diff --git a/sanguo_factor/batch_eval.py b/sanguo_factor/batch_eval.py
index 8a7a110..e004bb9 100644
--- a/sanguo_factor/batch_eval.py
+++ b/sanguo_factor/batch_eval.py
@@ -15,8 +15,9 @@ if _VNPY_SRC not in sys.path:
 
 import pandas as pd
 import polars as pl
+from datetime import datetime as _dt
 
-from .universe import load_universe_bars, evaluation_filter
+from .universe import load_universe_bars, WARMUP_BARS
 from .registry import get_factor
 from . import eval_store
 from .metrics import summarize_factor
@@ -57,7 +58,15 @@ def run_batch_eval(
 
     alpha_df = bars.select(["vt_symbol", "datetime", "open", "high", "low", "close",
                             "volume", "turnover", "open_interest", "vwap"])
-    eval_rows = evaluation_filter(bars, start, end).select(["vt_symbol", "datetime"])
+    # Pre-compute per-symbol warmup cutoff dates (bar_idx >= WARMUP_BARS 的首日)
+    # 用于替代 per-factor hash join，改为 pivot 后 pandas 广播掩码置 NaN
+    cutoffs = (
+        bars.filter(pl.col("bar_idx") >= WARMUP_BARS)
+        .group_by("vt_symbol").agg(pl.col("datetime").min().alias("cutoff"))
+    )
+    cutoff_map = dict(zip(cutoffs["vt_symbol"].to_list(), cutoffs["cutoff"].to_list()))
+    start_dt = _dt.strptime(start, "%Y-%m-%d")
+    end_dt = _dt.strptime(end, "%Y-%m-%d")
 
     close_wide = (
         bars.select(["datetime", "vt_symbol", "close"])
@@ -79,7 +88,7 @@ def run_batch_eval(
     done = 0
     buffer: list[dict] = []
     for i, name in enumerate(factor_names):
-        row = _eval_one(name, alpha_df, eval_rows, rets, calculate_by_expression)
+        row = _eval_one(name, alpha_df, cutoff_map, start_dt, end_dt, rets, calculate_by_expression)
         if "error" in row["metrics"]:
             errors.append(name)
         buffer.append(row)
@@ -101,24 +110,28 @@ def run_batch_eval(
     }
 
 
-def _eval_one(name: str, alpha_df: pl.DataFrame, eval_rows: pl.DataFrame,
-              rets: dict[int, pd.DataFrame], calculate_by_expression) -> dict:
+def _eval_one(name: str, alpha_df: pl.DataFrame, cutoff_map: dict[str, _dt],
+              start_dt: _dt, end_dt: _dt, rets: dict[int, pd.DataFrame], calculate_by_expression) -> dict:
     factor = get_factor(name)
     if factor is None:
         return {"factor": name, "category": "unknown", "expression": "",
                 "metrics": {"error": f"因子未注册: {name}"}}
     try:
         res = calculate_by_expression(alpha_df, factor["expression"])
-        f_long = res.join(eval_rows, on=["vt_symbol", "datetime"], how="inner")
-        F = (
-            f_long.pivot(index="datetime", on="vt_symbol", values="data")
-            .sort("datetime").to_pandas().set_index("datetime")
-        )
+        # 优化:先 pivot 全表，再 pandas 切窗口+预热期广播掩码置 NaN（替代 per-factor hash join）
+        # 语义等价性:join 版把「窗口外或 bar_idx<WARMUP」的行剔除；新版这些位置变 NaN
+        # 下游 rank_corr_rows/metrics 所有指标以 NaN=缺失自动剔除，两者等价
+        Fw = res.pivot(index="datetime", on="vt_symbol", values="data").sort("datetime")
+        F = Fw.to_pandas().set_index("datetime")
         F.index = pd.to_datetime(F.index)
-        if F.empty or len(F.index) == 0:
+        F = F.loc[start_dt:end_dt]  # 评估窗口切片
+        # 预热期前置 NaN:每列用对应 cutoff_map[vt_symbol] 进行广播比较
+        cut_arr = pd.Series([cutoff_map.get(s) for s in F.columns], index=F.columns).to_numpy()
+        F = F.where(F.index.to_numpy()[:, None] >= cut_arr[None, :])
+        if F.empty or F.shape[0] == 0 or F.shape[1] == 0:
             return {"factor": name, "category": factor["category"],
                     "expression": factor["expression"],
-                    "metrics": {"error": "因子矩阵为空(join 后无评估窗行,检查 datetime dtype/窗口)"}}
+                    "metrics": {"error": "因子矩阵为空(处理后无评估窗行,检查 datetime dtype/窗口)"}}
         metrics = summarize_factor(F, rets[1], rets[5], rets[10])
         return {"factor": name, "category": factor["category"],
                 "expression": factor["expression"], "metrics": metrics}