diff --git a/strategies/rotation/engine.py b/strategies/rotation/engine.py
index fef294d..82144fb 100644
--- a/strategies/rotation/engine.py
+++ b/strategies/rotation/engine.py
@@ -241,21 +241,29 @@ class RotationStrategy(BacktestStrategy):
         select_num = self.config["select_num"]
         trade_cost = self.config["trade_cost"]
 
-        # 计算策略日收益率
+        # 计算策略日收益率 - 处理NaN值
         if select_num == 1:
             def calc_return(row):
                 signal = row['信号']
                 if not signal or pd.isna(signal):
                     return 0.0
-                return row.get(f"日收益率_{signal}", 0.0)
+                ret = row.get(f"日收益率_{signal}", 0.0)
+                # 如果日收益率是NaN，返回0.0
+                return ret if pd.notna(ret) else 0.0
             result["轮动策略日收益率"] = result.apply(calc_return, axis=1)
         else:
             def calc_multi_return(row):
                 codes = [c for c in row["信号"].split(",") if c]  # 过滤空字符串
                 if not codes:
                     return 0.0
-                returns = [row.get(f"日收益率_{c}", 0.0) for c in codes]
-                return np.mean(returns)
+                # 获取各品种日收益率，忽略NaN值
+                returns = []
+                for c in codes:
+                    ret = row.get(f"日收益率_{c}", None)
+                    if ret is not None and pd.notna(ret):
+                        returns.append(ret)
+                # 如果所有品种日收益率都缺失，返回0.0
+                return np.mean(returns) if returns else 0.0
             result["轮动策略日收益率"] = result.apply(calc_multi_return, axis=1)
 
         # 扣除交易成本
@@ -281,10 +289,16 @@ class RotationStrategy(BacktestStrategy):
         # 计算净值
         result["轮动策略净值"] = (1 + result["轮动策略日收益率"]).cumprod()
 
-        # 各ETF单独净值
+        # 各ETF单独净值 - 使用第一个有效价格作为基准
         for code in self.valid_codes:
-            first_price = result[code].iloc[0]
-            result[f"净值_{code}"] = result[code] / first_price
+            # 获取第一个有效价格（非NaN）
+            valid_prices = result[code][result[code].notna()]
+            if len(valid_prices) > 0:
+                first_valid_price = valid_prices.iloc[0]
+                result[f"净值_{code}"] = result[code] / first_valid_price
+            else:
+                # 如果没有有效数据，净值列全部为NaN
+                result[f"净值_{code}"] = np.nan
 
         # 基准净值
         # benchmark_data 是 DataFrame，需要提取 close 列