添加批量解析

run_analysis.py

@@ -189,28 +189,9 @@ def extended_metrics(trades: pd.DataFrame) -> Dict[str, Any]:
 
 
 # =========================================================================== #
-# What-If 假设分析（通用场景）
+# 单段通用指标（从 mt5_report_parser 复用）
 # =========================================================================== #
-def _stats_from_net(net: pd.Series) -> Dict[str, float]:
-    """由净盈亏序列算关键统计。"""
-    n = len(net)
-    if n == 0:
-        return {"n": 0, "net": 0, "pf": 0, "win": 0, "dd": 0, "exp": 0}
-    wins = net[net > 0]
-    losses = net[net <= 0]
-    gp = wins.sum()
-    gl = -losses.sum()
-    pf = gp / gl if gl > 0 else np.inf
-    equity = net.cumsum()
-    dd = (equity - equity.cummax()).min()
-    return {
-        "n": int(n),
-        "net": float(net.sum()),
-        "pf": float(pf),
-        "win": float(len(wins) / n * 100),
-        "dd": float(dd),
-        "exp": float(net.mean()),
-    }
+from mt5_report_parser import compute_segment_metrics as _stats_from_net
 
 
 def whatif_scenarios(rep: mp.MT5Report) -> List[Dict[str, Any]]:
@@ -324,12 +305,16 @@ def monte_carlo_dd(net: pd.Series, n_sim: int = 1000, seed: int = 42) -> Dict[st
     n = len(arr)
     if n == 0:
         return {"p5": 0, "p50": 0, "p95": 0, "actual": 0, "mean": 0}
+    # 向量化：一次性生成 (n_sim, n) 置换矩阵，在 C 层完成
+    # 每行是一个随机打乱的交易顺序
+    perms = rng.integers(n, size=(n_sim, n))
+    # 每行按该行的索引排序得到 (n_sim, n) 的排列索引
+    idx = np.argsort(perms, axis=1)
     dds = np.empty(n_sim)
     for i in range(n_sim):
-        perm = rng.permutation(n)
-        eq = np.cumsum(arr[perm])
-        dd = (eq - np.maximum.accumulate(eq)).min()
-        dds[i] = dd
+        perm = arr[idx[i]]
+        eq = np.cumsum(perm)
+        dds[i] = (eq - np.maximum.accumulate(eq)).min()
     return {
         "p5": float(np.percentile(dds, 5)),
         "p50": float(np.percentile(dds, 50)),