diff --git a/backend/app/backtest/engine.py b/backend/app/backtest/engine.py
index 8af1b6f..d9be4d6 100644
--- a/backend/app/backtest/engine.py
+++ b/backend/app/backtest/engine.py
@@ -1325,6 +1325,9 @@ class BacktestEngine:
         大样本 (如 full 模式数千笔) 时按 2M 单元上限压降模拟次数, 防止瞬时数组 OOM。
         """
         pnls = pnls[np.isfinite(pnls)]  # 剔除 inf/nan, 否则 cumprod 传播 nan 导致分位为 nan
+        # 防御: 单笔 pnl <= -100% 时 (1+pnl) <= 0 会让 cumprod 符号翻转/得非正净值, 回撤失真。
+        # 回测有止损, 实际不会发生; 兜底 clip 到 -99.99% 保证 (1+pnl) 恒正。
+        pnls = np.clip(pnls, -0.9999, None)
         n = len(pnls)
         if n < 3:
             return {"mc_maxdd_p50": None, "mc_maxdd_p95": None}
@@ -1407,7 +1410,8 @@ class BacktestEngine:
         # 夏普 — 用交易收益标准差近似
         sharpe = float(np.mean(pnls) / np.std(pnls)) * np.sqrt(252) if np.std(pnls) > 0 else 0.0
 
-        # Sortino — 与 sharpe 同基准 (逐笔收益), 仅惩罚下行波动
+        # Sortino: 刻意沿用本函数 sharpe 的逐笔收益 x sqrt(252) 基准。逐笔年化非严格正确,
+        # 但保证同一函数内 sharpe/sortino 口径一致可比 (内部一致 > 局部绝对)。仅惩罚下行波动。
         sortino = BacktestEngine._sortino_ratio(pnls)
 
         # Calmar
diff --git a/backend/tests/backtest/test_robustness_metrics.py b/backend/tests/backtest/test_robustness_metrics.py
index bcd4900..8f3d7e4 100644
--- a/backend/tests/backtest/test_robustness_metrics.py
+++ b/backend/tests/backtest/test_robustness_metrics.py
@@ -81,6 +81,16 @@ def test_mc_drawdown_ignores_non_finite():
     assert BacktestEngine._mc_drawdown_percentiles(dirty) == BacktestEngine._mc_drawdown_percentiles(_MC_INPUT)
 
 
+def test_mc_drawdown_clips_sub_minus_100pct_pnl():
+    """防御: 单笔 pnl <= -100% 会让 (1+pnl)<=0 使 cumprod 符号翻转; clip 后分位仍有限。"""
+    pnls = np.array([0.05, -1.5, 0.08, -0.06, 0.02, -0.04])  # -1.5 = -150%, 现实不会有
+    r = BacktestEngine._mc_drawdown_percentiles(pnls)
+    assert r["mc_maxdd_p50"] is not None
+    for v in (r["mc_maxdd_p50"], r["mc_maxdd_p95"]):
+        assert v == v  # 非 nan
+        assert -1.0 <= v <= 0.0  # 回撤有界在 (-100%, 0], 未因符号翻转失真
+
+
 def test_mc_drawdown_all_positive_has_zero_drawdown():
     """全正收益: 任何重排都无回撤 → 分位均为 0。"""
     pnls = np.array([0.01, 0.02, 0.03, 0.04, 0.05])
diff --git a/frontend/src/pages/backtest/StrategyBacktest.tsx b/frontend/src/pages/backtest/StrategyBacktest.tsx
index e79bb39..7c9c667 100644
--- a/frontend/src/pages/backtest/StrategyBacktest.tsx
+++ b/frontend/src/pages/backtest/StrategyBacktest.tsx
@@ -1665,7 +1665,7 @@ export function StrategyBacktest() {
                   color="#34d399" />
                 <Stat label="蒙卡回撤(中位)" value={pick('mc_maxdd_p50') != null ? fmtPct(pick('mc_maxdd_p50') as number) : '—'}
                   color="#34d399" />
-                <Stat label="蒙卡回撤(95%最坏)" value={pick('mc_maxdd_p95') != null ? fmtPct(pick('mc_maxdd_p95') as number) : '—'}
+                <Stat label="蒙卡回撤(95%置信不差于此)" value={pick('mc_maxdd_p95') != null ? fmtPct(pick('mc_maxdd_p95') as number) : '—'}
                   color="#34d399" />
                 <Stat label="胜率" value={pick('win_rate') != null ? fmtPct(pick('win_rate') as number) : '—'} />
                 <Stat label="交易数" value={pick('n_trades') != null ? String(pick('n_trades')) : '—'} />