🛠️ refactor(data_downloader.py): 修正更新时间逻辑，并修复更新模式查询逻辑

data_downloader.py

@@ -339,7 +339,7 @@ def split_list_into_chunks(data_list, num_chunks):
     return chunks
 
 
-def process_stock_batch(batch, config, update_mode, start_date, end_date, update_time):
+def process_stock_batch(batch, config, update_mode, start_date, end_date):
     """
     处理一批股票的工作函数(在单独进程中运行)
 
@@ -349,7 +349,6 @@ def process_stock_batch(batch, config, update_mode, start_date, end_date, update
         update_mode: 更新模式 'full' 或 'incremental'
         start_date: 开始日期
         end_date: 结束日期
-        update_time: 当前更新时间戳
 
     Returns:
         dict: 包含处理结果统计信息的字典
@@ -373,6 +372,8 @@ def process_stock_batch(batch, config, update_mode, start_date, end_date, update
     total_stocks = len(batch)
 
     for i, stock_info in enumerate(batch):
+        # 当前更新时间
+        update_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
         # 根据更新模式获取不同的参数
         if update_mode == 'full':
             ts_code = stock_info
@@ -588,7 +589,7 @@ def perform_full_update(start_year=2020, processes=4, resume=False):
             query = """
             SELECT ts_code
             FROM stock_metadata
-            WHERE list_status = 'L' AND status = 2
+            WHERE list_status = 'L' AND status != 1 
             """
         else:
             # 查询所有已上市的股票
@@ -610,9 +611,6 @@ def perform_full_update(start_year=2020, processes=4, resume=False):
 
     print(f"共找到 {len(stock_codes)} 只股票需要{update_type}")
 
-    # 当前更新时间
-    update_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
-
     # 将股票列表分成多个批次
     batches = split_list_into_chunks(stock_codes, processes)
 
@@ -624,8 +622,7 @@ def perform_full_update(start_year=2020, processes=4, resume=False):
             config=config,
             update_mode='full',
             start_date=start_date,
-            end_date=end_date,
-            update_time=update_time
+            end_date=end_date
         )
 
         # 启动多进程处理并收集结果