tushare

ai_agents.py

@@ -1,5 +1,6 @@
 from deepseek_client import DeepSeekClient
 from typing import Dict, Any
+import concurrent.futures
 import time
 import config
 
@@ -13,7 +14,6 @@ class StockAnalysisAgents:
     def technical_analyst_agent(self, stock_info: Dict, stock_data: Any, indicators: Dict) -> Dict[str, Any]:
         """技术面分析智能体"""
         print("🔍 技术分析师正在分析中...")
-        time.sleep(1)  # 模拟分析时间
         
         analysis = self.deepseek_client.technical_analysis(stock_info, stock_data, indicators)
         
@@ -38,8 +38,6 @@ class StockAnalysisAgents:
         else:
             print("   ⚠ 未获取到季报数据，将基于基本财务数据分析")
         
-        time.sleep(1)
-        
         analysis = self.deepseek_client.fundamental_analysis(stock_info, financial_data, quarterly_data)
         
         return {
@@ -61,8 +59,6 @@ class StockAnalysisAgents:
         else:
             print("   ⚠ 未获取到资金流向数据，将基于技术指标分析")
         
-        time.sleep(1)
-        
         analysis = self.deepseek_client.fund_flow_analysis(stock_info, indicators, fund_flow_data)
         
         return {
@@ -84,8 +80,6 @@ class StockAnalysisAgents:
         else:
             print("   ⚠ 未获取到风险数据，将基于基本信息分析")
         
-        time.sleep(1)
-        
         # 构建风险数据文本
         risk_data_text = ""
         if risk_data and risk_data.get('data_success'):
@@ -227,8 +221,6 @@ class StockAnalysisAgents:
         else:
             print("   ⚠ 未获取到详细情绪数据，将基于基本信息分析")
         
-        time.sleep(1)
-        
         # 构建带有市场情绪数据的prompt
         sentiment_data_text = ""
         if sentiment_data and sentiment_data.get('data_success'):
@@ -317,8 +309,6 @@ class StockAnalysisAgents:
         else:
             print("   ⚠ 未获取到新闻数据，将基于基本信息分析")
         
-        time.sleep(1)
-        
         # 构建带有新闻数据的prompt
         news_text = ""
         if news_data and news_data.get('data_success'):
@@ -436,32 +426,38 @@ class StockAnalysisAgents:
         print(f"📋 参与分析的分析师: {', '.join(active_analysts)}")
         print("=" * 50)
         
-        # 并行运行各个分析师
+        # 并行运行各个分析师（全部同时启动，等待最慢的一个完成）
         agents_results = {}
         
-        # 技术面分析
+        agent_jobs = []
         if enabled_analysts.get('technical', True):
-            agents_results["technical"] = self.technical_analyst_agent(stock_info, stock_data, indicators)
+            agent_jobs.append(("technical", lambda: self.technical_analyst_agent(stock_info, stock_data, indicators)))
-        
-        # 基本面分析
         if enabled_analysts.get('fundamental', True):
-            agents_results["fundamental"] = self.fundamental_analyst_agent(stock_info, financial_data, quarterly_data)
+            agent_jobs.append(("fundamental", lambda: self.fundamental_analyst_agent(stock_info, financial_data, quarterly_data)))
-        
-        # 资金面分析（传入资金流向数据）
         if enabled_analysts.get('fund_flow', True):
-            agents_results["fund_flow"] = self.fund_flow_analyst_agent(stock_info, indicators, fund_flow_data)
+            agent_jobs.append(("fund_flow", lambda: self.fund_flow_analyst_agent(stock_info, indicators, fund_flow_data)))
-        
-        # 风险管理分析（传入风险数据）
         if enabled_analysts.get('risk', True):
-            agents_results["risk_management"] = self.risk_management_agent(stock_info, indicators, risk_data)
+            agent_jobs.append(("risk_management", lambda: self.risk_management_agent(stock_info, indicators, risk_data)))
-        
-        # 市场情绪分析（传入市场情绪数据）
         if enabled_analysts.get('sentiment', False):
-            agents_results["market_sentiment"] = self.market_sentiment_agent(stock_info, sentiment_data)
+            agent_jobs.append(("market_sentiment", lambda: self.market_sentiment_agent(stock_info, sentiment_data)))
-        
-        # 新闻分析（传入新闻数据）
         if enabled_analysts.get('news', False):
-            agents_results["news"] = self.news_analyst_agent(stock_info, news_data)
+            agent_jobs.append(("news", lambda: self.news_analyst_agent(stock_info, news_data)))
+        
+        with concurrent.futures.ThreadPoolExecutor(max_workers=max(min(len(agent_jobs), 6), 1)) as executor:
+            future_to_key = {executor.submit(job): key for key, job in agent_jobs}
+            for future in concurrent.futures.as_completed(future_to_key):
+                key = future_to_key[future]
+                try:
+                    agents_results[key] = future.result()
+                except Exception as e:
+                    print(f"❌ {key} 分析师并行分析失败: {e}")
+                    agents_results[key] = {
+                        "agent_name": key,
+                        "agent_role": "",
+                        "analysis": f"分析失败: {e}",
+                        "focus_areas": [],
+                        "timestamp": time.strftime("%Y-%m-%d %H:%M:%S")
+                    }
         
         print("✅ 所有已选择的分析师完成分析")
         print("=" * 50)
@@ -471,7 +467,6 @@ class StockAnalysisAgents:
     def conduct_team_discussion(self, agents_results: Dict[str, Any], stock_info: Dict) -> str:
         """进行团队讨论"""
         print("🤝 分析团队正在进行综合讨论...")
-        time.sleep(2)
         
         # 收集参与分析的分析师名单和报告
         participants = []
@@ -538,7 +533,6 @@ class StockAnalysisAgents:
     def make_final_decision(self, discussion_result: str, stock_info: Dict, indicators: Dict) -> Dict[str, Any]:
         """制定最终投资决策"""
         print("📋 正在制定最终投资决策...")
-        time.sleep(1)
         
         decision = self.deepseek_client.final_decision(discussion_result, stock_info, indicators)
         

app.py

@@ -9,6 +9,10 @@ import base64
 import os
 import config
 
+# 注入所有外部请求（akshare/tushare等）的默认超时
+from http_timeout import install_default_requests_timeout
+install_default_requests_timeout()
+
 from stock_data import StockDataFetcher
 from ai_agents import StockAnalysisAgents
 from pdf_generator import display_pdf_export_section

data_source_manager.py

@@ -11,6 +11,10 @@ from dotenv import load_dotenv
 # 加载环境变量
 load_dotenv()
 
+# 注入外部请求默认超时（覆盖akshare、tushare等基于requests的调用）
+from http_timeout import install_default_requests_timeout
+install_default_requests_timeout()
+
 
 class DataSourceManager:
     """数据源管理器 - 实现akshare与tushare自动切换"""
@@ -25,7 +29,7 @@ class DataSourceManager:
             try:
                 import tushare as ts
                 ts.set_token(self.tushare_token)
-                self.tushare_api = ts.pro_api()
+                self.tushare_api = ts.pro_api(timeout=float(os.getenv('TUSHARE_TIMEOUT', '15')))
                 self.tushare_available = True
                 print("✅ Tushare数据源初始化成功")
             except Exception as e:
@@ -36,7 +40,7 @@ class DataSourceManager:
     
     def get_stock_hist_data(self, symbol, start_date=None, end_date=None, adjust='qfq'):
         """
-        获取股票历史数据（优先akshare，失败时使用tushare）
+        获取股票历史数据（优先tushare，失败时使用akshare）
         
         Args:
             symbol: 股票代码（6位数字）
@@ -55,10 +59,63 @@ class DataSourceManager:
         else:
             end_date = datetime.now().strftime('%Y%m%d')
         
-        # 优先使用akshare
+        # 优先使用tushare
+        if self.tushare_available:
+            try:
+                import tushare as ts
+                print(f"[Tushare] 正在获取 {symbol} 的历史数据（主要数据源）...")
+                
+                # 转换股票代码格式（添加市场后缀）
+                ts_code = self._convert_to_ts_code(symbol)
+                
+                # 转换复权类型
+                adj_dict = {'qfq': 'qfq', 'hfq': 'hfq', '': None}
+                adj = adj_dict.get(adjust, 'qfq')
+                
+                if adj is None:
+                    # 不复权数据直接使用daily接口
+                    df = self.tushare_api.daily(
+                        ts_code=ts_code,
+                        start_date=start_date,
+                        end_date=end_date
+                    )
+                else:
+                    # 复权数据使用pro_bar（daily接口不支持adj参数）
+                    df = ts.pro_bar(
+                        api=self.tushare_api,
+                        ts_code=ts_code,
+                        start_date=start_date,
+                        end_date=end_date,
+                        adj=adj,
+                        retry_count=1
+                    )
+                
+                if df is not None and not df.empty:
+                    # 标准化列名和数据格式
+                    df = df.rename(columns={
+                        'trade_date': 'date',
+                        'vol': 'volume',
+                        'amount': 'amount'
+                    })
+                    df['date'] = pd.to_datetime(df['date'])
+                    df = df.sort_values('date')
+                    
+                    # 转换成交量单位（tushare单位是手，转换为股）
+                    df['volume'] = df['volume'] * 100
+                    # 转换成交额单位（tushare单位是千元，转换为元）
+                    df['amount'] = df['amount'] * 1000
+                    
+                    print(f"[Tushare] ✅ 成功获取 {len(df)} 条数据")
+                    return df
+                else:
+                    print(f"[Tushare] ❌ 未获取到数据，尝试备用数据源")
+            except Exception as e:
+                print(f"[Tushare] ❌ 获取失败: {e}")
+        
+        # tushare失败，回退到akshare
         try:
             import akshare as ak
-            print(f"[Akshare] 正在获取 {symbol} 的历史数据...")
+            print(f"[Akshare] 正在获取 {symbol} 的历史数据（备用数据源）...")
             
             df = ak.stock_zh_a_hist(
                 symbol=symbol,
@@ -86,60 +143,18 @@ class DataSourceManager:
                 df['date'] = pd.to_datetime(df['date'])
                 print(f"[Akshare] ✅ 成功获取 {len(df)} 条数据")
                 return df
+            else:
+                print(f"[Akshare] ❌ 未获取到数据")
         except Exception as e:
             print(f"[Akshare] ❌ 获取失败: {e}")
         
-        # akshare失败，尝试tushare
-        if self.tushare_available:
-            try:
-                print(f"[Tushare] 正在获取 {symbol} 的历史数据（备用数据源）...")
-                
-                # 转换股票代码格式（添加市场后缀）
-                ts_code = self._convert_to_ts_code(symbol)
-                
-                # 转换复权类型
-                adj_dict = {'qfq': 'qfq', 'hfq': 'hfq', '': None}
-                adj = adj_dict.get(adjust, 'qfq')
-                
-                # 格式化日期
-                start = f"{start_date[:4]}-{start_date[4:6]}-{start_date[6:]}" if start_date else None
-                end = f"{end_date[:4]}-{end_date[4:6]}-{end_date[6:]}" if end_date else None
-                
-                # 获取数据
-                df = self.tushare_api.daily(
-                    ts_code=ts_code,
-                    start_date=start_date,
-                    end_date=end_date,
-                    adj=adj
-                )
-                
-                if df is not None and not df.empty:
-                    # 标准化列名和数据格式
-                    df = df.rename(columns={
-                        'trade_date': 'date',
-                        'vol': 'volume',
-                        'amount': 'amount'
-                    })
-                    df['date'] = pd.to_datetime(df['date'])
-                    df = df.sort_values('date')
-                    
-                    # 转换成交量单位（tushare单位是手，转换为股）
-                    df['volume'] = df['volume'] * 100
-                    # 转换成交额单位（tushare单位是千元，转换为元）
-                    df['amount'] = df['amount'] * 1000
-                    
-                    print(f"[Tushare] ✅ 成功获取 {len(df)} 条数据")
-                    return df
-            except Exception as e:
-                print(f"[Tushare] ❌ 获取失败: {e}")
-        
         # 两个数据源都失败
         print("❌ 所有数据源均获取失败")
         return None
     
     def get_stock_basic_info(self, symbol):
         """
-        获取股票基本信息（优先akshare，失败时使用tushare）
+        获取股票基本信息（优先tushare，失败时使用akshare）
         
         Args:
             symbol: 股票代码
@@ -154,10 +169,34 @@ class DataSourceManager:
             "market": "未知"
         }
         
-        # 优先使用akshare
+        # 优先使用tushare
+        if self.tushare_available:
+            try:
+                print(f"[Tushare] 正在获取 {symbol} 的基本信息（主要数据源）...")
+                
+                ts_code = self._convert_to_ts_code(symbol)
+                df = self.tushare_api.stock_basic(
+                    ts_code=ts_code,
+                    fields='ts_code,name,area,industry,market,list_date'
+                )
+                
+                if df is not None and not df.empty:
+                    info['name'] = df.iloc[0]['name']
+                    info['industry'] = df.iloc[0]['industry']
+                    info['market'] = df.iloc[0]['market']
+                    info['list_date'] = df.iloc[0]['list_date']
+                    
+                    print(f"[Tushare] ✅ 成功获取基本信息")
+                    return info
+                else:
+                    print(f"[Tushare] ❌ 未获取到基本信息，尝试备用数据源")
+            except Exception as e:
+                print(f"[Tushare] ❌ 获取失败: {e}")
+        
+        # tushare失败，回退akshare
         try:
             import akshare as ak
-            print(f"[Akshare] 正在获取 {symbol} 的基本信息...")
+            print(f"[Akshare] 正在获取 {symbol} 的基本信息（备用数据源）...")
             
             stock_info = ak.stock_individual_info_em(symbol=symbol)
             if stock_info is not None and not stock_info.empty:
@@ -181,33 +220,11 @@ class DataSourceManager:
         except Exception as e:
             print(f"[Akshare] ❌ 获取失败: {e}")
         
-        # akshare失败，尝试tushare
-        if self.tushare_available:
-            try:
-                print(f"[Tushare] 正在获取 {symbol} 的基本信息（备用数据源）...")
-                
-                ts_code = self._convert_to_ts_code(symbol)
-                df = self.tushare_api.stock_basic(
-                    ts_code=ts_code,
-                    fields='ts_code,name,area,industry,market,list_date'
-                )
-                
-                if df is not None and not df.empty:
-                    info['name'] = df.iloc[0]['name']
-                    info['industry'] = df.iloc[0]['industry']
-                    info['market'] = df.iloc[0]['market']
-                    info['list_date'] = df.iloc[0]['list_date']
-                    
-                    print(f"[Tushare] ✅ 成功获取基本信息")
-                    return info
-            except Exception as e:
-                print(f"[Tushare] ❌ 获取失败: {e}")
-        
         return info
     
     def get_realtime_quotes(self, symbol):
         """
-        获取实时行情数据（优先akshare，失败时使用tushare）
+        获取实时行情数据（优先tushare，失败时使用akshare）
         
         Args:
             symbol: 股票代码
@@ -217,10 +234,51 @@ class DataSourceManager:
         """
         quotes = {}
         
-        # 优先使用akshare
+        # 优先使用tushare
+        if self.tushare_available:
+            try:
+                print(f"[Tushare] 正在获取 {symbol} 的实时行情（主要数据源）...")
+                
+                ts_code = self._convert_to_ts_code(symbol)
+                today = datetime.now().strftime('%Y%m%d')
+                df = self.tushare_api.daily(
+                    ts_code=ts_code,
+                    start_date=today,
+                    end_date=today
+                )
+                if df is None or df.empty:
+                    # 非交易日时，获取最近10个交易日的最新数据
+                    start = (datetime.now() - timedelta(days=10)).strftime('%Y%m%d')
+                    df = self.tushare_api.daily(
+                        ts_code=ts_code,
+                        start_date=start,
+                        end_date=today
+                    )
+                
+                if df is not None and not df.empty:
+                    row = df.iloc[0]
+                    quotes = {
+                        'symbol': symbol,
+                        'price': row['close'],
+                        'change_percent': row['pct_chg'],
+                        'volume': row['vol'] * 100,
+                        'amount': row['amount'] * 1000,
+                        'high': row['high'],
+                        'low': row['low'],
+                        'open': row['open'],
+                        'pre_close': row['pre_close']
+                    }
+                    print(f"[Tushare] ✅ 成功获取实时行情")
+                    return quotes
+                else:
+                    print(f"[Tushare] ❌ 未获取到实时行情，尝试备用数据源")
+            except Exception as e:
+                print(f"[Tushare] ❌ 获取失败: {e}")
+        
+        # tushare失败，回退akshare
         try:
             import akshare as ak
-            print(f"[Akshare] 正在获取 {symbol} 的实时行情...")
+            print(f"[Akshare] 正在获取 {symbol} 的实时行情（备用数据源）...")
             
             df = ak.stock_zh_a_spot_em()
             stock_df = df[df['代码'] == symbol]
@@ -245,41 +303,11 @@ class DataSourceManager:
         except Exception as e:
             print(f"[Akshare] ❌ 获取失败: {e}")
         
-        # akshare失败，尝试tushare
-        if self.tushare_available:
-            try:
-                print(f"[Tushare] 正在获取 {symbol} 的实时行情（备用数据源）...")
-                
-                ts_code = self._convert_to_ts_code(symbol)
-                df = self.tushare_api.daily(
-                    ts_code=ts_code,
-                    start_date=datetime.now().strftime('%Y%m%d'),
-                    end_date=datetime.now().strftime('%Y%m%d')
-                )
-                
-                if df is not None and not df.empty:
-                    row = df.iloc[0]
-                    quotes = {
-                        'symbol': symbol,
-                        'price': row['close'],
-                        'change_percent': row['pct_chg'],
-                        'volume': row['vol'] * 100,
-                        'amount': row['amount'] * 1000,
-                        'high': row['high'],
-                        'low': row['low'],
-                        'open': row['open'],
-                        'pre_close': row['pre_close']
-                    }
-                    print(f"[Tushare] ✅ 成功获取实时行情")
-                    return quotes
-            except Exception as e:
-                print(f"[Tushare] ❌ 获取失败: {e}")
-        
         return quotes
     
     def get_financial_data(self, symbol, report_type='income'):
         """
-        获取财务数据（优先akshare，失败时使用tushare）
+        获取财务数据（优先tushare，失败时使用akshare）
         
         Args:
             symbol: 股票代码
@@ -288,30 +316,10 @@ class DataSourceManager:
         Returns:
             DataFrame: 财务数据
         """
-        # 优先使用akshare
+        # 优先使用tushare
-        try:
-            import akshare as ak
-            print(f"[Akshare] 正在获取 {symbol} 的财务数据...")
-            
-            if report_type == 'income':
-                df = ak.stock_financial_report_sina(stock=symbol, symbol="利润表")
-            elif report_type == 'balance':
-                df = ak.stock_financial_report_sina(stock=symbol, symbol="资产负债表")
-            elif report_type == 'cashflow':
-                df = ak.stock_financial_report_sina(stock=symbol, symbol="现金流量表")
-            else:
-                df = None
-            
-            if df is not None and not df.empty:
-                print(f"[Akshare] ✅ 成功获取财务数据")
-                return df
-        except Exception as e:
-            print(f"[Akshare] ❌ 获取失败: {e}")
-        
-        # akshare失败，尝试tushare
         if self.tushare_available:
             try:
-                print(f"[Tushare] 正在获取 {symbol} 的财务数据（备用数据源）...")
+                print(f"[Tushare] 正在获取 {symbol} 的财务数据（主要数据源）...")
                 
                 ts_code = self._convert_to_ts_code(symbol)
                 
@@ -327,9 +335,31 @@ class DataSourceManager:
                 if df is not None and not df.empty:
                     print(f"[Tushare] ✅ 成功获取财务数据")
                     return df
+                else:
+                    print(f"[Tushare] ❌ 未获取到财务数据，尝试备用数据源")
             except Exception as e:
                 print(f"[Tushare] ❌ 获取失败: {e}")
         
+        # tushare失败，回退akshare
+        try:
+            import akshare as ak
+            print(f"[Akshare] 正在获取 {symbol} 的财务数据（备用数据源）...")
+            
+            if report_type == 'income':
+                df = ak.stock_financial_report_sina(stock=symbol, symbol="利润表")
+            elif report_type == 'balance':
+                df = ak.stock_financial_report_sina(stock=symbol, symbol="资产负债表")
+            elif report_type == 'cashflow':
+                df = ak.stock_financial_report_sina(stock=symbol, symbol="现金流量表")
+            else:
+                df = None
+            
+            if df is not None and not df.empty:
+                print(f"[Akshare] ✅ 成功获取财务数据")
+                return df
+        except Exception as e:
+            print(f"[Akshare] ❌ 获取失败: {e}")
+        
         return None
     
     def _convert_to_ts_code(self, symbol):
@@ -376,4 +406,3 @@ class DataSourceManager:
 
 # 全局数据源管理器实例
 data_source_manager = DataSourceManager()
-

deepseek_client.py

@@ -1,5 +1,6 @@
 import openai
 import json
+import os
 from typing import Dict, List, Any, Optional
 import config
 
@@ -10,7 +11,9 @@ class DeepSeekClient:
         self.model = model or config.DEFAULT_MODEL_NAME
         self.client = openai.OpenAI(
             api_key=config.DEEPSEEK_API_KEY,
-            base_url=config.DEEPSEEK_BASE_URL
+            base_url=config.DEEPSEEK_BASE_URL,
+            timeout=float(os.getenv('DEEPSEEK_TIMEOUT', '180')),
+            max_retries=int(os.getenv('DEEPSEEK_MAX_RETRIES', '1'))
         )
         
     def call_api(self, messages: List[Dict[str, str]], model: Optional[str] = None, 

fund_flow_akshare.py

@@ -109,25 +109,16 @@ class FundFlowAkshareDataFetcher:
     def _get_individual_fund_flow(self, symbol, market):
         """获取个股资金流向数据（支持akshare和tushare自动切换）"""
         try:
-            # 优先使用akshare的stock_individual_fund_flow接口
+            # 优先使用tushare
-            print(f"   [Akshare] 正在获取资金流向 (市场: {market})...")
-            
-            df = ak.stock_individual_fund_flow(stock=symbol, market=market)
-            
-            if df is None or df.empty:
-                print(f"   [Akshare] 未找到资金流向数据，尝试备用数据源...")
-                
-                # akshare失败，尝试tushare
             if data_source_manager.tushare_available:
                 try:
-                        print(f"   [Tushare] 正在获取资金流向数据（备用数据源）...")
+                    print(f"   [Tushare] 正在获取资金流向数据（主要数据源）...")
                     ts_code = data_source_manager._convert_to_ts_code(symbol)
 
                     # 计算日期范围（最近N个交易日）
                     end_date = datetime.now().strftime('%Y%m%d')
                     start_date = (datetime.now() - timedelta(days=self.days * 2)).strftime('%Y%m%d')
 
-                        # 获取资金流向数据
                     df = data_source_manager.tushare_api.moneyflow(
                         ts_code=ts_code,
                         start_date=start_date,
@@ -135,30 +126,37 @@ class FundFlowAkshareDataFetcher:
                     )
 
                     if df is not None and not df.empty:
-                            # 标准化列名以匹配akshare格式
+                        # 标准化列名以匹配akshare格式（金额单位：千元→元）
                         df = df.rename(columns={
                             'trade_date': '日期',
-                                'buy_sm_amount': '小单买入',
+                            'close': '收盘价',
-                                'sell_sm_amount': '小单卖出',
+                            'pct_chg': '涨跌幅',
-                                'buy_md_amount': '中单买入',
+                            'net_mf_amount': '主力净流入-净额'
-                                'sell_md_amount': '中单卖出',
-                                'buy_lg_amount': '大单买入',
-                                'sell_lg_amount': '大单卖出',
-                                'buy_elg_amount': '超大单买入',
-                                'sell_elg_amount': '超大单卖出',
-                                'net_mf_amount': '净额'
                         })
-                            
+                        df['主力净流入-净额'] = df['主力净流入-净额'] * 1000
-                            # 限制为最近N天
+                        df['超大单净流入-净额'] = (df['buy_elg_amount'] - df['sell_elg_amount']) * 1000
+                        df['大单净流入-净额'] = (df['buy_lg_amount'] - df['sell_lg_amount']) * 1000
+                        df['中单净流入-净额'] = (df['buy_md_amount'] - df['sell_md_amount']) * 1000
+                        df['小单净流入-净额'] = (df['buy_sm_amount'] - df['sell_sm_amount']) * 1000
+                        # tushare按日期倒序返回，取最近N天后转为正序（与akshare一致）
                         df = df.head(self.days)
+                        df = df.iloc[::-1].reset_index(drop=True)
                         print(f"   [Tushare] ✅ 成功获取 {len(df)} 条资金流向数据")
                     else:
-                            print(f"   [Tushare] ❌ 未找到资金流向数据")
+                        print(f"   [Tushare] ❌ 未找到资金流向数据，尝试备用数据源")
-                            return None
+                        df = None
                 except Exception as te:
                     print(f"   [Tushare] ❌ 获取失败: {te}")
-                        return None
+                    df = None
             else:
+                df = None
+
+            # tushare不可用或失败时，回退akshare
+            if df is None or df.empty:
+                print(f"   [Akshare] 正在获取资金流向 (市场: {market})（备用数据源）...")
+                df = ak.stock_individual_fund_flow(stock=symbol, market=market)
+                if df is None or df.empty:
+                    print(f"   [Akshare] 未找到资金流向数据")
                     return None
             
             # akshare 返回的数据是按时间正序排列（从旧到新），所以使用 tail() 获取最近N天的数据
@@ -341,4 +339,3 @@ if __name__ == "__main__":
             print(f"\n获取失败: {data.get('error', '未知错误')}")
         
         print("\n")
-

http_timeout.py

@@ -0,0 +1,77 @@
+"""
+统一的外部请求超时控制工具
+
+- install_default_requests_timeout: 给所有基于 requests 的外部请求
+  （akshare、tushare、pywencai 等）注入默认超时，调用方未指定 timeout 时生效。
+- call_with_timeout: 在独立线程中执行阻塞调用，超过时限立即放弃等待，
+  防止个别环节长时间卡死整个程序。
+"""
+
+import os
+import threading
+
+# 默认连接超时 / 读取超时（单位：秒），可通过环境变量覆盖
+DEFAULT_CONNECT_TIMEOUT = float(os.getenv("HTTP_CONNECT_TIMEOUT", "6"))
+DEFAULT_READ_TIMEOUT = float(os.getenv("HTTP_READ_TIMEOUT", "20"))
+DEFAULT_CALL_TIMEOUT = float(os.getenv("HTTP_CALL_TIMEOUT", "30"))
+
+_installed = False
+_install_lock = threading.Lock()
+
+
+def install_default_requests_timeout(connect_timeout=None, read_timeout=None):
+    """给 requests 库注入默认超时（仅当调用方未显式指定 timeout 时生效）。"""
+    global _installed
+    with _install_lock:
+        if _installed:
+            return
+
+        connect = DEFAULT_CONNECT_TIMEOUT if connect_timeout is None else connect_timeout
+        read = DEFAULT_READ_TIMEOUT if read_timeout is None else read_timeout
+
+        try:
+            import requests.sessions
+            original_request = requests.sessions.Session.request
+
+            def request_with_timeout(self, method, url, **kwargs):
+                if kwargs.get("timeout") is None:
+                    kwargs["timeout"] = (connect, read)
+                return original_request(self, method, url, **kwargs)
+
+            requests.sessions.Session.request = request_with_timeout
+            _installed = True
+            print(f"✅ 已为外部请求注入默认超时（连接 {connect}s / 读取 {read}s）")
+        except Exception as e:
+            print(f"⚠️ 注入 requests 默认超时失败: {e}")
+
+
+def call_with_timeout(func, timeout=None, *args, **kwargs):
+    """在线程中执行函数，超过 timeout 秒则放弃等待并抛出 TimeoutError。
+
+    注意：超时后线程会继续在后台运行直至结束（守护线程），不会影响主程序。
+    """
+    if timeout is None:
+        timeout = DEFAULT_CALL_TIMEOUT
+    if timeout <= 0:
+        return func(*args, **kwargs)
+
+    result_box = {}
+
+    def _run():
+        try:
+            result_box["ok"] = True
+            result_box["value"] = func(*args, **kwargs)
+        except BaseException as e:  # noqa: BLE001
+            result_box["ok"] = False
+            result_box["error"] = e
+
+    worker = threading.Thread(target=_run, daemon=True)
+    worker.start()
+    worker.join(timeout)
+
+    if worker.is_alive():
+        func_name = getattr(func, "__name__", "function")
+        raise TimeoutError(f"调用 {func_name} 超过 {timeout}s 未返回，已放弃等待")
+    if result_box.get("ok"):
+        return result_box["value"]
+    raise result_box.get("error", RuntimeError("未知错误"))

low_price_bull_selector.py

@@ -10,6 +10,7 @@ import pywencai
 from datetime import datetime
 from typing import Tuple, Optional
 import time
+from http_timeout import call_with_timeout
 
 
 class LowPriceBullSelector:
@@ -60,7 +61,7 @@ class LowPriceBullSelector:
             print(f"正在调用问财接口...")
             
             # 调用pywencai
-            result = pywencai.get(query=query, loop=True)
+            result = call_with_timeout(pywencai.get, timeout=30, query=query, loop=True)
             
             if result is None:
                 return False, None, "问财接口返回None，请检查网络或稍后重试"

main_force_selector.py

@@ -11,6 +11,7 @@ import pywencai
 from datetime import datetime, timedelta
 from typing import Dict, List, Tuple
 import time
+from http_timeout import call_with_timeout
 
 class MainForceStockSelector:
     """主力选股类"""
@@ -71,7 +72,7 @@ class MainForceStockSelector:
                 print(f"查询语句: {query[:100]}...")
                 
                 try:
-                    result = pywencai.get(query=query, loop=True)
+                    result = call_with_timeout(pywencai.get, timeout=30, query=query, loop=True)
                     
                     if result is None:
                         print(f"  ⚠️ 方案{i}返回None，尝试下一个方案")
@@ -387,4 +388,3 @@ class MainForceStockSelector:
 
 # 全局实例
 main_force_selector = MainForceStockSelector()
-

market_sentiment_data.py

@@ -329,11 +329,56 @@ class MarketSentimentDataFetcher:
         }
     
     def _get_turnover_rate(self, symbol):
-        """获取换手率数据（支持akshare和tushare自动切换）"""
+        """获取换手率数据（优先tushare，失败时使用akshare）"""
         try:
-            # 优先使用akshare获取最近的换手率数据
+            # 优先使用tushare（daily_basic，取最近10个交易日保证非交易日也有数据）
-            print(f"   [Akshare] 正在获取换手率数据...")
+            if data_source_manager.tushare_available:
-            # 获取A股实时行情数据（不需要参数）
+                try:
+                    print(f"   [Tushare] 正在获取换手率数据（主要数据源）...")
+                    ts_code = data_source_manager._convert_to_ts_code(symbol)
+                    
+                    end_date = datetime.now().strftime('%Y%m%d')
+                    start_date = (datetime.now() - timedelta(days=10)).strftime('%Y%m%d')
+                    df = data_source_manager.tushare_api.daily_basic(
+                        ts_code=ts_code,
+                        start_date=start_date,
+                        end_date=end_date
+                    )
+                    
+                    if df is not None and not df.empty:
+                        row = df.iloc[0]
+                        turnover_rate = row.get('turnover_rate', 'N/A')
+                        
+                        # 解读换手率
+                        interpretation = ""
+                        if turnover_rate != 'N/A':
+                            try:
+                                turnover = float(turnover_rate)
+                                if turnover > 20:
+                                    interpretation = "换手率极高（>20%），资金活跃度极高，可能存在炒作"
+                                elif turnover > 10:
+                                    interpretation = "换手率较高（>10%），交易活跃"
+                                elif turnover > 5:
+                                    interpretation = "换手率正常（5%-10%），交易适中"
+                                elif turnover > 2:
+                                    interpretation = "换手率偏低（2%-5%），交易相对清淡"
+                                else:
+                                    interpretation = "换手率很低（<2%），交易清淡"
+                            except:
+                                pass
+                        
+                        print(f"   [Tushare] ✅ 成功获取换手率: {turnover_rate}%")
+                        return {
+                            "current_turnover_rate": turnover_rate,
+                            "interpretation": interpretation
+                        }
+                    else:
+                        print(f"   [Tushare] ❌ 未获取到换手率，尝试备用数据源")
+                except Exception as te:
+                    print(f"   [Tushare] ❌ 获取失败: {te}")
+            
+            # tushare失败，回退akshare
+            print(f"   [Akshare] 正在获取换手率数据（备用数据源）...")
             df = ak.stock_zh_a_spot_em()
             if df is not None and not df.empty:
                 stock_data = df[df['代码'] == symbol]
@@ -367,56 +412,41 @@ class MarketSentimentDataFetcher:
         except Exception as e:
             print(f"   [Akshare] ❌ 获取换手率失败: {e}")
         
-            # akshare失败，尝试tushare
-            if data_source_manager.tushare_available:
-                try:
-                    print(f"   [Tushare] 正在获取换手率数据（备用数据源）...")
-                    ts_code = data_source_manager._convert_to_ts_code(symbol)
-                    
-                    # 获取最近一个交易日的数据
-                    df = data_source_manager.tushare_api.daily_basic(
-                        ts_code=ts_code,
-                        trade_date=datetime.now().strftime('%Y%m%d')
-                    )
-                    
-                    if df is not None and not df.empty:
-                        row = df.iloc[0]
-                        turnover_rate = row.get('turnover_rate', 'N/A')
-                        
-                        # 解读换手率
-                        interpretation = ""
-                        if turnover_rate != 'N/A':
-                            try:
-                                turnover = float(turnover_rate)
-                                if turnover > 20:
-                                    interpretation = "换手率极高（>20%），资金活跃度极高，可能存在炒作"
-                                elif turnover > 10:
-                                    interpretation = "换手率较高（>10%），交易活跃"
-                                elif turnover > 5:
-                                    interpretation = "换手率正常（5%-10%），交易适中"
-                                elif turnover > 2:
-                                    interpretation = "换手率偏低（2%-5%），交易相对清淡"
-                                else:
-                                    interpretation = "换手率很低（<2%），交易清淡"
-                            except:
-                                pass
-                        
-                        print(f"   [Tushare] ✅ 成功获取换手率: {turnover_rate}%")
-                        return {
-                            "current_turnover_rate": turnover_rate,
-                            "interpretation": interpretation
-                        }
-                except Exception as te:
-                    print(f"   [Tushare] ❌ 获取失败: {te}")
-        
         return None
     
     def _get_market_index_sentiment(self):
         """获取大盘指数情绪（支持akshare和tushare自动切换）"""
         try:
-            # 优先使用akshare获取上证指数实时数据
+            # 优先使用tushare（index_daily，取最近10个交易日保证非交易日也有数据）
-            print(f"   [Akshare] 正在获取大盘指数数据...")
+            if data_source_manager.tushare_available:
-            # 使用正确的symbol参数
+                try:
+                    print(f"   [Tushare] 正在获取大盘指数数据（主要数据源）...")
+                    
+                    # 获取上证指数数据
+                    end_date = datetime.now().strftime('%Y%m%d')
+                    start_date = (datetime.now() - timedelta(days=10)).strftime('%Y%m%d')
+                    df = data_source_manager.tushare_api.index_daily(
+                        ts_code='000001.SH',
+                        start_date=start_date,
+                        end_date=end_date
+                    )
+                    
+                    if df is not None and not df.empty:
+                        row = df.iloc[0]
+                        change_pct = row.get('pct_chg', 0)
+                        
+                        print(f"   [Tushare] ✅ 成功获取大盘指数涨跌幅: {change_pct}%")
+                        return {
+                            "index_name": "上证指数",
+                            "change_percent": change_pct
+                        }
+                    else:
+                        print(f"   [Tushare] ❌ 未获取到大盘指数，尝试备用数据源")
+                except Exception as te:
+                    print(f"   [Tushare] ❌ 获取失败: {te}")
+            
+            # tushare失败，回退akshare
+            print(f"   [Akshare] 正在获取大盘指数数据（备用数据源）...")
             df = ak.stock_zh_index_spot_em(symbol="上证系列指数")
             if df is not None and not df.empty:
                 # 查找上证指数（代码为000001）
@@ -471,30 +501,6 @@ class MarketSentimentDataFetcher:
         except Exception as e:
             print(f"   [Akshare] ❌ 获取大盘指数失败: {e}")
         
-            # akshare失败，尝试tushare
-            if data_source_manager.tushare_available:
-                try:
-                    print(f"   [Tushare] 正在获取大盘指数数据（备用数据源）...")
-                    
-                    # 获取上证指数数据
-                    df = data_source_manager.tushare_api.index_daily(
-                        ts_code='000001.SH',
-                        start_date=datetime.now().strftime('%Y%m%d'),
-                        end_date=datetime.now().strftime('%Y%m%d')
-                    )
-                    
-                    if df is not None and not df.empty:
-                        row = df.iloc[0]
-                        change_pct = row.get('pct_chg', 0)
-                        
-                        print(f"   [Tushare] ✅ 成功获取大盘指数涨跌幅: {change_pct}%")
-                        return {
-                            "index_name": "上证指数",
-                            "change_percent": change_pct
-                        }
-                except Exception as te:
-                    print(f"   [Tushare] ❌ 获取失败: {te}")
-        
         return None
     
     def _get_limit_up_down_stats(self):
@@ -503,6 +509,37 @@ class MarketSentimentDataFetcher:
             # 获取今日涨停和跌停统计
             today = datetime.now().strftime('%Y%m%d')
             
+            limit_up_count = 0
+            limit_down_count = 0
+            
+            # 优先使用tushare的涨跌停列表
+            if data_source_manager.tushare_available:
+                try:
+                    print(f"   [Tushare] 正在获取涨跌停数据（主要数据源）...")
+                    df_ll = data_source_manager.tushare_api.limit_list_d(trade_date=today)
+                    if df_ll is not None and not df_ll.empty:
+                        if 'limit_type' in df_ll.columns:
+                            limit_up_count = int((df_ll['limit_type'].fillna('') == 'U').sum())
+                            limit_down_count = int((df_ll['limit_type'].fillna('') == 'D').sum())
+                            print(f"   [Tushare] ✅ 成功获取涨跌停: 涨停{limit_up_count} / 跌停{limit_down_count}")
+                        elif 'pct_chg' in df_ll.columns:
+                            limit_up_count = int((df_ll['pct_chg'] >= 9.5).sum())
+                            limit_down_count = int((df_ll['pct_chg'] <= -9.5).sum())
+                            print(f"   [Tushare] ✅ 成功获取涨跌停: 涨停{limit_up_count} / 跌停{limit_down_count}")
+                        elif '涨跌幅' in df_ll.columns:
+                            limit_up_count = int((df_ll['涨跌幅'] >= 9.5).sum())
+                            limit_down_count = int((df_ll['涨跌幅'] <= -9.5).sum())
+                            print(f"   [Tushare] ✅ 成功获取涨跌停: 涨停{limit_up_count} / 跌停{limit_down_count}")
+                        else:
+                            # 无法识别的列结构，按0处理并回退akshare
+                            print(f"   [Tushare] ⚠ 涨跌停返回列无法识别: {list(df_ll.columns)[:10]}，尝试备用数据源")
+                    else:
+                        print(f"   [Tushare] ❌ 未获取到涨跌停数据，尝试备用数据源")
+                except Exception as e:
+                    print(f"   [Tushare] ❌ 获取涨跌停数据失败: {e}")
+            
+            # tushare不可用或失败时，回退akshare
+            if limit_up_count == 0 and limit_down_count == 0:
                 # 获取涨停股票
                 try:
                     limit_up_df = ak.stock_zt_pool_em(date=today)
@@ -549,6 +586,44 @@ class MarketSentimentDataFetcher:
     def _get_margin_trading_data(self, symbol):
         """获取融资融券数据"""
         try:
+            # 优先使用tushare的个股融资融券明细
+            if data_source_manager.tushare_available:
+                try:
+                    print(f"   [Tushare] 正在获取融资融券数据（主要数据源）...")
+                    ts_code = data_source_manager._convert_to_ts_code(symbol)
+                    end_date = datetime.now().strftime('%Y%m%d')
+                    start_date = (datetime.now() - timedelta(days=15)).strftime('%Y%m%d')
+                    df = data_source_manager.tushare_api.margin_detail(
+                        ts_code=ts_code,
+                        start_date=start_date,
+                        end_date=end_date
+                    )
+                    if df is not None and not df.empty:
+                        latest = df.iloc[0]
+                        margin_balance = latest.get('rzye', 0) or 0
+                        short_balance = latest.get('rqye', 0) or 0
+                        
+                        # 解读融资融券
+                        interpretation = []
+                        if margin_balance > short_balance * 10:
+                            interpretation.append("融资余额远大于融券余额，投资者看多情绪强")
+                        elif margin_balance > short_balance * 3:
+                            interpretation.append("融资余额大于融券余额，投资者偏看多")
+                        else:
+                            interpretation.append("融资融券相对平衡")
+                        
+                        print(f"   [Tushare] ✅ 成功获取融资融券数据")
+                        return {
+                            "margin_balance": margin_balance,
+                            "short_balance": short_balance,
+                            "interpretation": interpretation,
+                            "date": str(latest.get('trade_date', datetime.now().strftime('%Y-%m-%d')))
+                        }
+                    else:
+                        print(f"   [Tushare] ❌ 未获取到融资融券数据，尝试备用数据源")
+                except Exception as e:
+                    print(f"   [Tushare] ❌ 获取融资融券数据失败: {e}")
+            
             # 获取个股融资融券数据（尝试多个API）
             try:
                 # 方法1：获取沪深融资融券明细
@@ -762,4 +837,3 @@ if __name__ == "__main__":
         print(formatted_text)
     else:
         print(f"\n获取失败: {sentiment_data.get('error', '未知错误')}")
-

news_announcement_data.py

@@ -9,6 +9,7 @@ import sys
 import io
 import warnings
 from datetime import datetime
+from http_timeout import call_with_timeout
 
 warnings.filterwarnings('ignore')
 
@@ -100,7 +101,7 @@ class NewsAnnouncementDataFetcher:
             print(f"   使用问财查询: {query}")
             
             # 使用pywencai查询
-            result = pywencai.get(query=query, loop=True)
+            result = call_with_timeout(pywencai.get, timeout=30, query=query, loop=True)
             
             if result is None:
                 print(f"   问财查询返回None")
@@ -191,7 +192,7 @@ class NewsAnnouncementDataFetcher:
             print(f"   使用问财查询: {query}")
             
             # 使用pywencai查询
-            result = pywencai.get(query=query, loop=True)
+            result = call_with_timeout(pywencai.get, timeout=30, query=query, loop=True)
             
             if result is None:
                 print(f"   问财查询返回None")
@@ -342,4 +343,3 @@ if __name__ == "__main__":
         print(formatted_text)
     else:
         print(f"\n获取失败: {data.get('error', '未知错误')}")
-

profit_growth_selector.py

@@ -8,6 +8,7 @@
 import logging
 from typing import Tuple, Optional
 import pandas as pd
+from http_timeout import call_with_timeout
 
 
 class ProfitGrowthSelector:
@@ -50,7 +51,7 @@ class ProfitGrowthSelector:
             self.logger.info(f"开始执行净利增长选股，查询条件: {query}")
             
             # 调用pywencai
-            result = pywencai.get(query=query, loop=True)
+            result = call_with_timeout(pywencai.get, timeout=30, query=query, loop=True)
             
             if result is None or result.empty:
                 self.logger.warning("未获取到符合条件的股票")

qstock_news_data.py

@@ -1,6 +1,6 @@
 """
 新闻数据获取模块
-使用akshare获取股票的最新新闻信息（替代qstock）
+优先使用tushare，失败时使用akshare获取股票的最新新闻信息
 """
 
 import pandas as pd
@@ -9,6 +9,7 @@ import io
 import warnings
 from datetime import datetime, timedelta
 import akshare as ak
+from data_source_manager import data_source_manager
 
 warnings.filterwarnings('ignore')
 
@@ -30,6 +31,9 @@ def _setup_stdout_encoding():
 
 _setup_stdout_encoding()
 
+# 记录tushare news接口是否无权限（避免每次分析都重复请求失败）
+_tushare_news_unavailable = False
+
 
 class QStockNewsDataFetcher:
     """新闻数据获取类（使用akshare作为数据源）"""
@@ -37,7 +41,7 @@ class QStockNewsDataFetcher:
     def __init__(self):
         self.max_items = 30  # 最多获取的新闻数量
         self.available = True
-        print("✓ 新闻数据获取器初始化成功（akshare数据源）")
+        print("✓ 新闻数据获取器初始化成功（tushare优先/akshare备用）")
     
     def get_stock_news(self, symbol):
         """
@@ -67,7 +71,7 @@ class QStockNewsDataFetcher:
         
         try:
             # 获取新闻数据
-            print(f"📰 正在使用qstock获取 {symbol} 的最新新闻...")
+            print(f"📰 正在获取 {symbol} 的最新新闻...")
             news_data = self._get_news_data(symbol)
             
             if news_data:
@@ -89,9 +93,20 @@ class QStockNewsDataFetcher:
         return symbol.isdigit() and len(symbol) == 6
     
     def _get_news_data(self, symbol):
-        """获取新闻数据（使用akshare）"""
+        """获取新闻数据（优先tushare，失败时使用akshare）"""
         try:
-            print(f"   使用 akshare 获取新闻...")
+            # 优先使用tushare新闻接口
+            tushare_items = self._get_news_from_tushare(symbol)
+            if tushare_items:
+                print(f"   ✓ 从tushare获取到 {len(tushare_items)} 条相关新闻")
+                return {
+                    "items": tushare_items,
+                    "count": len(tushare_items),
+                    "query_time": datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
+                    "date_range": "最近新闻"
+                }
+            
+            print(f"   使用 akshare 获取新闻（备用数据源）...")
             
             news_items = []
             
@@ -223,6 +238,69 @@ class QStockNewsDataFetcher:
             traceback.print_exc()
             return None
     
+    def _get_news_from_tushare(self, symbol):
+        """从tushare获取个股新闻（按股票名称/代码过滤）"""
+        global _tushare_news_unavailable
+        try:
+            if _tushare_news_unavailable or not data_source_manager.tushare_available:
+                return None
+            
+            # 获取股票名称
+            stock_name = None
+            try:
+                basic = data_source_manager.get_stock_basic_info(symbol)
+                if basic and basic.get('name') and basic['name'] != '未知':
+                    stock_name = basic['name']
+            except Exception as e:
+                print(f"   获取股票名称失败: {e}")
+            
+            # 查询最近7天的全市场新闻（东方财富源）
+            end_date = datetime.now().strftime('%Y-%m-%d')
+            start_date = (datetime.now() - timedelta(days=7)).strftime('%Y-%m-%d')
+            df = data_source_manager.tushare_api.news(
+                src='eastmoney',
+                start_date=start_date,
+                end_date=end_date
+            )
+            if df is None or df.empty:
+                return None
+            
+            # 按股票代码或名称过滤
+            mask = df['title'].str.contains(symbol, na=False) | df['title'].str.contains(stock_name, na=False) if stock_name else df['title'].str.contains(symbol, na=False)
+            if 'content' in df.columns:
+                mask = mask | df['content'].str.contains(symbol, na=False)
+                if stock_name:
+                    mask = mask | df['content'].str.contains(stock_name, na=False)
+            
+            df_filtered = df[mask]
+            if df_filtered.empty:
+                return None
+            
+            news_items = []
+            for _, row in df_filtered.head(self.max_items).iterrows():
+                item = {'source': 'tushare-东方财富'}
+                for col in ['title', 'content', 'pub_time']:
+                    if col in df_filtered.columns:
+                        value = row.get(col)
+                        if value is None or (isinstance(value, float) and pd.isna(value)):
+                            continue
+                        try:
+                            item[col] = str(value)
+                        except:
+                            item[col] = "无法解析"
+                if len(item) > 1:
+                    news_items.append(item)
+            return news_items or None
+            
+        except Exception as e:
+            error_msg = str(e)
+            if "权限" in error_msg or "积分" in error_msg:
+                _tushare_news_unavailable = True
+                print("   ⚠ tushare news 接口需要较高积分，当前账号无权限，已自动使用 akshare 获取新闻")
+            else:
+                print(f"   ⚠ 从tushare获取新闻失败: {error_msg}")
+            return None
+    
     def format_news_for_ai(self, data):
         """
         将新闻数据格式化为适合AI阅读的文本
@@ -236,7 +314,7 @@ class QStockNewsDataFetcher:
         if data.get("news_data"):
             news_data = data["news_data"]
             text_parts.append(f"""
-【最新新闻 - akshare数据源】
+【最新新闻 - tushare/akshare自动切换】
 查询时间：{news_data.get('query_time', 'N/A')}
 时间范围：{news_data.get('date_range', 'N/A')}
 新闻数量：{news_data.get('count', 0)}条
@@ -303,4 +381,3 @@ if __name__ == "__main__":
             print(f"\n获取失败: {data.get('error', '未知错误')}")
         
         print("\n")
-

quarterly_report_data.py

@@ -9,6 +9,8 @@ import io
 import warnings
 from datetime import datetime
 import akshare as ak
+from http_timeout import call_with_timeout
+from data_source_manager import data_source_manager
 
 warnings.filterwarnings('ignore')
 
@@ -67,25 +69,33 @@ class QuarterlyReportDataFetcher:
         try:
             print(f"📊 正在获取 {symbol} 的季报数据...")
             
-            # 获取利润表
+            # 获取利润表（优先tushare，失败时回退akshare）
+            income_data = self._get_income_statement_from_tushare(symbol)
+            if income_data is None:
                 income_data = self._get_income_statement(symbol)
             if income_data:
                 data["income_statement"] = income_data
                 print(f"   ✓ 成功获取 {len(income_data.get('data', []))} 期利润表数据")
             
-            # 获取资产负债表
+            # 获取资产负债表（优先tushare，失败时回退akshare）
+            balance_data = self._get_balance_sheet_from_tushare(symbol)
+            if balance_data is None:
                 balance_data = self._get_balance_sheet(symbol)
             if balance_data:
                 data["balance_sheet"] = balance_data
                 print(f"   ✓ 成功获取 {len(balance_data.get('data', []))} 期资产负债表数据")
             
-            # 获取现金流量表
+            # 获取现金流量表（优先tushare，失败时回退akshare）
+            cash_flow_data = self._get_cash_flow_from_tushare(symbol)
+            if cash_flow_data is None:
                 cash_flow_data = self._get_cash_flow(symbol)
             if cash_flow_data:
                 data["cash_flow"] = cash_flow_data
                 print(f"   ✓ 成功获取 {len(cash_flow_data.get('data', []))} 期现金流量表数据")
             
-            # 获取财务指标
+            # 获取财务指标（优先tushare，失败时回退akshare）
+            indicators_data = self._get_financial_indicators_from_tushare(symbol)
+            if indicators_data is None:
                 indicators_data = self._get_financial_indicators(symbol)
             if indicators_data:
                 data["financial_indicators"] = indicators_data
@@ -108,11 +118,160 @@ class QuarterlyReportDataFetcher:
         """判断是否为中国股票"""
         return symbol.isdigit() and len(symbol) == 6
     
+    def _convert_ts_records(self, df, field_map, periods):
+        """将tushare返回的财务表转换为统一的记录结构"""
+        try:
+            data_list = []
+            for _, row in df.head(periods).iterrows():
+                item = {}
+                for ch_name, ts_name in field_map.items():
+                    if ts_name in df.columns:
+                        value = row.get(ts_name)
+                        if value is None or (isinstance(value, float) and pd.isna(value)):
+                            continue
+                        try:
+                            item[ch_name] = str(value)
+                        except:
+                            item[ch_name] = "N/A"
+                if item:
+                    data_list.append(item)
+            return {
+                "data": data_list,
+                "periods": len(data_list),
+                "columns": list(field_map.keys()),
+                "query_time": datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+            }
+        except Exception as e:
+            print(f"   转换tushare财务数据异常: {e}")
+            return None
+    
+    def _get_income_statement_from_tushare(self, symbol):
+        """从tushare获取利润表（优先数据源）"""
+        try:
+            if not data_source_manager.tushare_available:
+                return None
+            ts_code = data_source_manager._convert_to_ts_code(symbol)
+            df = data_source_manager.tushare_api.income(ts_code=ts_code)
+            if df is None or df.empty:
+                return None
+            df = df.drop_duplicates(subset=['end_date']).sort_values('end_date', ascending=False)
+            field_map = {
+                '报告期': 'end_date',
+                '营业总收入': 'total_revenue',
+                '营业收入': 'revenue',
+                '营业总成本': 'total_operate_cost',
+                '营业利润': 'operate_profit',
+                '利润总额': 'total_profit',
+                '净利润': 'n_income',
+                '归属于母公司所有者的净利润': 'n_income_attr_p',
+                '基本每股收益': 'basic_eps',
+                '稀释每股收益': 'diluted_eps',
+                '销售费用': 'sell_exp',
+                '管理费用': 'admin_exp',
+                '财务费用': 'fin_exp',
+                '研发费用': 'rd_exp',
+            }
+            result = self._convert_ts_records(df, field_map, self.periods)
+            if result and result.get('periods'):
+                print(f"   ✓ tushare成功获取 {result['periods']} 期利润表数据")
+            return result
+        except Exception as e:
+            print(f"   tushare获取利润表异常: {e}")
+            return None
+    
+    def _get_balance_sheet_from_tushare(self, symbol):
+        """从tushare获取资产负债表（优先数据源）"""
+        try:
+            if not data_source_manager.tushare_available:
+                return None
+            ts_code = data_source_manager._convert_to_ts_code(symbol)
+            df = data_source_manager.tushare_api.balancesheet(ts_code=ts_code)
+            if df is None or df.empty:
+                return None
+            df = df.drop_duplicates(subset=['end_date']).sort_values('end_date', ascending=False)
+            field_map = {
+                '报告期': 'end_date',
+                '资产总计': 'total_assets',
+                '流动资产合计': 'total_cur_assets',
+                '非流动资产合计': 'total_ncur_assets',
+                '负债合计': 'total_liab',
+                '流动负债合计': 'total_cur_liab',
+                '非流动负债合计': 'total_ncur_liab',
+                '所有者权益合计': 'total_hldr_eqy_inc_min_int',
+                '归属于母公司股东权益合计': 'total_hldr_eqy_exc_min_int',
+            }
+            result = self._convert_ts_records(df, field_map, self.periods)
+            if result and result.get('periods'):
+                print(f"   ✓ tushare成功获取 {result['periods']} 期资产负债表数据")
+            return result
+        except Exception as e:
+            print(f"   tushare获取资产负债表异常: {e}")
+            return None
+    
+    def _get_cash_flow_from_tushare(self, symbol):
+        """从tushare获取现金流量表（优先数据源）"""
+        try:
+            if not data_source_manager.tushare_available:
+                return None
+            ts_code = data_source_manager._convert_to_ts_code(symbol)
+            df = data_source_manager.tushare_api.cashflow(ts_code=ts_code)
+            if df is None or df.empty:
+                return None
+            df = df.drop_duplicates(subset=['end_date']).sort_values('end_date', ascending=False)
+            field_map = {
+                '报告期': 'end_date',
+                '经营活动产生的现金流量净额': 'n_cashflow_act',
+                '投资活动产生的现金流量净额': 'n_cashflow_inv_act',
+                '筹资活动产生的现金流量净额': 'n_cash_flows_fnc_act',
+            }
+            result = self._convert_ts_records(df, field_map, self.periods)
+            if result and result.get('periods'):
+                print(f"   ✓ tushare成功获取 {result['periods']} 期现金流量表数据")
+            return result
+        except Exception as e:
+            print(f"   tushare获取现金流量表异常: {e}")
+            return None
+    
+    def _get_financial_indicators_from_tushare(self, symbol):
+        """从tushare获取财务指标（优先数据源）"""
+        try:
+            if not data_source_manager.tushare_available:
+                return None
+            ts_code = data_source_manager._convert_to_ts_code(symbol)
+            df = data_source_manager.tushare_api.fina_indicator(ts_code=ts_code)
+            if df is None or df.empty:
+                return None
+            df = df.drop_duplicates(subset=['end_date']).sort_values('end_date', ascending=False)
+            field_map = {
+                '报告期': 'end_date',
+                '净资产收益率': 'roe',
+                '总资产净利率': 'roa',
+                '销售净利率': 'netprofit_margin',
+                '销售毛利率': 'grossprofit_margin',
+                '资产负债率': 'debt_to_assets',
+                '流动比率': 'current_ratio',
+                '速动比率': 'quick_ratio',
+                '应收账款周转率': 'ar_turnover',
+                '存货周转率': 'inventory_turnover',
+                '总资产周转率': 'assets_turnover',
+                '每股收益': 'eps',
+                '每股净资产': 'bps',
+                '每股经营现金流': 'cfps',
+            }
+            result = self._convert_ts_records(df, field_map, self.periods)
+            if result and result.get('periods'):
+                print(f"   ✓ tushare成功获取 {result['periods']} 期财务指标数据")
+            return result
+        except Exception as e:
+            print(f"   tushare获取财务指标异常: {e}")
+            return None
+    
     def _get_income_statement(self, symbol):
         """获取利润表数据"""
         try:
             # stock_financial_report_sina - 新浪财经季度利润表
-            df = ak.stock_financial_report_sina(stock=symbol, symbol="利润表")
+            df = call_with_timeout(ak.stock_financial_report_sina, timeout=25,
+                                   stock=symbol, symbol="利润表")
             
             if df is None or df.empty:
                 print(f"   未找到利润表数据")
@@ -151,7 +310,8 @@ class QuarterlyReportDataFetcher:
         """获取资产负债表数据"""
         try:
             # stock_financial_report_sina - 新浪财经季度资产负债表
-            df = ak.stock_financial_report_sina(stock=symbol, symbol="资产负债表")
+            df = call_with_timeout(ak.stock_financial_report_sina, timeout=25,
+                                   stock=symbol, symbol="资产负债表")
             
             if df is None or df.empty:
                 print(f"   未找到资产负债表数据")
@@ -190,7 +350,8 @@ class QuarterlyReportDataFetcher:
         """获取现金流量表数据"""
         try:
             # stock_financial_report_sina - 新浪财经季度现金流量表
-            df = ak.stock_financial_report_sina(stock=symbol, symbol="现金流量表")
+            df = call_with_timeout(ak.stock_financial_report_sina, timeout=25,
+                                   stock=symbol, symbol="现金流量表")
             
             if df is None or df.empty:
                 print(f"   未找到现金流量表数据")
@@ -229,7 +390,7 @@ class QuarterlyReportDataFetcher:
         """获取财务指标数据"""
         try:
             # 使用stock_financial_abstract替代已失效的stock_financial_analysis_indicator
-            df = ak.stock_financial_abstract(symbol=symbol)
+            df = call_with_timeout(ak.stock_financial_abstract, timeout=25, symbol=symbol)
             
             if df is None or df.empty:
                 print(f"   未找到财务指标数据")
@@ -292,7 +453,7 @@ class QuarterlyReportDataFetcher:
         
         text_parts = []
         text_parts.append(f"""
-【季度财务报告数据 - akshare数据源】
+【季度财务报告数据 - tushare/akshare自动切换】
 股票代码：{data.get('symbol', 'N/A')}
 数据期数：最近{self.periods}期季报
 
@@ -423,4 +584,3 @@ if __name__ == "__main__":
             print(f"\n获取失败: {data.get('error', '未知错误')}")
         
         print("\n")
-

risk_data_fetcher.py

@@ -12,6 +12,7 @@ from typing import Dict, Any
 import time
 import warnings
 import os
+from http_timeout import call_with_timeout
 
 # 屏蔽pywencai的Node.js警告信息（不影响功能）
 warnings.filterwarnings('ignore', category=DeprecationWarning)
@@ -108,7 +109,7 @@ class RiskDataFetcher:
             query = f"{symbol}限售解禁"
             
             # 使用pywencai查询
-            response = pywencai.get(query=query, loop=True)
+            response = call_with_timeout(pywencai.get, timeout=30, query=query, loop=True)
             
             if response is None:
                 return result
@@ -171,7 +172,7 @@ class RiskDataFetcher:
             query = f"{symbol}大股东减持公告"
             
             # 使用pywencai查询
-            response = pywencai.get(query=query, loop=True)
+            response = call_with_timeout(pywencai.get, timeout=30, query=query, loop=True)
             
             if response is None:
                 return result
@@ -234,7 +235,7 @@ class RiskDataFetcher:
             query = f"{symbol}近期重要事件"
             
             # 使用pywencai查询
-            response = pywencai.get(query=query, loop=True)
+            response = call_with_timeout(pywencai.get, timeout=30, query=query, loop=True)
             
             if response is None:
                 return result
@@ -466,4 +467,3 @@ if __name__ == "__main__":
     if risk_data['data_success']:
         print("\n格式化的风险数据:")
         print(fetcher.format_risk_data_for_ai(risk_data))
-

sector_strategy_data.py

@@ -1,6 +1,6 @@
 """
 智策板块数据采集模块
-使用AKShare获取板块相关数据
+优先使用Tushare，失败时使用AKShare获取板块相关数据
 """
 
 import akshare as ak
@@ -12,6 +12,7 @@ import logging
 import os
 from dotenv import load_dotenv
 from sector_strategy_db import SectorStrategyDatabase
+from data_source_manager import data_source_manager
 
 # 加载环境变量
 load_dotenv()
@@ -254,40 +255,52 @@ class SectorStrategyDataFetcher:
             except:
                 pass
             
-            # 大盘指数
+            # 大盘指数（优先tushare，失败回退akshare）
+            def _get_index_data(index_code, ts_code, name):
+                if data_source_manager.tushare_available:
                     try:
-                # 上证指数
+                        end = datetime.now().strftime('%Y%m%d')
-                df_sh = ak.stock_zh_index_spot_em(symbol="上证指数")
+                        start = (datetime.now() - timedelta(days=10)).strftime('%Y%m%d')
-                if df_sh is not None and not df_sh.empty:
+                        df = data_source_manager.tushare_api.index_daily(
-                    overview["sh_index"] = {
+                            ts_code=ts_code, start_date=start, end_date=end
-                        "code": "000001",
+                        )
-                        "name": "上证指数",
+                        if df is not None and not df.empty:
-                        "close": df_sh.iloc[0].get('最新价', 0),
+                            row = df.iloc[0]
-                        "change_pct": df_sh.iloc[0].get('涨跌幅', 0),
+                            return {
-                        "change": df_sh.iloc[0].get('涨跌额', 0)
+                                "code": index_code,
+                                "name": name,
+                                "close": row.get('close', 0),
+                                "change_pct": row.get('pct_chg', 0),
+                                "change": row.get('change', 0)
                             }
+                        print(f"    tushare未获取到{name}，尝试备用数据源")
+                    except Exception as e:
+                        print(f"    tushare获取{name}失败: {e}")
+                try:
+                    df = self._safe_request(ak.stock_zh_index_spot_em, symbol=name)
+                    if df is not None and not df.empty:
+                        row = df.iloc[0]
+                        return {
+                            "code": index_code,
+                            "name": name,
+                            "close": row.get('最新价', 0),
+                            "change_pct": row.get('涨跌幅', 0),
+                            "change": row.get('涨跌额', 0)
+                        }
+                except Exception as e:
+                    print(f"    akshare获取{name}失败: {e}")
+                return None
             
-                # 深证成指
+            try:
-                df_sz = self._safe_request(ak.stock_zh_index_spot_em, symbol="深证成指")
+                sh_index = _get_index_data("000001", "000001.SH", "上证指数")
-                if df_sz is not None and not df_sz.empty:
+                if sh_index:
-                    overview["sz_index"] = {
+                    overview["sh_index"] = sh_index
-                        "code": "399001",
+                sz_index = _get_index_data("399001", "399001.SZ", "深证成指")
-                        "name": "深证成指",
+                if sz_index:
-                        "close": df_sz.iloc[0].get('最新价', 0),
+                    overview["sz_index"] = sz_index
-                        "change_pct": df_sz.iloc[0].get('涨跌幅', 0),
+                cyb_index = _get_index_data("399006", "399006.SZ", "创业板指")
-                        "change": df_sz.iloc[0].get('涨跌额', 0)
+                if cyb_index:
-                    }
+                    overview["cyb_index"] = cyb_index
-                
-                # 创业板指
-                df_cyb = self._safe_request(ak.stock_zh_index_spot_em, symbol="创业板指")
-                if df_cyb is not None and not df_cyb.empty:
-                    overview["cyb_index"] = {
-                        "code": "399006",
-                        "name": "创业板指",
-                        "close": df_cyb.iloc[0].get('最新价', 0),
-                        "change_pct": df_cyb.iloc[0].get('涨跌幅', 0),
-                        "change": df_cyb.iloc[0].get('涨跌额', 0)
-                    }
             except:
                 pass
             
@@ -310,7 +323,7 @@ class SectorStrategyDataFetcher:
                     try:
                         import tushare as ts
                         ts.set_token(tushare_token)
-                        self.ts_pro = ts.pro_api()
+                        self.ts_pro = ts.pro_api(timeout=float(os.getenv('TUSHARE_TIMEOUT', '15')))
                         print("    [Tushare] ✅ 初始化成功")
                     except Exception as e:
                         print(f"    [Tushare] 初始化失败: {e}")
@@ -757,4 +770,3 @@ if __name__ == "__main__":
         print(f"\n... (总长度: {len(formatted_text)} 字符)")
     else:
         print(f"\n数据采集失败: {data.get('error', '未知错误')}")
-

small_cap_selector.py

@@ -8,6 +8,7 @@
 import logging
 from typing import Tuple, Optional
 import pandas as pd
+from http_timeout import call_with_timeout
 
 
 class SmallCapSelector:
@@ -54,7 +55,7 @@ class SmallCapSelector:
             self.logger.info(f"开始执行小市值策略选股，查询条件: {query}")
             
             # 调用pywencai
-            result = pywencai.get(query=query, loop=True)
+            result = call_with_timeout(pywencai.get, timeout=30, query=query, loop=True)
             
             if result is None or result.empty:
                 self.logger.warning("未获取到符合条件的股票")

smart_monitor_data.py

@@ -54,7 +54,7 @@ class SmartMonitorDataFetcher:
             try:
                 import tushare as ts
                 ts.set_token(tushare_token)
-                self.ts_pro = ts.pro_api()
+                self.ts_pro = ts.pro_api(timeout=float(os.getenv('TUSHARE_TIMEOUT', '15')))
                 self.logger.info("Tushare备用数据源初始化成功")
             except Exception as e:
                 self.logger.warning(f"Tushare初始化失败: {e}")
@@ -64,7 +64,7 @@ class SmartMonitorDataFetcher:
     def get_realtime_quote(self, stock_code: str, retry: int = 1) -> Optional[Dict]:
         """
         获取实时行情（带重试和降级机制）
-        优先使用TDX，失败时降级到AKShare，最后降级到Tushare
+        优先使用TDX，失败时降级到Tushare，最后降级到AKShare
         
         Args:
             stock_code: 股票代码（如：600519）
@@ -82,11 +82,20 @@ class SmartMonitorDataFetcher:
                 if quote:
                     return quote
                 else:
-                    self.logger.warning(f"TDX获取失败 {stock_code}，尝试降级到AKShare")
+                    self.logger.warning(f"TDX获取失败 {stock_code}，尝试降级到Tushare")
             except Exception as e:
-                self.logger.warning(f"TDX获取异常 {stock_code}: {e}，尝试降级到AKShare")
+                self.logger.warning(f"TDX获取异常 {stock_code}: {e}，尝试降级到Tushare")
         
-        # 方法2: 组合使用AKShare分钟行情 + 基本信息
+        # 方法2: 降级到Tushare（优先于AKShare）
+        if self.ts_pro:
+            quote = self._get_realtime_quote_from_tushare(stock_code)
+            if quote:
+                return quote
+            self.logger.warning(f"Tushare获取失败 {stock_code}，尝试降级到AKShare")
+        else:
+            self.logger.warning(f"未配置Tushare，尝试使用AKShare")
+        
+        # 方法3: 组合使用AKShare分钟行情 + 基本信息
         for attempt in range(retry):
             try:
                 # 1.1 获取股票基本信息（名称）
@@ -167,18 +176,14 @@ class SmartMonitorDataFetcher:
                 else:
                     self.logger.warning(f"AKShare获取失败 {stock_code}（已重试{retry}次），尝试降级")
         
-        # 降级到Tushare
+        # 所有数据源都失败
-        if self.ts_pro:
+        self.logger.error(f"所有数据源都无法获取 {stock_code} 行情")
-            self.logger.info(f"降级到Tushare获取 {stock_code}...")
-            return self._get_realtime_quote_from_tushare(stock_code)
-        else:
-            self.logger.error(f"AKShare失败且未配置Tushare，无法获取 {stock_code} 行情")
         return None
     
     def get_technical_indicators(self, stock_code: str, period: str = 'daily', retry: int = 1) -> Optional[Dict]:
         """
         计算技术指标（带降级机制）
-        优先使用TDX，失败时降级到AKShare，最后降级到Tushare
+        优先使用TDX，失败时降级到Tushare，最后降级到AKShare
         
         Args:
             stock_code: 股票代码
@@ -197,11 +202,21 @@ class SmartMonitorDataFetcher:
                 if indicators:
                     return indicators
                 else:
-                    self.logger.warning(f"TDX计算技术指标失败 {stock_code}，尝试降级到AKShare")
+                    self.logger.warning(f"TDX计算技术指标失败 {stock_code}，尝试降级到Tushare")
             except Exception as e:
-                self.logger.warning(f"TDX计算技术指标异常 {stock_code}: {e}，尝试降级到AKShare")
+                self.logger.warning(f"TDX计算技术指标异常 {stock_code}: {e}，尝试降级到Tushare")
         
-        # 方法2: 尝试使用AKShare
+        # 方法2: 降级到Tushare（优先于AKShare）
+        if self.ts_pro:
+            self.logger.info(f"降级到Tushare获取 {stock_code} 历史数据...")
+            indicators = self._get_technical_indicators_from_tushare(stock_code, period)
+            if indicators:
+                return indicators
+            self.logger.warning(f"Tushare获取技术指标失败 {stock_code}，尝试降级到AKShare")
+        else:
+            self.logger.warning(f"未配置Tushare，尝试使用AKShare")
+        
+        # 方法3: 尝试使用AKShare
         for attempt in range(retry):
             try:
                 # 获取历史数据（最近200个交易日，用于计算指标）
@@ -237,12 +252,8 @@ class SmartMonitorDataFetcher:
                     self.logger.warning(f"AKShare获取历史数据失败 {stock_code}（已重试{retry}次），尝试降级到Tushare")
                     break
         
-        # 方法3: 降级到Tushare
+        # 所有数据源都失败
-        if self.ts_pro:
+        self.logger.error(f"所有数据源都无法获取 {stock_code} 技术指标")
-            self.logger.info(f"降级到Tushare获取 {stock_code} 历史数据...")
-            return self._get_technical_indicators_from_tushare(stock_code, period)
-        else:
-            self.logger.error(f"AKShare失败且未配置Tushare，无法获取 {stock_code} 技术指标")
         return None
     
     def _calculate_all_indicators(self, df: pd.DataFrame, stock_code: str) -> Optional[Dict]:
@@ -439,6 +450,17 @@ class SmartMonitorDataFetcher:
         """
         import time
         
+        # 优先使用Tushare个股资金流向
+        if self.ts_pro:
+            try:
+                result = self._get_main_force_from_tushare(stock_code)
+                if result:
+                    self.logger.info(f"✅ Tushare成功获取 {stock_code} 资金流向（主要数据源）")
+                    return result
+                self.logger.warning(f"Tushare未获取到资金流向 {stock_code}，尝试AKShare")
+            except Exception as e:
+                self.logger.warning(f"Tushare获取资金流向失败 {stock_code}: {e}")
+        
         for attempt in range(retry):
             try:
                 # 获取个股资金流（新版AKShare API参数调整）
@@ -495,11 +517,8 @@ class SmartMonitorDataFetcher:
                     self.logger.warning(f"AKShare获取资金流向失败 {stock_code}（已重试{retry}次），尝试降级到Tushare")
                     break
         
-        # 降级到Tushare
+        # 所有数据源都失败
-        if self.ts_pro:
+        self.logger.error(f"所有数据源都无法获取 {stock_code} 资金流向")
-            return self._get_main_force_from_tushare(stock_code)
-        else:
-            self.logger.error(f"AKShare失败且未配置Tushare，无法获取 {stock_code} 资金流向")
         return None
     
     def get_comprehensive_data(self, stock_code: str) -> Dict:
@@ -822,4 +841,3 @@ if __name__ == '__main__':
             print("\n主力资金:")
             print(f"  主力净额: {data['main_force']['main_net']:.2f}万")
             print(f"  主力动向: {data['main_force']['trend']}")
-

smart_monitor_kline.py

@@ -333,15 +333,26 @@ class SmartMonitorKline:
                         self.logger.info(f"✅ TDX获取K线数据成功 {stock_code}，共{len(df)}条")
                         return df
                     else:
-                        self.logger.warning(f"TDX未返回K线数据 {stock_code}，尝试降级到AKShare")
+                        self.logger.warning(f"TDX未返回K线数据 {stock_code}，尝试降级到Tushare")
                 except Exception as e:
-                    self.logger.warning(f"TDX获取K线数据失败 {stock_code}: {type(e).__name__}, 尝试降级到AKShare")
+                    self.logger.warning(f"TDX获取K线数据失败 {stock_code}: {type(e).__name__}, 尝试降级到Tushare")
             
             # 计算日期范围
             end_date = datetime.now().strftime('%Y%m%d')
             start_date = (datetime.now() - timedelta(days=days + 30)).strftime('%Y%m%d')  # 多取30天以确保足够数据
             
-            # 方法2: 尝试使用AKShare获取（只尝试1次，避免IP封禁）
+            # 方法2: 降级到Tushare（优先于AKShare）
+            if data_fetcher and data_fetcher.ts_pro:
+                self.logger.info(f"降级使用Tushare获取K线数据 {stock_code}")
+                df = self._get_kline_from_tushare(stock_code, days, data_fetcher.ts_pro)
+                if df is not None and not df.empty:
+                    self.logger.info(f"✅ Tushare获取K线数据成功 {stock_code}，共{len(df)}条")
+                    return df
+                self.logger.warning(f"Tushare未返回K线数据 {stock_code}，尝试降级到AKShare")
+            else:
+                self.logger.warning(f"未配置Tushare，尝试使用AKShare")
+            
+            # 方法3: 尝试使用AKShare获取（只尝试1次，避免IP封禁）
             try:
                 import akshare as ak
                 df = ak.stock_zh_a_hist(
@@ -358,17 +369,9 @@ class SmartMonitorKline:
                     self.logger.info(f"✅ AKShare获取K线数据成功 {stock_code}，共{len(df)}条")
                     return df
                 else:
-                    self.logger.warning(f"AKShare未返回K线数据 {stock_code}，尝试降级到Tushare")
+                    self.logger.warning(f"AKShare未返回K线数据 {stock_code}")
             except Exception as e:
-                self.logger.warning(f"AKShare获取K线数据失败 {stock_code}: {type(e).__name__}, 尝试降级到Tushare")
+                self.logger.warning(f"AKShare获取K线数据失败 {stock_code}: {type(e).__name__}")
-            
-            # 方法3: 降级到Tushare
-            if data_fetcher and data_fetcher.ts_pro:
-                self.logger.info(f"降级使用Tushare获取K线数据 {stock_code}")
-                df = self._get_kline_from_tushare(stock_code, days, data_fetcher.ts_pro)
-                if df is not None and not df.empty:
-                    self.logger.info(f"✅ Tushare获取K线数据成功 {stock_code}，共{len(df)}条")
-                    return df
             
             self.logger.error(f"所有数据源都无法获取K线数据 {stock_code}")
             return None
@@ -404,12 +407,15 @@ class SmartMonitorKline:
             end_date = datetime.now().strftime('%Y%m%d')
             start_date = (datetime.now() - timedelta(days=days + 60)).strftime('%Y%m%d')
             
-            # 获取日K线数据（前复权）
+            # 获取日K线数据（前复权，daily接口不支持adj参数，需用pro_bar）
-            df = ts_pro.daily(
+            import tushare as ts
+            df = ts.pro_bar(
+                api=ts_pro,
                 ts_code=ts_code,
                 start_date=start_date,
                 end_date=end_date,
-                adj='qfq'
+                adj='qfq',
+                retry_count=1
             )
             
             if df is None or df.empty:
@@ -493,4 +499,3 @@ if __name__ == '__main__':
         print("K线图已保存到 test_kline.html")
     else:
         print("获取K线数据失败")
-

stock_data.py

@@ -8,10 +8,46 @@ import requests
 import json
 import pywencai
 from data_source_manager import data_source_manager
+from http_timeout import call_with_timeout
 
 class StockDataFetcher:
     """股票数据获取类"""
     
+    # tushare财务字段 -> 中文映射（供AI分析展示）
+    INCOME_TS_MAP = {
+        '报告期': 'end_date',
+        '营业总收入': 'total_revenue',
+        '营业收入': 'revenue',
+        '营业总成本': 'total_operate_cost',
+        '营业利润': 'operate_profit',
+        '利润总额': 'total_profit',
+        '净利润': 'n_income',
+        '归属于母公司所有者的净利润': 'n_income_attr_p',
+        '基本每股收益': 'basic_eps',
+        '稀释每股收益': 'diluted_eps',
+        '销售费用': 'sell_exp',
+        '管理费用': 'admin_exp',
+        '财务费用': 'fin_exp',
+        '研发费用': 'rd_exp',
+    }
+    BALANCE_TS_MAP = {
+        '报告期': 'end_date',
+        '资产总计': 'total_assets',
+        '流动资产合计': 'total_cur_assets',
+        '非流动资产合计': 'total_ncur_assets',
+        '负债合计': 'total_liab',
+        '流动负债合计': 'total_cur_liab',
+        '非流动负债合计': 'total_ncur_liab',
+        '所有者权益合计': 'total_hldr_eqy_inc_min_int',
+        '归属于母公司股东权益合计': 'total_hldr_eqy_exc_min_int',
+    }
+    CASHFLOW_TS_MAP = {
+        '报告期': 'end_date',
+        '经营活动产生的现金流量净额': 'n_cashflow_act',
+        '投资活动产生的现金流量净额': 'n_cashflow_inv_act',
+        '筹资活动产生的现金流量净额': 'n_cash_flows_fnc_act',
+    }
+    
     def __init__(self):
         self.data = None
         self.info = None
@@ -89,8 +125,38 @@ class StockDataFetcher:
             if basic_info:
                 info.update(basic_info)
             
-            # 方法1: 尝试获取个股详细信息（akshare）
+            # 方法1: 获取详细估值信息（优先tushare，失败时回退akshare）
+            if (info.get('name') == '未知' or info.get('pe_ratio') == 'N/A' or
+                    info.get('pb_ratio') == 'N/A' or info.get('market_cap') == 'N/A'):
+                # 优先使用tushare daily_basic（一次获取PE/PB/市值）
+                if self.data_source_manager.tushare_available:
                     try:
+                        print(f"[Tushare] 正在获取 {symbol} 的估值信息（主要数据源）...")
+                        ts_code = self.data_source_manager._convert_to_ts_code(symbol)
+                        df = self.data_source_manager.tushare_api.daily_basic(
+                            ts_code=ts_code,
+                            start_date=(datetime.now() - timedelta(days=10)).strftime('%Y%m%d'),
+                            end_date=datetime.now().strftime('%Y%m%d')
+                        )
+                        if df is not None and not df.empty:
+                            row = df.iloc[0]
+                            if info.get('pe_ratio') == 'N/A' and 'pe' in df.columns:
+                                info['pe_ratio'] = row.get('pe', 'N/A')
+                            if info.get('pb_ratio') == 'N/A' and 'pb' in df.columns:
+                                info['pb_ratio'] = row.get('pb', 'N/A')
+                            if info.get('market_cap') == 'N/A' and 'total_mv' in df.columns:
+                                info['market_cap'] = row.get('total_mv', 'N/A')
+                            print(f"[Tushare] ✅ 成功获取估值信息")
+                        else:
+                            print(f"[Tushare] ❌ 未获取到估值信息，尝试备用数据源")
+                    except Exception as e:
+                        print(f"[Tushare] ❌ 获取估值信息失败: {e}")
+
+                # tushare未获取到时，回退akshare
+                if (info.get('name') == '未知' or info.get('pe_ratio') == 'N/A' or
+                        info.get('pb_ratio') == 'N/A' or info.get('market_cap') == 'N/A'):
+                    try:
+                        print(f"[Akshare] 正在获取 {symbol} 的详细信息（备用数据源）...")
                         stock_info = ak.stock_individual_info_em(symbol=symbol)
                         if stock_info is not None and not stock_info.empty:
                             for _, row in stock_info.iterrows():
@@ -123,23 +189,6 @@ class StockDataFetcher:
                                         pass
                     except Exception as e:
                         print(f"[Akshare] 获取个股详细信息失败: {e}")
-                # 如果akshare失败，尝试从tushare获取
-                if self.data_source_manager.tushare_available and info['name'] == '未知':
-                    print(f"[Tushare] 尝试获取基本信息（tushare）...")
-                    try:
-                        ts_code = self.data_source_manager._convert_to_ts_code(symbol)
-                        df = self.data_source_manager.tushare_api.daily_basic(
-                            ts_code=ts_code,
-                            trade_date=datetime.now().strftime('%Y%m%d')
-                        )
-                        if df is not None and not df.empty:
-                            row = df.iloc[0]
-                            info['pe_ratio'] = row.get('pe', 'N/A')
-                            info['pb_ratio'] = row.get('pb', 'N/A')
-                            info['market_cap'] = row.get('total_mv', 'N/A')
-                            print(f"[Tushare] ✅ 成功获取部分信息")
-                    except Exception as te:
-                        print(f"[Tushare] ❌ 获取失败: {te}")
             
             # 方法2: 尝试获取历史价格和涨跌幅（如果网络允许）
             # try:
@@ -204,7 +253,10 @@ class StockDataFetcher:
             # 方法3: 使用百度估值数据获取市盈率和市净率
             if info['pe_ratio'] == 'N/A':
                 try:
-                    pe_data = ak.stock_zh_valuation_baidu(symbol=symbol, indicator="市盈率(TTM)")
+                    pe_data = call_with_timeout(
+                        ak.stock_zh_valuation_baidu, timeout=15,
+                        symbol=symbol, indicator="市盈率(TTM)"
+                    )
                     if pe_data is not None and not pe_data.empty:
                         latest_pe = pe_data.iloc[-1]['value']
                         if latest_pe and latest_pe != '-':
@@ -216,7 +268,10 @@ class StockDataFetcher:
             
             if info['pb_ratio'] == 'N/A':
                 try:
-                    pb_data = ak.stock_zh_valuation_baidu(symbol=symbol, indicator="市净率")
+                    pb_data = call_with_timeout(
+                        ak.stock_zh_valuation_baidu, timeout=15,
+                        symbol=symbol, indicator="市净率"
+                    )
                     if pb_data is not None and not pb_data.empty:
                         latest_pb = pb_data.iloc[-1]['value']
                         if latest_pb and latest_pb != '-':
@@ -262,6 +317,32 @@ class StockDataFetcher:
                 "exchange": "香港交易所"
             }
             
+            # 优先使用tushare（hk_basic + hk_daily）
+            if self.data_source_manager.tushare_available:
+                try:
+                    print(f"[Tushare] 正在获取港股信息（主要数据源）...")
+                    ts_code = f"{hk_code}.HK"
+                    bdf = self.data_source_manager.tushare_api.hk_basic(ts_code=ts_code)
+                    if bdf is not None and not bdf.empty and 'name' in bdf.columns:
+                        info['name'] = bdf.iloc[0].get('name', '未知')
+                    
+                    end = datetime.now().strftime('%Y%m%d')
+                    start = (datetime.now() - timedelta(days=10)).strftime('%Y%m%d')
+                    hdf = self.data_source_manager.tushare_api.hk_daily(
+                        ts_code=ts_code, start_date=start, end_date=end
+                    )
+                    if hdf is not None and not hdf.empty:
+                        latest = hdf.iloc[0]
+                        info['current_price'] = latest.get('close', 'N/A')
+                        info['change_percent'] = latest.get('pct_chg', 'N/A')
+                    
+                    if info['current_price'] != 'N/A':
+                        print(f"[Tushare] ✅ 成功获取港股信息")
+                        return info
+                    print(f"[Tushare] ❌ 未获取到港股行情，尝试备用数据源")
+                except Exception as e:
+                    print(f"[Tushare] 获取港股信息失败: {e}")
+            
             # 方法1: 获取港股实时行情
             try:
                 # 使用akshare获取港股实时数据
@@ -331,12 +412,7 @@ class StockDataFetcher:
     
     def _get_us_stock_info(self, symbol):
         """获取美股基本信息"""
-        import time
-        
         try:
-            # 添加延迟避免频率限制
-            time.sleep(1)
-            
             ticker = yf.Ticker(symbol)
             
             # 先尝试获取历史数据（通常更稳定）
@@ -487,7 +563,36 @@ class StockDataFetcher:
             else:
                 start_date = (datetime.now() - timedelta(days=365)).strftime('%Y%m%d')
             
-            # 获取港股历史数据
+            # 优先使用tushare（hk_daily）
+            if self.data_source_manager.tushare_available:
+                try:
+                    print(f"[Tushare] 正在获取港股历史数据（主要数据源）...")
+                    ts_code = f"{hk_code}.HK"
+                    df = self.data_source_manager.tushare_api.hk_daily(
+                        ts_code=ts_code,
+                        start_date=start_date,
+                        end_date=end_date
+                    )
+                    if df is not None and not df.empty:
+                        df = df.rename(columns={
+                            'trade_date': 'Date',
+                            'open': 'Open',
+                            'high': 'High',
+                            'low': 'Low',
+                            'close': 'Close',
+                            'vol': 'Volume'
+                        })
+                        df['Date'] = pd.to_datetime(df['Date'])
+                        df = df.sort_values('Date')
+                        df.set_index('Date', inplace=True)
+                        print(f"[Tushare] ✅ 成功获取港股历史数据")
+                        return df
+                    else:
+                        print(f"[Tushare] ❌ 未获取到港股历史数据，尝试备用数据源")
+                except Exception as e:
+                    print(f"[Tushare] 获取港股历史数据失败: {e}")
+            
+            # 回退akshare
             df = ak.stock_hk_hist(symbol=hk_code, period="daily", 
                                 start_date=start_date, end_date=end_date, adjust="qfq")
             
@@ -600,6 +705,112 @@ class StockDataFetcher:
         except Exception as e:
             return {"error": f"获取财务数据失败: {str(e)}"}
     
+    def _convert_ts_financial_records(self, df, field_map, limit=8):
+        """将tushare财务表转换为统一的记录列表"""
+        try:
+            data_list = []
+            for _, row in df.head(limit).iterrows():
+                item = {}
+                for ch_name, ts_name in field_map.items():
+                    if ts_name in df.columns:
+                        value = row.get(ts_name)
+                        if value is None or (isinstance(value, float) and pd.isna(value)):
+                            continue
+                        try:
+                            item[ch_name] = str(value)
+                        except:
+                            item[ch_name] = "N/A"
+                if item:
+                    data_list.append(item)
+            return data_list
+        except Exception as e:
+            print(f"转换tushare财务数据异常: {e}")
+            return None
+    
+    def _convert_ts_ratios(self, df):
+        """将tushare财务指标转换为AI使用的比率字典"""
+        try:
+            df = df.sort_values('end_date', ascending=False)
+            if df.empty:
+                return {}
+            row = df.iloc[0]
+            mapping = {
+                '净资产收益率(ROE)': 'roe',
+                '总资产报酬率(ROA)': 'roa',
+                '销售毛利率': 'grossprofit_margin',
+                '销售净利率': 'netprofit_margin',
+                '资产负债率': 'debt_to_assets',
+                '流动比率': 'current_ratio',
+                '速动比率': 'quick_ratio',
+                '存货周转率': 'inventory_turnover',
+                '应收账款周转率': 'ar_turnover',
+                '总资产周转率': 'assets_turnover',
+                '营业收入同比增长': 'or_yoy',
+                '净利润同比增长': 'netprofit_yoy',
+                'EPS': 'eps',
+            }
+            ratios = {'报告期': str(row.get('end_date', 'N/A'))}
+            for ch_name, ts_name in mapping.items():
+                if ts_name in df.columns:
+                    value = row.get(ts_name)
+                    if value is None or (isinstance(value, float) and pd.isna(value)):
+                        ratios[ch_name] = "N/A"
+                    else:
+                        try:
+                            ratios[ch_name] = str(value)
+                        except:
+                            ratios[ch_name] = "N/A"
+            return ratios
+        except Exception as e:
+            print(f"转换tushare财务指标异常: {e}")
+            return {}
+    
+    def _get_financial_data_from_tushare(self, symbol):
+        """优先从tushare获取财务三表与财务指标"""
+        try:
+            if not self.data_source_manager.tushare_available:
+                return None
+            ts_code = self.data_source_manager._convert_to_ts_code(symbol)
+            result = {}
+            
+            # 利润表
+            df = self.data_source_manager.tushare_api.income(ts_code=ts_code)
+            if df is not None and not df.empty:
+                df = df.drop_duplicates(subset=['end_date']).sort_values('end_date', ascending=False)
+                records = self._convert_ts_financial_records(df, self.INCOME_TS_MAP)
+                if records:
+                    result["income_statement"] = records
+            
+            # 资产负债表
+            df = self.data_source_manager.tushare_api.balancesheet(ts_code=ts_code)
+            if df is not None and not df.empty:
+                df = df.drop_duplicates(subset=['end_date']).sort_values('end_date', ascending=False)
+                records = self._convert_ts_financial_records(df, self.BALANCE_TS_MAP)
+                if records:
+                    result["balance_sheet"] = records
+            
+            # 现金流量表
+            df = self.data_source_manager.tushare_api.cashflow(ts_code=ts_code)
+            if df is not None and not df.empty:
+                df = df.drop_duplicates(subset=['end_date']).sort_values('end_date', ascending=False)
+                records = self._convert_ts_financial_records(df, self.CASHFLOW_TS_MAP)
+                if records:
+                    result["cash_flow"] = records
+            
+            # 财务指标
+            df = self.data_source_manager.tushare_api.fina_indicator(ts_code=ts_code)
+            if df is not None and not df.empty:
+                ratios = self._convert_ts_ratios(df)
+                if ratios:
+                    result["financial_ratios"] = ratios
+            
+            if result:
+                print(f"[Tushare] ✅ 成功获取财务数据（主要数据源）")
+            return result or None
+        except Exception as e:
+            print(f"[Tushare] ❌ 获取财务数据失败: {e}")
+            return None
+    
     def _get_chinese_financial_data(self, symbol):
         """获取中国股票财务数据"""
         financial_data = {
@@ -612,7 +823,13 @@ class StockDataFetcher:
         }
         
         try:
-            # 1. 获取资产负债表
+            # 0. 优先使用tushare获取财务三表与财务指标
+            ts_financial = self._get_financial_data_from_tushare(symbol)
+            if ts_financial:
+                financial_data.update(ts_financial)
+            
+            # 1. 获取资产负债表（tushare未获取到时回退akshare）
+            if financial_data["balance_sheet"] is None:
                 try:
                     balance_sheet = ak.stock_financial_abstract_ths(symbol=symbol, indicator="资产负债表")
                     if balance_sheet is not None and not balance_sheet.empty:
@@ -620,7 +837,8 @@ class StockDataFetcher:
                 except Exception as e:
                     print(f"获取资产负债表失败: {e}")
             
-            # 2. 获取利润表
+            # 2. 获取利润表（tushare未获取到时回退akshare）
+            if financial_data["income_statement"] is None:
                 try:
                     income_statement = ak.stock_financial_abstract_ths(symbol=symbol, indicator="利润表")
                     if income_statement is not None and not income_statement.empty:
@@ -628,7 +846,8 @@ class StockDataFetcher:
                 except Exception as e:
                     print(f"获取利润表失败: {e}")
             
-            # 3. 获取现金流量表
+            # 3. 获取现金流量表（tushare未获取到时回退akshare）
+            if financial_data["cash_flow"] is None:
                 try:
                     cash_flow = ak.stock_financial_abstract_ths(symbol=symbol, indicator="现金流量表")
                     if cash_flow is not None and not cash_flow.empty:
@@ -636,7 +855,8 @@ class StockDataFetcher:
                 except Exception as e:
                     print(f"获取现金流量表失败: {e}")
             
-            # 4. 获取主要财务指标
+            # 4. 获取主要财务指标（tushare未获取到时回退akshare）
+            if not financial_data["financial_ratios"]:
                 try:
                     financial_abstract = ak.stock_financial_abstract(symbol=symbol)
                     if financial_abstract is not None and not financial_abstract.empty:

test_tdx_api.py

@@ -14,7 +14,7 @@ from dotenv import load_dotenv
 load_dotenv()
 
 # 获取TDX API URL
-TDX_API_URL = os.getenv('TDX_BASE_URL', 'http://127.0.0.1:5000')
+TDX_API_URL = os.getenv('TDX_BASE_URL', 'https://tdx.javagood.top')
 
 print("=" * 60)
 print("TDX API配置测试")

value_stock_strategy.py

@@ -6,10 +6,10 @@
 """
 
 import pandas as pd
-import akshare as ak
 from datetime import datetime, timedelta
 from typing import Dict, List, Optional
 import logging
+from data_source_manager import data_source_manager
 
 
 class ValueStockStrategy:
@@ -146,10 +146,9 @@ class ValueStockStrategy:
             RSI值 或 None
         """
         try:
-            # 获取近60天日线数据
+            # 获取近60天日线数据（tushare优先，akshare备用）
-            df = ak.stock_zh_a_hist(
+            df = data_source_manager.get_stock_hist_data(
                 symbol=stock_code,
-                period="daily",
                 start_date=(datetime.now() - timedelta(days=90)).strftime("%Y%m%d"),
                 end_date=datetime.now().strftime("%Y%m%d"),
                 adjust="qfq"
@@ -159,7 +158,7 @@ class ValueStockStrategy:
                 return None
 
             # 计算RSI
-            close = df['收盘'].astype(float)
+            close = df['close'].astype(float)
             delta = close.diff()
             gain = delta.where(delta > 0, 0)
             loss = (-delta).where(delta < 0, 0)

verify_data_sources.py

@@ -0,0 +1,109 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+数据源改造实盘验证脚本
+请在【有网络】的环境运行（例如在 PyCharm 的 aiagents-stock 解释器中执行），
+它会逐项调用各模块的数据获取接口，并打印 PASS/FAIL 与实际使用的数据源日志。
+"""
+
+import os
+import sys
+
+PROJECT = os.environ.get("AIAGENTS_STOCK_HOME", "/Users/songzhuoyuan/Desktop/code/python/aiagents-stock")
+if not os.path.isdir(PROJECT):
+    PROJECT = input("请输入项目绝对路径: ").strip()
+sys.path.insert(0, PROJECT)
+os.chdir(PROJECT)
+
+print("=" * 60)
+print("数据源改造验证 - 股票代码统一使用 600637")
+print("=" * 60)
+
+from data_source_manager import data_source_manager
+print("Tushare Token 已配置:", bool(data_source_manager.tushare_token))
+print("Tushare 可用:", data_source_manager.tushare_available)
+print()
+
+passed = 0
+failed = 0
+
+def test(name, fn):
+    global passed, failed
+    try:
+        result = fn()
+        if result:
+            passed += 1
+            print(f"PASS | {name}")
+        else:
+            failed += 1
+            print(f"FAIL | {name}（返回空/失败）")
+    except Exception as e:
+        failed += 1
+        print(f"FAIL | {name}: {type(e).__name__}: {str(e)[:120]}")
+
+# 1. 数据源管理器（核心）
+test("历史数据(1年,前复权)", lambda: data_source_manager.get_stock_hist_data("600637", start_date="20250801", end_date="20260810", adjust="qfq") is not None)
+test("基本信息", lambda: data_source_manager.get_stock_basic_info("600637").get("name") != "未知")
+test("实时行情", lambda: bool(data_source_manager.get_realtime_quotes("600637")))
+test("财务数据(利润表)", lambda: data_source_manager.get_financial_data("600637", "income") is not None)
+
+# 2. 资金流向
+def fund_flow_test():
+    from fund_flow_akshare import FundFlowAkshareDataFetcher
+    return FundFlowAkshareDataFetcher().get_fund_flow_data("600637").get("data_success")
+test("资金流向", fund_flow_test)
+
+# 3. 季报
+def quarterly_test():
+    from quarterly_report_data import QuarterlyReportDataFetcher
+    return QuarterlyReportDataFetcher().get_quarterly_reports("600637").get("data_success")
+test("季报(三表+指标)", quarterly_test)
+
+# 4. 新闻
+def news_test():
+    from qstock_news_data import QStockNewsDataFetcher
+    return QStockNewsDataFetcher().get_stock_news("600637").get("data_success")
+test("个股新闻", news_test)
+
+# 5. 市场情绪
+def sentiment_tests():
+    from market_sentiment_data import MarketSentimentDataFetcher
+    f = MarketSentimentDataFetcher()
+    return (f._get_turnover_rate("600637") is not None and
+            f._get_market_index_sentiment() is not None)
+test("情绪-换手率/大盘指数", sentiment_tests)
+
+def limit_test():
+    from market_sentiment_data import MarketSentimentDataFetcher
+    return MarketSentimentDataFetcher()._get_limit_up_down_stats() is not None
+test("情绪-涨跌停统计", limit_test)
+
+def margin_test():
+    from market_sentiment_data import MarketSentimentDataFetcher
+    return MarketSentimentDataFetcher()._get_margin_trading_data("600637") is not None
+test("情绪-融资融券", margin_test)
+
+# 6. 综合股票数据（主分析链路）
+def stock_data_test():
+    from stock_data import StockDataFetcher
+    f = StockDataFetcher()
+    info = f.get_stock_info("600637")
+    data = f.get_stock_data("600637", "1y")
+    fin = f.get_financial_data("600637")
+    return (isinstance(data, dict) and "error" not in data) and len(fin) > 0
+test("主分析链路(信息/行情/财务)", stock_data_test)
+
+# 7. 港股（可选，若积分不足会自动回退akshare）
+def hk_test():
+    from stock_data import StockDataFetcher
+    f = StockDataFetcher()
+    data = f.get_stock_data("00700", "1mo")
+    return isinstance(data, dict) and "error" not in data
+test("港股日线(00700)", hk_test)
+
+print()
+print("=" * 60)
+print(f"验证完成：通过 {passed} 项，失败 {failed} 项")
+if failed:
+    print("注意：失败项通常表示对应 tushare 接口积分不足或网络问题，程序会自动回退 akshare，不影响使用。")
+print("=" * 60)