feat(tushare): 新增ETF后复权价格和交易日历获取方法

新增方法:
- fetch_etf_adj: 获取ETF后复权价格数据，消除份额拆分对收益率的影响
  通过 fund_daily + fund_adj 手动计算后复权价格
  fund_adj 单次限2000条，按5年分段请求
- fetch_trade_cal: 获取A股SSE官方交易日历

验证结果:
- 纳指ETF后复权正确识别2022-01-14拆分(复权因子5.0)
- 累计收益100.54%与纳指100指数一致

datasource/tushare_source.py

@@ -245,4 +245,139 @@ class TushareSource:
         elif self.is_futures(code):
             return self.fetch_futures(code, start_date, end_date)
         else:
-            return None
+            return None
+
+    def fetch_etf_adj(self, code: str, start_date: str, end_date: str) -> Optional[pd.DataFrame]:
+        """
+        获取 ETF 后复权价格数据
+        
+        通过 fund_daily + fund_adj 手动计算后复权价格，消除份额折算（拆分）对收益率的影响。
+        fund_adj 单次限 2000 条，按 5 年分段请求再拼接。
+        
+        Args:
+            code: ETF代码，如 '159915.SZ', '518880.SH'
+            start_date: 开始日期 'YYYY-MM-DD'
+            end_date: 结束日期 'YYYY-MM-DD'
+        
+        Returns:
+            DataFrame with columns: date, open, close, adj_factor, close_hfq
+        """
+        import tushare as ts
+        from datetime import datetime
+        
+        original_proxy = self._clear_proxy()
+        
+        try:
+            pro = self._get_pro_api()
+            ts_code = code.replace('.SS', '.SH')
+            
+            # 获取 fund_daily 数据
+            df_daily = pro.fund_daily(
+                ts_code=ts_code,
+                start_date=start_date.replace('-', ''),
+                end_date=end_date.replace('-', '')
+            )
+            
+            if df_daily is None or len(df_daily) == 0:
+                return None
+            
+            # 获取 fund_adj 数据（分段请求，单次限2000条）
+            # 按5年分段
+            start_dt = datetime.strptime(start_date, '%Y-%m-%d')
+            end_dt = datetime.strptime(end_date, '%Y-%m-%d')
+            
+            adj_chunks = []
+            chunk_start = start_dt
+            while chunk_start < end_dt:
+                chunk_end = min(chunk_start.replace(year=chunk_start.year + 5), end_dt)
+                chunk_start_str = chunk_start.strftime('%Y%m%d')
+                chunk_end_str = chunk_end.strftime('%Y%m%d')
+                
+                df_adj_chunk = pro.fund_adj(
+                    ts_code=ts_code,
+                    start_date=chunk_start_str,
+                    end_date=chunk_end_str
+                )
+                
+                if df_adj_chunk is not None and len(df_adj_chunk) > 0:
+                    adj_chunks.append(df_adj_chunk)
+                
+                chunk_start = chunk_end
+            
+            if not adj_chunks:
+                # 无复权因子，返回原始数据
+                df = df_daily.rename(columns={'trade_date': 'date', 'vol': 'volume'})
+                df['date'] = pd.to_datetime(df['date'])
+                df = df.set_index('date').sort_index()
+                df['adj_factor'] = 1.0
+                df['close_hfq'] = df['close']
+                return df[['code', 'open', 'close', 'adj_factor', 'close_hfq']]
+            
+            # 合并所有复权因子
+            df_adj = pd.concat(adj_chunks, ignore_index=True)
+            df_adj = df_adj.rename(columns={'trade_date': 'date'})
+            df_adj['date'] = pd.to_datetime(df_adj['date'])
+            df_adj = df_adj.set_index('date').sort_index()
+            
+            # 合并 daily 和 adj
+            df_daily = df_daily.rename(columns={'trade_date': 'date', 'vol': 'volume'})
+            df_daily['date'] = pd.to_datetime(df_daily['date'])
+            df_daily = df_daily.set_index('date').sort_index()
+            
+            # 复权因子对齐（用最新值）
+            df_adj_aligned = df_adj.reindex(df_daily.index, method='ffill')
+            df_adj_aligned['adj_factor'] = df_adj_aligned['adj_factor'].fillna(1.0)
+            
+            # 计算后复权价格
+            df = df_daily.copy()
+            df['adj_factor'] = df_adj_aligned['adj_factor']
+            df['close_hfq'] = df['close'] * df['adj_factor']
+            df['code'] = code
+            
+            return df[['code', 'open', 'close', 'adj_factor', 'close_hfq']]
+            
+        except Exception as e:
+            print(f"Tushare下载ETF复权数据 {code} 失败: {e}")
+            return None
+            
+        finally:
+            self._restore_proxy(original_proxy)
+
+    def fetch_trade_cal(self, start_date: str, end_date: str) -> pd.DatetimeIndex:
+        """
+        获取 A 股（上交所 SSE）官方交易日历
+        
+        Args:
+            start_date: 开始日期 'YYYY-MM-DD'
+            end_date: 结束日期 'YYYY-MM-DD'
+        
+        Returns:
+            DatetimeIndex: A股交易日日期序列
+        """
+        import tushare as ts
+        
+        original_proxy = self._clear_proxy()
+        
+        try:
+            pro = self._get_pro_api()
+            
+            df = pro.trade_cal(
+                exchange='SSE',
+                start_date=start_date.replace('-', ''),
+                end_date=end_date.replace('-', ''),
+                is_open='1'
+            )
+            
+            if df is None or len(df) == 0:
+                return pd.DatetimeIndex([])
+            
+            # 提取交易日并转换为 DatetimeIndex
+            trade_dates = pd.to_datetime(df['cal_date'])
+            return pd.DatetimeIndex(trade_dates.sort_values())
+            
+        except Exception as e:
+            print(f"Tushare下载交易日历失败: {e}")
+            return pd.DatetimeIndex([])
+            
+        finally:
+            self._restore_proxy(original_proxy)