test: define post-execution daily ledger contract

tests/test_research_pipeline.py

@@ -7,8 +7,10 @@ import pytest
 
 from quant_engine.execution import ExecutionConfig
 from quant_engine.research_pipeline import (
+    FactorBacktestResult,
     FactorExecutionResult,
     TargetWeightSchedule,
+    run_factor_backtest_research,
     run_factor_execution_research,
     schedule_target_weights,
 )
@@ -149,3 +151,85 @@ def test_factor_execution_research_accepts_empty_scores() -> None:
 
     assert result.schedule.execution_weights.empty
     assert result.execution.positions == ()
+
+
+def test_factor_backtest_research_runs_signal_to_daily_performance_without_lookahead() -> None:
+    """信号日保持现金，下一日开盘成交后才参与当日收盘收益。"""
+    dates = _calendar()
+    scores = pd.DataFrame({"A": [2.0], "B": [1.0]}, index=dates[:1])
+    opens = pd.DataFrame(
+        {"A": [1.0, 10.0, 10.0, 10.0], "B": [1.0, 20.0, 20.0, 20.0]},
+        index=dates,
+    )
+    closes = pd.DataFrame(
+        {"A": [500.0, 11.0, 12.0, 12.0], "B": [500.0, 20.0, 20.0, 20.0]},
+        index=dates,
+    )
+    config = ExecutionConfig(
+        commission_bps=0,
+        stamp_tax_bps=0,
+        slippage_bps=0,
+        min_trade_amount=0,
+    )
+
+    result = run_factor_backtest_research(
+        scores,
+        execution_prices=opens,
+        valuation_prices=closes,
+        top_k=1,
+        execution_price_field="open",
+        valuation_price_field="close",
+        initial_cash=1_000.0,
+        config=config,
+    )
+
+    assert isinstance(result, FactorBacktestResult)
+    assert result.execution_price_field == "open"
+    assert result.valuation_price_field == "close"
+    pd.testing.assert_series_equal(
+        result.nav,
+        pd.Series([1.0, 1.1, 1.2, 1.2], index=dates, name="nav"),
+    )
+    pd.testing.assert_series_equal(
+        result.returns,
+        pd.Series([0.0, 0.1, 1.2 / 1.1 - 1.0, 0.0], index=dates, name="returns"),
+    )
+    assert result.stats()["n_days"] == 4
+    assert result.execution.daily_executions[0].executions == ()
+    assert result.execution.daily_executions[1].executions[0].price == 10.0
+
+
+def test_factor_backtest_result_snapshots_both_price_semantics() -> None:
+    scores = pd.DataFrame({"A": [1.0]}, index=_calendar()[:1])
+    opens = pd.DataFrame({"A": [10.0, 10.0, 10.0, 10.0]}, index=_calendar())
+    closes = pd.DataFrame({"A": [10.0, 11.0, 12.0, 13.0]}, index=_calendar())
+
+    result = run_factor_backtest_research(
+        scores,
+        execution_prices=opens,
+        valuation_prices=closes,
+        top_k=1,
+        execution_price_field="open",
+        valuation_price_field="close",
+    )
+    opens.iloc[1, 0] = 999.0
+    closes.iloc[1, 0] = 999.0
+
+    assert result.execution_prices.iloc[1, 0] == 10.0
+    assert result.valuation_prices.iloc[1, 0] == 11.0
+
+
+def test_factor_backtest_research_requires_matching_daily_calendars() -> None:
+    scores = pd.DataFrame({"A": [1.0]}, index=_calendar()[:1])
+    opens = pd.DataFrame({"A": [10.0, 10.0, 10.0, 10.0]}, index=_calendar())
+    closes = pd.DataFrame({"A": [10.0, 11.0, 12.0]}, index=_calendar()[:3])
+
+    with pytest.raises(ValueError, match="matching trading calendars"):
+        run_factor_backtest_research(
+            scores,
+            execution_prices=opens,
+            valuation_prices=closes,
+            top_k=1,
+            execution_price_field="open",
+            valuation_price_field="close",
+        )