增加主力选股-批量分析功能

2025-10-21 17:27:29 +08:00
parent 04de9c28bb
commit acc00ec702
5 changed files with 1011 additions and 304 deletions
@@ -13,6 +13,11 @@ 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
+    
    st.markdown("## 🎯 主力选股 - 智能筛选优质标的")
    st.markdown("---")
    
@@ -270,6 +275,50 @@ 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",
+                options=[10, 20, 30, 50],
+                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", use_container_width=True):
+                # 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 = []
+                for code in raw_codes:
+                    # 去掉后缀（如果有的话）
+                    if isinstance(code, str):
+                        # 去掉 .SH, .SZ, .BJ 等后缀
+                        clean_code = code.split('.')[0] if '.' in code else code
+                        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:
@@ -417,3 +466,421 @@ def format_number(value, unit='', suffix=''):
    except (ValueError, TypeError):
        return str(value)

+
+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:
+            if st.button("🔙 返回主力选股", use_container_width=True):
+                # 清除所有批量分析相关状态
+                if 'main_force_batch_trigger' in st.session_state:
+                    del st.session_state.main_force_batch_trigger
+                if 'main_force_batch_codes' in st.session_state:
+                    del st.session_state.main_force_batch_codes
+                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", use_container_width=True):
+                # 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
+        if 'main_force_batch_trigger' in st.session_state:
+            del st.session_state.main_force_batch_trigger
+        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",
+            options=["sequential", "parallel"],
+            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(
+                "并行线程数",
+                min_value=2,
+                max_value=5,
+                value=3,
+                help="同时分析的股票数量"
+            )
+        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", use_container_width=True):
+            start_analysis = True
+    
+    with col_cancel:
+        if st.button("❌ 取消", type="secondary", use_container_width=True):
+            # 清除所有批量分析相关状态
+            if 'main_force_batch_trigger' in st.session_state:
+                del st.session_state.main_force_batch_trigger
+            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")
+            st.write(f"**股票代码列表**: {stock_codes}")
+            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,
+            'fundamental': True,
+            'fund_flow': True,
+            'risk': True,
+            'sentiment': False,  # 禁用以提升速度
+            'news': False  # 禁用以提升速度
+        }
+        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(
+                        symbol=code,
+                        period=period,
+                        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）...")
+            
+            def analyze_one(code):
+                try:
+                    result = analyze_single_stock_for_batch(
+                        symbol=code,
+                        period=period,
+                        enabled_analysts_config=enabled_analysts_config,
+                        selected_model=selected_model
+                    )
+                    return result
+                except Exception as 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
+                    completed += 1
+                    progress_bar.progress(completed / len(stock_codes))
+                    status_text.text(f"已完成 {completed}/{len(stock_codes)} ({code})")
+                    
+                    try:
+                        result = future.result()
+                        results.append(result)
+                    except Exception as e:
+                        results.append({"symbol": code, "success": False, "error": str(e)})
+        
+        # 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')}")
+        
+        # 保存结果到session_state
+        st.session_state.main_force_batch_results = {
+            "results": results,
+            "total": len(results),
+            "success": success_count,
+            "failed": failed_count,
+            "elapsed_time": elapsed_time,
+            "analysis_mode": analysis_mode
+        }
+        
+        time.sleep(1)
+        
+        # 重新渲染以显示结果
+        st.rerun()
+
+
+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']
+    failed = batch_results['failed']
+    elapsed_time = batch_results['elapsed_time']
+    
+    st.markdown("## 📊 批量分析结果")
+    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 = {
+                '强烈买入': '🔥',
+                '买入': '✅',
+                '持有': '⏸️',
+                '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', ''),
+                '评级': f"{rating_emoji} {rating}",
+                '信心度': final_decision.get('confidence_level', 'N/A'),
+                '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'),
+                '目标价': final_decision.get('target_price', 'N/A')
+            })
+        
+        df_display = pd.DataFrame(display_data)
+        st.dataframe(df_display, use_container_width=True, 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')
+            rating_emoji = {
+                '强烈买入': '🔥',
+                '买入': '✅',
+                '持有': '⏸️',
+                '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('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
+                    entry_range = final_decision.get('entry_range', '')
+                    entry_min, entry_max = None, None
+                    if entry_range and isinstance(entry_range, str) and "-" in entry_range:
+                        try:
+                            parts = entry_range.split("-")
+                            entry_min = float(parts[0].strip())
+                            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
+                    if take_profit_str:
+                        try:
+                            numbers = re.findall(r'\d+\.?\d*', str(take_profit_str))
+                            if numbers:
+                                take_profit = float(numbers[0])
+                        except:
+                            pass
+                    
+                    stop_loss_str = final_decision.get('stop_loss', '')
+                    stop_loss = None
+                    if stop_loss_str:
+                        try:
+                            numbers = re.findall(r'\d+\.?\d*', str(stop_loss_str))
+                            if numbers:
+                                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,
+                            name=name,
+                            rating=rating,
+                            entry_range=entry_range_dict if entry_range_dict else None,
+                            take_profit=take_profit,
+                            stop_loss=stop_loss,
+                            notes=f"主力选股批量分析 | {rating}"
+                        )
+                        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, use_container_width=True)
+