diff --git a/scripts/generate_legacy_report.py b/scripts/generate_legacy_report.py
index f377c4d..ac4ab67 100644
--- a/scripts/generate_legacy_report.py
+++ b/scripts/generate_legacy_report.py
@@ -71,17 +71,25 @@ def run_with_legacy_report():
     
     # 1. 基准净值和基准日收益率
     benchmark_data = data.get('benchmark_data')
-    if benchmark_data is not None and not benchmark_data.empty:
-        # 对齐基准数据到回测日期
-        benchmark_close = benchmark_data['close'] if 'close' in benchmark_data.columns else benchmark_data.iloc[:, 0]
-        benchmark_close_aligned = benchmark_close.reindex(backtest_result.index, method='ffill')
+    if benchmark_data is not None:
+        # benchmark_data 已经是 Series（close 价格）
+        if isinstance(benchmark_data, pd.Series):
+            benchmark_close = benchmark_data
+        elif isinstance(benchmark_data, pd.DataFrame):
+            benchmark_close = benchmark_data['close'] if 'close' in benchmark_data.columns else benchmark_data.iloc[:, 0]
+        else:
+            benchmark_close = None
         
-        # 计算基准净值
-        benchmark_nav = (1 + benchmark_close_aligned.pct_change()).cumprod()
-        benchmark_nav = benchmark_nav / benchmark_nav.dropna().iloc[0]  # 归一化起点为1
-        
-        backtest_result['基准净值'] = benchmark_nav.values
-        backtest_result['基准日收益率'] = benchmark_close_aligned.pct_change().values
+        if benchmark_close is not None and len(benchmark_close) > 0:
+            # 对齐基准数据到回测日期
+            benchmark_close_aligned = benchmark_close.reindex(backtest_result.index, method='ffill')
+            
+            # 计算基准净值
+            benchmark_nav = (1 + benchmark_close_aligned.pct_change()).cumprod()
+            benchmark_nav = benchmark_nav / benchmark_nav.dropna().iloc[0]  # 归一化起点为1
+            
+            backtest_result['基准净值'] = benchmark_nav.values
+            backtest_result['基准日收益率'] = benchmark_close_aligned.pct_change().values
     
     # 2. 各标的净值（指数价格）- 使用index_data而非index_close
     # index_close可能对齐有问题，直接从index_data获取
@@ -143,14 +151,26 @@ def run_with_legacy_report():
                 price_aligned = etf_data[etf_code].reindex(backtest_result.index, method='ffill')
                 etf_price_data[idx_code] = price_aligned.values
     
-    if etf_nav_data is not None:
-        # ETF净值数据列名是ETF代码，需要用etf_code_map映射
-        # 并对齐到回测日期
+    # ETF净值数据现在是字典格式 {etf_code: DataFrame}
+    etf_nav_data_raw = None
+    
+    if etf_nav_data and len(etf_nav_data) > 0:
+        # etf_nav_data 是字典 {etf_code: DataFrame}
         etf_nav_data_raw = pd.DataFrame(index=backtest_result.index)
         for idx_code, etf_code in etf_code_map.items():
-            if etf_code in etf_nav_data.columns:
+            if etf_code in etf_nav_data:
+                # 从字典中获取净值 DataFrame
+                nav_df = etf_nav_data[etf_code]
+                if isinstance(nav_df, pd.DataFrame) and 'nav' in nav_df.columns:
+                    nav_series = nav_df['nav']
+                elif isinstance(nav_df, pd.DataFrame):
+                    nav_series = nav_df.iloc[:, 0]
+                elif isinstance(nav_df, pd.Series):
+                    nav_series = nav_df
+                else:
+                    continue
                 # 对齐净值数据到回测日期（使用ffill处理日期差异）
-                nav_aligned = etf_nav_data[etf_code].reindex(backtest_result.index, method='ffill')
+                nav_aligned = nav_series.reindex(backtest_result.index, method='ffill')
                 etf_nav_data_raw[idx_code] = nav_aligned.values
     
     # 生成原引擎格式的报告