commit;

manage-instances.sh

@@ -11,7 +11,7 @@ show_help() {
     echo "  start [实例ID]        启动指定实例或所有实例"
     echo "  stop [实例ID]         停止指定实例或所有实例"
     echo "  restart [实例ID]      重启指定实例或所有实例"
-    echo "  logs [实例ID]         查看指定实例的日志"
+    echo "  logs [实例ID]         实时查看指定实例的日志 (Ctrl+C 退出)"
     echo "  status               显示实例状态概览"
     echo "  remove [实例ID]       删除指定实例或所有实例"
     echo "  rebuild [数量]        重新构建镜像并部署指定数量的实例"
@@ -68,8 +68,9 @@ restart_instance() {
 
 # 函数：查看实例日志
 view_logs() {
-    echo "实例 $1 的日志:"
+    echo "正在实时显示实例 $1 的日志 (按 Ctrl+C 退出):"
-    docker logs stock-app-$1
+    echo "----------------------------------------"
+    docker logs -f stock-app-$1
 }
 
 # 函数：显示状态概览

src/app.py

@@ -2897,21 +2897,64 @@ def get_pep_stock_info_by_shortname():
 
 @app.route('/api/pep_stock_info_by_code', methods=['GET'])
 def get_pep_stock_info_by_code():
-    """根据股票代码查询Redis中的实时行情并返回指定结构"""
+    """根据股票代码查询Redis中的实时行情并返回指定结构，支持A股和港股"""
     short_code = request.args.get('code')
     if not short_code:
-        return jsonify({'success': False, 'message': '缺少必要参数: short_code'}), 400
+        return jsonify({'success': False, 'message': '缺少必要参数: code'}), 400
+    
     try:
-        # 兼容600001.SH/SH600001等格式
+        # 判断股票类型并调用相应的查询函数
-        from src.quantitative_analysis.batch_stock_price_collector import get_stock_realtime_info_from_redis
+        if is_hk_stock_code(short_code):
-        result = get_stock_realtime_info_from_redis(short_code)
+            # 港股代码
-        if result:
+            from src.quantitative_analysis.hk_stock_price_collector import get_hk_stock_realtime_info_from_redis
-            return jsonify(result)
+            result = get_hk_stock_realtime_info_from_redis(short_code)
+            if result:
+                return jsonify(result)
+            else:
+                return jsonify({'success': False, 'message': f'未找到港股 {short_code} 的实时行情'}), 404
         else:
-            return jsonify({'success': False, 'message': f'未找到股票 {short_code} 的实时行情'}), 404
+            # A股代码
+            from src.quantitative_analysis.batch_stock_price_collector import get_stock_realtime_info_from_redis
+            result = get_stock_realtime_info_from_redis(short_code)
+            if result:
+                return jsonify(result)
+            else:
+                return jsonify({'success': False, 'message': f'未找到A股 {short_code} 的实时行情'}), 404
     except Exception as e:
         return jsonify({'success': False, 'message': f'服务器错误: {str(e)}'}), 500
 
+def is_hk_stock_code(stock_code):
+    """
+    判断是否为港股代码
+    支持格式：00700, 00700.HK, HK00700, 0700.HK等
+    """
+    if not stock_code:
+        return False
+    
+    stock_code = stock_code.upper().strip()
+    
+    # 港股代码特征：
+    # 1. 包含.HK后缀
+    if '.HK' in stock_code:
+        return True
+    
+    # 2. 以HK开头
+    if stock_code.startswith('HK'):
+        return True
+    
+    # 3. 纯数字且长度为4-5位（港股代码通常是4-5位数字）
+    if stock_code.isdigit() and 4 <= len(stock_code) <= 5:
+        # 进一步判断：港股代码通常以0、1、2、3、6、8、9开头
+        if stock_code[0] in ['0', '1', '2', '3', '6', '8', '9']:
+            return True
+    
+    # 4. 特殊港股代码（如腾讯00700、阿里巴巴09988等）
+    common_hk_codes = ['00700', '09988', '03690', '09888', '06618', '02318', '02020', '01810']
+    if stock_code in common_hk_codes:
+        return True
+    
+    return False
+
 @app.route('/api/industry/crowding/filter', methods=['GET'])
 def filter_industry_crowding():
     """根据拥挤度百分位区间筛选行业和概念板块"""
@@ -2991,7 +3034,7 @@ def get_momentum_by_plate():
 def run_batch_stock_price_collection():
     """批量采集A股行情并保存到数据库"""
     try:
-        fetch_and_store_stock_data()
+        fetch_and_store_stock_data(use_proxy=True)
         return jsonify({"status": "success", "message": "批量采集A股行情并保存到数据库成功"})
     except Exception as e:
         logger.error(f"批量采集A股行情失败: {str(e)}")
@@ -3001,7 +3044,7 @@ def run_batch_stock_price_collection():
 def run_batch_hk_stock_price_collection():
     """批量采集港股行情并保存到数据库"""
     try:
-        fetch_and_store_hk_stock_data()
+        fetch_and_store_hk_stock_data(use_proxy=True)
         return jsonify({"status": "success", "message": "批量采集A股行情并保存到数据库成功"})
     except Exception as e:
         logger.error(f"批量采集A股行情失败: {str(e)}")

src/quantitative_analysis/batch_stock_price_collector.py

@@ -5,14 +5,14 @@ import sys
 import os
 import redis
 import json
+import threading
+from concurrent.futures import ThreadPoolExecutor, as_completed
+import time
 
 # 添加项目根目录到路径，便于导入scripts.config
 project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 sys.path.append(project_root)
 
-# 导入代理管理器
-from src.scripts.ProxyIP import EnhancedProxyManager
-
 # 读取雪球headers和Redis配置
 try:
     from src.scripts.config import XUEQIU_HEADERS
@@ -31,8 +31,13 @@ except ImportError:
 
 REDIS_KEY = 'xq_stock_changes_latest'  # 存放行情的主键
 
-# 创建全局代理管理器实例
+# 条件导入代理管理器
-proxy_manager = EnhancedProxyManager()
+proxy_manager = None
+try:
+    from src.scripts.ProxyIP import EnhancedProxyManager
+    proxy_manager = EnhancedProxyManager()
+except ImportError:
+    print("代理管理器导入失败，将使用直接请求模式")
 
 
 def get_redis_conn():
@@ -47,58 +52,130 @@ def get_redis_conn():
     return redis.Redis(connection_pool=pool)
 
 
-def fetch_and_store_stock_data(page_size=90):
+def fetch_and_store_stock_data(page_size=90, max_workers=10, use_proxy=False):
     """
     批量采集雪球A股（上证、深证、科创板）股票的最新行情数据，并保存到Redis。
+    使用线程池并行请求，提高采集效率。
+    
     :param page_size: 每页采集数量
+    :param max_workers: 线程池最大工作线程数
+    :param use_proxy: 是否使用代理（默认False）
     """
     base_url = 'https://stock.xueqiu.com/v5/stock/screener/quote/list.json'
     types = ['sha', 'sza', 'kcb']  # 上证、深证、科创板
     headers = XUEQIU_HEADERS
 
     all_data = []
+    data_lock = threading.Lock()  # 线程安全锁
 
-    for stock_type in types:
+    def fetch_page_data(stock_type, page):
+        """获取单页数据的函数"""
         params = {
-            'page': 1,
+            'page': page,
             'size': page_size,
             'order': 'desc',
             'order_by': 'dividend_yield',
             'market': 'CN',
             'type': stock_type
         }
-
+        
-        # 初次请求以获取总页数，使用代理
+        try:
-        response = proxy_manager.request_with_proxy('get', base_url, headers=headers, params=params)
+            # 根据配置选择是否使用代理
-        # response = requests.get(base_url, headers=headers, params=params)
+            if use_proxy and proxy_manager:
-        if response.status_code != 200:
+                response = proxy_manager.request_with_proxy('get', base_url, headers=headers, params=params)
-            print(f"请求 {stock_type} 数据失败，状态码：{response.status_code}")
+            else:
-            continue
+                response = requests.get(base_url, headers=headers, params=params, timeout=10)
-
+                
-        data = response.json()
-        total_count = data['data']['count']
-        total_pages = (total_count // page_size) + 1
-
-        for page in range(1, total_pages + 1):
-            params['page'] = page
-            # response = requests.get(base_url, headers=headers, params=params)
-            response = proxy_manager.request_with_proxy('get', base_url, headers=headers, params=params)
             if response.status_code == 200:
                 data = response.json()
-                all_data.extend(data['data']['list'])
+                page_data = data['data']['list']
-                print(f"成功采集第 {page}/{total_pages} 页数据")
+                
+                # 线程安全地添加数据
+                with data_lock:
+                    all_data.extend(page_data)
+                
+                print(f"成功采集 {stock_type} 第 {page} 页数据，获取 {len(page_data)} 条记录")
+                return len(page_data)
             else:
                 print(f"请求 {stock_type} 数据第 {page} 页失败，状态码：{response.status_code}")
+                return 0
+        except Exception as e:
+            print(f"请求 {stock_type} 数据第 {page} 页异常：{e}")
+            return 0
+
+    # 使用线程池并行采集数据
+    with ThreadPoolExecutor(max_workers=max_workers) as executor:
+        futures = []
+        
+        for stock_type in types:
+            # 先获取总页数
+            params = {
+                'page': 1,
+                'size': page_size,
+                'order': 'desc',
+                'order_by': 'dividend_yield',
+                'market': 'CN',
+                'type': stock_type
+            }
+            
+            try:
+                # 根据配置选择是否使用代理
+                if use_proxy and proxy_manager:
+                    response = proxy_manager.request_with_proxy('get', base_url, headers=headers, params=params)
+                else:
+                    response = requests.get(base_url, headers=headers, params=params, timeout=10)
+                    
+                if response.status_code != 200:
+                    print(f"请求 {stock_type} 数据失败，状态码：{response.status_code}")
+                    continue
+                
+                data = response.json()
+                total_count = data['data']['count']
+                total_pages = (total_count // page_size) + 1
+                
+                print(f"开始采集 {stock_type} 数据，共 {total_pages} 页，总计 {total_count} 条记录")
+                
+                # 提交所有页面的采集任务
+                for page in range(1, total_pages + 1):
+                    future = executor.submit(fetch_page_data, stock_type, page)
+                    futures.append(future)
+                    
+            except Exception as e:
+                print(f"获取 {stock_type} 总页数失败：{e}")
+                continue
+        
+        # 等待所有任务完成
+        print(f"正在并行采集数据，使用 {max_workers} 个线程...")
+        start_time = time.time()
+        
+        completed_count = 0
+        for future in as_completed(futures):
+            completed_count += 1
+            try:
+                result = future.result()
+                if result > 0:
+                    print(f"进度: {completed_count}/{len(futures)} 页完成")
+            except Exception as e:
+                print(f"采集任务异常：{e}")
+        
+        end_time = time.time()
+        print(f"数据采集完成，耗时: {end_time - start_time:.2f} 秒")
+
     # 转换为 DataFrame
     df = pd.DataFrame(all_data)
 
     if not df.empty:
         df['fetch_time'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+        
         # 存入Redis，使用hash结构，key为symbol，value为json字符串
         r = get_redis_conn()
         pipe = r.pipeline()
+        
         # 先清空旧数据
         r.delete(REDIS_KEY)
+        
+        print(f"正在将 {len(df)} 条记录写入Redis...")
+        
         for _, row in df.iterrows():
             symbol = row.get('symbol')
             if not symbol:
@@ -106,6 +183,7 @@ def fetch_and_store_stock_data(page_size=90):
             # 只保留必要字段，也可直接存row.to_dict()
             value = row.to_dict()
             pipe.hset(REDIS_KEY, symbol, json.dumps(value, ensure_ascii=False))
+        
         pipe.execute()
         print(f"成功将数据写入Redis哈希 {REDIS_KEY}，共{len(df)}条记录。")
         
@@ -192,5 +270,35 @@ def get_stock_realtime_info_from_redis(stock_code):
     return result
 
 
+def fetch_and_store_stock_data_optimized(page_size=90, max_workers=15, use_proxy=False):
+    """
+    优化版本的批量采集函数，支持更灵活的配置
+    
+    :param page_size: 每页采集数量
+    :param max_workers: 线程池最大工作线程数（建议10-20之间）
+    :param use_proxy: 是否使用代理（默认False）
+    """
+    print(f"开始批量采集A股数据...")
+    print(f"配置: 每页 {page_size} 条记录，最大线程数 {max_workers}")
+    print(f"代理模式: {'启用' if use_proxy else '禁用'}")
+    print(f"预计采集: 上证、深证、科创板所有股票数据")
+    print("-" * 50)
+    
+    try:
+        result = fetch_and_store_stock_data(page_size, max_workers, use_proxy)
+        if not result.empty:
+            print(f"采集完成！共获取 {len(result)} 只股票的数据")
+            print(f"数据已保存到Redis键: {REDIS_KEY}")
+        else:
+            print("采集完成，但未获取到数据")
+    except Exception as e:
+        print(f"采集过程中发生错误: {e}")
+        return None
+    
+    return result
+
+
 if __name__ == '__main__':
-    fetch_and_store_stock_data() 
+    # 可以根据需要调整参数
+    # fetch_and_store_stock_data_optimized(page_size=100, max_workers=15, use_proxy=True)
+    fetch_and_store_stock_data_optimized(use_proxy=False)  # 默认不使用代理 

src/quantitative_analysis/hk_stock_price_collector.py

@@ -5,14 +5,14 @@ import sys
 import os
 import redis
 import json
+import threading
+from concurrent.futures import ThreadPoolExecutor, as_completed
+import time
 
 # 添加项目根目录到路径，便于导入scripts.config
 project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 sys.path.append(project_root)
 
-# 导入代理管理器
-from src.scripts.ProxyIP import EnhancedProxyManager
-
 # 读取雪球headers和Redis配置
 try:
     from src.scripts.config import XUEQIU_HEADERS
@@ -31,8 +31,13 @@ except ImportError:
 
 REDIS_KEY = 'xq_hk_stock_changes_latest'  # 存放港股行情的主键
 
-# 创建全局代理管理器实例
+# 条件导入代理管理器
-proxy_manager = EnhancedProxyManager()
+proxy_manager = None
+try:
+    from src.scripts.ProxyIP import EnhancedProxyManager
+    proxy_manager = EnhancedProxyManager()
+except ImportError:
+    print("代理管理器导入失败，将使用直接请求模式")
 
 
 def get_redis_conn():
@@ -47,67 +52,128 @@ def get_redis_conn():
     return redis.Redis(connection_pool=pool)
 
 
-def fetch_and_store_hk_stock_data(page_size=90):
+def fetch_and_store_hk_stock_data(page_size=90, max_workers=10, use_proxy=False):
     """
     批量采集雪球港股所有股票的最新行情数据，并保存到Redis。
+    使用线程池并行请求，提高采集效率。
+    
     :param page_size: 每页采集数量
+    :param max_workers: 线程池最大工作线程数
+    :param use_proxy: 是否使用代理（默认False）
     """
     base_url = 'https://stock.xueqiu.com/v5/stock/screener/quote/list.json'
     headers = XUEQIU_HEADERS
 
     all_data = []
+    data_lock = threading.Lock()  # 线程安全锁
 
-    # 使用港股API参数
+    def fetch_page_data(page):
+        """获取单页数据的函数"""
+        params = {
+            'page': page,
+            'size': page_size,
+            'order': 'desc',
+            'order_by': 'dividend_yield',
+            'market': 'HK',  # 港股市场
+            'type': 'hk'     # 港股类型
+        }
+        
+        try:
+            # 根据配置选择是否使用代理
+            if use_proxy and proxy_manager:
+                response = proxy_manager.request_with_proxy('get', base_url, headers=headers, params=params)
+            else:
+                response = requests.get(base_url, headers=headers, params=params, timeout=10)
+                
+            if response.status_code == 200:
+                data = response.json()
+                page_data = data['data']['list']
+                
+                # 线程安全地添加数据
+                with data_lock:
+                    all_data.extend(page_data)
+                
+                print(f"成功采集港股第 {page} 页数据，获取 {len(page_data)} 条记录")
+                return len(page_data)
+            else:
+                print(f"请求港股数据第 {page} 页失败，状态码：{response.status_code}")
+                return 0
+        except Exception as e:
+            print(f"请求港股数据第 {page} 页异常：{e}")
+            return 0
+
+    # 先获取总页数
     params = {
         'page': 1,
         'size': page_size,
         'order': 'desc',
         'order_by': 'dividend_yield',
-        'market': 'HK',  # 港股市场
+        'market': 'HK',
-        'type': 'hk'     # 港股类型
+        'type': 'hk'
     }
-
+    
-    # 初次请求以获取总页数，使用代理
     try:
-        response = proxy_manager.request_with_proxy('get', base_url, headers=headers, params=params)
+        # 根据配置选择是否使用代理
+        if use_proxy and proxy_manager:
+            response = proxy_manager.request_with_proxy('get', base_url, headers=headers, params=params)
+        else:
+            response = requests.get(base_url, headers=headers, params=params, timeout=10)
+            
         if response.status_code != 200:
             print(f"请求港股数据失败，状态码：{response.status_code}")
-            return
+            return pd.DataFrame()
+        
+        data = response.json()
+        total_count = data['data']['count']
+        total_pages = (total_count // page_size) + 1
+        
+        print(f"开始采集港股数据，共 {total_pages} 页，总计 {total_count} 条记录")
+        
+        # 使用线程池并行采集数据
+        with ThreadPoolExecutor(max_workers=max_workers) as executor:
+            futures = []
+            
+            # 提交所有页面的采集任务
+            for page in range(1, total_pages + 1):
+                future = executor.submit(fetch_page_data, page)
+                futures.append(future)
+            
+            # 等待所有任务完成
+            print(f"正在并行采集港股数据，使用 {max_workers} 个线程...")
+            start_time = time.time()
+            
+            completed_count = 0
+            for future in as_completed(futures):
+                completed_count += 1
+                try:
+                    result = future.result()
+                    if result > 0:
+                        print(f"进度: {completed_count}/{len(futures)} 页完成")
+                except Exception as e:
+                    print(f"采集任务异常：{e}")
+            
+            end_time = time.time()
+            print(f"港股数据采集完成，耗时: {end_time - start_time:.2f} 秒")
+            
     except Exception as e:
-        print(f"请求港股数据时发生异常：{e}")
+        print(f"获取港股总页数失败：{e}")
-        return
+        return pd.DataFrame()
 
-    data = response.json()
-    total_count = data['data']['count']
-    total_pages = (total_count // page_size) + 1
-
-    print(f"开始采集港股数据，共 {total_pages} 页，{total_count} 条记录")
-
-    # 循环获取所有页面的数据
-    for page in range(1, total_pages + 1):
-        params['page'] = page
-        try:
-            response = proxy_manager.request_with_proxy('get', base_url, headers=headers, params=params)
-            if response.status_code == 200:
-                data = response.json()
-                all_data.extend(data['data']['list'])
-                print(f"成功采集港股第 {page}/{total_pages} 页数据")
-            else:
-                print(f"请求港股数据第 {page} 页失败，状态码：{response.status_code}")
-        except Exception as e:
-            print(f"请求港股数据第 {page} 页时发生异常：{e}")
-            continue
-    
     # 转换为 DataFrame
     df = pd.DataFrame(all_data)
 
     if not df.empty:
         df['fetch_time'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+        
         # 存入Redis，使用hash结构，key为symbol，value为json字符串
         r = get_redis_conn()
         pipe = r.pipeline()
+        
         # 先清空旧数据
         r.delete(REDIS_KEY)
+        
+        print(f"正在将 {len(df)} 条港股记录写入Redis...")
+        
         for _, row in df.iterrows():
             symbol = row.get('symbol')
             if not symbol:
@@ -115,10 +181,15 @@ def fetch_and_store_hk_stock_data(page_size=90):
             # 只保留必要字段，也可直接存row.to_dict()
             value = row.to_dict()
             pipe.hset(REDIS_KEY, symbol, json.dumps(value, ensure_ascii=False))
+        
         pipe.execute()
         print(f"成功将港股数据写入Redis哈希 {REDIS_KEY}，共{len(df)}条记录。")
+        
+        # 返回DataFrame供其他脚本使用
+        return df
     else:
         print("未获取到任何港股数据。")
+        return pd.DataFrame()
 
 
 def format_hk_stock_code(stock_code):
@@ -175,7 +246,7 @@ def get_hk_stock_realtime_info_from_redis(stock_code):
     result["crawlDate"] = data.get("fetch_time")
     result["marketValue"] = data.get("market_capital")
     result["maxPrice"] = data.get("high") if "high" in data else data.get("high52w")
-    result["minPrice"] = data.get("low") if "low" in data else data.get("low52w")
+    result["minPrice"] = data.get("low") if "low" in data else data.get("high52w")
     result["nowPrice"] = data.get("current")
     result["pbRate"] = data.get("pb")
     result["rangeRiseAndFall"] = data.get("percent")
@@ -193,5 +264,35 @@ def get_hk_stock_realtime_info_from_redis(stock_code):
     return result
 
 
+def fetch_and_store_hk_stock_data_optimized(page_size=90, max_workers=15, use_proxy=False):
+    """
+    优化版本的港股批量采集函数，支持更灵活的配置
+    
+    :param page_size: 每页采集数量
+    :param max_workers: 线程池最大工作线程数（建议10-20之间）
+    :param use_proxy: 是否使用代理（默认False）
+    """
+    print(f"开始批量采集港股数据...")
+    print(f"配置: 每页 {page_size} 条记录，最大线程数 {max_workers}")
+    print(f"代理模式: {'启用' if use_proxy else '禁用'}")
+    print(f"预计采集: 港股所有股票数据")
+    print("-" * 50)
+    
+    try:
+        result = fetch_and_store_hk_stock_data(page_size, max_workers, use_proxy)
+        if not result.empty:
+            print(f"港股采集完成！共获取 {len(result)} 只股票的数据")
+            print(f"数据已保存到Redis键: {REDIS_KEY}")
+        else:
+            print("港股采集完成，但未获取到数据")
+    except Exception as e:
+        print(f"港股采集过程中发生错误: {e}")
+        return None
+    
+    return result
+
+
 if __name__ == '__main__':
-    fetch_and_store_hk_stock_data() 
+    # 可以根据需要调整参数
+    # fetch_and_store_hk_stock_data_optimized(page_size=100, max_workers=15, use_proxy=True)
+    fetch_and_store_hk_stock_data_optimized(use_proxy=False)  # 默认不使用代理 

src/scripts/ProxyIP.py

@@ -339,4 +339,37 @@ class EnhancedProxyManager:
             'manual_proxies': self.redis_conn.hlen(manual_key),
             'auto_refresh': self.auto_refresh,
             'last_update': datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-        }
+        }
+    
+    def get_multiple_proxies(self, count: int = 5) -> List[Dict]:
+        """
+        获取多个代理，用于并发请求
+        
+        :param count: 需要的代理数量
+        :return: 代理列表
+        """
+        proxies = []
+        manual_key = self._get_redis_key('manual')
+        
+        # 获取手动代理池中的所有代理
+        manual_proxies = self.redis_conn.hgetall(manual_key)
+        active_proxies = []
+        
+        for proxy_json in manual_proxies.values():
+            proxy = json.loads(proxy_json)
+            if proxy.get('status') == 'active':
+                active_proxies.append(proxy)
+        
+        if not active_proxies:
+            return []
+        
+        # 随机选择指定数量的代理
+        selected_count = min(count, len(active_proxies))
+        selected_proxies = random.sample(active_proxies, selected_count)
+        
+        # 为每个代理添加Redis键信息
+        for proxy in selected_proxies:
+            proxy['_redis_key'] = self._get_redis_key(proxy['source'])
+            proxies.append(proxy)
+        
+        return proxies

src/scripts/config.py

@@ -11,7 +11,7 @@ XUEQIU_HEADERS = {
     'Accept-Encoding': 'gzip, deflate, br, zstd',
     'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8',
     'Client-Version': 'v2.44.75',
-    'Cookie': 'cookiesu=811743062689927; device_id=33fa3c7fca4a65f8f4354e10ed6b7470; smidV2=20250327160437f244626e8b47ca2a7992f30f389e4e790074ae48656a22f10; HMACCOUNT=8B64A2E3C307C8C0; s=c611ttmqlj; xq_is_login=1; u=8493411634; bid=4065a77ca57a69c83405d6e591ab5449_m8r2nhs8; __utma=1.434320573.1747189698.1747189698.1747189698.1; __utmc=1; __utmz=1.1747189698.1.1.utmcsr=(direct)|utmccn=(direct)|utmcmd=(none); snbim_minify=true; _c_WBKFRo=dsWgHR8i8KGPbIyhFlN51PHOzVuuNytvUAFppfkD; _nb_ioWEgULi=; xq_a_token=ada154d4707b8d3f8aa521ff0c960aa7f81cbf9e; xqat=ada154d4707b8d3f8aa521ff0c960aa7f81cbf9e; xq_id_token=eyJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiJ9.eyJ1aWQiOjg0OTM0MTE2MzQsImlzcyI6InVjIiwiZXhwIjoxNzU2MDAyNjgyLCJjdG0iOjE3NTM0MTA2ODI0MTQsImNpZCI6ImQ5ZDBuNEFadXAifQ.AlnzQSY7oGKGABfaQcFLg0lAJsDdvBMiwUbgpCMCBlbx6VZPKhzERxWiylQb4dFIyyECvRRJ73SbO9cD46fAqgzOgTxArNHtTKD4lQapTnyb11diDADnpb_nzzaRr4k_BYQRKXWtcJxdUMzde2WLy-eAkSf76QkXmKrwS3kvRm5gfqhdye44whw5XMEGoZ_lXHzGLWGz_PludHZp6W3v-wwZc_0wLU6cTb_KdrwWUWT_8jw5JHXnJEmuZmQI8QWf60DtiHIYCYXarxv8XtyHK7lLKhIAa3C2QmGWw5wv2HGz4I5DPqm2uMPKumgkQxycfAk56-RWviLZ8LAPF-XcbA; xq_r_token=92527e51353f90ba14d5fd16581e5a7a2780baa2; acw_tc=1a0c655917546366986673411e68d25d3c69c1719d6d1d6283c7271cc1529f; is_overseas=0; Hm_lvt_1db88642e346389874251b5a1eded6e3=1754636834; Hm_lpvt_1db88642e346389874251b5a1eded6e3=1754636837; .thumbcache_f24b8bbe5a5934237bbc0eda20c1b6e7=Hvg6Ac+qmPnDgzOvFuCePWwm7reK8TPoE9ayL8cyLnFg+Jhg1RJO2WnkeH2T8Q18+iV9bDh+UAq222GxdelHBg%3D%3D; ssxmod_itna=1-eqGxBDnGKYuxcD4kDRgxYq7ueYKS8DBP01Dp2xQyP08D60DB40Q0P6Dw1PtDCuqbKOOQYMxPsMKjqDsqze4GzDiLPGhDBWAFdYjdqN4NCtAoqzWWF2ruqe8bOZqKKFS96SM6sXUGQKhexGLDY=DCuXiieGGU4GwDGoD34DiDDpLD03Db4D_nWrD7ORQMluokjeDQ4GyDiUk3ObDm4DfDDLorA6osQ4DGqDSFcyTxD3DfRb4DDN4CIDu_mDDbObt5jcbUx7OBCGxIeDMixGXzGC4InyRNvDrgjMXvzEKH1aDtqD9_au4XxKdr3NEAEP4KGGpC0inpge_5neOQDqix1oeee4eQvxQ5O7Gv0DOGDz0G4ix_jwP_RUWjiihW9PeGAShXZ=E/ZND6q3mi40weUmXjmvYIzSQzWDW9wsemhYedCrwihQYbKYvWRD3YD; ssxmod_itna2=1-eqGxBDnGKYuxcD4kDRgxYq7ueYKS8DBP01Dp2xQyP08D60DB40Q0P6Dw1PtDCuqbKOOQYMxPsMKe4DWhzmxhTKRDjR_xWs_DDs6KmhfHjRKnZkBxNA3TIO4Arip5wU2kO0SwUfkEzryfSk6Rzud3ARD49fiKFd344obYvCv1lxYhY3qdzQe3vWD',
+    'Cookie': 'cookiesu=811743062689927; device_id=33fa3c7fca4a65f8f4354e10ed6b7470; smidV2=20250327160437f244626e8b47ca2a7992f30f389e4e790074ae48656a22f10; HMACCOUNT=8B64A2E3C307C8C0; s=c611ttmqlj; xq_is_login=1; u=8493411634; bid=4065a77ca57a69c83405d6e591ab5449_m8r2nhs8; __utma=1.434320573.1747189698.1747189698.1747189698.1; __utmc=1; __utmz=1.1747189698.1.1.utmcsr=(direct)|utmccn=(direct)|utmcmd=(none); snbim_minify=true; _c_WBKFRo=dsWgHR8i8KGPbIyhFlN51PHOzVuuNytvUAFppfkD; _nb_ioWEgULi=; Hm_lvt_1db88642e346389874251b5a1eded6e3=1754636834; xq_a_token=4ea8af8f9cb5850af2ba654c5255cbf6bf797b39; xqat=4ea8af8f9cb5850af2ba654c5255cbf6bf797b39; xq_id_token=eyJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiJ9.eyJ1aWQiOjg0OTM0MTE2MzQsImlzcyI6InVjIiwiZXhwIjoxNzU3NjM3OTA4LCJjdG0iOjE3NTUwNDU5MDg0ODksImNpZCI6ImQ5ZDBuNEFadXAifQ.jAeKlW2r1xRuyoZ3cuy2rTgfSoW_79wGJeuup7I7sZMSH5QeRDJrGx5JWXO4373YRpNW0qnAXR51Ygd8Plmko1u99iN8LifGzyMtblXDPgs17aS0zyHr6cMAsURU984wCXkmZxdvRMCHdevc8XWNHnuqeGfQNSgBSdO6Zv7Xc5-t965TJba96UOsNBpv2GghV9B2mcrUQyW3edi9kRAN_Fxmx5M1Iri4Yfppcaj-VSZYkdZtUpizrN5BbVYujcnQjj4kceUYYAl3Ccs273KVNSMFKpHMIOJcMJATY6PRgLvbEu8_ttIfBnbG4mmZ71bU7RXigleXIj1qhcDL2rDzQQ; xq_r_token=2b5db3e3897cb3e46b8fa2fa384471b334ec59cb; acw_tc=ac11000117550489614555169ef3ec63ec008e1bfba0fe5321bc8a30c2deb8; Hm_lpvt_1db88642e346389874251b5a1eded6e3=1755050072; .thumbcache_f24b8bbe5a5934237bbc0eda20c1b6e7=kE/XuROkIJ4APDhfxUYOb9lRiDFNJT8KxiXYwJAuoCeNlkaxlcytBSuiCXGjqxhydALLguC/FB4qIXfLut408Q%3D%3D; ssxmod_itna=1-eqGxBDnGKYuxcD4kDRgxYq7ueYKS8DBP01Dp2xQyP08D60DB40Q0P6Dw1PtDCuuqxGeR0qxPehAjqDsqze4GzDiLPGhDBWAFdYjw7ivq8RG5pxMBrGHuhLMEzoiqds=iCpxusTh1K/Zjw=KmoeK4xGLDY=DCTKq1QeD4S3Dt4DIDAYDDxDWU4DLDYoDY3nYxGP=xpWTcmRbD0YDzqDgUEe=xi3DA4DjnehqYiTdwDDBDGtO=9aDG4GfSmDD0wDLoGQQoDGWnCneE6mkiFIr6TTDjqPD/Shc59791vGW56CM9zo3paFDtqD90aAFn=GrvFaE_n93e4F4qibH7GYziTmrzt4xmrKi44mBDmAQQ0TKe4Bxq3DPQDhtTH7OY8FYS_Qqx4Gn/lHDcnDd7YerPCYC70bYbC42q3i8WTx3e8/rijIosDPRGrE0WdoqzGh_YPeD; ssxmod_itna2=1-eqGxBDnGKYuxcD4kDRgxYq7ueYKS8DBP01Dp2xQyP08D60DB40Q0P6Dw1PtDCuuqxGeR0qxPehAe4DWhYeRonANsR7vNG4g2BxpQTxTiD',
     'Referer': 'https://weibo.com/u/7735765253',
     'Sec-Ch-Ua': '"Chromium";v="122", "Not(A:Brand";v="24", "Google Chrome";v="122"',
     'Sec-Ch-Ua-Mobile': '?0',

src/scripts/stock_price_change_analyzer.py

@@ -184,55 +184,6 @@ def analyze_price_changes(db_url):
     except Exception as e:
         print("分析数据时发生错误: {}".format(str(e)))
 
-def get_previous_sector_ranks(engine):
-    """获取上一次的板块排名"""
-    try:
-        query = text("""
-            SELECT sector_name, rank_num
-            FROM sector_performance
-            WHERE DATE(add_time) = CURDATE()
-            ORDER BY add_time DESC
-            LIMIT 1
-        """)
-        
-        result = pd.read_sql_query(query, engine)
-        if result.empty:
-            return {}
-            
-        return dict(zip(result['sector_name'], result['rank_num']))
-    except Exception as e:
-        print("获取上一次排名数据时发生错误: {}".format(str(e)))
-        return {}
-
-def calculate_rank_change(row, previous_ranks):
-    """计算排名变化"""
-    previous_rank = previous_ranks.get(row['sector_name'])
-    if previous_rank is None:
-        return 0
-    return previous_rank - row['rank_num']
-
-def get_cache_mark():
-    """获取当前时间对应的缓存标记"""
-    current_minute = datetime.now().minute
-    mark = (current_minute % 10) // 2 * 2
-    return "{}m".format(mark)
-
-def save_sector_cache(engine, df_result, cache_mark):
-    """保存板块数据到缓存表"""
-    try:
-        df_cache = df_result.copy()
-        df_cache['cache_mark'] = cache_mark
-        
-        with engine.connect() as conn:
-            delete_query = text("DELETE FROM sector_performance_cache WHERE cache_mark = :cache_mark")
-            conn.execute(delete_query, {'cache_mark': cache_mark})
-            conn.commit()
-        
-        df_cache.to_sql('sector_performance_cache', con=engine, if_exists='append', index=False)
-        print(f"缓存数据已保存，标记: {cache_mark}")
-    except Exception as e:
-        print("保存缓存数据时发生错误: {}".format(str(e)))
-
 def main(db_url):
     """主函数"""
     engine = create_engine(db_url)

src/scripts/stock_sector_analysis.py

@@ -1,62 +1,74 @@
 # coding:utf-8
 
-import requests
 import pandas as pd
 from sqlalchemy import create_engine, text
 from datetime import datetime
-from config import XUEQIU_HEADERS
+import redis
+import json
+import sys
+import os
 
-def fetch_and_store_stock_data(db_url, table_name='stock_changes', page_size=90):
+# 添加项目根目录到路径，便于导入config
-    """获取雪球数据并保存到数据库"""
+project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-    base_url = 'https://stock.xueqiu.com/v5/stock/screener/quote/list.json'
+sys.path.append(project_root)
-    types = ['sha', 'sza', 'kcb']  # 数据类型
-    headers = XUEQIU_HEADERS
 
-    all_data = []
+# 读取配置
+try:
+    from src.valuation_analysis.config import REDIS_CONFIG
+except ImportError:
+    REDIS_CONFIG = {
+        'host': 'localhost',
+        'port': 6379,
+        'db': 0,
+        'password': None
+    }
 
-    for stock_type in types:
+REDIS_KEY = 'xq_stock_changes_latest'  # Redis中存放行情的主键
-        params = {
-            'page': 1,
-            'size': page_size,
-            'order': 'desc',
-            'order_by': 'percent',
-            'market': 'CN',
-            'type': stock_type
-        }
 
-        # 初次请求以获取总页数
+def get_redis_conn():
-        response = requests.get(base_url, headers=headers, params=params)
+    """获取Redis连接"""
-        if response.status_code != 200:
+    pool = redis.ConnectionPool(
-            print(f"请求 {stock_type} 数据失败，状态码：{response.status_code}")
+        host=REDIS_CONFIG['host'],
-            continue
+        port=REDIS_CONFIG['port'],
+        db=REDIS_CONFIG.get('db', 0),
+        password=REDIS_CONFIG.get('password', None),
+        decode_responses=True
+    )
+    return redis.Redis(connection_pool=pool)
 
-        data = response.json()
+def fetch_stock_data_from_redis():
-        total_count = data['data']['count']
+    """从Redis获取股票数据"""
-        total_pages = (total_count // page_size) + 1
+    try:
-
+        r = get_redis_conn()
-        for page in range(1, total_pages + 1):
+        
-            params['page'] = page
+        # 从Redis获取所有股票数据
-            response = requests.get(base_url, headers=headers, params=params)
+        all_stock_data = r.hgetall(REDIS_KEY)
-            if response.status_code == 200:
+        
-                data = response.json()
+        if not all_stock_data:
-                all_data.extend(data['data']['list'])
+            print("Redis中没有找到股票数据")
-            else:
+            return pd.DataFrame()
-                print(f"请求 {stock_type} 数据第 {page} 页失败，状态码：{response.status_code}")
+        
-    # 转换为 DataFrame
+        # 转换为DataFrame
-    df = pd.DataFrame(all_data)
+        stock_list = []
-
+        for symbol, value in all_stock_data.items():
-    if not df.empty:
+            try:
-        # 添加 id 列
+                stock_data = json.loads(value)
-        df['id'] = range(1, len(df) + 1)
+                stock_list.append(stock_data)
-
+            except json.JSONDecodeError:
-        # 创建数据库连接
+                print(f"解析股票数据失败: {symbol}")
-        engine = create_engine(db_url)
+                continue
-
+        
-        # 将数据写入数据库表
+        if stock_list:
-        df.to_sql(table_name, con=engine, if_exists='replace', index=False)
+            df = pd.DataFrame(stock_list)
-        print(f"成功将数据写入数据库表 {table_name}。")
+            print(f"从Redis成功获取 {len(df)} 条股票数据")
+            return df
     else:
-        print("未获取到任何数据。")
+            print("Redis数据解析失败")
+            return pd.DataFrame()
+            
+    except Exception as e:
+        print(f"从Redis获取数据时发生错误: {str(e)}")
+        return pd.DataFrame()
 
 def get_cache_mark():
     """获取当前时间对应的缓存标记（0m, 2m, 4m, 6m, 8m）"""
@@ -164,21 +176,43 @@ def get_high_performance_stocks(db_url):
         # 确保缓存表存在
         ensure_cache_table_exists(engine)
 
-        # 先获取最新数据
+        # 从Redis获取股票数据
-        fetch_and_store_stock_data(db_url)
+        print("从Redis获取股票数据...")
+        stock_changes_df = fetch_stock_data_from_redis()
+        
+        if stock_changes_df.empty:
+            print("Redis数据获取失败，无法进行分析")
+            return []
 
-        # 读取数据
+        print(f"Redis数据获取成功，共 {len(stock_changes_df)} 条记录")
-        stock_changes_df = pd.read_sql_table('stock_changes', con=engine)
+        
+        # 读取板块信息
         gp_gnbk_df = pd.read_sql_table('gp_gnbk', con=engine)
 
-        # 去掉 symbol 字段的前两个字符
+        # 处理symbol字段 - 根据Redis数据格式，symbol已经是完整格式（如SZ002916）
-        stock_changes_df['symbol'] = stock_changes_df['symbol'].str[2:]
+        if 'symbol' in stock_changes_df.columns:
+            # 保持原始格式用于关联板块表，因为板块表使用的是完整格式（如SZ000973）
+            stock_changes_df['symbol_clean'] = stock_changes_df['symbol']
+        else:
+            print("股票数据中缺少symbol字段")
+            return []
 
         # 筛选涨幅超过 1.5% 的股票
         high_performance_stocks = stock_changes_df[stock_changes_df['percent'] > 1.5]
+        print(f"筛选结果: {len(high_performance_stocks)} 只股票涨幅超过1.5%")
+        
+        if len(high_performance_stocks) == 0:
+            print("没有股票涨幅超过1.5%，尝试降低阈值到0.5%...")
+            high_performance_stocks = stock_changes_df[stock_changes_df['percent'] > 0.5]
+            print(f"筛选结果: {len(high_performance_stocks)} 只股票涨幅超过0.5%")
+            
+            if len(high_performance_stocks) == 0:
+                print("仍然没有股票涨幅超过0.5%，尝试获取所有上涨股票...")
+                high_performance_stocks = stock_changes_df[stock_changes_df['percent'] > 0]
+                print(f"筛选结果: {len(high_performance_stocks)} 只股票上涨")
 
         # 关联两个表，获取 bk_name
-        merged_df = high_performance_stocks.merge(gp_gnbk_df, left_on='symbol', right_on='gp_code')
+        merged_df = high_performance_stocks.merge(gp_gnbk_df, left_on='symbol_clean', right_on='gp_code')
 
         # 统计每个 bk_name 的数量
         total_counts = gp_gnbk_df['bk_name'].value_counts()
@@ -215,6 +249,7 @@ def get_high_performance_stocks(db_url):
         save_sector_cache(engine, df_result, cache_mark)
 
         # 输出结果
+        print("\n板块分析结果:")
         for _, row in df_result.iterrows():
             print("板块名称: {}, 上涨家数: {}, 总数: {}, 比重: {:.2%}, 排名: {}, 排名变化: {}".format(
                 row['sector_name'], row['up_count'], row['total_count'],
@@ -251,21 +286,31 @@ def get_top_industries_and_stocks(db_url, top_start=1, top_end=10):
         # 提取指定范围的行名称
         top_industry_names = [industry[0] for industry in top_industries[top_start-1:top_end]]
 
-        # 读取数据
+        # 从Redis获取股票数据
-        stock_changes_df = pd.read_sql_table('stock_changes', con=engine)
+        stock_changes_df = fetch_stock_data_from_redis()
+        if stock_changes_df.empty:
+            print("无法获取股票数据")
+            return
+            
+        # 读取板块信息
         gp_gnbk_df = pd.read_sql_table('gp_gnbk', con=engine)
 
-        # 去掉 symbol 字段的前两个字符
+        # 处理symbol字段
-        stock_changes_df['symbol'] = stock_changes_df['symbol'].str[2:]
+        if 'symbol' in stock_changes_df.columns:
+            # 保持原始格式用于关联板块表
+            stock_changes_df['symbol_clean'] = stock_changes_df['symbol']
+        else:
+            print("股票数据中缺少symbol字段")
+            return
 
         # 关联两个表，获取 bk_name 和 gp_name
-        merged_df = stock_changes_df.merge(gp_gnbk_df, left_on='symbol', right_on='gp_code')
+        merged_df = stock_changes_df.merge(gp_gnbk_df, left_on='symbol_clean', right_on='gp_code')
 
         # 筛选指定范围的行业的股票
         filtered_df = merged_df[merged_df['bk_name'].isin(top_industry_names)]
 
         # 统计每只股票命中行业的数量
-        stock_industry_counts = filtered_df.groupby(['symbol', 'gp_name'])['bk_name'].nunique().sort_values(ascending=False).head(10)
+        stock_industry_counts = filtered_df.groupby(['symbol', 'name'])['bk_name'].nunique().sort_values(ascending=False).head(10)
 
         # 获取每只股票的命中行业数量
         stock_industry_list = stock_industry_counts.reset_index()
@@ -332,5 +377,17 @@ if __name__ == "__main__":
     # 清理历史数据
     clean_historical_data(engine)
     
-    # 执行主要分析
+    # 执行主要分析 - 完全使用Redis数据源
-    get_top_industries_and_stocks(db_url, 1, 10)
+    print("=" * 60)
+    print("板块分析脚本启动 - Redis数据源版本")
+    print("=" * 60)
+    print("使用Redis作为数据源")
+    print("确保batch_stock_price_collector.py脚本正在运行")
+    print("-" * 60)
+    
+    # 执行分析
+    get_top_industries_and_stocks(db_url, 1, 10)
+    
+    print("=" * 60)
+    print("板块分析完成")
+    print("=" * 60)

src/static/js/bigscreen_v2.js

@@ -172,6 +172,13 @@ $(function() {
         // 隐藏重置按钮
         $('#resetViewBtn').hide();
         
+        // 静默请求接口，不传递行业参数
+        $.get('https://spb.bmbs.tech/api/dify/webSelectStockIndustry')
+            .fail(function(xhr, status, error) {
+                // 静默处理错误，不显示给用户
+                console.log('返回默认视图通知接口调用失败:', error);
+            });
+        
         // 恢复第二行的原始布局
         const rowContainer = document.querySelector('.row.d-flex2');
         rowContainer.innerHTML = `
@@ -223,6 +230,9 @@ $(function() {
                 element.remove();
             }
         });
+        
+        // 重新绑定持仓点击事件
+        setTimeout(bindIndustryHoldingsClick, 1000); // 延迟1秒绑定，确保数据加载完成
     }
 
     // 加载行业详情函数
@@ -399,10 +409,10 @@ $(function() {
                     
                     <!-- 持仓和因子的左右布局 -->
                     <div class="factor-holding-row">
-                        <!-- 持仓数据 -->
+                    <!-- 持仓数据 -->
                         <div class="holding-section">
-                            <h5>持仓情况</h5>
+                        <h5>持仓情况</h5>
-                            <div class="holding-details">
+                        <div class="holding-details">
             `;
             
             if (stockHoldings.length > 0) {
@@ -431,9 +441,21 @@ $(function() {
                             <div class="factor-details">
             `;
             
-            if (factorInfo && factorInfo.details) {
+            if (factorInfo && factorInfo.factors_details) {
-                Object.entries(factorInfo.details).forEach(([factor, value]) => {
+                factorInfo.factors_details.forEach((factorDetail) => {
-                    html += `<p><strong>${factor}:</strong> ${value}</p>`;
+                    const factor = factorDetail.factor;
+                    const detail = factorDetail.detail;
+                    const signal = factorDetail.signal;
+                    
+                    // 根据signal显示箭头
+                    let arrowIcon = '';
+                    if (signal === '买') {
+                        arrowIcon = '<span class="factor-arrow buy">⬆</span>';
+                    } else if (signal === '卖') {
+                        arrowIcon = '<span class="factor-arrow sell">⬇</span>';
+                    }
+                    
+                    html += `<p>${arrowIcon}<strong>${factor}:</strong> ${detail}</p>`;
                 });
             } else {
                 html += '<p style="color: #999;">暂无因子数据</p>';
@@ -485,10 +507,6 @@ $(function() {
         container.innerHTML = html;
     }
     
-    // 这个函数已经不再使用，删除
-    
-    // 这个函数已经不再使用，删除
-    
     function convertStockCode(stockCode) {
         // 将 600584.SH 格式转换为 SH600584 格式
         const parts = stockCode.split('.');
@@ -702,13 +720,16 @@ $(function() {
     function bindIndustryHoldingsClick() {
         // 为每个持仓容器添加点击事件
         const holdingsContainers = ['holdings_xjfz', 'holdings_xp', 'holdings_xfdz', 'holdings_jqr'];
-        const industries = ['半导体', '自动化设备', '军工电子', '消费电子']; // 默认行业，实际会根据持仓动态更新
         
         holdingsContainers.forEach((containerId, index) => {
             const container = document.getElementById(containerId);
             if (container) {
-                container.style.cursor = 'pointer';
+                // 移除之前的事件监听器，避免重复绑定
-                container.addEventListener('click', function() {
+                container.replaceWith(container.cloneNode(true));
+                const newContainer = document.getElementById(containerId);
+                
+                newContainer.style.cursor = 'pointer';
+                newContainer.addEventListener('click', function() {
                     // 获取当前显示的行业名称
                     const industryTitle = this.parentElement.querySelector('.chart-title');
                     if (industryTitle) {
@@ -752,7 +773,7 @@ $(function() {
             html = '<div class="notice-item" style="text-align: center; color: #999; padding: 20px;">暂无重要提醒</div>';
         } else {
             // 只显示一次内容，不再重复
-            notices.forEach(notice => {
+            notices.forEach((notice, index) => {
                 html += `<div class="notice-item">${notice}</div>`;
             });
         }
@@ -1120,34 +1141,34 @@ $(function() {
 });
 
 // 弹窗相关函数 - 移到全局作用域
-function showNoticeModal() {
+    function showNoticeModal() {
-    const modal = document.getElementById('noticeModal');
+        const modal = document.getElementById('noticeModal');
-    const modalContent = document.getElementById('modalNoticeContent');
+        const modalContent = document.getElementById('modalNoticeContent');
-    
+        
-    // 获取当前所有提醒数据
+        // 获取当前所有提醒数据
-    const notices = getCurrentNotices();
+        const notices = getCurrentNotices();
-    
+        
-    // 生成弹窗内容
+        // 生成弹窗内容
-    let html = '';
+        let html = '';
-    notices.forEach(notice => {
+        notices.forEach(notice => {
-        html += `<div class="modal-notice-item">${notice}</div>`;
+            html += `<div class="modal-notice-item">${notice}</div>`;
-    });
+        });
-    modalContent.innerHTML = html;
+        modalContent.innerHTML = html;
-    
+        
-    // 显示弹窗
+        // 显示弹窗
-    modal.style.display = 'flex';
+        modal.style.display = 'flex';
-    
+        
-    // 阻止背景滚动
+        // 阻止背景滚动
-    document.body.style.overflow = 'hidden';
+        document.body.style.overflow = 'hidden';
-}
+    }
 
-function closeNoticeModal() {
+    function closeNoticeModal() {
-    const modal = document.getElementById('noticeModal');
+        const modal = document.getElementById('noticeModal');
-    modal.style.display = 'none';
+        modal.style.display = 'none';
-    
+        
-    // 恢复背景滚动
+        // 恢复背景滚动
-    document.body.style.overflow = 'auto';
+        document.body.style.overflow = 'auto';
-}
+    }
 
 function closeIndustryHoldingsModal() {
     const modal = document.getElementById('industryHoldingsModal');
@@ -1157,14 +1178,14 @@ function closeIndustryHoldingsModal() {
     document.body.style.overflow = 'auto';
 }
 
-function getCurrentNotices() {
+    function getCurrentNotices() {
-    // 从当前显示的提醒框中获取数据，或者使用默认数据
+        // 从当前显示的提醒框中获取数据，或者使用默认数据
-    const noticeItems = document.querySelectorAll('.notice-item');
+        const noticeItems = document.querySelectorAll('.notice-item');
-    if (noticeItems.length > 0) {
+        if (noticeItems.length > 0) {
-        return Array.from(noticeItems).map(item => item.textContent.trim());
+            return Array.from(noticeItems).map(item => item.textContent.trim());
+        }
+        return getDefaultNotices();
     }
-    return getDefaultNotices();
-}
 
 // 页面加载完成后绑定事件
 $(function() {

src/templates/bigscreen_v2.html

@@ -103,6 +103,14 @@
             border-left: 3px solid #5470c6;
         }
 
+        .holding-item:nth-child(odd) {
+            background-color: #f8f9fa;
+        }
+
+        .holding-item:nth-child(even) {
+            background-color: #ffffff;
+        }
+
         .holding-name {
             font-weight: bold;
             color: #333;
@@ -162,6 +170,20 @@
             line-height: 1.4;
         }
 
+        .notice-item:nth-child(odd) {
+            background-color: #f8f9fa;
+            padding: 8px 10px;
+            margin: 0 -10px;
+            border-radius: 4px;
+        }
+
+        .notice-item:nth-child(even) {
+            background-color: #ffffff;
+            padding: 8px 10px;
+            margin: 0 -10px;
+            border-radius: 4px;
+        }
+
         .notice-item:last-child {
             border-bottom: none;
         }
@@ -566,6 +588,22 @@
             color: #34495e;
         }
         
+        /* 因子箭头样式 */
+        .factor-arrow {
+            display: inline-block;
+            font-weight: bold;
+            margin-right: 8px;
+            font-size: 14px;
+        }
+        
+        .factor-arrow.buy {
+            color: #ff4444;
+        }
+        
+        .factor-arrow.sell {
+            color: #44cc44;
+        }
+        
         .holding-detail-item {
             margin-bottom: 3px;
             padding: 3px;

src/templates/个股指数对比分析.html

@@ -0,0 +1,313 @@
+<!DOCTYPE html>
+<html>
+<head>
+    <meta charset="UTF-8">
+    <title>个股指数对比分析</title>
+    <!-- 引入 ECharts -->
+    <script src="../static/js/echarts.min.js"></script>
+    <style>
+        .container {
+            padding: 20px;
+            font-family: Arial, sans-serif;
+        }
+        .input-group {
+            margin-bottom: 20px;
+        }
+        .input-group label {
+            display: inline-block;
+            width: 100px;
+            margin-right: 10px;
+        }
+        .input-group input, .input-group select {
+            padding: 5px;
+            margin-right: 10px;
+        }
+        .result-panel {
+            background-color: #f5f5f5;
+            padding: 15px;
+            border-radius: 5px;
+            margin-bottom: 20px;
+        }
+        .strength-indicator {
+            font-size: 24px;
+            font-weight: bold;
+            color: #ff4d4f;
+        }
+        .charts-container {
+            display: flex;
+            gap: 20px;
+        }
+        .chart-item {
+            flex: 1;
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <h1>个股指数对比分析</h1>
+        
+        <!-- 输入控制区 -->
+        <div class="input-group">
+            <label>个股代码:</label>
+            <input type="text" id="stockCode" placeholder="输入股票代码">
+        </div>
+        
+        <div class="input-group">
+            <label>对比指数:</label>
+            <select id="indexSelect">
+                <option value="000001|1">000001 - 上证指数</option>
+                <option value="399001|0">399001 - 深证成指</option>
+                <option value="399006|0">399006 - 创业板指</option>
+                <option value="000688|1">000688 - 科创50指数</option>
+            </select>
+        </div>
+        
+        <div class="input-group">
+            <button onclick="fetchAndAnalyze()" style="padding: 10px 20px; background-color: #1890ff; color: white; border: none; border-radius: 4px; cursor: pointer;">开始分析</button>
+        </div>
+
+        <!-- 结果展示区 -->
+        <div class="result-panel" id="resultPanel" style="display: none;">
+            <h3>逆市强度分析结果</h3>
+            <div>
+                <span>逆市强度: </span>
+                <span class="strength-indicator" id="strengthValue">-</span>
+            </div>
+            <div style="margin-top: 10px; font-size: 14px; color: #666;">
+                指数弱势次数: <span id="indexWeakCount">-</span> | 
+                个股逆市次数: <span id="stockStrongCount">-</span>
+            </div>
+        </div>
+
+        <!-- 图表展示区 -->
+        <div class="charts-container">
+            <div class="chart-item">
+                <h3>个股买卖方能量</h3>
+                <div id="stockChart" style="width: 100%; height: 400px;"></div>
+            </div>
+            <div class="chart-item">
+                <h3>指数买卖方能量</h3>
+                <div id="indexChart" style="width: 100%; height: 400px;"></div>
+            </div>
+        </div>
+    </div>
+
+    <script>
+        // 初始化图表
+        var stockChart = echarts.init(document.getElementById('stockChart'));
+        var indexChart = echarts.init(document.getElementById('indexChart'));
+
+        // 获取分时数据
+        async function fetchStockData(code, market) {
+            var baseUrl = "http://push2ex.eastmoney.com/getStockFenShi?pagesize=6000&ut=7eea3edcaed734bea9cbfc24409ed989&dpt=wzfscj&pageindex=0&id=3007792&sort=1&ft=1&code=REPLACE_CODE&market=REPLACE_MARKET";
+            var url = baseUrl.replace("REPLACE_CODE", code).replace("REPLACE_MARKET", market);
+            
+            try {
+                const response = await fetch(url);
+                const jsonData = await response.json();
+                return jsonData.data.data;
+            } catch (error) {
+                console.log('Request failed for ' + code, error);
+                return null;
+            }
+        }3
+
+        // 处理数据：按分钟聚合买卖方数据
+        function processTradeData(data) {
+            var buyData = {};
+            var sellData = {};
+            
+            if (!data) return { buyData, sellData };
+            
+            data.forEach(function(item) {
+                var minute = Math.floor(item.t / 100);
+                
+                if (item.bs === 1) {
+                    // 卖方
+                    if (!sellData[minute]) sellData[minute] = 0;
+                    sellData[minute] += item.v;
+                } else if (item.bs === 2) {
+                    // 买方
+                    if (!buyData[minute]) buyData[minute] = 0;
+                    buyData[minute] += item.v;
+                }
+            });
+            
+            return { buyData, sellData };
+        }
+
+        // 计算逆市强度
+        function calculateCounterTrendStrength(stockBuyData, stockSellData, indexBuyData, indexSellData) {
+            var indexWeakCount = 0;  // 指数弱势次数（卖方 > 买方）
+            var stockStrongCount = 0; // 个股逆市强势次数
+            
+            // 获取所有时间点
+            var allMinutes = new Set([
+                ...Object.keys(stockBuyData), 
+                ...Object.keys(stockSellData),
+                ...Object.keys(indexBuyData), 
+                ...Object.keys(indexSellData)
+            ]);
+            
+            allMinutes.forEach(function(minute) {
+                var stockBuy = stockBuyData[minute] || 0;
+                var stockSell = stockSellData[minute] || 0;
+                var indexBuy = indexBuyData[minute] || 0;
+                var indexSell = indexSellData[minute] || 0;
+                
+                // 指数弱势：卖方量 > 买方量
+                if (indexSell > indexBuy) {
+                    indexWeakCount++;
+                    
+                    // 个股强势：买方量 > 卖方量
+                    if (stockBuy > stockSell) {
+                        stockStrongCount++;
+                    }
+                }
+            });
+            
+            var strength = indexWeakCount > 0 ? (stockStrongCount / indexWeakCount * 100) : 0;
+            
+            return {
+                strength: strength,
+                indexWeakCount: indexWeakCount,
+                stockStrongCount: stockStrongCount
+            };
+        }
+
+        // 生成图表配置
+        function generateChartOption(buyData, sellData, title) {
+            var allMinutes = new Set([...Object.keys(buyData), ...Object.keys(sellData)]);
+            var sortedMinutes = Array.from(allMinutes).map(Number).sort((a, b) => a - b);
+            
+            var categories = [];
+            var buyValues = [];
+            var sellValues = [];
+            
+            sortedMinutes.forEach(function(minute) {
+                var timeStr = minute.toString().padStart(6, '0');
+                var formattedTime = timeStr.substring(0,2) + ':' + timeStr.substring(2,4) + ':' + timeStr.substring(4,6);
+                categories.push(formattedTime);
+                buyValues.push(buyData[minute] || 0);
+                sellValues.push(sellData[minute] || 0);
+            });
+
+            return {
+                title: {
+                    text: title
+                },
+                tooltip: {
+                    trigger: 'axis',
+                    axisPointer: {
+                        type: 'shadow'
+                    }
+                },
+                legend: {
+                    data: ['买方', '卖方']
+                },
+                xAxis: {
+                    data: categories,
+                    axisLabel: {
+                        rotate: 45
+                    }
+                },
+                yAxis: {
+                    type: 'value',
+                    name: '交易量'
+                },
+                series: [{
+                    name: '买方',
+                    type: 'bar',
+                    data: buyValues,
+                    stack: '总量',
+                    itemStyle: {
+                        color: '#ff4d4f'
+                    }
+                }, {
+                    name: '卖方',
+                    type: 'bar',
+                    data: sellValues,
+                    stack: '总量',
+                    itemStyle: {
+                        color: '#52c41a'
+                    }
+                }],
+                dataZoom: [{
+                    type: 'inside',
+                    start: 0,
+                    end: 100
+                }, {
+                    type: 'slider',
+                    start: 0,
+                    end: 100
+                }]
+            };
+        }
+
+        // 主分析函数
+        async function fetchAndAnalyze() {
+            var stockCode = document.getElementById('stockCode').value;
+            var indexInfo = document.getElementById('indexSelect').value.split('|');
+            var indexCode = indexInfo[0];
+            var indexMarket = indexInfo[1];
+            
+            if (!stockCode) {
+                alert('请输入股票代码');
+                return;
+            }
+
+            // 确定个股市场
+            var stockMarket = stockCode.startsWith('6') ? '1' : '0';
+            
+            console.log('开始获取数据...');
+            console.log('个股:', stockCode, '市场:', stockMarket);
+            console.log('指数:', indexCode, '市场:', indexMarket);
+
+            try {
+                // 并行获取个股和指数数据
+                const [stockData, indexData] = await Promise.all([
+                    fetchStockData(stockCode, stockMarket),
+                    fetchStockData(indexCode, indexMarket)
+                ]);
+
+                if (!stockData || !indexData) {
+                    alert('数据获取失败，请检查代码是否正确');
+                    return;
+                }
+
+                // 处理数据
+                var stockResult = processTradeData(stockData);
+                var indexResult = processTradeData(indexData);
+
+                // 计算逆市强度
+                var analysis = calculateCounterTrendStrength(
+                    stockResult.buyData, 
+                    stockResult.sellData,
+                    indexResult.buyData, 
+                    indexResult.sellData
+                );
+
+                // 显示结果
+                document.getElementById('resultPanel').style.display = 'block';
+                document.getElementById('strengthValue').textContent = analysis.strength.toFixed(2) + '%';
+                document.getElementById('indexWeakCount').textContent = analysis.indexWeakCount;
+                document.getElementById('stockStrongCount').textContent = analysis.stockStrongCount;
+
+                // 生成图表
+                var stockOption = generateChartOption(stockResult.buyData, stockResult.sellData, '个股: ' + stockCode);
+                var indexOption = generateChartOption(indexResult.buyData, indexResult.sellData, '指数: ' + indexCode);
+
+                stockChart.setOption(stockOption);
+                indexChart.setOption(indexOption);
+
+                console.log('分析完成');
+                console.log('逆市强度:', analysis.strength.toFixed(2) + '%');
+
+            } catch (error) {
+                console.error('分析过程中出现错误:', error);
+                alert('分析过程中出现错误，请重试');
+            }
+        }
+    </script>
+</body>
+</html> 

src/templates/买方卖方能量v2.html

@@ -0,0 +1,133 @@
+<!DOCTYPE html>
+<html>
+<head>
+    <meta charset="UTF-8">
+    <title>ECharts 示例</title>
+    <!-- 引入 ECharts -->
+    <script src="../static/js/echarts.min.js"></script>
+</head>
+<body>
+    <!-- 输入框和按钮 -->
+    <div>
+        <input type="text" id="stockCode" placeholder="输入股票代码">
+        <input type="text" id="jysl" value="0" placeholder="输入交易量阈值">
+        <button onclick="fetchData()">查询</button>
+    </div>
+    <!-- 为ECharts准备一个具备大小（宽高）的Dom -->
+    <div id="main" style="width: 1000px;height:600px;"></div>
+    <script>
+        // ECharts图表初始化
+        var myChart = echarts.init(document.getElementById('main'));
+
+        function fetchData() {
+
+            let inputValue = document.getElementById('jysl').value;
+            var stockCode = document.getElementById('stockCode').value;
+            // 检查code是否以'6'开头，据此设置market值
+            var market = stockCode.startsWith('6') ? '1' : '0';
+            var baseUrl = "http://push2ex.eastmoney.com/getStockFenShi?pagesize=6000&ut=7eea3edcaed734bea9cbfc24409ed989&dpt=wzfscj&pageindex=0&id=3007792&sort=1&ft=1&code=REPLACE_CODE&market=REPLACE_MARKET";
+            var url = baseUrl.replace("REPLACE_CODE", stockCode).replace("REPLACE_MARKET", market);
+            
+            fetch(url)
+                .then(function(response) {
+                    return response.json();
+                })
+                .then(function(jsonData) {
+                    // 这里的jsonData就是你从服务器获取的数据
+                    // 下面是数据处理的代码，可能需要根据实际数据结构进行调整
+
+                    var data = jsonData.data.data; // 根据实际结构调整路径
+
+                    // 数据处理：按每分钟聚合
+                    var buyData = {};  // 买方数据
+                    var sellData = {}; // 卖方数据
+                    data.forEach(function(item) {
+                        var minute = Math.floor(item.t / 100); // 取整到分钟
+                        if (item.v >= inputValue) { // 只有当v值大于或等于100时才进行处理
+                            if (item.bs === 1) {
+                                if (!sellData[minute]) sellData[minute] = 0;
+                                sellData[minute] += item.v;
+                            } else if (item.bs === 2) {
+                                if (!buyData[minute]) buyData[minute] = 0;
+                                buyData[minute] += item.v;
+                            }
+                        }
+                    });
+
+                    // 准备ECharts数据
+                    var categories = [];
+                    var buyValues = [];
+                    var sellValues = [];
+                    
+                    // 获取所有时间点并按数字大小排序
+                    var allMinutes = new Set([...Object.keys(buyData), ...Object.keys(sellData)]);
+                    var sortedMinutes = Array.from(allMinutes).map(Number).sort((a, b) => a - b);
+                    
+                    sortedMinutes.forEach(function(minute) {
+                        // 格式化时间显示 (例如: 093000 -> 09:30:00)
+                        var timeStr = minute.toString().padStart(6, '0');
+                        var formattedTime = timeStr.substring(0,2) + ':' + timeStr.substring(2,4) + ':' + timeStr.substring(4,6);
+                        categories.push(formattedTime);
+                        buyValues.push(buyData[minute] || 0);
+                        sellValues.push(sellData[minute] || 0);
+                    });
+
+                    // 设置ECharts选项
+                    var option = {
+                        tooltip: {
+                            trigger: 'axis',
+                            axisPointer: {
+                                type: 'shadow'
+                            }
+                        },
+                        legend: {
+                            data:['买方', '卖方']
+                        },
+                        xAxis: {
+                            data: categories,
+                            axisLabel: {
+                                rotate: 45 // 旋转标签以避免重叠
+                            }
+                        },
+                        yAxis: {
+                            type: 'value',
+                            name: '交易量'
+                        },
+                        series: [{
+                            name: '买方',
+                            type: 'bar',
+                            data: buyValues,  // 修复：买方显示买方数据
+                            stack: '总量',
+                            itemStyle: {
+                                color: '#ff4d4f' // 红色表示买方
+                            }
+                        }, {
+                            name: '卖方',
+                            type: 'bar',
+                            data: sellValues, // 修复：卖方显示卖方数据
+                            stack: '总量',
+                            itemStyle: {
+                                color: '#52c41a' // 绿色表示卖方
+                            }
+                        }],
+                        dataZoom: [{
+                            type: 'inside', // 内置于坐标系中，通过鼠标滚轮或触控板操作进行数据区域缩放
+                            start: 0,       // 数据窗口范围的起始百分比，表示数据窗口包含数据序列的起始部分
+                            end: 100        // 数据窗口范围的结束百分比，初始设置为100%，即展示全部数据
+                        }, {
+                            type: 'slider', // 滑动条型数据区域缩放组件
+                            start: 0,
+                            end: 100
+                        }],
+                    };
+
+                    // 显示图表。
+                    myChart.setOption(option);
+                })
+                .catch(function(error) {
+                    console.log('Request failed', error);
+                });
+        }
+    </script>
+</body>
+</html>

src/valuation_analysis/portfolio_analyzer.py

@@ -43,7 +43,7 @@ class PortfolioAnalyzer:
             pool_recycle=3600
         )
         self.api_url = ("https://to.bmbs.tech/aim/app/v1/derivativeTrading/getTradingRecordList?"
-                        "projectId=182AE38B8C254BC88C715D86C643A6DD,C9B533175D294648A2372CB2966BCC96")
+                        "projectId=182AE38B8C254BC88C715D86C643A6DD,C9B533175D294648A2372CB2966BCC96&dataScope=all")
         logger.info("持仓分析器初始化完成")
     
     def get_trading_records(self) -> Optional[Dict]: