fix: 改为每次请求一支股票，间隔9秒

- 移除批量请求逻辑
- 每次只请求一支股票
- 请求完成后休息9秒再请求下一支
- 显示预估耗时

fetch_history.py

@@ -24,11 +24,8 @@ LOGS_DIR.mkdir(exist_ok=True)
 START_DATE = '20100101'
 END_DATE = datetime.now().strftime('%Y%m%d')
 
-# 每批次获取的股票数量（tushare限制）
-BATCH_SIZE = 50
-
-# 请求间隔（秒）- 避免频繁请求
-REQUEST_INTERVAL = 0.3
+# 请求间隔（秒）- tushare积分限制
+REQUEST_INTERVAL = 9
 
 
 def setup_tushare(token=None):
@@ -94,29 +91,32 @@ def get_stock_codes_with_suffix(df):
 
 
 def fetch_daily_data(pro, codes, start_date, end_date):
-    """批量获取日线数据"""
+    """逐个获取日线数据（每次一支股票）"""
     all_data = []
     total = len(codes)
     
-    for i in range(0, total, BATCH_SIZE):
-        batch_codes = codes[i:i + BATCH_SIZE]
-        ts_codes = ','.join(batch_codes)
-        
+    print(f"\n共 {total} 只股票需要获取")
+    print(f"预计耗时: {total * REQUEST_INTERVAL / 60:.1f} 分钟")
+    print("-" * 50)
+    
+    for i, ts_code in enumerate(codes):
         try:
-            print(f"获取第 {i+1}-{min(i+BATCH_SIZE, total)} 只股票数据...")
-            df = pro.daily(ts_code=ts_codes, start_date=start_date, end_date=end_date)
+            print(f"[{i+1}/{total}] 获取 {ts_code}...", end=' ')
+            
+            df = pro.daily(ts_code=ts_code, start_date=start_date, end_date=end_date)
             
             if df is not None and len(df) > 0:
                 all_data.append(df)
-                print(f"  成功获取 {len(df)} 条记录")
+                print(f"成功，{len(df)} 条记录")
             else:
-                print(f"  无数据")
+                print("无数据")
                 
         except Exception as e:
-            print(f"  错误: {e}")
+            print(f"错误: {e}")
             
-        # 避免请求过快
-        time.sleep(REQUEST_INTERVAL)
+        # 每次请求后休息9秒
+        if i < total - 1:  # 最后一个不需要等待
+            time.sleep(REQUEST_INTERVAL)
     
     return all_data