diff --git a/src/fxhnt/application/equity_backtest_runner.py b/src/fxhnt/application/equity_backtest_runner.py
index 00b4212..f2aa194 100644
--- a/src/fxhnt/application/equity_backtest_runner.py
+++ b/src/fxhnt/application/equity_backtest_runner.py
@@ -7,10 +7,13 @@ from __future__ import annotations
 
 import datetime as dt
 from dataclasses import dataclass
+from typing import Any
 
 import numpy as np
 
 from fxhnt.adapters.warehouse.duckdb_feature_store import DuckDbFeatureStore
+from fxhnt.domain.backtest import compute_stats
+from fxhnt.domain.gauntlet.core import evaluate
 from fxhnt.domain.equity_backtest import (
     _epoch_day,
     daily_returns_for_weights,
@@ -128,6 +131,45 @@ class EquityBacktestRunner:
         )
 
 
+def evaluate_constructions(
+    result: BacktestRunResult,
+    *,
+    oos_fraction: float = 0.40,
+    dsr_min: float = 0.95,
+    oos_min_sharpe: float = 0.0,
+    max_is_oos_decay: float = 0.50,
+) -> dict[str, Any]:
+    """Stack each construction's daily returns, split IS/OOS, run the gauntlet.
+    n_trials = the number of constructions tested (multiple-testing correction)."""
+    n_trials = len(result.returns_by_construction)
+    out: dict[str, Any] = {
+        "constructions": {},
+        "rebalance_dates": result.rebalance_dates,
+        "n_names_avg": result.n_names_avg,
+        "settings": {
+            "oos_fraction": oos_fraction, "dsr_min": dsr_min,
+            "oos_min_sharpe": oos_min_sharpe, "max_is_oos_decay": max_is_oos_decay,
+            "n_trials": n_trials,
+        },
+    }
+    for c, r in result.returns_by_construction.items():
+        stats = compute_stats(r)
+        split = int((1.0 - oos_fraction) * len(r))
+        is_r, oos_r = r[:split], r[split:]
+        sr_variance = (1.0 / len(r)) if len(r) > 1 else 0.0
+        verdict = evaluate(
+            is_r, oos_r, n_trials=n_trials, sr_variance=sr_variance,
+            dsr_min=dsr_min, oos_min_sharpe=oos_min_sharpe,
+            max_is_oos_decay=max_is_oos_decay,
+            has_economic_rationale=True,
+        )
+        out["constructions"][c] = {
+            "stats": stats.model_dump(),
+            "verdict": verdict.model_dump(),
+        }
+    return out
+
+
 def _turnover(prev: dict[str, float], cur: dict[str, float]) -> float:
     """TWO-WAY (round-trip) turnover: the full sum of absolute weight changes
     across all names. A complete A->B switch (sell 1.0 of A, buy 1.0 of B) scores
diff --git a/tests/integration/test_equity_backtest_runner.py b/tests/integration/test_equity_backtest_runner.py
index 4d96dd9..43a0c7b 100644
--- a/tests/integration/test_equity_backtest_runner.py
+++ b/tests/integration/test_equity_backtest_runner.py
@@ -3,7 +3,10 @@ import datetime as dt
 import numpy as np
 
 from fxhnt.adapters.warehouse.duckdb_feature_store import DuckDbFeatureStore
-from fxhnt.application.equity_backtest_runner import EquityBacktestRunner
+from fxhnt.application.equity_backtest_runner import (
+    EquityBacktestRunner,
+    evaluate_constructions,
+)
 from fxhnt.domain.equity_backtest import month_end_rebalance_dates
 
 _SPD = 86_400
@@ -89,3 +92,23 @@ def test_momentum_min_history_gates_inclusion(tmp_path):
     assert result.n_names_avg == 0.0
     for series in result.returns_by_construction.values():
         assert len(series) == 0
+
+
+def test_evaluate_constructions_emits_stats_and_verdict_per_construction(tmp_path):
+    store = _build_warehouse(str(tmp_path / "wh.duckdb"))
+    runner = EquityBacktestRunner(store, n=10, cost_bps_per_turnover=15.0,
+                                  borrow_annual=0.0, momentum_min_history=252)
+    result = runner.run()
+    store.close()
+    report = evaluate_constructions(result, oos_fraction=0.40, dsr_min=0.95,
+                                    oos_min_sharpe=0.0, max_is_oos_decay=0.50)
+    assert set(report["constructions"]) == {"long", "ls", "tilt"}
+    for c, block in report["constructions"].items():
+        assert {"stats", "verdict"} <= set(block)
+        assert {"sharpe", "cagr", "max_drawdown", "n_obs"} <= set(block["stats"])
+        assert {"passed", "dsr", "is_sharpe", "oos_sharpe", "n_trials", "reasons"} <= set(block["verdict"])
+        assert block["verdict"]["n_trials"] == 3
+    assert report["n_names_avg"] >= 0.0
+    assert isinstance(report["rebalance_dates"], list)
+    import json
+    json.dumps(report)  # must not raise