diff --git a/strategy_lab.py b/strategy_lab.py
index 7a1bdabb..8bb6e1e9 100644
--- a/strategy_lab.py
+++ b/strategy_lab.py
@@ -1057,20 +1057,52 @@ def max_concurrency(trades):
 
 MIN_POSITION = 50000  # 单仓可行性下限（A股一手+手续费，约5万）
 
-def portfolio_sim_full(trades, capital=1000000):
-    """全参与组合模拟（可行版）：仓位槽 = min(信号流最大并发, 总资产/单仓下限)。
-    消除"选哪几笔上车"的运气成分，同时保证单仓金额实际可操作。"""
+def portfolio_sim_full(trades, capital=1000000, runs=50):
+    """全参与组合模拟（Monte Carlo 随机选装版）：
+    仓位槽 = min(信号流最大并发, 总资产/单仓下限)；
+    同日竞争时随机洗牌选装，重复 runs 次取均值±方差。
+    彻底消灭评分挑选偏差（2026-07-29 老爸：v1.0高分信号被偏爱=虚高收益）"""
     if not trades:
         return {}
     natural = max_concurrency(trades)
     affordable = max(1, int(capital / MIN_POSITION))
     slots = min(natural, affordable)
-    r = portfolio_sim(trades, capital, slots)
-    r['slots'] = slots
-    r['natural_concurrency'] = natural
-    r['min_position'] = MIN_POSITION
-    r['mode'] = 'full_feasible'
-    return r
+    # 交易少时直接单跑(无需MC)
+    if len(trades) <= slots * 2:
+        r = portfolio_sim(trades, capital, slots)
+        r.update({'slots': slots, 'natural_concurrency': natural, 'mode': 'full_single',
+                  'std': 0.0, 'p10': r['total_return_pct'], 'p90': r['total_return_pct']})
+        return r
+    rets = []
+    finals = []
+    dds = []
+    taken = []
+    for k in range(runs):
+        r = portfolio_sim(trades, capital, slots, random_seed=42 + k)
+        rets.append(r['total_return_pct'])
+        finals.append(r['capital_final'])
+        dds.append(r['portfolio_max_dd_pct'])
+        taken.append(r['positions_taken'])
+    rets_s = sorted(rets)
+    mean_ret = sum(rets) / len(rets)
+    mean_dd = sum(dds) / len(dds)
+    std = (sum((x - mean_ret) ** 2 for x in rets) / len(rets)) ** 0.5
+    return {
+        'capital_final': round(sum(finals) / len(finals), 0),
+        'total_return_pct': round(mean_ret, 1),
+        'cagr_pct': round((((1 + mean_ret / 100) ** 0.5) - 1) * 100, 1),
+        'portfolio_max_dd_pct': round(mean_dd, 1),
+        'positions_taken': round(sum(taken) / len(taken)),
+        'positions_skipped': len(trades) - round(sum(taken) / len(taken)),
+        'slots': slots,
+        'natural_concurrency': natural,
+        'min_position': MIN_POSITION,
+        'mode': 'full_mc',
+        'mc_runs': runs,
+        'std': round(std, 1),
+        'p10': round(rets_s[int(len(rets_s) * 0.1)], 1),
+        'p90': round(rets_s[int(len(rets_s) * 0.9)], 1),
+    }
 
 
 COST_RATE = 0.002  # 往返交易费用率（佣金+印花税+滑点≈0.2%）
@@ -1079,7 +1111,7 @@ def _trade_legs(t):
     """波段类出场按2次往返计费"""
     return 2 if t.get('exit_reason') in ('swing_re', 'swing_ptp') else 1
 
-def portfolio_sim(trades, capital=1000000, max_positions=10, cost=True):
+def portfolio_sim(trades, capital=1000000, max_positions=10, cost=True, random_seed=None):
     """组合级模拟：固定等分仓位，按交易日历执行，返回最终资产/总收益/资产曲线回撤
     规则：每日先结算到期仓位 → 再执行当日入场（仓位满跳过）→ 持仓按成本估值
     含交易费用：每笔往返扣 COST_RATE（2026-07-29 老爸：高频策略必须上费用天平）"""
@@ -1096,12 +1128,20 @@ def portfolio_sim(trades, capital=1000000, max_positions=10, cost=True):
             return d
         return cal[min(i + n, len(cal) - 1)]
 
-    # 按入场日组织（同日高分优先）
+    # 按入场日组织
+    # random_seed 设置时：同日信号随机洗牌（消灭评分挑选偏差, Monte Carlo用）
+    # 否则：同日高分优先（原行为, 集中仓位模拟用）
     entries = {}
     for t in trades:
         entries.setdefault(t['entry_date'], []).append(t)
-    for d in entries:
-        entries[d].sort(key=lambda x: -x.get('score', 0))
+    if random_seed is not None:
+        import random as _rnd
+        rng = _rnd.Random(random_seed)
+        for d in entries:
+            rng.shuffle(entries[d])
+    else:
+        for d in entries:
+            entries[d].sort(key=lambda x: -x.get('score', 0))
 
     cash = capital
     open_pos = []   # {'exit_date','alloc','pnl'}