diff --git a/sanguo_factor/alpha_datasets.py b/sanguo_factor/alpha_datasets.py
index aac644b..1c2a1ee 100644
--- a/sanguo_factor/alpha_datasets.py
+++ b/sanguo_factor/alpha_datasets.py
@@ -48,7 +48,7 @@ def _mount(dataset_cls, category: str) -> int:
 
 
 def mount_alpha101() -> int:
-    """挂载 WorldQuant Alpha101(100 个),category=alpha101."""
+    """挂载 WorldQuant Alpha101(82 个),category=alpha101."""
     from vnpy.alpha.dataset.datasets.alpha_101 import Alpha101
     return _mount(Alpha101, "alpha101")
 
diff --git a/sanguo_factor/metrics.py b/sanguo_factor/metrics.py
index 8ca78e4..f41ec19 100644
--- a/sanguo_factor/metrics.py
+++ b/sanguo_factor/metrics.py
@@ -51,12 +51,12 @@ def factor_turnover(F: pd.DataFrame) -> float:
     return float(1.0 - corr.mean()) if len(corr) else 0.0
 
 
-def _quantile_mask(F: pd.DataFrame, lo_frac: float, hi_frac: float) -> pd.DataFrame:
-    """按行把因子值分位选mask(基于升序秩/当日有效数)."""
+def _quantile_mask(F: pd.DataFrame, top: bool) -> pd.DataFrame:
+    """按行把因子值分位选mask(基于升序秩/当日有效数). top=True选最大10%,False选最小10%."""
     ranks = F.rank(axis=1, ascending=False)  # 1=最大
     n = ranks.notna().sum(axis=1)
     k = np.maximum((n * 0.1).round().astype(int), 1)
-    if lo_frac == 0.0:
+    if top:
         return ranks.le(k, axis=0) & ranks.notna()
     return ranks.ge(n - k + 1, axis=0) & ranks.notna()
 
@@ -66,8 +66,8 @@ def long_short_annual_return(F: pd.DataFrame, R: pd.DataFrame) -> float | None:
     cols = F.columns.intersection(R.columns)
     idx = F.index.intersection(R.index)
     f, r = F.loc[idx, cols], R.loc[idx, cols]
-    top = _quantile_mask(f, 0.0, 0.1)
-    bot = _quantile_mask(f, 0.9, 1.0)
+    top = _quantile_mask(f, top=True)
+    bot = _quantile_mask(f, top=False)
     daily = (r.where(top).mean(axis=1) - r.where(bot).mean(axis=1)).dropna()
     if daily.empty:
         return None