1.修复docker运行无法打开页面问题 (#6)

2.支持下载DM文件
3.支持硅基流动，阿里百炼模型

docker-compose.yml

@@ -7,7 +7,7 @@ services:
       dockerfile: Dockerfile
     container_name: agentsstock1
     ports:
-      - "8503:8501"
+      - "8503:8503"
     volumes:
       # 数据和数据库持久化 - 使用目录挂载而不是文件挂载
       - ./data:/app/data
@@ -20,7 +20,7 @@ services:
     networks:
       - agentsstock-network
     healthcheck:
-      test: ["CMD", "curl", "-f", "http://localhost:8501/_stcore/health"]
+      test: ["CMD", "curl", "-f", "http://localhost:8503/_stcore/health"]
       interval: 30s
       timeout: 10s
       retries: 3

longhubang_ui.py

@@ -136,9 +136,12 @@ def display_analysis_tab():
             )
     
     with col3:
+        # 导入model_config.py中定义的model_options
+        from model_config import model_options as app_model_options
         selected_model = st.selectbox(
             "AI模型",
-            ["deepseek-chat", "deepseek-reasoner"],
+            list(app_model_options.keys()),
+            format_func=lambda x: app_model_options[x],
             help="Reasoner模型提供更强的推理能力"
         )
     
@@ -721,12 +724,12 @@ def display_visualizations(result):
 def display_pdf_export_section(result):
     """显示PDF导出功能"""
     
-    st.markdown("### 📄 导出PDF报告")
+    st.markdown("### 📄 导出报告")
     
-    col1, col2 = st.columns([3, 1])
+    col1, col2, col3 = st.columns([2, 1, 1])
     
     with col1:
-        st.info("💡 点击按钮生成并下载专业的PDF分析报告")
+        st.info("💡 点击按钮生成并下载专业分析报告")
     
     with col2:
         if st.button("📥 生成PDF", type="primary", width='stretch'):
@@ -752,6 +755,154 @@ def display_pdf_export_section(result):
                 
                 except Exception as e:
                     st.error(f"❌ PDF生成失败: {str(e)}")
+    
+    with col3:
+        if st.button("📝 生成Markdown", type="secondary", width='stretch'):
+            with st.spinner("正在生成Markdown报告..."):
+                try:
+                    # 生成Markdown内容
+                    markdown_content = generate_markdown_report(result)
+                    
+                    # 提供下载
+                    st.download_button(
+                        label="📥 下载Markdown报告",
+                        data=markdown_content,
+                        file_name=f"智瞰龙虎报告_{datetime.now().strftime('%Y%m%d_%H%M%S')}.md",
+                        mime="text/markdown",
+                        width='stretch'
+                    )
+                    
+                    st.success("✅ Markdown报告生成成功！")
+                
+                except Exception as e:
+                    st.error(f"❌ Markdown生成失败: {str(e)}")
+
+
+def generate_markdown_report(result_data: dict) -> str:
+    """生成龙虎榜分析Markdown报告"""
+    
+    # 获取当前时间
+    current_time = datetime.now().strftime("%Y年%m月%d日 %H:%M:%S")
+    
+    # 标题页
+    markdown_content = f"""# 智瞰龙虎榜分析报告
+
+**AI驱动的龙虎榜多维度分析系统**
+
+---
+
+## 📊 报告概览
+
+- **生成时间**: {current_time}
+- **数据记录**: {result_data.get('data_info', {}).get('total_records', 0)} 条
+- **涉及股票**: {result_data.get('data_info', {}).get('total_stocks', 0)} 只
+- **涉及游资**: {result_data.get('data_info', {}).get('total_youzi', 0)} 个
+- **AI分析师**: 5位专业分析师团队
+- **分析模型**: DeepSeek AI Multi-Agent System
+
+> ⚠️ 本报告由AI系统基于龙虎榜公开数据自动生成，仅供参考，不构成投资建议。市场有风险，投资需谨慎。
+
+---
+
+## 📈 数据概况
+
+本次分析共涵盖 **{result_data.get('data_info', {}).get('total_records', 0)}** 条龙虎榜记录，
+涉及 **{result_data.get('data_info', {}).get('total_stocks', 0)}** 只股票和 
+**{result_data.get('data_info', {}).get('total_youzi', 0)}** 个游资席位。
+
+"""
+    
+    # 资金概况
+    summary = result_data.get('data_info', {}).get('summary', {})
+    markdown_content += f"""
+### 💰 资金概况
+
+- **总买入金额**: {summary.get('total_buy_amount', 0):,.2f} 元
+- **总卖出金额**: {summary.get('total_sell_amount', 0):,.2f} 元
+- **净流入金额**: {summary.get('total_net_inflow', 0):,.2f} 元
+
+"""
+    
+    # TOP游资
+    if summary.get('top_youzi'):
+        markdown_content += "### 🏆 活跃游资 TOP10\n\n| 排名 | 游资名称 | 净流入金额(元) |\n|------|----------|---------------|\n"
+        for idx, (name, amount) in enumerate(list(summary['top_youzi'].items())[:10], 1):
+            markdown_content += f"| {idx} | {name} | {amount:,.2f} |\n"
+        markdown_content += "\n"
+    
+    # TOP股票
+    if summary.get('top_stocks'):
+        markdown_content += "### 📈 资金净流入 TOP20 股票\n\n| 排名 | 股票代码 | 股票名称 | 净流入金额(元) |\n|------|----------|----------|---------------|\n"
+        for idx, stock in enumerate(summary['top_stocks'][:20], 1):
+            markdown_content += f"| {idx} | {stock['code']} | {stock['name']} | {stock['net_inflow']:,.2f} |\n"
+        markdown_content += "\n"
+    
+    # 热门概念
+    if summary.get('hot_concepts'):
+        markdown_content += "### 🔥 热门概念 TOP15\n\n"
+        for idx, (concept, count) in enumerate(list(summary['hot_concepts'].items())[:15], 1):
+            markdown_content += f"{idx}. {concept} ({count}次)  \n"
+        markdown_content += "\n"
+    
+    # 推荐股票
+    recommended = result_data.get('recommended_stocks', [])
+    if recommended:
+        markdown_content += f"""
+## 🎯 AI推荐股票
+
+基于5位AI分析师的综合分析，系统识别出以下 **{len(recommended)}** 只潜力股票，
+这些股票在资金流向、游资关注度、题材热度等多个维度表现突出。
+
+### 推荐股票清单
+
+| 排名 | 股票代码 | 股票名称 | 净流入金额 | 确定性 | 持有周期 |
+|------|----------|----------|------------|--------|----------|
+"""
+        for stock in recommended[:10]:
+            markdown_content += f"| {stock.get('rank', '-')} | {stock.get('code', '-')} | {stock.get('name', '-')} | {stock.get('net_inflow', 0):,.0f} | {stock.get('confidence', '-')} | {stock.get('hold_period', '-')} |\n"
+        
+        markdown_content += "\n### 推荐理由详解\n\n"
+        for stock in recommended[:5]:  # 只详细展示前5只
+            markdown_content += f"**{stock.get('rank', '-')}. {stock.get('name', '-')} ({stock.get('code', '-')})**\n\n"
+            markdown_content += f"- 推荐理由: {stock.get('reason', '暂无')}\n"
+            markdown_content += f"- 确定性: {stock.get('confidence', '-')}\n"
+            markdown_content += f"- 持有周期: {stock.get('hold_period', '-')}\n\n"
+    
+    # AI分析师报告
+    agents_analysis = result_data.get('agents_analysis', {})
+    if agents_analysis:
+        markdown_content += "## 🤖 AI分析师报告\n\n"
+        markdown_content += "本报告由5位AI专业分析师从不同维度进行分析，综合形成投资建议：\n\n"
+        markdown_content += "- **游资行为分析师** - 分析游资操作特征和意图\n"
+        markdown_content += "- **个股潜力分析师** - 挖掘次日大概率上涨的股票\n"
+        markdown_content += "- **题材追踪分析师** - 识别热点题材和轮动机会\n"
+        markdown_content += "- **风险控制专家** - 识别高风险股票和市场陷阱\n"
+        markdown_content += "- **首席策略师** - 综合研判并给出最终建议\n\n"
+        
+        agent_titles = {
+            'youzi': '游资行为分析师',
+            'stock': '个股潜力分析师',
+            'theme': '题材追踪分析师',
+            'risk': '风险控制专家',
+            'chief': '首席策略师综合研判'
+        }
+        
+        for agent_key, agent_title in agent_titles.items():
+            agent_data = agents_analysis.get(agent_key, {})
+            if agent_data:
+                markdown_content += f"### {agent_title}\n\n"
+                analysis_text = agent_data.get('analysis', '暂无分析')
+                # 处理文本中的换行
+                analysis_text = analysis_text.replace('\n', '\n\n')
+                markdown_content += f"{analysis_text}\n\n"
+    
+    markdown_content += """
+---
+
+*报告由智瞰龙虎AI系统自动生成*
+"""
+    
+    return markdown_content
 
 
 def display_history_tab():

main_force_ui.py

@@ -13,17 +13,17 @@ import pandas as pd
 
 def display_main_force_selector():
     """显示主力选股界面"""
-    
+
     # 检查是否触发批量分析（不立即删除标志）
     if st.session_state.get('main_force_batch_trigger'):
         run_main_force_batch_analysis()
         return
-    
+
     # 检查是否查看历史记录
     if st.session_state.get('main_force_view_history'):
         display_batch_history()
         return
-    
+
     # 页面标题和历史记录按钮
     col_title, col_history = st.columns([4, 1])
     with col_title:
@@ -33,9 +33,9 @@ def display_main_force_selector():
         if st.button("📚 批量分析历史", width='content'):
             st.session_state.main_force_view_history = True
             st.rerun()
-    
+
     st.markdown("---")
-    
+
     st.markdown("""
     ### 功能说明
     
@@ -53,18 +53,18 @@ def display_main_force_selector():
     - ✅ 行业前景明朗
     - ✅ 综合素质优秀
     """)
-    
+
     st.markdown("---")
-    
+
     # 参数设置
     col1, col2, col3 = st.columns(3)
-    
+
     with col1:
         date_option = st.selectbox(
             "选择时间区间",
             ["最近3个月", "最近6个月", "最近1年", "自定义日期"]
         )
-        
+
         if date_option == "最近3个月":
             days_ago = 90
             start_date = None
@@ -81,7 +81,7 @@ def display_main_force_selector():
             )
             start_date = f"{custom_date.year}年{custom_date.month}月{custom_date.day}日"
             days_ago = None
-    
+
     with col2:
         final_n = st.slider(
             "最终精选数量",
@@ -91,14 +91,14 @@ def display_main_force_selector():
             step=1,
             help="最终推荐的股票数量"
         )
-    
+
     with col3:
         st.info("💡 系统将获取前100名股票，进行整体分析后精选优质标的")
-    
+
     # 高级选项
     with st.expander("⚙️ 高级筛选参数"):
         col1, col2, col3 = st.columns(3)
-        
+
         with col1:
             max_change = st.number_input(
                 "最大涨跌幅(%)",
@@ -108,7 +108,7 @@ def display_main_force_selector():
                 step=5.0,
                 help="过滤掉涨幅过高的股票，避免追高"
             )
-        
+
         with col2:
             min_cap = st.number_input(
                 "最小市值(亿)",
@@ -117,7 +117,7 @@ def display_main_force_selector():
                 value=50.0,
                 step=10.0
             )
-        
+
         with col3:
             max_cap = st.number_input(
                 "最大市值(亿)",
@@ -126,24 +126,27 @@ def display_main_force_selector():
                 value=5000.0,
                 step=100.0
             )
-    
+
     # 模型选择
+    # 导入model_config.py中定义的model_options
+    from model_config import model_options as app_model_options
     model = st.selectbox(
         "选择AI模型",
-        ["deepseek-chat", "deepseek-reasoner"],
+        list(app_model_options.keys()),
+        format_func=lambda x: app_model_options[x],
         help="deepseek-chat速度快，deepseek-reasoner推理能力强"
     )
-    
+
     st.markdown("---")
-    
+
     # 开始分析按钮
     if st.button("🚀 开始主力选股", type="primary", width='content'):
-        
+
         with st.spinner("正在获取数据并分析，这可能需要几分钟..."):
-            
+
             # 创建分析器
             analyzer = MainForceAnalyzer(model=model)
-            
+
             # 运行分析
             result = analyzer.run_full_analysis(
                 start_date=start_date,
@@ -153,83 +156,83 @@ def display_main_force_selector():
                 min_market_cap=min_cap,
                 max_market_cap=max_cap
             )
-            
+
             # 保存结果到session_state
             st.session_state.main_force_result = result
             st.session_state.main_force_analyzer = analyzer
-        
+
         # 显示结果
         if result['success']:
             st.success(f"✅ 分析完成！共筛选出 {len(result['final_recommendations'])} 只优质标的")
             st.rerun()
         else:
             st.error(f"❌ 分析失败: {result.get('error', '未知错误')}")
-    
+
     # 显示分析结果
     if 'main_force_result' in st.session_state:
         result = st.session_state.main_force_result
-        
+
         if result['success']:
             display_analysis_results(result, st.session_state.get('main_force_analyzer'))
 
 def display_analysis_results(result: dict, analyzer):
     """显示分析结果"""
-    
+
     st.markdown("---")
     st.markdown("## 📊 分析结果")
-    
+
     # 统计信息
     col1, col2, col3 = st.columns(3)
-    
+
     with col1:
         st.metric("获取股票数", result['total_stocks'])
-    
+
     with col2:
         st.metric("筛选后", result['filtered_stocks'])
-    
+
     with col3:
         st.metric("最终推荐", len(result['final_recommendations']))
-    
+
     st.markdown("---")
-    
+
     # 显示AI分析师完整报告
     if analyzer and hasattr(analyzer, 'fund_flow_analysis'):
         display_analyst_reports(analyzer)
-    
+
     st.markdown("---")
-    
+
     # 显示推荐股票
     if result['final_recommendations']:
         st.markdown("### ⭐ 精选推荐")
-        
+
         for rec in result['final_recommendations']:
             with st.expander(
-                f"【第{rec['rank']}名】{rec['symbol']} - {rec['name']}", 
+                f"【第{rec['rank']}名】{rec['symbol']} - {rec['name']}",
                 expanded=(rec['rank'] <= 3)
             ):
                 display_recommendation_detail(rec)
-    
+
     # 显示候选股票列表
     if analyzer and analyzer.raw_stocks is not None and not analyzer.raw_stocks.empty:
         st.markdown("---")
         st.markdown("### 📋 候选股票列表（筛选后）")
-        
+
         # 选择关键列显示
         display_cols = ['股票代码', '股票简称']
-        
+
         # 添加行业列
         industry_cols = [col for col in analyzer.raw_stocks.columns if '行业' in col]
         if industry_cols:
             display_cols.append(industry_cols[0])
-        
+
         # 添加区间主力资金净流入（智能匹配）
         main_fund_col = None
         main_fund_patterns = [
             '区间主力资金流向',      # 实际列名
-            '区间主力资金净流入', 
+            '区间主力资金净流入',
             '主力资金流向',
-            '主力资金净流入', 
+            '主力资金净流入',
-            '主力净流入', 
+            '主力净流入',
             '主力资金'
         ]
         for pattern in main_fund_patterns:
@@ -239,11 +242,11 @@ def display_analysis_results(result: dict, analyzer):
                 break
         if main_fund_col:
             display_cols.append(main_fund_col)
-        
+
         # 添加区间涨跌幅（前复权）（智能匹配）
         interval_pct_col = None
         interval_pct_patterns = [
-            '区间涨跌幅:前复权', '区间涨跌幅:前复权(%)', '区间涨跌幅(%)', 
+            '区间涨跌幅:前复权', '区间涨跌幅:前复权(%)', '区间涨跌幅(%)',
             '区间涨跌幅', '涨跌幅:前复权', '涨跌幅:前复权(%)', '涨跌幅(%)', '涨跌幅'
         ]
         for pattern in interval_pct_patterns:
@@ -253,16 +256,16 @@ def display_analysis_results(result: dict, analyzer):
                 break
         if interval_pct_col:
             display_cols.append(interval_pct_col)
-        
+
         # 添加市值、市盈率、市净率
         for col_name in ['总市值', '市盈率', '市净率']:
             matching_cols = [col for col in analyzer.raw_stocks.columns if col_name in col]
             if matching_cols:
                 display_cols.append(matching_cols[0])
-        
+
         # 选择存在的列
         final_cols = [col for col in display_cols if col in analyzer.raw_stocks.columns]
-        
+
         # 调试信息：显示找到的列名
         with st.expander("🔍 调试信息 - 查看数据列", expanded=False):
             st.caption("所有可用列:")
@@ -277,14 +280,14 @@ def display_analysis_results(result: dict, analyzer):
                 st.success(f"✅ 找到涨跌幅列: {interval_pct_col}")
             else:
                 st.warning("⚠️ 未找到涨跌幅列")
-        
+
         # 显示DataFrame
         display_df = analyzer.raw_stocks[final_cols].copy()
         st.dataframe(display_df, width='content', height=400)
-        
+
         # 显示统计
         st.caption(f"共 {len(display_df)} 只候选股票，显示 {len(final_cols)} 个字段")
-        
+
         # 下载按钮
         csv = display_df.to_csv(index=False, encoding='utf-8-sig')
         st.download_button(
@@ -293,15 +296,15 @@ def display_analysis_results(result: dict, analyzer):
             file_name=f"main_force_stocks_{datetime.now().strftime('%Y%m%d')}.csv",
             mime="text/csv"
         )
-        
+
         # 批量分析功能区
         st.markdown("---")
-        
+
         col_batch1, col_batch2, col_batch3 = st.columns([2, 1, 1])
         with col_batch1:
             st.markdown("#### 🚀 批量深度分析")
             st.caption("对主力资金净流入TOP股票进行完整的AI团队分析，获取投资评级和关键价位")
-        
+
         with col_batch2:
             batch_count = st.selectbox(
                 "分析数量",
@@ -309,18 +312,18 @@ def display_analysis_results(result: dict, analyzer):
                 index=1,  # 默认20只
                 help="选择分析主力资金净流入前N只股票"
             )
-        
+
         with col_batch3:
             st.write("")  # 占位
             if st.button("🚀 开始批量分析", type="primary", width='content'):
                 # 准备数据：按主力资金净流入排序
                 df_sorted = analyzer.raw_stocks.copy()
-                
+
                 # 确保主力资金列是数值类型并排序
                 if main_fund_col:
                     df_sorted[main_fund_col] = pd.to_numeric(df_sorted[main_fund_col], errors='coerce')
                     df_sorted = df_sorted.sort_values(by=main_fund_col, ascending=False)
-                
+
                 # 提取股票代码并去掉市场后缀（.SH, .SZ等）
                 raw_codes = df_sorted.head(batch_count)['股票代码'].tolist()
                 stock_codes = []
@@ -332,55 +335,55 @@ def display_analysis_results(result: dict, analyzer):
                         stock_codes.append(clean_code)
                     else:
                         stock_codes.append(str(code))
-                
+
                 # 存储到session_state，触发批量分析
                 st.session_state.main_force_batch_codes = stock_codes
                 st.session_state.main_force_batch_trigger = True
                 st.rerun()
-    
+
     # 显示PDF报告下载区域
     if analyzer and result:
         display_report_download_section(analyzer, result)
 
 def display_recommendation_detail(rec: dict):
     """显示单个推荐股票的详细信息"""
-    
+
     col1, col2 = st.columns([1, 1])
-    
+
     with col1:
         st.markdown("#### 📌 推荐理由")
         for reason in rec.get('reasons', []):
             st.markdown(f"- {reason}")
-        
+
         st.markdown("#### 💡 投资亮点")
         st.info(rec.get('highlights', 'N/A'))
-    
+
     with col2:
         st.markdown("#### 📊 投资建议")
         st.markdown(f"**建议仓位**: {rec.get('position', 'N/A')}")
         st.markdown(f"**投资周期**: {rec.get('investment_period', 'N/A')}")
-        
+
         st.markdown("#### ⚠️ 风险提示")
         st.warning(rec.get('risks', 'N/A'))
-    
+
     # 显示股票详细数据
     if 'stock_data' in rec:
         st.markdown("---")
         st.markdown("#### 📊 股票详细数据")
-        
+
         stock_data = rec['stock_data']
-        
+
         # 创建数据展示
         col1, col2, col3 = st.columns(3)
-        
+
         with col1:
             st.metric("股票代码", stock_data.get('股票代码', 'N/A'))
-            
+
             # 显示行业
             industry_keys = [k for k in stock_data.keys() if '行业' in k]
             if industry_keys:
                 st.metric("所属行业", stock_data.get(industry_keys[0], 'N/A'))
-        
+
         with col2:
             # 显示主力资金
             fund_keys = [k for k in stock_data.keys() if '主力' in k and '净流入' in k]
@@ -390,7 +393,7 @@ def display_recommendation_detail(rec: dict):
                     st.metric("主力资金净流入", f"{fund_value/100000000:.2f}亿")
                 else:
                     st.metric("主力资金净流入", str(fund_value))
-        
+
         with col3:
             # 显示涨跌幅
             change_keys = [k for k in stock_data.keys() if '涨跌幅' in k]
@@ -400,23 +403,23 @@ def display_recommendation_detail(rec: dict):
                     st.metric("区间涨跌幅", f"{change_value:.2f}%")
                 else:
                     st.metric("区间涨跌幅", str(change_value))
-        
+
         # 显示其他关键指标
         st.markdown("**其他关键指标：**")
         metrics_col1, metrics_col2, metrics_col3 = st.columns(3)
-        
+
         with metrics_col1:
             if '市盈率' in stock_data or any('市盈率' in k for k in stock_data.keys()):
                 pe_keys = [k for k in stock_data.keys() if '市盈率' in k]
                 if pe_keys:
                     st.caption(f"市盈率: {stock_data.get(pe_keys[0], 'N/A')}")
-        
+
         with metrics_col2:
             if '市净率' in stock_data or any('市净率' in k for k in stock_data.keys()):
                 pb_keys = [k for k in stock_data.keys() if '市净率' in k]
                 if pb_keys:
                     st.caption(f"市净率: {stock_data.get(pb_keys[0], 'N/A')}")
-        
+
         with metrics_col3:
             if '总市值' in stock_data or any('总市值' in k for k in stock_data.keys()):
                 cap_keys = [k for k in stock_data.keys() if '总市值' in k]
@@ -425,12 +428,12 @@ def display_recommendation_detail(rec: dict):
 
 def display_analyst_reports(analyzer):
     """显示AI分析师完整报告"""
-    
+
     st.markdown("### 🤖 AI分析师团队完整报告")
-    
+
     # 创建三个标签页
     tab1, tab2, tab3 = st.tabs(["💰 资金流向分析", "📊 行业板块分析", "📈 财务基本面分析"])
-    
+
     with tab1:
         st.markdown("#### 💰 资金流向分析师报告")
         st.markdown("---")
@@ -438,7 +441,7 @@ def display_analyst_reports(analyzer):
             st.markdown(analyzer.fund_flow_analysis)
         else:
             st.info("暂无资金流向分析报告")
-    
+
     with tab2:
         st.markdown("#### 📊 行业板块及市场热点分析师报告")
         st.markdown("---")
@@ -446,7 +449,7 @@ def display_analyst_reports(analyzer):
             st.markdown(analyzer.industry_analysis)
         else:
             st.info("暂无行业板块分析报告")
-    
+
     with tab3:
         st.markdown("#### 📈 财务基本面分析师报告")
         st.markdown("---")
@@ -459,10 +462,10 @@ def format_number(value, unit='', suffix=''):
     """格式化数字显示"""
     if value is None or value == 'N/A':
         return 'N/A'
-    
+
     try:
         num = float(value)
-        
+
         # 如果单位是亿，需要转换
         if unit == '亿':
             if abs(num) >= 100000000:  # 大于1亿（以元为单位）
@@ -471,7 +474,7 @@ def format_number(value, unit='', suffix=''):
                 pass
             else:  # 100-100000000之间，可能是万
                 num = num / 10000
-        
+
         # 格式化显示
         if abs(num) >= 1000:
             formatted = f"{num:,.2f}"
@@ -479,7 +482,7 @@ def format_number(value, unit='', suffix=''):
             formatted = f"{num:.2f}"
         else:
             formatted = f"{num:.4f}"
-        
+
         return f"{formatted}{suffix}"
     except (ValueError, TypeError):
         return str(value)
@@ -489,14 +492,14 @@ def run_main_force_batch_analysis():
     """执行主力选股TOP股票批量分析（遵循统一调用规范）"""
     import time
     import re
-    
+
     st.markdown("## 🚀 主力选股TOP股票批量分析")
     st.markdown("---")
-    
+
     # 检查是否已有分析结果
     if st.session_state.get('main_force_batch_results'):
         display_main_force_batch_results(st.session_state.main_force_batch_results)
-        
+
         # 返回按钮
         col_back, col_clear = st.columns(2)
         with col_back:
@@ -509,28 +512,28 @@ def run_main_force_batch_analysis():
                 if 'main_force_batch_results' in st.session_state:
                     del st.session_state.main_force_batch_results
                 st.rerun()
-        
+
         with col_clear:
             if st.button("🔄 重新分析", width='content'):
                 # 清除结果，保留触发标志和代码
                 if 'main_force_batch_results' in st.session_state:
                     del st.session_state.main_force_batch_results
                 st.rerun()
-        
+
         return
-    
+
     # 获取股票代码列表
     stock_codes = st.session_state.get('main_force_batch_codes', [])
-    
+
     if not stock_codes:
         st.error("未找到股票代码列表")
         # 清除触发标志
         if 'main_force_batch_trigger' in st.session_state:
             del st.session_state.main_force_batch_trigger
         return
-    
+
     st.info(f"即将分析 {len(stock_codes)} 只股票：{', '.join(stock_codes[:10])}{'...' if len(stock_codes) > 10 else ''}")
-    
+
     # 返回按钮
     if st.button("🔙 取消返回", type="secondary"):
         # 清除所有批量分析相关状态
@@ -539,12 +542,12 @@ def run_main_force_batch_analysis():
         if 'main_force_batch_codes' in st.session_state:
             del st.session_state.main_force_batch_codes
         st.rerun()
-    
+
     st.markdown("---")
-    
+
     # 分析选项
     col1, col2 = st.columns(2)
-    
+
     with col1:
         analysis_mode = st.selectbox(
             "分析模式",
@@ -552,7 +555,7 @@ def run_main_force_batch_analysis():
             format_func=lambda x: "顺序分析（稳定）" if x == "sequential" else "并行分析（快速）",
             help="顺序分析较慢但稳定，并行分析更快但消耗更多资源"
         )
-    
+
     with col2:
         if analysis_mode == "parallel":
             max_workers = st.number_input(
@@ -564,17 +567,17 @@ def run_main_force_batch_analysis():
             )
         else:
             max_workers = 1
-    
+
     st.markdown("---")
-    
+
     # 开始分析按钮
     col_confirm, col_cancel = st.columns(2)
-    
+
     start_analysis = False
     with col_confirm:
         if st.button("🚀 确认开始分析", type="primary", width='content'):
             start_analysis = True
-    
+
     with col_cancel:
         if st.button("❌ 取消", type="secondary", width='content'):
             # 清除所有批量分析相关状态
@@ -583,16 +586,16 @@ def run_main_force_batch_analysis():
             if 'main_force_batch_codes' in st.session_state:
                 del st.session_state.main_force_batch_codes
             st.rerun()
-    
+
     if start_analysis:
         # 导入统一分析函数（遵循统一规范）
         from app import analyze_single_stock_for_batch
         import concurrent.futures
         import time
-        
+
         st.markdown("---")
         st.info("⏳ 正在执行批量分析，请稍候...")
-        
+
         # 显示即将分析的股票代码（调试用）
         with st.expander("🔍 调试信息", expanded=True):
             st.write(f"**股票代码数量**: {len(stock_codes)} 只")
@@ -600,7 +603,7 @@ def run_main_force_batch_analysis():
             st.write(f"**代码格式检查**: {'✅ 无后缀，格式正确' if all('.' not in str(c) for c in stock_codes) else '❌ 包含后缀，可能有问题'}")
             st.write(f"**分析模式**: {analysis_mode}")
             st.write(f"**线程数**: {max_workers if analysis_mode == 'parallel' else 1}")
-        
+
         # 配置分析师参数
         enabled_analysts_config = {
             'technical': True,
@@ -612,23 +615,23 @@ def run_main_force_batch_analysis():
         }
         selected_model = 'deepseek-chat'
         period = '1y'
-        
+
         # 创建进度显示
         progress_bar = st.progress(0)
         status_text = st.empty()
-        
+
         # 存储结果
         results = []
-        
+
         # 记录开始时间
         start_time = time.time()
-        
+
         if analysis_mode == "sequential":
             # 顺序分析
             for i, code in enumerate(stock_codes):
                 status_text.text(f"正在分析 {code} ({i+1}/{len(stock_codes)})")
                 progress_bar.progress((i + 1) / len(stock_codes))
-                
+
                 try:
                     # 调用统一分析函数
                     result = analyze_single_stock_for_batch(
@@ -637,23 +640,23 @@ def run_main_force_batch_analysis():
                         enabled_analysts_config=enabled_analysts_config,
                         selected_model=selected_model
                     )
-                    
+
                     results.append(result)
-                    
+
                 except Exception as e:
                     results.append({
                         "symbol": code,
                         "success": False,
                         "error": str(e)
                     })
-        
+
         else:
             # 并行分析
             status_text.text(f"并行分析 {len(stock_codes)} 只股票（{max_workers}线程）...")
             print(f"\n{'='*60}")
             print(f"🚀 开始并行分析 {len(stock_codes)} 只股票")
             print(f"{'='*60}")
-            
+
             def analyze_one(code):
                 try:
                     print(f"  开始分析: {code}")
@@ -668,10 +671,10 @@ def run_main_force_batch_analysis():
                 except Exception as e:
                     print(f"  分析失败: {code} - {str(e)}")
                     return {"symbol": code, "success": False, "error": str(e)}
-            
+
             with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
                 futures = {executor.submit(analyze_one, code): code for code in stock_codes}
-                
+
                 completed = 0
                 for future in concurrent.futures.as_completed(futures):
                     code = futures[future]  # 获取对应的股票代码
@@ -679,48 +682,48 @@ def run_main_force_batch_analysis():
                     progress = completed / len(stock_codes)
                     progress_bar.progress(progress)
                     status_text.text(f"已完成 {completed}/{len(stock_codes)} ({code})")
-                    
+
                     print(f"  进度更新: {completed}/{len(stock_codes)} ({progress*100:.1f}%) - {code}")
-                    
+
                     try:
                         result = future.result()
                         results.append(result)
                     except Exception as e:
                         print(f"  获取结果失败: {code} - {str(e)}")
                         results.append({"symbol": code, "success": False, "error": str(e)})
-            
+
             print(f"\n✅ 所有并行任务已完成")
             print(f"   完成数: {completed}")
             print(f"   结果数: {len(results)}")
             print(f"{'='*60}\n")
-        
+
         # 清除进度
         progress_bar.empty()
         status_text.empty()
-        
+
         # 计算统计
         elapsed_time = time.time() - start_time
         success_count = sum(1 for r in results if r.get("success", False))
         failed_count = len(results) - success_count
-        
+
         # 显示完成信息
         if success_count > 0:
             st.success(f"✅ 批量分析完成！成功 {success_count} 只，失败 {failed_count} 只，耗时 {elapsed_time/60:.1f} 分钟")
         else:
             st.error(f"❌ 批量分析完成，但所有 {failed_count} 只股票都分析失败！")
-            
+
             # 显示失败原因（调试用）
             with st.expander("❌ 查看失败原因", expanded=True):
                 for r in results:
                     if not r.get("success", False):
                         st.error(f"**{r.get('symbol', 'N/A')}**: {r.get('error', '未知错误')}")
-        
+
         # 先保存到数据库历史记录（在 rerun 之前完成）
         save_success = False
         save_error = None
         try:
             from main_force_batch_db import batch_db
-            
+
             # 调试信息
             print(f"\n{'='*60}")
             print(f"📝 准备保存批量分析结果到历史记录")
@@ -731,17 +734,17 @@ def run_main_force_batch_analysis():
             print(f"失败数: {failed_count}")
             print(f"总耗时: {elapsed_time:.2f}秒")
             print(f"结果数: {len(results)}")
-            
+
             # 检查结果数据类型
             print(f"\n检查结果数据类型:")
             for i, result in enumerate(results[:3]):  # 只检查前3个
                 print(f"  结果 {i+1}:")
                 for key, value in list(result.items())[:5]:  # 只检查前5个字段
                     print(f"    - {key}: {type(value).__name__}")
-            
+
             print(f"\n开始保存到数据库...")
             save_start = time.time()
-            
+
             # 保存到数据库
             record_id = batch_db.save_batch_analysis(
                 batch_count=len(stock_codes),
@@ -751,14 +754,14 @@ def run_main_force_batch_analysis():
                 total_time=elapsed_time,
                 results=results
             )
-            
+
             save_elapsed = time.time() - save_start
             print(f"✅ 批量分析结果已保存到历史记录")
             print(f"   记录ID: {record_id}")
             print(f"   保存耗时: {save_elapsed:.2f}秒")
             print(f"{'='*60}\n")
             save_success = True
-            
+
         except Exception as e:
             import traceback
             save_error = str(e)
@@ -769,7 +772,7 @@ def run_main_force_batch_analysis():
             print(f"详细错误:")
             print(traceback.format_exc())
             print(f"{'='*60}\n")
-        
+
         # 保存结果到session_state
         st.session_state.main_force_batch_results = {
             "results": results,
@@ -781,9 +784,9 @@ def run_main_force_batch_analysis():
             "saved_to_history": save_success,
             "save_error": save_error
         }
-        
+
         time.sleep(0.5)
-        
+
         # 重新渲染以显示结果
         st.rerun()
 
@@ -791,7 +794,7 @@ def run_main_force_batch_analysis():
 def display_main_force_batch_results(batch_results):
     """显示主力选股批量分析结果"""
     import re
-    
+
     results = batch_results['results']
     total = batch_results['total']
     success = batch_results['success']
@@ -799,46 +802,46 @@ def display_main_force_batch_results(batch_results):
     elapsed_time = batch_results['elapsed_time']
     saved_to_history = batch_results.get('saved_to_history', False)
     save_error = batch_results.get('save_error')
-    
+
     st.markdown("## 📊 批量分析结果")
-    
+
     # 显示保存状态
     if saved_to_history:
         st.success("✅ 分析结果已自动保存到历史记录，可点击右上角'📚 批量分析历史'查看")
     elif save_error:
         st.warning(f"⚠️ 历史记录保存失败: {save_error}，但结果仍可查看")
-    
+
     st.markdown("---")
-    
+
     # 统计信息
     col1, col2, col3, col4 = st.columns(4)
-    
+
     with col1:
         st.metric("总计分析", f"{total} 只")
-    
+
     with col2:
         st.metric("成功分析", f"{success} 只", delta=f"{success/total*100:.1f}%")
-    
+
     with col3:
         st.metric("失败分析", f"{failed} 只")
-    
+
     with col4:
         st.metric("总耗时", f"{elapsed_time/60:.1f} 分钟")
-    
+
     st.markdown("---")
-    
+
     # 成功分析的股票
     successful_results = [r for r in results if r['success']]
-    
+
     if successful_results:
         st.markdown(f"### ✅ 成功分析的股票 ({len(successful_results)}只)")
-        
+
         # 创建DataFrame展示
         display_data = []
         for result in successful_results:
             stock_info = result.get('stock_info', {})
             final_decision = result.get('final_decision', {})
-            
+
             # 提取评级emoji
             rating = final_decision.get('rating', '未知')
             rating_emoji = {
@@ -848,7 +851,7 @@ def display_main_force_batch_results(batch_results):
                 '卖出': '⚠️',
                 '强烈卖出': '🚫'
             }.get(rating, '❓')
-            
+
             display_data.append({
                 '股票代码': stock_info.get('symbol', ''),
                 '股票名称': stock_info.get('name', ''),
@@ -859,9 +862,9 @@ def display_main_force_batch_results(batch_results):
                 '止损位': final_decision.get('stop_loss', 'N/A'),
                 '目标价': final_decision.get('target_price', 'N/A')
             })
-        
+
         df_display = pd.DataFrame(display_data)
-        
+
         # 类型统一，避免Arrow序列化错误
         numeric_cols = ['信心度', '止盈位', '止损位', '目标价']
         for col in numeric_cols:
@@ -872,17 +875,17 @@ def display_main_force_batch_results(batch_results):
         for col in text_cols:
             if col in df_display.columns:
                 df_display[col] = df_display[col].astype(str)
-        
+
         st.dataframe(df_display, width='content', height=400)
-        
+
         # 详细分析结果（可展开）
         st.markdown("---")
         st.markdown("### 📋 详细分析报告")
-        
+
         for result in successful_results:
             stock_info = result.get('stock_info', {})
             final_decision = result.get('final_decision', {})
-            
+
             symbol = stock_info.get('symbol', '')
             name = stock_info.get('name', '')
             rating = final_decision.get('rating', '未知')
@@ -893,34 +896,34 @@ def display_main_force_batch_results(batch_results):
                 '卖出': '⚠️',
                 '强烈卖出': '🚫'
             }.get(rating, '❓')
-            
+
             with st.expander(f"{rating_emoji} {symbol} - {name} | {rating}"):
                 # 关键信息
                 col1, col2, col3 = st.columns(3)
-                
+
                 with col1:
                     st.metric("信心度", final_decision.get('confidence_level', 'N/A'))
-                
+
                 with col2:
                     st.metric("进场区间", final_decision.get('entry_range', 'N/A'))
-                
+
                 with col3:
                     st.metric("目标价", final_decision.get('target_price', 'N/A'))
-                
+
                 # 止盈止损
                 col1, col2 = st.columns(2)
-                
+
                 with col1:
                     st.metric("止盈位", final_decision.get('take_profit', 'N/A'))
-                
+
                 with col2:
                     st.metric("止损位", final_decision.get('stop_loss', 'N/A'))
-                
+
                 # 投资建议
                 st.markdown("#### 💡 投资建议")
                 advice = final_decision.get('operation_advice', final_decision.get('advice', '暂无建议'))
                 st.info(advice)
-                
+
                 # 加入监测按钮
                 if st.button(f"➕ 加入监测列表", key=f"monitor_{symbol}"):
                     # 解析进场区间
@@ -933,7 +936,7 @@ def display_main_force_batch_results(batch_results):
                             entry_max = float(parts[1].strip())
                         except:
                             pass
-                    
+
                     # 解析止盈止损
                     take_profit_str = final_decision.get('take_profit', '')
                     take_profit = None
@@ -944,7 +947,7 @@ def display_main_force_batch_results(batch_results):
                                 take_profit = float(numbers[0])
                         except:
                             pass
-                    
+
                     stop_loss_str = final_decision.get('stop_loss', '')
                     stop_loss = None
                     if stop_loss_str:
@@ -954,16 +957,16 @@ def display_main_force_batch_results(batch_results):
                                 stop_loss = float(numbers[0])
                         except:
                             pass
-                    
+
                     # 调用监测管理器添加
                     from monitor_db import monitor_db
-                    
+
                     try:
                         # 准备进场区间数据
                         entry_range_dict = {}
                         if entry_min and entry_max:
                             entry_range_dict = {"min": entry_min, "max": entry_max}
-                        
+
                         # 添加到监测列表
                         monitor_db.add_monitored_stock(
                             symbol=symbol,
@@ -976,21 +979,21 @@ def display_main_force_batch_results(batch_results):
                         st.success(f"✅ {symbol} - {name} 已加入监测列表")
                     except Exception as e:
                         st.error(f"❌ 添加失败: {str(e)}")
-    
+
     # 失败的股票
     failed_results = [r for r in results if not r['success']]
-    
+
     if failed_results:
         st.markdown("---")
         st.markdown(f"### ❌ 分析失败的股票 ({len(failed_results)}只)")
-        
+
         failed_data = []
         for result in failed_results:
             failed_data.append({
                 '股票代码': result.get('symbol', ''),
                 '失败原因': result.get('error', '未知错误')
             })
-        
+
         df_failed = pd.DataFrame(failed_data)
         st.dataframe(df_failed, width='content')
 

model_config.py

@@ -0,0 +1,24 @@
+"""
+模型配置文件
+包含所有可用的AI模型选项
+"""
+
+model_options = {
+    "deepseek-chat": "DeepSeek Chat (默认)",
+    "deepseek-reasoner": "DeepSeek Reasoner (推理增强)",
+    "qwen-plus": "qwen-plus (阿里百炼)",
+    "qwen-plus-latest": "qwen-plus-latest (阿里百炼)",
+    "qwen-flash": "qwen-flash (阿里百炼)",
+    "qwen-turbo": "qwen-turbo (阿里百炼)",
+    "qwen3-max": "qwen-max (阿里百炼)",
+    "qwen-long": "qwen-long (阿里百炼)",
+    "deepseek-ai/DeepSeek-R1-0528-Qwen3-8B": "DeepSeek-R1 免费(硅基流动)",
+    "Qwen/Qwen2.5-7B-Instruct": "Qwen 免费(硅基流动)",
+    "Pro/deepseek-ai/DeepSeek-V3.1-Terminus": "DeepSeek-V3.1-Terminus (硅基流动)",
+    "deepseek-ai/DeepSeek-R1": "DeepSeek-R1 (硅基流动)",
+    "Qwen/Qwen3-235B-A22B-Thinking-2507": "Qwen3-235B (硅基流动)",
+    "zai-org/GLM-4.6": "智谱(硅基流动)",
+    "moonshotai/Kimi-K2-Instruct-0905": "Kimi (硅基流动)",
+    "Ring-1T": "蚂蚁百灵 (硅基流动)",
+    "step3": "阶跃星辰(硅基流动)"
+}

pdf_generator.py

@@ -259,6 +259,128 @@ def create_download_link(pdf_content, filename):
     href = f'<a href="data:application/pdf;base64,{b64}" download="{filename}" style="display: inline-block; padding: 15px 30px; background-color: #e74c3c; color: white; text-decoration: none; border-radius: 8px; font-weight: bold; font-size: 16px; margin: 10px;">📄 下载PDF报告</a>'
     return href
 
+def generate_markdown_report(stock_info, agents_results, discussion_result, final_decision):
+    """生成Markdown格式的分析报告"""
+    
+    # 获取当前时间
+    current_time = datetime.now().strftime("%Y年%m月%d日 %H:%M:%S")
+    
+    markdown_content = f"""
+# AI股票分析报告
+
+**生成时间**: {current_time}
+
+---
+
+## 📊 股票基本信息
+
+| 项目 | 值 |
+|------|-----|
+| **股票代码** | {stock_info.get('symbol', 'N/A')} |
+| **股票名称** | {stock_info.get('name', 'N/A')} |
+| **当前价格** | {stock_info.get('current_price', 'N/A')} |
+| **涨跌幅** | {stock_info.get('change_percent', 'N/A')}% |
+| **市盈率(PE)** | {stock_info.get('pe_ratio', 'N/A')} |
+| **市净率(PB)** | {stock_info.get('pb_ratio', 'N/A')} |
+| **市值** | {stock_info.get('market_cap', 'N/A')} |
+| **市场** | {stock_info.get('market', 'N/A')} |
+| **交易所** | {stock_info.get('exchange', 'N/A')} |
+
+---
+
+## 🔍 各分析师详细分析
+
+"""
+
+    # 添加各分析师的分析结果
+    agent_names = {
+        'technical': '📈 技术分析师',
+        'fundamental': '📊 基本面分析师',
+        'fund_flow': '💰 资金面分析师',
+        'risk_management': '⚠️ 风险管理师',
+        'market_sentiment': '📈 市场情绪分析师'
+    }
+    
+    for agent_key, agent_name in agent_names.items():
+        if agent_key in agents_results:
+            agent_result = agents_results[agent_key]
+            if isinstance(agent_result, dict):
+                analysis_text = agent_result.get('analysis', '暂无分析')
+            else:
+                analysis_text = str(agent_result)
+            
+            markdown_content += f"""
+### {agent_name}
+
+{analysis_text}
+
+---
+
+"""
+
+    # 添加团队讨论结果
+    markdown_content += f"""
+## 🤝 团队综合讨论
+
+{discussion_result}
+
+---
+
+## 📋 最终投资决策
+
+"""
+    
+    # 处理最终决策的显示
+    if isinstance(final_decision, dict) and "decision_text" not in final_decision:
+        # JSON格式的决策
+        markdown_content += f"""
+**投资评级**: {final_decision.get('rating', '未知')}
+
+**目标价位**: {final_decision.get('target_price', 'N/A')}
+
+**操作建议**: {final_decision.get('operation_advice', '暂无建议')}
+
+**进场区间**: {final_decision.get('entry_range', 'N/A')}
+
+**止盈位**: {final_decision.get('take_profit', 'N/A')}
+
+**止损位**: {final_decision.get('stop_loss', 'N/A')}
+
+**持有周期**: {final_decision.get('holding_period', 'N/A')}
+
+**仓位建议**: {final_decision.get('position_size', 'N/A')}
+
+**信心度**: {final_decision.get('confidence_level', 'N/A')}/10
+
+**风险提示**: {final_decision.get('risk_warning', '无')}
+"""
+    else:
+        # 文本格式的决策
+        decision_text = final_decision.get('decision_text', str(final_decision))
+        markdown_content += decision_text
+
+    markdown_content += """
+
+---
+
+## 📝 免责声明
+
+本报告由AI系统生成，仅供参考，不构成投资建议。投资有风险，入市需谨慎。请在做出投资决策前咨询专业的投资顾问。
+
+---
+
+*报告生成时间: {current_time}*
+*AI股票分析系统 v1.0*
+"""
+
+    return markdown_content
+
+def create_markdown_download_link(markdown_content, filename):
+    """创建Markdown下载链接"""
+    b64 = base64.b64encode(markdown_content.encode()).decode()
+    href = f'<a href="data:text/markdown;base64,{b64}" download="{filename}" style="display: inline-block; padding: 15px 30px; background-color: #9b59b6; color: white; text-decoration: none; border-radius: 8px; font-weight: bold; font-size: 16px; margin: 10px;">📝 下载Markdown报告</a>'
+    return href
+
 def display_pdf_export_section(stock_info, agents_results, discussion_result, final_decision):
     """显示PDF导出区域"""
     
@@ -269,8 +391,11 @@ def display_pdf_export_section(stock_info, agents_results, discussion_result, fi
     
     with col2:
         # 生成PDF报告按钮（使用股票代码作为key的一部分，确保唯一性）
-        button_key = f"pdf_btn_{stock_info.get('symbol', 'unknown')}"
+        pdf_button_key = f"pdf_btn_{stock_info.get('symbol', 'unknown')}"
-        if st.button("📄 生成并下载PDF报告", type="primary", width='content', key=button_key):
+        markdown_button_key = f"markdown_btn_{stock_info.get('symbol', 'unknown')}"
+        
+        # 生成PDF报告按钮
+        if st.button("📄 生成并下载PDF报告", type="primary", width='content', key=pdf_button_key):
             with st.spinner("正在生成PDF报告..."):
                 try:
                     # 生成PDF内容
@@ -300,3 +425,36 @@ def display_pdf_export_section(stock_info, agents_results, discussion_result, fi
                     st.error(f"❌ 生成PDF报告时出错: {str(e)}")
                     import traceback
                     st.error(f"详细错误信息: {traceback.format_exc()}")
+        
+        # 生成Markdown报告按钮
+        if st.button("📝 生成并下载Markdown报告", type="secondary", width='content', key=markdown_button_key):
+            with st.spinner("正在生成Markdown报告..."):
+                try:
+                    # 生成Markdown内容
+                    markdown_content = generate_markdown_report(stock_info, agents_results, discussion_result, final_decision)
+                    
+                    # 生成文件名
+                    stock_symbol = stock_info.get('symbol', 'unknown')
+                    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+                    filename = f"股票分析报告_{stock_symbol}_{timestamp}.md"
+                    
+                    st.success("✅ Markdown报告生成成功！")
+                    st.balloons()
+                    
+                    # 显示下载链接
+                    st.markdown("### 📄 报告下载")
+                    
+                    download_link = create_markdown_download_link(markdown_content, filename)
+                    st.markdown(f"""
+                    <div style="text-align: center; margin: 20px 0;">
+                        {download_link}
+                    </div>
+                    """, unsafe_allow_html=True)
+                    
+                    st.info("💡 提示：点击上方按钮即可下载Markdown格式的完整分析报告")
+                    
+                except Exception as e:
+                    st.error(f"❌ 生成Markdown报告时出错: {str(e)}")
+                    import traceback
+                    st.error(f"详细错误信息: {traceback.format_exc()}")
+

pdf_generator_fixed.py

@@ -133,151 +133,6 @@ def create_html_download_link(content, filename, link_text):
     href = f'<a href="data:text/html;base64,{b64}" download="{filename}" style="display: inline-block; padding: 10px 20px; background-color: #2196F3; color: white; text-decoration: none; border-radius: 5px; margin: 5px;">{link_text}</a>'
     return href
 
-def generate_html_content(markdown_content):
-    """将Markdown转换为HTML"""
-    html_content = f"""
-<!DOCTYPE html>
-<html>
-<head>
-    <meta charset="UTF-8">
-    <title>AI股票分析报告</title>
-    <style>
-        body {{
-            font-family: 'Microsoft YaHei', Arial, sans-serif;
-            line-height: 1.6;
-            max-width: 800px;
-            margin: 0 auto;
-            padding: 20px;
-            background-color: #f5f5f5;
-        }}
-        .container {{
-            background-color: white;
-            padding: 30px;
-            border-radius: 10px;
-            box-shadow: 0 2px 10px rgba(0,0,0,0.1);
-        }}
-        h1 {{
-            color: #2c3e50;
-            border-bottom: 3px solid #3498db;
-            padding-bottom: 10px;
-        }}
-        h2 {{
-            color: #34495e;
-            border-left: 4px solid #3498db;
-            padding-left: 15px;
-            margin-top: 30px;
-        }}
-        h3 {{
-            color: #2980b9;
-            margin-top: 25px;
-        }}
-        table {{
-            width: 100%;
-            border-collapse: collapse;
-            margin: 20px 0;
-        }}
-        th, td {{
-            border: 1px solid #ddd;
-            padding: 12px;
-            text-align: left;
-        }}
-        th {{
-            background-color: #3498db;
-            color: white;
-        }}
-        tr:nth-child(even) {{
-            background-color: #f9f9f9;
-        }}
-        .disclaimer {{
-            background-color: #fff3cd;
-            border: 1px solid #ffeaa7;
-            border-radius: 5px;
-            padding: 15px;
-            margin-top: 30px;
-        }}
-        .footer {{
-            text-align: center;
-            margin-top: 30px;
-            color: #7f8c8d;
-            font-style: italic;
-        }}
-        hr {{
-            border: none;
-            height: 2px;
-            background-color: #ecf0f1;
-            margin: 20px 0;
-        }}
-        strong {{
-            color: #2c3e50;
-        }}
-    </style>
-</head>
-<body>
-    <div class="container">
-"""
-    
-    # 简单的Markdown到HTML转换
-    html_body = markdown_content
-    html_body = html_body.replace('\n# ', '\n<h1>').replace('\n## ', '\n<h2>').replace('\n### ', '\n<h3>')
-    html_body = html_body.replace('# ', '<h1>').replace('## ', '<h2>').replace('### ', '<h3>')
-    html_body = html_body.replace('\n---\n', '\n<hr>\n')
-    
-    # 处理粗体文本
-    html_body = re.sub(r'\*\*(.*?)\*\*', r'<strong>\1</strong>', html_body)
-    
-    # 处理表格
-    lines = html_body.split('\n')
-    in_table = False
-    processed_lines = []
-    
-    for line in lines:
-        if '|' in line and not in_table and line.strip().startswith('|'):
-            processed_lines.append('<table>')
-            in_table = True
-            cells = [cell.strip() for cell in line.split('|')[1:-1]]
-            processed_lines.append('<tr>')
-            for cell in cells:
-                processed_lines.append(f'<th>{cell}</th>')
-            processed_lines.append('</tr>')
-        elif '|' in line and in_table:
-            if '---' not in line:
-                cells = [cell.strip() for cell in line.split('|')[1:-1]]
-                processed_lines.append('<tr>')
-                for cell in cells:
-                    processed_lines.append(f'<td>{cell}</td>')
-                processed_lines.append('</tr>')
-        elif in_table and '|' not in line:
-            processed_lines.append('</table>')
-            processed_lines.append(line)
-            in_table = False
-        else:
-            processed_lines.append(line)
-    
-    if in_table:
-        processed_lines.append('</table>')
-    
-    html_body = '\n'.join(processed_lines)
-    
-    # 处理段落
-    paragraphs = html_body.split('\n\n')
-    processed_paragraphs = []
-    for para in paragraphs:
-        para = para.strip()
-        if para and not para.startswith('<') and not para.startswith('---'):
-            processed_paragraphs.append(f'<p>{para}</p>')
-        else:
-            processed_paragraphs.append(para)
-    
-    html_body = '\n'.join(processed_paragraphs)
-    
-    html_content += html_body + """
-    </div>
-</body>
-</html>
-"""
-    
-    return html_content
-
 def display_pdf_export_section(stock_info, agents_results, discussion_result, final_decision):
     """显示PDF导出区域 - 修复报告生成问题"""
     
@@ -290,8 +145,11 @@ def display_pdf_export_section(stock_info, agents_results, discussion_result, fi
         # 生成报告按钮
         import uuid
         import time
-        button_key = f"generate_report_btn_{int(time.time())}_{uuid.uuid4().hex[:8]}"
+        pdf_button_key = f"generate_report_btn_{int(time.time())}_{uuid.uuid4().hex[:8]}"
-        if st.button("📊 生成并下载报告", type="primary", width='content', key=button_key):
+        markdown_button_key = f"generate_markdown_btn_{int(time.time())}_{uuid.uuid4().hex[:8]}"
+        
+        # 生成PDF和HTML报告按钮
+        if st.button("📊 生成并下载报告(PDF/HTML)", type="primary", width='content', key=pdf_button_key):
             with st.spinner("正在生成报告..."):
                 try:
                     # 生成Markdown内容
@@ -337,4 +195,44 @@ def display_pdf_export_section(stock_info, agents_results, discussion_result, fi
                 except Exception as e:
                     st.error(f"❌ 生成报告时出错: {str(e)}")
                     import traceback
-                    st.error(f"详细错误信息: {traceback.format_exc()}")
+                    st.error(f"详细错误信息: {traceback.format_exc()}")
+        
+        # 单独生成Markdown报告按钮
+        if st.button("📝 生成并下载Markdown报告", type="secondary", width='content', key=markdown_button_key):
+            with st.spinner("正在生成Markdown报告..."):
+                try:
+                    # 生成Markdown内容
+                    markdown_content = generate_markdown_report(stock_info, agents_results, discussion_result, final_decision)
+                    
+                    # 生成文件名
+                    stock_symbol = stock_info.get('symbol', 'unknown')
+                    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+                    filename = f"股票分析报告_{stock_symbol}_{timestamp}.md"
+                    
+                    st.success("✅ Markdown报告生成成功！")
+                    st.balloons()
+                    
+                    # 显示下载链接
+                    st.markdown("### 📄 报告下载")
+                    
+                    # 创建下载链接
+                    md_link = create_download_link(
+                        markdown_content, 
+                        filename, 
+                        "📝 下载Markdown报告"
+                    )
+                    
+                    # 显示下载链接
+                    st.markdown(f"""
+                    <div style="text-align: center; margin: 20px 0;">
+                        {md_link}
+                    </div>
+                    """, unsafe_allow_html=True)
+                    
+                    st.info("💡 提示：点击上方按钮即可下载Markdown格式的报告文件")
+                    
+                except Exception as e:
+                    st.error(f"❌ 生成Markdown报告时出错: {str(e)}")
+                    import traceback
+                    st.error(f"详细错误信息: {traceback.format_exc()}")
+

pdf_generator_pandoc.py

@@ -267,12 +267,47 @@ def display_pdf_export_section(stock_info, agents_results, discussion_result, fi
     col1, col2, col3 = st.columns([1, 2, 1])
     
     with col2:
-        if st.button("📊 生成并下载报告", type="primary", width='content', key="generate_report_btn"):
+        pdf_button_key = "generate_report_btn"
+        markdown_button_key = "generate_markdown_btn"
+        
+        # 生成PDF报告按钮
+        if st.button("📊 生成并下载报告(PDF/HTML)", type="primary", width='content', key=pdf_button_key):
             st.session_state.show_download_links = True
             with st.spinner("正在生成报告..."):
                 success = generate_pdf_report(stock_info, agents_results, discussion_result, final_decision)
                 if success:
                     st.balloons()
+        
+        # 生成Markdown报告按钮
+        if st.button("📝 生成并下载Markdown报告", type="secondary", width='content', key=markdown_button_key):
+            with st.spinner("正在生成Markdown报告..."):
+                try:
+                    # 生成Markdown内容
+                    markdown_content = generate_markdown_report(stock_info, agents_results, discussion_result, final_decision)
+                    
+                    # 生成文件名
+                    stock_symbol = stock_info.get('symbol', 'unknown')
+                    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+                    filename = f"股票分析报告_{stock_symbol}_{timestamp}.md"
+                    
+                    st.success("✅ Markdown报告生成成功！")
+                    st.balloons()
+                    
+                    # 显示下载链接
+                    st.markdown("### 📄 报告下载")
+                    
+                    # Markdown下载链接
+                    md_download_link = create_download_link(
+                        markdown_content, 
+                        filename, 
+                        "📝 下载Markdown报告"
+                    )
+                    st.markdown(md_download_link, unsafe_allow_html=True)
+                    
+                    st.info("💡 提示：点击上方按钮即可下载Markdown格式的报告文件")
+                    
+                except Exception as e:
+                    st.error(f"❌ 生成Markdown报告时出错: {str(e)}")
     
     # 如果已经生成了报告，显示下载链接
     if st.session_state.show_download_links:

sector_strategy_ui.py

@@ -116,9 +116,12 @@ def display_analysis_tab():
     col1, col2, col3 = st.columns([2, 2, 2])
     
     with col1:
+        # 导入model_config.py中定义的model_options
+        from model_config import model_options as app_model_options
         selected_model = st.selectbox(
-            "选择AI模型",
+            "AI模型",
-            ["deepseek-chat", "deepseek-reasoner"],
+            list(app_model_options.keys()),
+            format_func=lambda x: app_model_options[x],
             help="Reasoner模型提供更强的推理能力"
         )
     
@@ -774,10 +777,10 @@ def display_pdf_export_section(result):
     """显示PDF导出部分"""
     st.subheader("📄 导出报告")
     
-    col1, col2, col3 = st.columns([2, 1, 1])
+    col1, col2, col3, col4 = st.columns([2, 1, 1, 1])
     
     with col1:
-        st.write("将分析报告导出为PDF文件，方便保存和分享")
+        st.write("将分析报告导出为PDF或Markdown文件，方便保存和分享")
     
     with col2:
         if st.button("📥 生成PDF报告", type="primary", width='content'):
@@ -802,6 +805,23 @@ def display_pdf_export_section(result):
                     st.error(f"❌ PDF生成失败: {str(e)}")
     
     with col3:
+        if st.button("📝 生成Markdown", type="secondary", width='content'):
+            with st.spinner("正在生成Markdown报告..."):
+                try:
+                    # 生成Markdown内容
+                    markdown_content = generate_sector_markdown_report(result)
+                    
+                    # 保存到session_state
+                    st.session_state.sector_markdown_data = markdown_content
+                    st.session_state.sector_markdown_filename = f"智策报告_{result.get('timestamp', datetime.now().strftime('%Y%m%d_%H%M%S')).replace(':', '').replace(' ', '_')}.md"
+                    
+                    st.success("✅ Markdown报告生成成功！")
+                    st.rerun()
+                    
+                except Exception as e:
+                    st.error(f"❌ Markdown生成失败: {str(e)}")
+    
+    with col4:
         # 如果已经生成了PDF，显示下载按钮
         if 'sector_pdf_data' in st.session_state:
             st.download_button(
@@ -811,6 +831,197 @@ def display_pdf_export_section(result):
                         mime="application/pdf",
                         width='content'
                     )
+        
+        # 如果已经生成了Markdown，显示下载按钮
+        if 'sector_markdown_data' in st.session_state:
+            st.download_button(
+                        label="💾 下载Markdown",
+                        data=st.session_state.sector_markdown_data,
+                        file_name=st.session_state.sector_markdown_filename,
+                        mime="text/markdown",
+                        width='content'
+                    )
+
+
+def generate_sector_markdown_report(result_data: dict) -> str:
+    """生成智策分析Markdown报告"""
+    
+    # 获取当前时间
+    current_time = datetime.now().strftime("%Y年%m月%d日 %H:%M:%S")
+    
+    # 标题页
+    markdown_content = f"""# 智策板块策略分析报告
+
+**AI驱动的多维度板块投资决策支持系统**
+
+---
+
+## 📊 报告信息
+
+- **生成时间**: {current_time}
+- **分析周期**: 当日市场数据
+- **AI模型**: DeepSeek Multi-Agent System
+- **分析维度**: 宏观·板块·资金·情绪
+
+> ⚠️ 本报告由AI系统自动生成，仅供参考，不构成投资建议。投资有风险，决策需谨慎。
+
+---
+
+## 📈 市场概况
+
+本报告基于{result_data.get('timestamp', 'N/A')}的实时市场数据，
+通过四位AI智能体的多维度分析，为您提供板块投资策略建议。
+
+### 分析师团队:
+
+- **宏观策略师** - 分析宏观经济、政策导向、新闻事件
+- **板块诊断师** - 分析板块走势、估值水平、轮动特征
+- **资金流向分析师** - 分析主力资金、北向资金流向
+- **市场情绪解码员** - 分析市场情绪、热度、赚钱效应
+
+"""
+    
+    # 核心预测
+    predictions = result_data.get('final_predictions', {})
+    
+    if predictions.get('prediction_text'):
+        # 文本格式预测
+        markdown_content += f"""
+## 🎯 核心预测
+
+{predictions.get('prediction_text', '')}
+
+"""
+    else:
+        # JSON格式预测
+        markdown_content += "## 🎯 核心预测\n\n"
+        
+        # 1. 板块多空预测
+        long_short = predictions.get('long_short', {})
+        bullish = long_short.get('bullish', [])
+        bearish = long_short.get('bearish', [])
+        
+        markdown_content += "### 📊 板块多空预测\n\n"
+        
+        if bullish:
+            markdown_content += "#### 🟢 看多板块\n\n"
+            for idx, item in enumerate(bullish, 1):
+                markdown_content += f"{idx}. **{item.get('sector', 'N/A')}** (信心度: {item.get('confidence', 0)}/10)\n"
+                markdown_content += f"   - 理由: {item.get('reason', 'N/A')}\n"
+                markdown_content += f"   - 风险: {item.get('risk', 'N/A')}\n\n"
+        
+        if bearish:
+            markdown_content += "#### 🔴 看空板块\n\n"
+            for idx, item in enumerate(bearish, 1):
+                markdown_content += f"{idx}. **{item.get('sector', 'N/A')}** (信心度: {item.get('confidence', 0)}/10)\n"
+                markdown_content += f"   - 理由: {item.get('reason', 'N/A')}\n"
+                markdown_content += f"   - 风险: {item.get('risk', 'N/A')}\n\n"
+        
+        # 2. 板块轮动预测
+        rotation = predictions.get('rotation', {})
+        current_strong = rotation.get('current_strong', [])
+        potential = rotation.get('potential', [])
+        declining = rotation.get('declining', [])
+        
+        markdown_content += "### 🔄 板块轮动预测\n\n"
+        
+        if current_strong:
+            markdown_content += "#### 💪 当前强势板块\n\n"
+            for item in current_strong:
+                markdown_content += f"- **{item.get('sector', 'N/A')}**\n"
+                markdown_content += f"  - 轮动逻辑: {item.get('logic', 'N/A')}\n"
+                markdown_content += f"  - 时间窗口: {item.get('time_window', 'N/A')}\n"
+                markdown_content += f"  - 操作建议: {item.get('advice', 'N/A')}\n\n"
+        
+        if potential:
+            markdown_content += "#### 🌱 潜力接力板块\n\n"
+            for item in potential:
+                markdown_content += f"- **{item.get('sector', 'N/A')}**\n"
+                markdown_content += f"  - 轮动逻辑: {item.get('logic', 'N/A')}\n"
+                markdown_content += f"  - 时间窗口: {item.get('time_window', 'N/A')}\n"
+                markdown_content += f"  - 操作建议: {item.get('advice', 'N/A')}\n\n"
+        
+        if declining:
+            markdown_content += "#### 📉 衰退板块\n\n"
+            for item in declining:
+                markdown_content += f"- **{item.get('sector', 'N/A')}**\n"
+                markdown_content += f"  - 轮动逻辑: {item.get('logic', 'N/A')}\n"
+                markdown_content += f"  - 时间窗口: {item.get('time_window', 'N/A')}\n"
+                markdown_content += f"  - 操作建议: {item.get('advice', 'N/A')}\n\n"
+        
+        # 3. 板块热度排行
+        heat = predictions.get('heat', {})
+        hottest = heat.get('hottest', [])
+        heating = heat.get('heating', [])
+        cooling = heat.get('cooling', [])
+        
+        markdown_content += "### 🔥 板块热度排行\n\n"
+        
+        if hottest:
+            markdown_content += "#### 最热板块\n\n| 排名 | 板块 | 热度评分 | 趋势 | 持续性 |\n|------|------|----------|------|--------|\n"
+            for idx, item in enumerate(hottest[:10], 1):
+                markdown_content += f"| {idx} | {item.get('sector', 'N/A')} | {item.get('score', 0)} | {item.get('trend', 'N/A')} | {item.get('sustainability', 'N/A')} |\n"
+            markdown_content += "\n"
+        
+        if heating:
+            markdown_content += "#### 升温板块\n\n"
+            for idx, item in enumerate(heating[:5], 1):
+                markdown_content += f"{idx}. {item.get('sector', 'N/A')} (评分: {item.get('score', 0)})\n"
+            markdown_content += "\n"
+        
+        if cooling:
+            markdown_content += "#### 降温板块\n\n"
+            for idx, item in enumerate(cooling[:5], 1):
+                markdown_content += f"{idx}. {item.get('sector', 'N/A')} (评分: {item.get('score', 0)})\n"
+            markdown_content += "\n"
+        
+        # 4. 策略总结
+        summary = predictions.get('summary', {})
+        if summary:
+            markdown_content += "### 📝 策略总结\n\n"
+            
+            if summary.get('market_view'):
+                markdown_content += f"**市场观点:** {summary.get('market_view', '')}\n\n"
+            
+            if summary.get('key_opportunity'):
+                markdown_content += f"**核心机会:** {summary.get('key_opportunity', '')}\n\n"
+            
+            if summary.get('major_risk'):
+                markdown_content += f"**主要风险:** {summary.get('major_risk', '')}\n\n"
+            
+            if summary.get('strategy'):
+                markdown_content += f"**整体策略:** {summary.get('strategy', '')}\n\n"
+    
+    # AI智能体分析
+    agents_analysis = result_data.get('agents_analysis', {})
+    if agents_analysis:
+        markdown_content += "## 🤖 AI智能体分析\n\n"
+        
+        for key, agent_data in agents_analysis.items():
+            agent_name = agent_data.get('agent_name', '未知分析师')
+            agent_role = agent_data.get('agent_role', '')
+            focus_areas = ', '.join(agent_data.get('focus_areas', []))
+            analysis = agent_data.get('analysis', '')
+            
+            markdown_content += f"### {agent_name}\n\n"
+            markdown_content += f"- **职责**: {agent_role}\n"
+            markdown_content += f"- **关注领域**: {focus_areas}\n\n"
+            markdown_content += f"{analysis}\n\n"
+            markdown_content += "---\n\n"
+    
+    # 综合研判
+    comprehensive_report = result_data.get('comprehensive_report', '')
+    if comprehensive_report:
+        markdown_content += "## 📊 综合研判\n\n"
+        markdown_content += f"{comprehensive_report}\n\n"
+    
+    markdown_content += """
+---
+
+*报告由智策AI系统自动生成*
+"""
+    
+    return markdown_content
 
 
 def display_scheduler_settings():