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