rebalance_at_prices(
+    date: str,
+    targets: Mapping[str, float],
+    prices: Mapping[str, float],
+    cash: float,
+    holdings: dict[str, float],
+    config: ExecutionConfig,
+) -> tuple[float, tuple[ExecutionResult, ...], float, float]:
+    """在单一执行时点按目标权重差额调仓，并原地更新 holdings。"""
+    normalized_targets = _validate_target_weights(date, targets)
+    for held_code in holdings:
+        held_price = prices.get(held_code)
+        if held_price is None or not math.isfinite(held_price) or held_price <= 0:
+            raise ValueError(f"missing price for held asset {held_code} on {date!r}")
+
+    nav_before = cash + sum(
+        shares * prices.get(stock_code, 0.0)
+        for stock_code, shares in holdings.items()
+    )
+    effective_targets = dict.fromkeys(holdings, 0.0)
+    effective_targets.update(normalized_targets)
+    buy_weights: dict[str, float] = {}
+    sell_weights: dict[str, float] = {}
+    rejected: list[ExecutionResult] = []
+
+    for stock_code, target_weight in effective_targets.items():
+        price = prices.get(stock_code)
+        target_value = float(target_weight) * nav_before
+        if price is None or not math.isfinite(price) or price <= 0:
+            if target_value != 0 or holdings.get(stock_code, 0.0) != 0:
+                rejected.append(_blocked_execution(stock_code, target_value, "missing_price"))
+            continue
+
+        current_value = holdings.get(stock_code, 0.0) * price
+        trade_value = target_value - current_value
+        if abs(trade_value) < config.min_trade_amount or math.isclose(
+            trade_value, 0.0, abs_tol=1e-12
+        ):
+            continue
+        if nav_before == 0:
+            rejected.append(_blocked_execution(stock_code, trade_value, "zero_nav"))
+            continue
+        destination = buy_weights if trade_value > 0 else sell_weights
+        destination[stock_code] = trade_value / nav_before
+
+    sell_executions = simulate_execution(sell_weights, nav_before, config)
+    filled: list[ExecutionResult] = []
+    for raw_execution in sell_executions:
+        price = prices[raw_execution.stock_code]
+        quantity = abs(raw_execution.target_value) / price
+        execution = replace(
+            raw_execution,
+            side="sell",
+            quantity=quantity,
+            price=price,
+        )
+        held = holdings.get(execution.stock_code, 0.0)
+        holdings[execution.stock_code] = max(0.0, held - quantity)
+        if holdings[execution.stock_code] < 1e-6:
+            del holdings[execution.stock_code]
+        cash += execution.net_cash_flow
+        filled.append(execution)
+
+    desired_buys = simulate_execution(buy_weights, nav_before, config)
+    required_cash = sum(-execution.net_cash_flow for execution in desired_buys)
+    buy_fill_pct = min(1.0, max(cash, 0.0) / required_cash) if required_cash > 0 else 1.0
+    for desired in desired_buys:
+        if buy_fill_pct == 0:
+            rejected.append(
+                _blocked_execution(desired.stock_code, desired.target_value, "insufficient_cash")
+            )
+            continue
+        raw_execution = (
+            desired
+            if buy_fill_pct == 1.0
+            else _partially_fill_buy(desired, buy_fill_pct, config)
+        )
+        price = prices[raw_execution.stock_code]
+        quantity = abs(raw_execution.target_value) * raw_execution.partial_fill_pct / price
+        execution = replace(
+            raw_execution,
+            side="buy",
+            quantity=quantity,
+            price=price,
+        )
+        holdings[execution.stock_code] = holdings.get(execution.stock_code, 0.0) + quantity
+        cash += execution.net_cash_flow
+        if math.isclose(cash, 0.0, abs_tol=1e-9):
+            cash = 0.0
+        filled.append(execution)
+
+    executions = (*filled, *rejected)
+    nav_after = cash + sum(
+        shares * prices.get(stock_code, 0.0)
+        for stock_code, shares in holdings.items()
+    )
+    return cash, executions, nav_before, nav_after
+
+
+def _validate_sparse_daily_histories(
+    target_weights_history: list[tuple[str, dict[str, float]]],
+    execution_price_history: list[tuple[str, dict[str, float]]],
+    valuation_price_history: list[tuple[str, dict[str, float]]],
+) -> tuple[
+    dict[str, dict[str, float]],
+    dict[str, dict[str, float]],
+    list[tuple[str, dict[str, float]]],
+]:
+    """校验稀疏调仓与完整估值日历，并隔离调用方可变输入。"""
+    target_dates = [date for date, _ in target_weights_history]
+    execution_dates = [date for date, _ in execution_price_history]
+    valuation_dates = [date for date, _ in valuation_price_history]
+    if len(set(target_dates)) != len(target_dates):
+        raise ValueError("target_weights_history must contain unique dates")
+    if len(set(execution_dates)) != len(execution_dates):
+        raise ValueError("execution_price_history must contain unique dates")
+    if len(set(valuation_dates)) != len(valuation_dates):
+        raise ValueError("valuation_price_history must contain unique dates")
+    if execution_dates != target_dates:
+        raise ValueError("execution price dates must exactly match target weight dates")
+
+    valuation_positions = {date: index for index, date in enumerate(valuation_dates)}
+    missing_dates = [date for date in target_dates if date not in valuation_positions]
+    if missing_dates:
+        raise ValueError(f"target dates must belong to valuation calendar: {missing_dates}")
+    positions = [valuation_positions[date] for date in target_dates]
+    if positions != sorted(positions):
+        raise ValueError("target weights must follow valuation calendar order")
+
+    targets = {date: dict(values) for date, values in target_weights_history}
+    execution_prices = {date: dict(values) for date, values in execution_price_history}
+    valuation_prices = [(date, dict(values)) for date, values in valuation_price_history]
+    return targets, execution_prices, valuation_prices
+
+
+def _simulate_daily_ledger(
+    target_weights_history: list[tuple[str, dict[str, float]]],
+    execution_price_history: list[tuple[str, dict[str, float]]],
+    valuation_price_history: list[tuple[str, dict[str, float]]],
+    initial_cash: float,
+    config: ExecutionConfig,
+) -> ExecutionSimulationResult:
+    targets_by_date, execution_prices_by_date, valuation_history = (
+        _validate_sparse_daily_histories(
+            target_weights_history,
+            execution_price_history,
+            valuation_price_history,
+        )
+    )
+    cash = initial_cash
+    holdings: dict[str, float] = {}
+    positions: list[DailyPosition] = []
+    daily_executions: list[DailyExecution] = []
+
+    for date, valuation_prices in valuation_history:
+        targets = targets_by_date.get(date)
+        if targets is None:
+            executions: tuple[ExecutionResult, ...] = ()
+            nav_before = 0.0
+            nav_after = 0.0
+            rebalance_triggered = False
+        else:
+            cash, executions, nav_before, nav_after = _rebalance_at_prices(
+                date,
+                targets,
+                execution_prices_by_date[date],
+                cash,
+                holdings,
+                config,
+            )
+            rebalance_triggered = any(execution.quantity > 0 for execution in executions)
+
+        for held_code in holdings:
+            valuation_price = valuation_prices.get(held_code)
+            if (
+                valuation_price is None
+                or not math.isfinite(valuation_price)
+                or valuation_price <= 0
+            ):
+                raise ValueError(
+                    f"missing valuation price for held asset {held_code} on {date!r}"
+                )
+        portfolio_value = cash + sum(
+            shares * valuation_prices[stock_code]
+            for stock_code, shares in holdings.items()
+        )
+        if targets is None:
+            nav_before = portfolio_value
+            nav_after = portfolio_value
+        positions.append(DailyPosition(date, cash, dict(holdings), portfolio_value))
+        daily_executions.append(
+            DailyExecution(
+                date=date,
+                executions=executions,
+                nav_before=nav_before,
+                nav_after=nav_after,
+                rebalance_triggered=rebalance_triggered,
+            )
+        )
+
+    return ExecutionSimulationResult(
+        initial_cash=initial_cash,
+        positions=tuple(positions),
+        daily_executions=tuple(daily_executions),
+    )
+
+
+def simulate_daily_ledger_with_audit(
+    target_weights_history: list[tuple[str, dict[str, float]]],
+    execution_price_history: list[tuple[str, dict[str, float]]],
+    valuation_price_history: list[tuple[str, dict[str, float]]],
+    initial_cash: float,
+    config: ExecutionConfig | None = None,
+) -> ExecutionSimulationResult:
+    """以稀疏调仓和完整日历运行成交后持仓 Ledger。
+
+    执行价只用于调仓日现金与股数变化，估值价用于每个交易日日末 NAV；二者
+    显式分离，从而支持“下一日 open 成交、同日 close 估值”的无前视研究。
+    """
+    if not math.isfinite(initial_cash) or initial_cash <= 0:
+        raise ValueError(f"initial_cash must be positive and finite, got {initial_cash}")
+    return _simulate_daily_ledger(
+        target_weights_history,
+        execution_price_history,
+        valuation_price_history,
+        initial_cash,
+        ExecutionConfig() if config is None else config,
+    )
+
+
 def simulate_multi_day_with_audit(
     target_weights_history: list[tuple[str, dict[str, float]]],
     price_history: list[tuple[str, dict[str, float]]],
@@ -488,122 +772,21 @@ def simulate_multi_day_with_audit(
         - 每日先按当日 close 估值，再交易“目标市值 - 当前市值”的差额
         - 此处简化为当日 close 成交；调用方必须传入已正确滞后的目标权重
     """
-    if config is None:
-        config = ExecutionConfig()
     if not math.isfinite(initial_cash) or initial_cash < 0:
         raise ValueError(f"initial_cash must be finite and non-negative, got {initial_cash}")
     if len(target_weights_history) != len(price_history):
         raise ValueError("target_weights_history and price_history must have same length")
-    if not target_weights_history:
+    for (date, _), (price_date, _) in zip(target_weights_history, price_history, strict=True):
-        return ExecutionSimulationResult(initial_cash, (), ())
-
-    cash = initial_cash
-    holdings: dict[str, float] = {}
-    positions: list[DailyPosition] = []
-    daily_executions: list[DailyExecution] = []
-
-    for (date, targets), (price_date, prices) in zip(
-        target_weights_history, price_history, strict=True
-    ):
         if date != price_date:
             raise ValueError(
                 f"target and price dates must match, got {date!r} and {price_date!r}"
             )
-
+    return _simulate_daily_ledger(
-        normalized_targets = _validate_target_weights(date, targets)
+        target_weights_history,
-        for held_code in holdings:
+        price_history,
-            held_price = prices.get(held_code)
+        price_history,
-            if held_price is None or not math.isfinite(held_price) or held_price <= 0:
+        initial_cash,
-                raise ValueError(f"missing price for held asset {held_code} on {date!r}")
+        ExecutionConfig() if config is None else config,
-
-        nav_before = cash + sum(
-            shares * prices.get(stock_code, 0.0)
-            for stock_code, shares in holdings.items()
-        )
-        effective_targets = dict.fromkeys(holdings, 0.0)
-        effective_targets.update(normalized_targets)
-        buy_weights: dict[str, float] = {}
-        sell_weights: dict[str, float] = {}
-        rejected: list[ExecutionResult] = []
-
-        for stock_code, target_weight in effective_targets.items():
-            price = prices.get(stock_code)
-            target_value = float(target_weight) * nav_before
-            if price is None or not math.isfinite(price) or price <= 0:
-                if target_value != 0 or holdings.get(stock_code, 0.0) != 0:
-                    rejected.append(_blocked_execution(stock_code, target_value, "missing_price"))
-                continue
-
-            current_value = holdings.get(stock_code, 0.0) * price
-            trade_value = target_value - current_value
-            if abs(trade_value) < config.min_trade_amount or math.isclose(
-                trade_value, 0.0, abs_tol=1e-12
-            ):
-                continue
-            if nav_before == 0:
-                rejected.append(_blocked_execution(stock_code, trade_value, "zero_nav"))
-                continue
-            destination = buy_weights if trade_value > 0 else sell_weights
-            destination[stock_code] = trade_value / nav_before
-
-        sell_executions = simulate_execution(sell_weights, nav_before, config)
-        filled: list[ExecutionResult] = []
-        for execution in sell_executions:
-            price = prices[execution.stock_code]
-            share_change = abs(execution.target_value) / price
-            held = holdings.get(execution.stock_code, 0.0)
-            holdings[execution.stock_code] = max(0.0, held - share_change)
-            if holdings[execution.stock_code] < 1e-6:
-                del holdings[execution.stock_code]
-            cash += execution.net_cash_flow
-            filled.append(execution)
-
-        desired_buys = simulate_execution(buy_weights, nav_before, config)
-        required_cash = sum(-execution.net_cash_flow for execution in desired_buys)
-        buy_fill_pct = min(1.0, max(cash, 0.0) / required_cash) if required_cash > 0 else 1.0
-        for desired in desired_buys:
-            if buy_fill_pct == 0:
-                rejected.append(
-                    _blocked_execution(desired.stock_code, desired.target_value, "insufficient_cash")
-                )
-                continue
-            execution = (
-                desired
-                if buy_fill_pct == 1.0
-                else _partially_fill_buy(desired, buy_fill_pct, config)
-            )
-            price = prices[execution.stock_code]
-            share_change = (
-                execution.target_value * execution.partial_fill_pct / price
-            )
-            holdings[execution.stock_code] = (
-                holdings.get(execution.stock_code, 0.0) + share_change
-            )
-            cash += execution.net_cash_flow
-            if math.isclose(cash, 0.0, abs_tol=1e-9):
-                cash = 0.0
-            filled.append(execution)
-
-        executions = (*filled, *rejected)
-        nav_after = cash + sum(
-            shares * prices.get(stock_code, 0.0)
-            for stock_code, shares in holdings.items()
-        )
-        positions.append(DailyPosition(date, cash, dict(holdings), nav_after))
-        daily_executions.append(
-            DailyExecution(
-                date=date,
-                executions=executions,
-                nav_before=nav_before,
-                nav_after=nav_after,
-                rebalance_triggered=bool(filled),
-            )
-        )
-
-    return ExecutionSimulationResult(
-        initial_cash=initial_cash,
-        positions=tuple(positions),
-        daily_executions=tuple(daily_executions),
     )
 
 
@@ -785,6 +968,7 @@ __all__ = [
     "DailyPosition",
     "DailyExecution",
     "ExecutionSimulationResult",
+    "simulate_daily_ledger_with_audit",
     "simulate_multi_day",
     "simulate_multi_day_with_audit",
     "run_end_to_end_poc",

src/quant_engine/research_pipeline.py

@@ -7,6 +7,7 @@
 
 from __future__ import annotations
 
+from collections.abc import Mapping
 from dataclasses import dataclass
 
 import numpy as np
@@ -16,15 +17,19 @@ from pandas.api.types import is_numeric_dtype
 from quant_engine.execution import (
     ExecutionConfig,
     ExecutionSimulationResult,
+    simulate_daily_ledger_with_audit,
     simulate_multi_day_with_audit,
 )
+from quant_engine.metrics import summary as metrics_summary
 from quant_engine.portfolio_construction import scores_to_weight_table
 
 __all__ = [
     "TargetWeightSchedule",
     "FactorExecutionResult",
+    "FactorBacktestResult",
     "schedule_target_weights",
     "run_factor_execution_research",
+    "run_factor_backtest_research",
 ]
 
 
@@ -49,6 +54,41 @@ class FactorExecutionResult:
     execution: ExecutionSimulationResult
 
 
+@dataclass(frozen=True, slots=True, eq=False)
+class FactorBacktestResult:
+    """因子、成交后日频 Ledger 与绩效的一次可复现快照。"""
+
+    factor_scores: pd.DataFrame
+    execution_prices: pd.DataFrame
+    valuation_prices: pd.DataFrame
+    schedule: TargetWeightSchedule
+    execution_price_field: str
+    valuation_price_field: str
+    execution: ExecutionSimulationResult
+
+    @property
+    def nav(self) -> pd.Series:
+        """返回以初始资金归一化为 1 的日频 NAV。"""
+        return pd.Series(
+            self.execution.normalized_nav_series.to_numpy(copy=True),
+            index=self.valuation_prices.index.copy(),
+            name="nav",
+        )
+
+    @property
+    def returns(self) -> pd.Series:
+        """返回包含首日成本影响的日频收益。"""
+        return pd.Series(
+            self.execution.daily_returns.to_numpy(copy=True),
+            index=self.valuation_prices.index.copy(),
+            name="returns",
+        )
+
+    def stats(self, rf: float = 0.0) -> Mapping[str, float]:
+        """复用标准绩效口径计算指标。"""
+        return metrics_summary(self.returns, rf)
+
+
 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")
@@ -215,3 +255,84 @@ def run_factor_execution_research(
         execution_price_field=price_field,
         execution=execution,
     )
+
+
+def run_factor_backtest_research(
+    factor_scores: pd.DataFrame,
+    execution_prices: pd.DataFrame,
+    valuation_prices: pd.DataFrame,
+    *,
+    top_k: int,
+    execution_price_field: str,
+    valuation_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,
+) -> FactorBacktestResult:
+    """运行 PIT 因子到成交后日频 Ledger、收益与绩效的可信研究链路。"""
+    execution_field = execution_price_field.strip()
+    valuation_field = valuation_price_field.strip()
+    if not execution_field:
+        raise ValueError("execution_price_field must be non-empty")
+    if not valuation_field:
+        raise ValueError("valuation_price_field must be non-empty")
+
+    execution_calendar = _validate_execution_prices(execution_prices)
+    valuation_calendar = _validate_execution_prices(valuation_prices)
+    if not execution_calendar.equals(valuation_calendar):
+        raise ValueError("execution and valuation prices must use matching trading calendars")
+    if not execution_prices.columns.equals(valuation_prices.columns):
+        raise ValueError("execution and valuation prices must use matching asset labels")
+
+    factor_snapshot = factor_scores.copy(deep=True)
+    execution_snapshot = execution_prices.copy(deep=True)
+    valuation_snapshot = valuation_prices.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,
+        execution_calendar,
+        lag_sessions=lag_sessions,
+    )
+
+    target_history: list[tuple[str, dict[str, float]]] = []
+    execution_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 = execution_snapshot.loc[execution_date]
+        execution_history.append(
+            (date_label, {asset: float(price) for asset, price in prices.items()})
+        )
+
+    valuation_history = [
+        (
+            str(pd.Timestamp(valuation_date)),
+            {asset: float(price) for asset, price in prices.items()},
+        )
+        for valuation_date, prices in valuation_snapshot.iterrows()
+    ]
+    execution = simulate_daily_ledger_with_audit(
+        target_history,
+        execution_history,
+        valuation_history,
+        initial_cash,
+        config,
+    )
+    return FactorBacktestResult(
+        factor_scores=factor_snapshot,
+        execution_prices=execution_snapshot,
+        valuation_prices=valuation_snapshot,
+        schedule=schedule,
+        execution_price_field=execution_field,
+        valuation_price_field=valuation_field,
+        execution=execution,
+    )