docs: document unified backtest result workflow

README.md

@@ -60,6 +60,7 @@ from quant_engine.alpha_factors import alpha_001, alpha_005, ALPHA158_REGISTRY
 from quant_engine.execution import (
     ExecutionConfig, simulate_with_daily_data, compute_realized_pnl,
 )
+from quant_engine.backtest import run_weight_backtest
 from quant_engine.indicators import macd, bollinger, kdj
 from quant_engine.data_adapter import (
     long_to_wide, wide_to_long, rename_tushare_columns,
@@ -72,6 +73,16 @@ from quant_engine.data_adapter import (
 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)
+
+# 权重回测 → 稳定结果对象 → 绩效/基准分析
+backtest = run_weight_backtest(
+    weights=rebalance_weights,
+    stock_returns=daily_returns,
+    initial_capital=1_000_000.0,
+    benchmark_nav=benchmark_nav,
+)
+print(backtest.stats())
+print(backtest.benchmark_report())
 ```
 
 ## 与 research_results 的关系

src/quant_engine/backtest.py

@@ -13,29 +13,28 @@
 
 ```python
 from quant_engine.backtest import (
-    compute_nav_from_weights,    # 调仓表 → 净值
+    rebalance_periodic,           # 周期性再平衡
-    rebalance_table,              # 周期性再平衡
+    run_weight_backtest,          # 权重 → 统一结果对象
-    compare_to_benchmark,         # 策略 vs 基准
     weights_to_long_short,        # 多空组合
 )
 
-# 1. 调仓表 → 净值
+# 调仓表 → 净值、收益、绩效与基准报告
-nav = compute_nav_from_weights(
+rebalance_table = rebalance_periodic(target_weights, rebalance_dates, returns.index)
+result = run_weight_backtest(
     weights=rebalance_table,    # 每周/每月调仓
     stock_returns=returns,      # 个股日收益
     initial_capital=1.0,
+    benchmark_nav=benchmark_nav,
 )
-
+print(result.stats())
-# 2. 跟基准比
+print(result.benchmark_report())
-result = compare_to_benchmark(nav, benchmark_nav)
-print(result.summary())
 ```
 """
 
 from __future__ import annotations
 
-from pathlib import Path
 from collections.abc import Mapping, Sequence
+from dataclasses import dataclass
 
 import numpy as np
 import pandas as pd
@@ -46,6 +45,26 @@ from quant_engine.metrics import summary as metrics_summary
 logger = get_logger(__name__)
 
 
+@dataclass(frozen=True, slots=True, eq=False)
+class BacktestResult:
+    """一次权重回测的稳定结果快照。"""
+
+    nav: pd.Series
+    returns: pd.Series
+    weights: pd.DataFrame
+    benchmark_nav: pd.Series | None = None
+
+    def stats(self, rf: float = 0.0) -> Mapping[str, float]:
+        """返回标准绩效指标。"""
+        return metrics_summary(self.returns, rf)
+
+    def benchmark_report(self, rf: float = 0.0) -> pd.DataFrame:
+        """返回策略与基准的对比报告。"""
+        if self.benchmark_nav is None:
+            raise ValueError("benchmark_nav is required for benchmark comparison")
+        return compare_to_benchmark(self.nav, self.benchmark_nav, rf)
+
+
 # ── 调仓表 → 净值 ──────────────────────────────────────
 
 
@@ -113,6 +132,29 @@ def compute_returns_from_nav(nav: pd.Series) -> pd.Series:
     return nav.pct_change().fillna(0.0)
 
 
+def run_weight_backtest(
+    weights: pd.DataFrame,
+    stock_returns: pd.DataFrame,
+    initial_capital: float = 1.0,
+    tc_rate: float = 0.0,
+    benchmark_nav: pd.Series | None = None,
+) -> BacktestResult:
+    """执行权重回测并返回隔离于调用方输入的结果快照。"""
+    weights_snapshot = weights.copy(deep=True)
+    nav = compute_nav_from_weights(
+        weights=weights_snapshot,
+        stock_returns=stock_returns,
+        initial_capital=initial_capital,
+        tc_rate=tc_rate,
+    )
+    return BacktestResult(
+        nav=nav,
+        returns=compute_returns_from_nav(nav),
+        weights=weights_snapshot,
+        benchmark_nav=None if benchmark_nav is None else benchmark_nav.copy(deep=True),
+    )
+
+
 # ── 调仓工具 ──────────────────────────────────────
 
 

tests/test_backtest.py

@@ -6,10 +6,12 @@ import pandas as pd
 import pytest
 
 from quant_engine.backtest import (
+    BacktestResult,
     compare_to_benchmark,
     compute_nav_from_weights,
     compute_returns_from_nav,
     rebalance_periodic,
+    run_weight_backtest,
     weights_to_long_short,
 )
 
@@ -122,3 +124,86 @@ def test_compare_to_benchmark_rejects_non_overlapping_dates() -> None:
 
     with pytest.raises(ValueError, match="overlapping dates"):
         compare_to_benchmark(strategy, benchmark)
+
+
+# ── 统一回测结果门面 ──────────────────────────────────────
+
+
+def test_run_weight_backtest_returns_nav_returns_and_input_snapshot() -> None:
+    dates = pd.date_range("2026-01-05", periods=3, freq="B")
+    weights = pd.DataFrame({"A": [1.0]}, index=dates[:1])
+    stock_returns = pd.DataFrame({"A": [0.10, -0.10, 0.20]}, index=dates)
+
+    result = run_weight_backtest(weights, stock_returns, initial_capital=100.0)
+
+    assert isinstance(result, BacktestResult)
+    pd.testing.assert_series_equal(
+        result.nav,
+        pd.Series([110.0, 99.0, 118.8], index=dates),
+    )
+    pd.testing.assert_series_equal(
+        result.returns,
+        pd.Series([0.0, -0.1, 0.2], index=dates),
+    )
+    pd.testing.assert_frame_equal(result.weights, weights)
+
+
+def test_backtest_result_stats_reuses_standard_metrics_contract() -> None:
+    dates = pd.date_range("2026-01-05", periods=3, freq="B")
+    result = run_weight_backtest(
+        pd.DataFrame({"A": [1.0]}, index=dates[:1]),
+        pd.DataFrame({"A": [0.10, -0.10, 0.20]}, index=dates),
+    )
+
+    stats = result.stats(rf=0.02)
+
+    assert stats["n_days"] == 3
+    assert stats["ann_return"] == pytest.approx(
+        (1.0 * 0.9 * 1.2) ** (252 / 3) - 1.0
+    )
+    assert "sharpe" in stats
+    assert stats["drawback"] == stats["max_drawdown"]
+
+
+def test_backtest_result_builds_benchmark_report() -> None:
+    dates = pd.date_range("2026-01-05", periods=3, freq="B")
+    benchmark = pd.Series([1.0, 1.05, 1.10], index=dates, name="benchmark")
+    result = run_weight_backtest(
+        pd.DataFrame({"A": [1.0]}, index=dates[:1]),
+        pd.DataFrame({"A": [0.10, -0.10, 0.20]}, index=dates),
+        benchmark_nav=benchmark,
+    )
+
+    report = result.benchmark_report()
+
+    assert report.columns.tolist() == ["策略", "基准"]
+    assert report.loc["累计收益", "基准"] == pytest.approx(0.10)
+
+
+def test_backtest_result_requires_benchmark_for_comparison() -> None:
+    dates = pd.date_range("2026-01-05", periods=2, freq="B")
+    result = run_weight_backtest(
+        pd.DataFrame({"A": [1.0]}, index=dates[:1]),
+        pd.DataFrame({"A": [0.0, 0.0]}, index=dates),
+    )
+
+    with pytest.raises(ValueError, match="benchmark_nav"):
+        result.benchmark_report()
+
+
+def test_backtest_result_isolated_from_mutated_caller_inputs() -> None:
+    dates = pd.date_range("2026-01-05", periods=2, freq="B")
+    weights = pd.DataFrame({"A": [1.0]}, index=dates[:1])
+    benchmark = pd.Series([1.0, 1.1], index=dates)
+    result = run_weight_backtest(
+        weights,
+        pd.DataFrame({"A": [0.0, 0.0]}, index=dates),
+        benchmark_nav=benchmark,
+    )
+
+    weights.iloc[0, 0] = 0.0
+    benchmark.iloc[1] = 99.0
+
+    assert result.weights.iloc[0, 0] == 1.0
+    assert result.benchmark_nav is not None
+    assert result.benchmark_nav.iloc[1] == 1.1