oficcejo
420 lines
15 KiB
Python
420 lines
15 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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主力选股UI模块
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"""
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import streamlit as st
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from datetime import datetime, timedelta
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from main_force_analysis import MainForceAnalyzer
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from main_force_pdf_generator import display_report_download_section
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import pandas as pd
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def display_main_force_selector():
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"""显示主力选股界面"""
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st.markdown("## 🎯 主力选股 - 智能筛选优质标的")
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st.markdown("---")
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st.markdown("""
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### 功能说明
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本功能通过以下步骤筛选优质股票：
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1. **数据获取**: 使用问财获取指定日期以来主力资金净流入前100名股票
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2. **智能筛选**: 过滤掉涨幅过高、市值不符的股票
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3. **AI分析**: 调用资金流向、行业板块、财务基本面三大分析师团队
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4. **综合决策**: 资深研究员综合评估，精选3-5只优质标的
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**筛选标准**:
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- ✅ 主力资金净流入较多
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- ✅ 区间涨跌幅适中（避免追高）
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- ✅ 财务基本面良好
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- ✅ 行业前景明朗
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- ✅ 综合素质优秀
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""")
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st.markdown("---")
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# 参数设置
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col1, col2, col3 = st.columns(3)
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with col1:
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date_option = st.selectbox(
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"选择时间区间",
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["最近3个月", "最近6个月", "最近1年", "自定义日期"]
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)
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if date_option == "最近3个月":
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days_ago = 90
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start_date = None
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elif date_option == "最近6个月":
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days_ago = 180
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start_date = None
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elif date_option == "最近1年":
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days_ago = 365
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start_date = None
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else:
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custom_date = st.date_input(
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"选择开始日期",
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value=datetime.now() - timedelta(days=90)
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)
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start_date = f"{custom_date.year}年{custom_date.month}月{custom_date.day}日"
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days_ago = None
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with col2:
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final_n = st.slider(
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"最终精选数量",
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min_value=3,
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max_value=10,
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value=5,
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step=1,
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help="最终推荐的股票数量"
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)
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with col3:
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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")
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# 高级选项
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with st.expander("⚙️ 高级筛选参数"):
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col1, col2, col3 = st.columns(3)
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with col1:
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max_change = st.number_input(
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"最大涨跌幅(%)",
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min_value=10.0,
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max_value=100.0,
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value=30.0,
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step=5.0,
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help="过滤掉涨幅过高的股票，避免追高"
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)
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with col2:
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min_cap = st.number_input(
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"最小市值(亿)",
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min_value=10.0,
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max_value=500.0,
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value=50.0,
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step=10.0
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)
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with col3:
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max_cap = st.number_input(
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"最大市值(亿)",
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min_value=100.0,
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max_value=50000.0,
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value=5000.0,
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step=100.0
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)
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# 模型选择
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model = st.selectbox(
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"选择AI模型",
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["deepseek-chat", "deepseek-reasoner"],
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help="deepseek-chat速度快，deepseek-reasoner推理能力强"
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)
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st.markdown("---")
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# 开始分析按钮
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if st.button("🚀 开始主力选股", type="primary", use_container_width=True):
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with st.spinner("正在获取数据并分析，这可能需要几分钟..."):
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# 创建分析器
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analyzer = MainForceAnalyzer(model=model)
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# 运行分析
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result = analyzer.run_full_analysis(
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start_date=start_date,
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days_ago=days_ago,
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final_n=final_n
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)
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# 保存结果到session_state
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st.session_state.main_force_result = result
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st.session_state.main_force_analyzer = analyzer
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# 显示结果
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if result['success']:
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st.success(f"✅ 分析完成！共筛选出 {len(result['final_recommendations'])} 只优质标的")
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st.rerun()
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else:
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st.error(f"❌ 分析失败: {result.get('error', '未知错误')}")
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# 显示分析结果
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if 'main_force_result' in st.session_state:
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result = st.session_state.main_force_result
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if result['success']:
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display_analysis_results(result, st.session_state.get('main_force_analyzer'))
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def display_analysis_results(result: dict, analyzer):
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"""显示分析结果"""
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st.markdown("---")
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st.markdown("## 📊 分析结果")
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# 统计信息
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col1, col2, col3 = st.columns(3)
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with col1:
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st.metric("获取股票数", result['total_stocks'])
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with col2:
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st.metric("筛选后", result['filtered_stocks'])
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with col3:
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st.metric("最终推荐", len(result['final_recommendations']))
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st.markdown("---")
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# 显示AI分析师完整报告
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if analyzer and hasattr(analyzer, 'fund_flow_analysis'):
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display_analyst_reports(analyzer)
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st.markdown("---")
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# 显示推荐股票
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if result['final_recommendations']:
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st.markdown("### ⭐ 精选推荐")
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for rec in result['final_recommendations']:
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with st.expander(
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f"【第{rec['rank']}名】{rec['symbol']} - {rec['name']}",
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expanded=(rec['rank'] <= 3)
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):
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display_recommendation_detail(rec)
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# 显示候选股票列表
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if analyzer and analyzer.raw_stocks is not None and not analyzer.raw_stocks.empty:
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st.markdown("---")
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st.markdown("### 📋 候选股票列表（筛选后）")
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# 选择关键列显示
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display_cols = ['股票代码', '股票简称']
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# 添加行业列
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industry_cols = [col for col in analyzer.raw_stocks.columns if '行业' in col]
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if industry_cols:
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display_cols.append(industry_cols[0])
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# 添加区间主力资金净流入（智能匹配）
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main_fund_col = None
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main_fund_patterns = [
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'区间主力资金流向', # 实际列名
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'区间主力资金净流入',
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'主力资金流向',
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'主力资金净流入',
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'主力净流入',
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'主力资金'
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]
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for pattern in main_fund_patterns:
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matching = [col for col in analyzer.raw_stocks.columns if pattern in col]
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if matching:
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main_fund_col = matching[0]
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break
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if main_fund_col:
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display_cols.append(main_fund_col)
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# 添加区间涨跌幅（前复权）（智能匹配）
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interval_pct_col = None
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interval_pct_patterns = [
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'区间涨跌幅:前复权', '区间涨跌幅:前复权(%)', '区间涨跌幅(%)',
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'区间涨跌幅', '涨跌幅:前复权', '涨跌幅:前复权(%)', '涨跌幅(%)', '涨跌幅'
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]
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for pattern in interval_pct_patterns:
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matching = [col for col in analyzer.raw_stocks.columns if pattern in col]
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if matching:
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interval_pct_col = matching[0]
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break
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if interval_pct_col:
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display_cols.append(interval_pct_col)
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# 添加市值、市盈率、市净率
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for col_name in ['总市值', '市盈率', '市净率']:
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matching_cols = [col for col in analyzer.raw_stocks.columns if col_name in col]
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if matching_cols:
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display_cols.append(matching_cols[0])
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# 选择存在的列
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final_cols = [col for col in display_cols if col in analyzer.raw_stocks.columns]
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# 调试信息：显示找到的列名
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with st.expander("🔍 调试信息 - 查看数据列", expanded=False):
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st.caption("所有可用列:")
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cols_list = list(analyzer.raw_stocks.columns)
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st.write(cols_list)
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st.caption(f"\n已选择显示的列: {final_cols}")
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if main_fund_col:
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st.success(f"✅ 找到主力资金列: {main_fund_col}")
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else:
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st.warning("⚠️ 未找到主力资金列")
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if interval_pct_col:
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st.success(f"✅ 找到涨跌幅列: {interval_pct_col}")
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else:
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st.warning("⚠️ 未找到涨跌幅列")
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# 显示DataFrame
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display_df = analyzer.raw_stocks[final_cols].copy()
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st.dataframe(display_df, use_container_width=True, height=400)
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# 显示统计
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st.caption(f"共 {len(display_df)} 只候选股票，显示 {len(final_cols)} 个字段")
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# 下载按钮
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csv = display_df.to_csv(index=False, encoding='utf-8-sig')
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st.download_button(
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label="📥 下载候选列表CSV",
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data=csv,
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file_name=f"main_force_stocks_{datetime.now().strftime('%Y%m%d')}.csv",
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mime="text/csv"
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)
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# 显示PDF报告下载区域
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if analyzer and result:
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display_report_download_section(analyzer, result)
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def display_recommendation_detail(rec: dict):
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"""显示单个推荐股票的详细信息"""
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col1, col2 = st.columns([1, 1])
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with col1:
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st.markdown("#### 📌 推荐理由")
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for reason in rec.get('reasons', []):
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st.markdown(f"- {reason}")
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st.markdown("#### 💡 投资亮点")
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st.info(rec.get('highlights', 'N/A'))
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with col2:
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st.markdown("#### 📊 投资建议")
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st.markdown(f"**建议仓位**: {rec.get('position', 'N/A')}")
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st.markdown(f"**投资周期**: {rec.get('investment_period', 'N/A')}")
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st.markdown("#### ⚠️ 风险提示")
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st.warning(rec.get('risks', 'N/A'))
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# 显示股票详细数据
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if 'stock_data' in rec:
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st.markdown("---")
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st.markdown("#### 📊 股票详细数据")
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stock_data = rec['stock_data']
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# 创建数据展示
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col1, col2, col3 = st.columns(3)
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with col1:
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st.metric("股票代码", stock_data.get('股票代码', 'N/A'))
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# 显示行业
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industry_keys = [k for k in stock_data.keys() if '行业' in k]
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if industry_keys:
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st.metric("所属行业", stock_data.get(industry_keys[0], 'N/A'))
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with col2:
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# 显示主力资金
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fund_keys = [k for k in stock_data.keys() if '主力' in k and '净流入' in k]
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if fund_keys:
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fund_value = stock_data.get(fund_keys[0], 'N/A')
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if isinstance(fund_value, (int, float)):
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st.metric("主力资金净流入", f"{fund_value/100000000:.2f}亿")
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else:
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st.metric("主力资金净流入", str(fund_value))
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with col3:
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# 显示涨跌幅
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change_keys = [k for k in stock_data.keys() if '涨跌幅' in k]
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if change_keys:
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change_value = stock_data.get(change_keys[0], 'N/A')
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if isinstance(change_value, (int, float)):
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st.metric("区间涨跌幅", f"{change_value:.2f}%")
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else:
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st.metric("区间涨跌幅", str(change_value))
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# 显示其他关键指标
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st.markdown("**其他关键指标：**")
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metrics_col1, metrics_col2, metrics_col3 = st.columns(3)
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with metrics_col1:
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if '市盈率' in stock_data or any('市盈率' in k for k in stock_data.keys()):
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pe_keys = [k for k in stock_data.keys() if '市盈率' in k]
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if pe_keys:
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st.caption(f"市盈率: {stock_data.get(pe_keys[0], 'N/A')}")
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with metrics_col2:
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if '市净率' in stock_data or any('市净率' in k for k in stock_data.keys()):
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pb_keys = [k for k in stock_data.keys() if '市净率' in k]
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if pb_keys:
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st.caption(f"市净率: {stock_data.get(pb_keys[0], 'N/A')}")
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with metrics_col3:
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if '总市值' in stock_data or any('总市值' in k for k in stock_data.keys()):
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cap_keys = [k for k in stock_data.keys() if '总市值' in k]
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if cap_keys:
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st.caption(f"总市值: {stock_data.get(cap_keys[0], 'N/A')}")
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def display_analyst_reports(analyzer):
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"""显示AI分析师完整报告"""
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st.markdown("### 🤖 AI分析师团队完整报告")
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# 创建三个标签页
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tab1, tab2, tab3 = st.tabs(["💰 资金流向分析", "📊 行业板块分析", "📈 财务基本面分析"])
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with tab1:
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st.markdown("#### 💰 资金流向分析师报告")
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st.markdown("---")
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if hasattr(analyzer, 'fund_flow_analysis') and analyzer.fund_flow_analysis:
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st.markdown(analyzer.fund_flow_analysis)
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else:
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st.info("暂无资金流向分析报告")
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with tab2:
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st.markdown("#### 📊 行业板块及市场热点分析师报告")
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st.markdown("---")
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if hasattr(analyzer, 'industry_analysis') and analyzer.industry_analysis:
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st.markdown(analyzer.industry_analysis)
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else:
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st.info("暂无行业板块分析报告")
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with tab3:
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st.markdown("#### 📈 财务基本面分析师报告")
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st.markdown("---")
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if hasattr(analyzer, 'fundamental_analysis') and analyzer.fundamental_analysis:
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st.markdown(analyzer.fundamental_analysis)
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else:
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st.info("暂无财务基本面分析报告")
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def format_number(value, unit='', suffix=''):
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"""格式化数字显示"""
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if value is None or value == 'N/A':
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return 'N/A'
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try:
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num = float(value)
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# 如果单位是亿，需要转换
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if unit == '亿':
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if abs(num) >= 100000000: # 大于1亿（以元为单位）
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num = num / 100000000
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elif abs(num) < 100: # 小于100，可能已经是亿
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pass
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else: # 100-100000000之间，可能是万
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num = num / 10000
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# 格式化显示
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if abs(num) >= 1000:
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formatted = f"{num:,.2f}"
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elif abs(num) >= 1:
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formatted = f"{num:.2f}"
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else:
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formatted = f"{num:.4f}"
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return f"{formatted}{suffix}"
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except (ValueError, TypeError):
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return str(value)
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