feat(backtest): add AttributionAnalyzer with Brinson, factor, cost attribution

Co-Authored-By: Claude <noreply@anthropic.com>

src/easy_tdx/backtest/attribution.py

@@ -0,0 +1,217 @@
+"""归因分析模块。"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+
+import numpy as np
+import pandas as pd
+
+
+@dataclass
+class AttributionReport:
+    """归因分析报告。"""
+
+    total_return: float = 0.0
+    # Brinson 归因
+    allocation_return: float = 0.0
+    selection_return: float = 0.0
+    interaction_return: float = 0.0
+    # 因子归因
+    factor_returns: dict[str, float] = field(default_factory=dict)
+    specific_return: float = 0.0
+    # 成本归因
+    total_trade_cost: float = 0.0
+    slippage_cost: float = 0.0
+    commission_cost: float = 0.0
+    stamp_tax_cost: float = 0.0
+
+
+class AttributionAnalyzer:
+    """收益归因分析器。
+
+    支持三种归因视角：
+    1. 成本归因：分解交易成本的来源（佣金/滑点/印花税）
+    2. Brinson 归因：分解超额收益（配置 vs 选股）
+    3. 因子归因：分解收益为因子贡献 + 特质收益
+    """
+
+    def __init__(
+        self,
+        trades: pd.DataFrame,
+        equity_curve: pd.DataFrame,
+        benchmark: pd.DataFrame | None = None,
+        factor_exposures: pd.DataFrame | None = None,
+        factor_returns: pd.DataFrame | None = None,
+        groups: pd.DataFrame | None = None,
+    ) -> None:
+        self._trades = trades
+        self._equity_curve = equity_curve
+        self._benchmark = benchmark
+        self._factor_exposures = factor_exposures
+        self._factor_returns = factor_returns
+        self._groups = groups
+
+    def cost_attribution(self) -> AttributionReport:
+        """成本归因：分解交易成本。"""
+        if self._trades.empty:
+            return AttributionReport()
+
+        valid = (
+            self._trades[~self._trades["rejected"]]
+            if "rejected" in self._trades.columns
+            else self._trades
+        )
+
+        slippage_cost = float(valid["slippage"].sum()) if "slippage" in valid.columns else 0.0
+        commission_cost = float(valid["commission"].sum()) if "commission" in valid.columns else 0.0
+
+        total_trade_cost = slippage_cost + commission_cost
+
+        # 估算印花税（卖出交易 0.1%）
+        sell_mask = (
+            valid["direction"] == "SELL" if "direction" in valid.columns else pd.Series(dtype=bool)
+        )
+        stamp_tax_cost = 0.0
+        if sell_mask.any():
+            sell_trades = valid[sell_mask]
+            if "price" in sell_trades.columns and "size" in sell_trades.columns:
+                stamp_tax_cost = float((sell_trades["price"] * sell_trades["size"] * 0.001).sum())
+
+        total_return = 0.0
+        if not self._equity_curve.empty and "total" in self._equity_curve.columns:
+            total_arr = self._equity_curve["total"].to_numpy()
+            if len(total_arr) >= 2 and total_arr[0] > 0:
+                total_return = float((total_arr[-1] / total_arr[0]) - 1)
+
+        return AttributionReport(
+            total_return=total_return,
+            total_trade_cost=total_trade_cost,
+            slippage_cost=slippage_cost,
+            commission_cost=commission_cost,
+            stamp_tax_cost=stamp_tax_cost,
+        )
+
+    def brinson_attribution(self) -> AttributionReport:
+        """Brinson-Hood-Beebower 归因分解。"""
+        cost_report = self.cost_attribution()
+
+        if self._benchmark is None:
+            return cost_report
+
+        if self._equity_curve.empty:
+            return cost_report
+
+        total_arr = self._equity_curve["total"].to_numpy()
+        if len(total_arr) < 2 or total_arr[0] <= 0:
+            return cost_report
+
+        portfolio_return = float((total_arr[-1] / total_arr[0]) - 1)
+
+        benchmark_return = 0.0
+        if "total" in self._benchmark.columns:
+            bench_arr = self._benchmark["total"].to_numpy()
+            if len(bench_arr) >= 2 and bench_arr[0] > 0:
+                benchmark_return = float((bench_arr[-1] / bench_arr[0]) - 1)
+
+        if self._groups is not None and not self._groups.empty:
+            allocation, selection, interaction = self._compute_grouped_brinson(
+                portfolio_return,
+                benchmark_return,
+            )
+        else:
+            excess_return = portfolio_return - benchmark_return
+            allocation = 0.0
+            selection = excess_return
+            interaction = 0.0
+
+        return AttributionReport(
+            total_return=portfolio_return,
+            allocation_return=allocation,
+            selection_return=selection,
+            interaction_return=interaction,
+            total_trade_cost=cost_report.total_trade_cost,
+            slippage_cost=cost_report.slippage_cost,
+            commission_cost=cost_report.commission_cost,
+            stamp_tax_cost=cost_report.stamp_tax_cost,
+        )
+
+    def _compute_grouped_brinson(
+        self,
+        portfolio_return: float,
+        benchmark_return: float,
+    ) -> tuple[float, float, float]:
+        """按组计算 Brinson 归因。"""
+        if self._groups is None or self._groups.empty:
+            return 0.0, portfolio_return - benchmark_return, 0.0
+
+        allocation = 0.0
+        selection = 0.0
+        interaction = 0.0
+
+        if (
+            "portfolio_weight" in self._groups.columns
+            and "benchmark_weight" in self._groups.columns
+        ):
+            pw = self._groups["portfolio_weight"].to_numpy()
+            bw = self._groups["benchmark_weight"].to_numpy()
+
+            if (
+                "portfolio_return" in self._groups.columns
+                and "benchmark_return" in self._groups.columns
+            ):
+                pr = self._groups["portfolio_return"].to_numpy()
+                br = self._groups["benchmark_return"].to_numpy()
+
+                allocation = float(np.sum((pw - bw) * br))
+                selection = float(np.sum(bw * (pr - br)))
+                interaction = float(np.sum((pw - bw) * (pr - br)))
+
+        return allocation, selection, interaction
+
+    def factor_attribution(self) -> AttributionReport:
+        """因子归因分解。"""
+        cost_report = self.cost_attribution()
+
+        if self._factor_exposures is None or self._factor_returns is None:
+            return cost_report
+
+        if self._factor_exposures.empty or self._factor_returns.empty:
+            return cost_report
+
+        factor_contributions: dict[str, float] = {}
+        common_factors = set(self._factor_exposures.columns) & set(self._factor_returns.columns)
+        for factor_name in common_factors:
+            exposures = self._factor_exposures[factor_name].to_numpy()
+            returns = self._factor_returns[factor_name].to_numpy()
+            min_len = min(len(exposures), len(returns))
+            if min_len > 0:
+                contrib = float(np.sum(exposures[:min_len] * returns[:min_len]))
+                factor_contributions[factor_name] = contrib
+
+        total_factor_return = sum(factor_contributions.values())
+
+        total_arr = self._equity_curve["total"].to_numpy()
+        total_return = 0.0
+        if len(total_arr) >= 2 and total_arr[0] > 0:
+            total_return = float((total_arr[-1] / total_arr[0]) - 1)
+
+        specific_return = total_return - total_factor_return
+
+        return AttributionReport(
+            total_return=total_return,
+            factor_returns=factor_contributions,
+            specific_return=specific_return,
+            total_trade_cost=cost_report.total_trade_cost,
+            slippage_cost=cost_report.slippage_cost,
+            commission_cost=cost_report.commission_cost,
+            stamp_tax_cost=cost_report.stamp_tax_cost,
+        )
+
+    def full_report(self) -> AttributionReport:
+        """完整归因报告。"""
+        if self._factor_exposures is not None and self._factor_returns is not None:
+            return self.factor_attribution()
+        if self._benchmark is not None:
+            return self.brinson_attribution()
+        return self.cost_attribution()