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)
-        
-        # 基本面分析
+            agent_jobs.append(("technical", lambda: self.technical_analyst_agent(stock_info, stock_data, indicators)))
         if enabled_analysts.get('fundamental', True):
-            agents_results["fundamental"] = self.fundamental_analyst_agent(stock_info, financial_data, quarterly_data)
-        
-        # 资金面分析（传入资金流向数据）
+            agent_jobs.append(("fundamental", lambda: self.fundamental_analyst_agent(stock_info, financial_data, quarterly_data)))
         if enabled_analysts.get('fund_flow', True):
-            agents_results["fund_flow"] = self.fund_flow_analyst_agent(stock_info, indicators, fund_flow_data)
-        
-        # 风险管理分析（传入风险数据）
+            agent_jobs.append(("fund_flow", lambda: self.fund_flow_analyst_agent(stock_info, indicators, fund_flow_data)))
         if enabled_analysts.get('risk', True):
-            agents_results["risk_management"] = self.risk_management_agent(stock_info, indicators, risk_data)
-        
-        # 市场情绪分析（传入市场情绪数据）
+            agent_jobs.append(("risk_management", lambda: self.risk_management_agent(stock_info, indicators, risk_data)))
         if enabled_analysts.get('sentiment', False):
-            agents_results["market_sentiment"] = self.market_sentiment_agent(stock_info, sentiment_data)
-        
-        # 新闻分析（传入新闻数据）
+            agent_jobs.append(("market_sentiment", lambda: self.market_sentiment_agent(stock_info, sentiment_data)))
         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)