diff --git a/tests/test_execution.py b/tests/test_execution.py
index e1fdd6b..dc9cf15 100644
--- a/tests/test_execution.py
+++ b/tests/test_execution.py
@@ -20,6 +20,7 @@ from quant_engine.execution import (
     compute_realized_pnl,
     run_end_to_end_poc,
     simulate_execution,
+    simulate_daily_ledger_with_audit,
     simulate_multi_day,
     simulate_multi_day_with_audit,
     simulate_with_daily_data,
@@ -470,6 +471,135 @@ def test_simulate_multi_day_with_audit_requires_matching_dates():
         )
 
 
+# ── 逐交易日 Ledger：成交时点与估值时点分离 ─────────────────
+
+
+def test_daily_ledger_marks_every_session_after_sparse_open_execution() -> None:
+    """下一日开盘成交后，应按每日收盘价持续盯市，而非只记录调仓日。"""
+    config = ExecutionConfig(
+        commission_bps=0,
+        stamp_tax_bps=0,
+        slippage_bps=0,
+        min_trade_amount=0,
+    )
+
+    result = simulate_daily_ledger_with_audit(
+        target_weights_history=[("d1", {"A": 1.0})],
+        execution_price_history=[("d1", {"A": 10.0})],
+        valuation_price_history=[
+            ("d0", {"A": 9.0}),
+            ("d1", {"A": 11.0}),
+            ("d2", {"A": 12.0}),
+        ],
+        initial_cash=1_000.0,
+        config=config,
+    )
+
+    assert [position.date for position in result.positions] == ["d0", "d1", "d2"]
+    assert [position.portfolio_value for position in result.positions] == pytest.approx(
+        [1_000.0, 1_100.0, 1_200.0]
+    )
+    assert [len(day.executions) for day in result.daily_executions] == [0, 1, 0]
+    fill = result.daily_executions[1].executions[0]
+    assert fill.side == "buy"
+    assert fill.quantity == pytest.approx(100.0)
+    assert fill.price == pytest.approx(10.0)
+    pd.testing.assert_series_equal(
+        result.normalized_nav_series,
+        pd.Series([1.0, 1.1, 1.2], index=["d0", "d1", "d2"], dtype=float),
+    )
+    pd.testing.assert_series_equal(
+        result.daily_returns,
+        pd.Series([0.0, 0.1, 1.2 / 1.1 - 1.0], index=["d0", "d1", "d2"]),
+    )
+
+
+def test_daily_ledger_first_session_cost_reduces_first_return() -> None:
+    """首个估值日发生交易时，费用必须进入相对初始资金的首日收益。"""
+    result = simulate_daily_ledger_with_audit(
+        target_weights_history=[("d0", {"A": 1.0})],
+        execution_price_history=[("d0", {"A": 10.0})],
+        valuation_price_history=[("d0", {"A": 10.0})],
+        initial_cash=1_000.0,
+    )
+
+    assert result.total_costs > 0
+    assert result.daily_returns.iloc[0] == pytest.approx(
+        result.final_portfolio_value / result.initial_cash - 1.0
+    )
+    assert result.daily_returns.iloc[0] < 0
+
+
+def test_daily_ledger_nav_is_rebuildable_and_trades_are_projectable() -> None:
+    """Ledger 必须同时支持现金守恒校验和平台成交表投影。"""
+    config = ExecutionConfig(
+        commission_bps=0,
+        stamp_tax_bps=0,
+        slippage_bps=0,
+        min_trade_amount=0,
+    )
+    result = simulate_daily_ledger_with_audit(
+        target_weights_history=[
+            ("d1", {"A": 1.0, "B": 0.0}),
+            ("d2", {"A": 0.0, "B": 1.0}),
+        ],
+        execution_price_history=[
+            ("d1", {"A": 10.0, "B": 20.0}),
+            ("d2", {"A": 11.0, "B": 22.0}),
+        ],
+        valuation_price_history=[
+            ("d0", {"A": 9.0, "B": 19.0}),
+            ("d1", {"A": 10.5, "B": 21.0}),
+            ("d2", {"A": 12.0, "B": 24.0}),
+        ],
+        initial_cash=1_000.0,
+        config=config,
+    )
+
+    close_prices = {
+        "d0": {"A": 9.0, "B": 19.0},
+        "d1": {"A": 10.5, "B": 21.0},
+        "d2": {"A": 12.0, "B": 24.0},
+    }
+    for position in result.positions:
+        rebuilt = position.cash + sum(
+            shares * close_prices[position.date][asset]
+            for asset, shares in position.holdings.items()
+        )
+        assert position.portfolio_value == pytest.approx(rebuilt)
+
+    trades = result.trades_frame
+    assert trades.columns.tolist() == [
+        "trade_date",
+        "ts_code",
+        "side",
+        "qty",
+        "price",
+        "amount",
+        "fee",
+        "slippage",
+    ]
+    assert trades["side"].tolist() == ["buy", "sell", "buy"]
+    assert (trades["qty"] > 0).all()
+
+
+def test_daily_ledger_rejects_missing_close_for_held_asset() -> None:
+    """已有持仓缺少收盘估值价时必须 fail closed。"""
+    with pytest.raises(ValueError, match="missing valuation price for held asset A"):
+        simulate_daily_ledger_with_audit(
+            target_weights_history=[("d0", {"A": 1.0})],
+            execution_price_history=[("d0", {"A": 10.0})],
+            valuation_price_history=[("d0", {"A": 10.0}), ("d1", {})],
+            initial_cash=1_000.0,
+        )
+
+
+def test_daily_ledger_requires_positive_initial_cash() -> None:
+    """可信收益曲线需要正初始资金作为归一化基准。"""
+    with pytest.raises(ValueError, match="initial_cash must be positive"):
+        simulate_daily_ledger_with_audit([], [], [], initial_cash=0.0)
+
+
 # ── v1.2.0 Phase 1：端到端 POC（run_end_to_end_poc） ─────
 
 
diff --git a/tests/test_research_pipeline.py b/tests/test_research_pipeline.py
index b7f4e72..98eb37e 100644
--- a/tests/test_research_pipeline.py
+++ b/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",
+        )