优化获取股票最新价格的函数，增加重试机制和错误处理，改进缓存逻辑以提高稳定性

us_stock.py

@@ -155,8 +155,14 @@ def reduce_columns(df, columns_to_keep):
 # 创建缓存字典
 _price_cache = {}
 
-def get_last_minute_stock_price(symbol: str) -> float:
-    """获取股票最新价格，使用30分钟缓存"""
+def get_last_minute_stock_price(symbol: str, max_retries=3) -> float:
+    """获取股票最新价格，使用30分钟缓存，并包含重试机制"""
+
+    if not symbol:
+        return -1.0
+    if symbol == "NONE_SYMBOL_FOUND":
+        return -1.0
+    
     current_time = datetime.now()
     
     # 检查缓存
@@ -166,17 +172,39 @@ def get_last_minute_stock_price(symbol: str) -> float:
         if current_time - cached_time < timedelta(minutes=30):
             return cached_price
 
-    # 缓存无效或不存在，从yfinance获取新数据
-    stock_data = yfinance.download(symbol, period='1d', interval='5m')
-    if stock_data.empty:
-        return -1.0
-    
-    latest_price = float(stock_data['Close'].iloc[-1])
-    
-    # 更新缓存
-    _price_cache[symbol] = (latest_price, current_time)
+    # 重试机制
+    for attempt in range(max_retries):
+        try:
+            # 缓存无效或不存在，从yfinance获取新数据
+            stock_data = yfinance.download(
+                symbol, 
+                period='1d', 
+                interval='5m',
+                progress=False,  # 禁用进度条
+                timeout=10  # 设置超时时间
+            )
+            
+            if stock_data.empty:
+                print(f"Warning: Empty data received for {symbol}, attempt {attempt + 1}/{max_retries}")
+                if attempt == max_retries - 1:
+                    return -1.0
+                time.sleep(1)  # 等待1秒后重试
+                continue
+                
+            latest_price = float(stock_data['Close'].iloc[-1])
+            
+            # 更新缓存
+            _price_cache[symbol] = (latest_price, current_time)
+            
+            return latest_price
+
+        except Exception as e:
+            print(f"Error fetching price for {symbol}, attempt {attempt + 1}/{max_retries}: {str(e)}")
+            if attempt == max_retries - 1:
+                return -1.0
+            time.sleep(1)  # 等待1秒后重试
     
-    return latest_price
+    return -1.0
 
 # 返回个股历史数据
 def get_stock_history(symbol, news_date, retries=10):