oficcejo
1149 lines
41 KiB
Python
1149 lines
41 KiB
Python
"""
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智策UI界面模块
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展示板块分析结果和预测
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"""
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import streamlit as st
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import time
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import plotly.graph_objects as go
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import plotly.express as px
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import pandas as pd
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from datetime import datetime, time as dt_time
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import time
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import base64
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import json
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from sector_strategy_data import SectorStrategyDataFetcher
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from sector_strategy_engine import SectorStrategyEngine
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from sector_strategy_pdf import SectorStrategyPDFGenerator
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from sector_strategy_db import SectorStrategyDatabase
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from sector_strategy_scheduler import sector_strategy_scheduler
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def _parse_json_field(value, default):
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"""将可能的JSON字符串安全转换为Python对象"""
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try:
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if isinstance(value, (dict, list)):
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return value
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if value is None:
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return default
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if isinstance(value, str):
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v = value.strip()
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if not v:
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return default
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return json.loads(v)
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return default
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except Exception:
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return default
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def display_sector_strategy():
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"""显示智策板块分析主界面"""
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st.markdown("""
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<div class="top-nav">
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<h1 class="nav-title">🎯 智策 - AI驱动的板块策略分析</h1>
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<p class="nav-subtitle">Multi-Agent Sector Strategy Analysis | 板块多空·轮动·热度预测</p>
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</div>
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""", unsafe_allow_html=True)
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st.markdown("---")
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# 创建标签页
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tab1, tab2 = st.tabs(["📊 智策分析", "📋 历史报告"])
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with tab1:
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display_analysis_tab()
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with tab2:
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display_history_tab()
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def display_analysis_tab():
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"""显示分析标签页"""
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# 定时任务设置区域
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display_scheduler_settings()
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# 功能说明
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with st.expander("💡 智策系统介绍", expanded=False):
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st.markdown("""
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### 🌟 系统特色
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**智策**是基于多AI智能体的板块策略分析系统，通过四位专业分析师的协同工作，为您提供全方位的板块投资决策支持。
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### 🤖 AI智能体团队
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1. **🌐 宏观策略师**
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- 分析宏观经济形势和政策导向
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- 解读财经新闻对市场的影响
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- 识别行业发展趋势
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2. **📊 板块诊断师**
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- 深入分析板块走势和估值
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- 评估板块基本面和成长性
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- 预判板块轮动方向
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3. **💰 资金流向分析师**
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- 跟踪主力资金的板块流向
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- 分析北向资金的偏好
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- 识别资金轮动信号
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4. **📈 市场情绪解码员**
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- 量化市场情绪指标
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- 识别恐慌贪婪信号
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- 评估板块热度
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### 📊 核心预测
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- **板块多空**: 看多/看空板块推荐
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- **板块轮动**: 强势/潜力/衰退板块识别
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- **板块热度**: 热度排行和升降温趋势
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### 📈 数据来源
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所有数据来自**AKShare**开源库，包括：
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- 行业板块和概念板块行情
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- 板块资金流向数据
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- 北向资金数据
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- 市场统计数据
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- 财经新闻数据
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""")
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st.markdown("---")
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# 操作按钮
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col1, col2 = st.columns([2, 2])
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with col1:
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analyze_button = st.button("🚀 开始智策分析", type="primary", width='content')
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with col2:
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if st.button("🔄 清除结果", width='content'):
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if 'sector_strategy_result' in st.session_state:
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del st.session_state.sector_strategy_result
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st.success("已清除分析结果")
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st.rerun()
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st.markdown("---")
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# 开始分析（使用.env中配置的默认模型）
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if analyze_button:
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# 清除之前的结果
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if 'sector_strategy_result' in st.session_state:
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del st.session_state.sector_strategy_result
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run_sector_strategy_analysis()
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# 显示分析结果
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if 'sector_strategy_result' in st.session_state:
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result = st.session_state.sector_strategy_result
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if result.get("success"):
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display_analysis_results(result)
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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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def display_history_tab():
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"""显示历史报告标签页"""
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st.markdown("### 📋 智策历史报告")
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st.markdown("查看和管理历史分析报告")
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try:
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# 初始化引擎以获取历史报告
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engine = SectorStrategyEngine()
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# 获取历史报告
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reports = engine.get_historical_reports(limit=20)
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if reports.empty:
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st.info("📝 暂无历史报告")
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st.markdown("""
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**提示**:
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- 运行智策分析后，报告将自动保存到历史记录中
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- 您可以在此查看和管理所有历史分析报告
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""")
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return
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st.success(f"📊 共找到 {len(reports)} 份历史报告")
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# 报告列表（精简摘要展示）
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for i, report in reports.iterrows():
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report_id = report['id'] if 'id' in report else None
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created_at = report['created_at'] if 'created_at' in report else ''
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data_date_range = report['data_date_range'] if 'data_date_range' in report else ''
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summary = report['summary'] if 'summary' in report else '智策板块分析报告'
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confidence_score = report['confidence_score'] if 'confidence_score' in report else 0
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risk_level = report['risk_level'] if 'risk_level' in report else '中等'
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market_outlook = report['market_outlook'] if 'market_outlook' in report else '谨慎乐观'
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with st.container():
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st.markdown(f"**📊 报告 #{report_id}**")
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st.caption(f"生成时间: {created_at} | 数据区间: {data_date_range}")
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col1, col2, col3 = st.columns([1, 1, 1])
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with col1:
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st.metric("置信度", f"{confidence_score:.1%}")
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with col2:
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st.metric("风险等级", risk_level)
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with col3:
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st.metric("市场展望", market_outlook)
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# 操作区：加载到分析视图 / 删除
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op1, op2 = st.columns([1, 1])
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with op1:
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if st.button("📥 加载到分析视图", key=f"load_{report_id}"):
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# 获取报告详情并写入session以展示到分析视图
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detail = engine.get_report_detail(report_id)
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if detail and isinstance(detail.get('analysis_content_parsed'), dict):
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st.session_state.sector_strategy_result = detail['analysis_content_parsed']
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st.session_state.sector_strategy_result_source = 'from_history'
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st.session_state.loaded_report_id = report_id
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st.success("✅ 已加载到分析视图，请切换到‘智策分析’标签查看")
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time.sleep(0.5)
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st.rerun()
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else:
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st.error("❌ 加载失败：报告内容缺失")
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with op2:
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if st.button(f"🗑️ 删除", key=f"delete_{report_id}"):
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if engine.delete_report(report_id):
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st.success("报告已删除")
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st.rerun()
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else:
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st.error("删除失败")
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# 改进的摘要展示逻辑，突出看多板块信息
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st.markdown("**📝 报告摘要**")
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summary_text = summary or "智策板块分析报告"
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# 解析摘要中的看多板块信息
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if "看多板块:" in summary_text:
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parts = summary_text.split("，看多板块:")
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main_summary = parts[0]
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bullish_info = parts[1] if len(parts) > 1 else ""
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# 显示主要摘要信息
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st.markdown(f"🔹 {main_summary}")
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# 特别突出显示看多板块
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if bullish_info:
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st.markdown(f"📈 **看多板块**: :green[{bullish_info}]")
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else:
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# 原有的简单展示方式
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short = summary_text if len(summary_text) <= 120 else (summary_text[:120] + "...")
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with st.expander(f"{short}", expanded=False):
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st.write(summary_text)
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st.markdown("-")
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except Exception as e:
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st.error(f"❌ 加载历史报告失败: {e}")
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def display_report_detail(report_id):
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"""详细报告页面已移除：保留占位以避免旧调用报错"""
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st.info("当前版本仅提供报告摘要，详细页面已移除。")
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def run_sector_strategy_analysis(model=None):
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"""运行智策分析"""
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import config
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model = model or config.DEFAULT_MODEL_NAME
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# 进度显示
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progress_bar = st.progress(0)
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status_text = st.empty()
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try:
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# 1. 获取数据
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status_text.text("📊 正在获取市场数据...")
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progress_bar.progress(10)
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fetcher = SectorStrategyDataFetcher()
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# 使用带缓存回退的获取逻辑
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data = fetcher.get_cached_data_with_fallback()
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if not data.get("success"):
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st.error("❌ 数据获取失败")
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return
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progress_bar.progress(30)
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status_text.text("✓ 数据获取完成")
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# 显示数据摘要（含缓存提示）
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display_data_summary(data)
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# 2. 运行AI分析
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status_text.text("🤖 AI智能体团队正在分析，预计需要10分钟...")
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progress_bar.progress(40)
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||
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engine = SectorStrategyEngine(model=model)
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result = engine.run_comprehensive_analysis(data)
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# 传递缓存元信息到结果以便页面提示
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||
if data.get("from_cache") or data.get("cache_warning"):
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result["cache_meta"] = {
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"from_cache": bool(data.get("from_cache")),
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"cache_warning": data.get("cache_warning", ""),
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"data_timestamp": data.get("timestamp")
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}
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progress_bar.progress(90)
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||
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if result.get("success"):
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# 保存结果
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||
st.session_state.sector_strategy_result = result
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||
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progress_bar.progress(100)
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status_text.text("✅ 分析完成！")
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||
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||
time.sleep(1)
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status_text.empty()
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progress_bar.empty()
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||
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# 自动刷新显示结果
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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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||
|
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except Exception as e:
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||
st.error(f"❌ 分析过程出错: {str(e)}")
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import traceback
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||
st.code(traceback.format_exc())
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finally:
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progress_bar.empty()
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||
status_text.empty()
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||
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def display_data_summary(data):
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"""显示数据摘要"""
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st.subheader("📊 市场数据概览")
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# 缓存提示横幅
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||
if data.get("from_cache") or data.get("cache_warning"):
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st.warning(data.get("cache_warning", "当前数据来自缓存，可能不是最新信息"))
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col1, col2, col3, col4 = st.columns(4)
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market = data.get("market_overview", {})
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with col1:
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if market.get("sh_index"):
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sh = market["sh_index"]
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st.metric(
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"上证指数",
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f"{sh['close']:.2f}",
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f"{sh['change_pct']:+.2f}%"
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)
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||
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with col2:
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if market.get("up_count"):
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st.metric(
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"上涨股票",
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market['up_count'],
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f"{market['up_ratio']:.1f}%"
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)
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||
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with col3:
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||
sectors_count = len(data.get("sectors", {}))
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st.metric("行业板块", sectors_count)
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||
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with col4:
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||
concepts_count = len(data.get("concepts", {}))
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||
st.metric("概念板块", concepts_count)
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||
|
||
|
||
def display_saved_report_summary(saved_report: dict):
|
||
"""在主页面显示保存的报告摘要（标题、时间、关键指标）"""
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st.subheader("📝 报告摘要")
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summary = saved_report.get('summary', '智策板块分析报告')
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||
created_at = saved_report.get('created_at', '')
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||
data_date_range = saved_report.get('data_date_range', '')
|
||
confidence_score = saved_report.get('confidence_score', 0)
|
||
risk_level = saved_report.get('risk_level', '中等')
|
||
market_outlook = saved_report.get('market_outlook', '谨慎乐观')
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st.caption(f"生成时间: {created_at} | 数据区间: {data_date_range}")
|
||
|
||
# 使用改进的摘要展示逻辑，突出看多板块信息
|
||
summary_text = summary or "智策板块分析报告"
|
||
|
||
# 解析摘要中的看多板块信息
|
||
if "看多板块:" in summary_text:
|
||
parts = summary_text.split("，看多板块:")
|
||
main_summary = parts[0]
|
||
bullish_info = parts[1] if len(parts) > 1 else ""
|
||
|
||
# 显示主要摘要信息
|
||
st.markdown(f"🔹 {main_summary}")
|
||
|
||
# 特别突出显示看多板块
|
||
if bullish_info:
|
||
st.markdown(f"📈 **看多板块**: :green[{bullish_info}]")
|
||
else:
|
||
# 原有的简单展示方式
|
||
st.info(summary_text)
|
||
|
||
col1, col2, col3 = st.columns(3)
|
||
with col1:
|
||
st.metric("置信度", f"{confidence_score:.1%}")
|
||
with col2:
|
||
st.metric("风险等级", risk_level)
|
||
with col3:
|
||
st.metric("市场展望", market_outlook)
|
||
|
||
|
||
def display_analysis_results(result):
|
||
"""显示分析结果"""
|
||
|
||
st.success("✅ 智策分析完成！")
|
||
st.info(f"📅 分析时间: {result.get('timestamp', 'N/A')}")
|
||
# 显示缓存提示（如果本次分析使用了缓存数据）
|
||
cache_meta = result.get("cache_meta")
|
||
if cache_meta and (cache_meta.get("from_cache") or cache_meta.get("cache_warning")):
|
||
st.warning(cache_meta.get("cache_warning", "当前分析基于缓存数据，可能不是最新信息"))
|
||
|
||
# 如果内容源自历史报告，给出返回入口
|
||
if st.session_state.get('sector_strategy_result_source') == 'from_history':
|
||
loaded_id = st.session_state.get('loaded_report_id')
|
||
st.info(f"🗂️ 当前展示为历史报告内容（ID: {loaded_id}）")
|
||
if st.button("↩️ 返回历史报告列表"):
|
||
# 清除已加载的历史报告并返回
|
||
for key in ['sector_strategy_result', 'sector_strategy_result_source', 'loaded_report_id']:
|
||
if key in st.session_state:
|
||
del st.session_state[key]
|
||
st.rerun()
|
||
|
||
# 显示引擎回传的保存报告摘要（用于主页面动态更新）
|
||
saved_report = result.get("saved_report")
|
||
if saved_report:
|
||
display_saved_report_summary(saved_report)
|
||
|
||
# PDF导出功能
|
||
display_pdf_export_section(result)
|
||
|
||
st.markdown("---")
|
||
|
||
# 创建标签页
|
||
tab1, tab2, tab3, tab4 = st.tabs([
|
||
"📋 核心预测",
|
||
"🤖 智能体分析",
|
||
"📊 综合研判",
|
||
"📈 数据可视化"
|
||
])
|
||
|
||
# Tab 1: 核心预测
|
||
with tab1:
|
||
display_predictions(result.get("final_predictions", {}))
|
||
|
||
# Tab 2: 智能体分析
|
||
with tab2:
|
||
display_agents_reports(result.get("agents_analysis", {}))
|
||
|
||
# Tab 3: 综合研判
|
||
with tab3:
|
||
display_comprehensive_report(result.get("comprehensive_report", ""))
|
||
|
||
# Tab 4: 数据可视化
|
||
with tab4:
|
||
display_visualizations(result.get("final_predictions", {}))
|
||
|
||
|
||
def display_predictions(predictions):
|
||
"""显示核心预测"""
|
||
|
||
st.subheader("🎯 智策核心预测")
|
||
|
||
if not predictions or predictions.get("prediction_text"):
|
||
# 文本格式
|
||
st.markdown("### 预测报告")
|
||
st.write(predictions.get("prediction_text", "暂无预测"))
|
||
return
|
||
|
||
# JSON格式预测
|
||
|
||
# 1. 板块多空
|
||
st.markdown("### 📊 板块多空预测")
|
||
|
||
col1, col2 = st.columns(2)
|
||
|
||
with col1:
|
||
st.markdown("#### 🟢 看多板块")
|
||
bullish = predictions.get("long_short", {}).get("bullish", [])
|
||
if bullish:
|
||
for item in bullish:
|
||
st.markdown(f"""
|
||
<div class="agent-card" style="border-left-color: #4caf50;">
|
||
<h4>{item.get('sector', 'N/A')} <span style="color: #4caf50;">↑</span></h4>
|
||
<p><strong>信心度:</strong> {item.get('confidence', 0)}/10</p>
|
||
<p><strong>理由:</strong> {item.get('reason', '')}</p>
|
||
<p><strong>风险:</strong> {item.get('risk', '')}</p>
|
||
</div>
|
||
""", unsafe_allow_html=True)
|
||
else:
|
||
st.info("暂无看多板块")
|
||
|
||
with col2:
|
||
st.markdown("#### 🔴 看空板块")
|
||
bearish = predictions.get("long_short", {}).get("bearish", [])
|
||
if bearish:
|
||
for item in bearish:
|
||
st.markdown(f"""
|
||
<div class="agent-card" style="border-left-color: #f44336;">
|
||
<h4>{item.get('sector', 'N/A')} <span style="color: #f44336;">↓</span></h4>
|
||
<p><strong>信心度:</strong> {item.get('confidence', 0)}/10</p>
|
||
<p><strong>理由:</strong> {item.get('reason', '')}</p>
|
||
<p><strong>风险:</strong> {item.get('risk', '')}</p>
|
||
</div>
|
||
""", unsafe_allow_html=True)
|
||
else:
|
||
st.info("暂无看空板块")
|
||
|
||
st.markdown("---")
|
||
|
||
# 2. 板块轮动
|
||
st.markdown("### 🔄 板块轮动预测")
|
||
|
||
rotation = predictions.get("rotation", {})
|
||
|
||
col1, col2, col3 = st.columns(3)
|
||
|
||
with col1:
|
||
st.markdown("#### 💪 当前强势")
|
||
current_strong = rotation.get("current_strong", [])
|
||
for item in current_strong:
|
||
st.markdown(f"""
|
||
**{item.get('sector', 'N/A')}**
|
||
- 时间窗口: {item.get('time_window', 'N/A')}
|
||
- 逻辑: {item.get('logic', '')[:50]}...
|
||
- 建议: {item.get('advice', '')}
|
||
""")
|
||
|
||
with col2:
|
||
st.markdown("#### 🌱 潜力接力")
|
||
potential = rotation.get("potential", [])
|
||
for item in potential:
|
||
st.markdown(f"""
|
||
**{item.get('sector', 'N/A')}**
|
||
- 时间窗口: {item.get('time_window', 'N/A')}
|
||
- 逻辑: {item.get('logic', '')[:50]}...
|
||
- 建议: {item.get('advice', '')}
|
||
""")
|
||
|
||
with col3:
|
||
st.markdown("#### 📉 衰退板块")
|
||
declining = rotation.get("declining", [])
|
||
for item in declining:
|
||
st.markdown(f"""
|
||
**{item.get('sector', 'N/A')}**
|
||
- 时间窗口: {item.get('time_window', 'N/A')}
|
||
- 逻辑: {item.get('logic', '')[:50]}...
|
||
- 建议: {item.get('advice', '')}
|
||
""")
|
||
|
||
st.markdown("---")
|
||
|
||
# 3. 板块热度
|
||
st.markdown("### 🔥 板块热度排行")
|
||
|
||
heat = predictions.get("heat", {})
|
||
|
||
col1, col2, col3 = st.columns(3)
|
||
|
||
with col1:
|
||
st.markdown("#### 🔥 最热板块")
|
||
hottest = heat.get("hottest", [])
|
||
for idx, item in enumerate(hottest, 1):
|
||
st.metric(
|
||
f"{idx}. {item.get('sector', 'N/A')}",
|
||
f"{item.get('score', 0)}分",
|
||
f"{item.get('trend', 'N/A')}"
|
||
)
|
||
|
||
with col2:
|
||
st.markdown("#### 📈 升温板块")
|
||
heating = heat.get("heating", [])
|
||
for idx, item in enumerate(heating, 1):
|
||
st.metric(
|
||
f"{idx}. {item.get('sector', 'N/A')}",
|
||
f"{item.get('score', 0)}分",
|
||
"↗️ 升温"
|
||
)
|
||
|
||
with col3:
|
||
st.markdown("#### 📉 降温板块")
|
||
cooling = heat.get("cooling", [])
|
||
for idx, item in enumerate(cooling, 1):
|
||
st.metric(
|
||
f"{idx}. {item.get('sector', 'N/A')}",
|
||
f"{item.get('score', 0)}分",
|
||
"↘️ 降温"
|
||
)
|
||
|
||
st.markdown("---")
|
||
|
||
# 4. 总结建议
|
||
summary = predictions.get("summary", {})
|
||
if summary:
|
||
st.markdown("### 📝 策略总结")
|
||
|
||
col1, col2 = st.columns(2)
|
||
|
||
with col1:
|
||
st.markdown(f"""
|
||
<div class="decision-card">
|
||
<h4>💡 市场观点</h4>
|
||
<p>{summary.get('market_view', 'N/A')}</p>
|
||
</div>
|
||
""", unsafe_allow_html=True)
|
||
|
||
st.markdown(f"""
|
||
<div class="agent-card" style="border-left-color: #2196f3;">
|
||
<h4>🎯 核心机会</h4>
|
||
<p>{summary.get('key_opportunity', 'N/A')}</p>
|
||
</div>
|
||
""", unsafe_allow_html=True)
|
||
|
||
with col2:
|
||
st.markdown(f"""
|
||
<div class="warning-card">
|
||
<h4>⚠️ 主要风险</h4>
|
||
<p>{summary.get('major_risk', 'N/A')}</p>
|
||
</div>
|
||
""", unsafe_allow_html=True)
|
||
|
||
st.markdown(f"""
|
||
<div class="agent-card" style="border-left-color: #ff9800;">
|
||
<h4>📋 整体策略</h4>
|
||
<p>{summary.get('strategy', 'N/A')}</p>
|
||
</div>
|
||
""", unsafe_allow_html=True)
|
||
|
||
|
||
def display_agents_reports(agents_analysis):
|
||
"""显示智能体分析报告"""
|
||
|
||
st.subheader("🤖 AI智能体分析报告")
|
||
|
||
if not agents_analysis:
|
||
st.info("暂无智能体分析数据")
|
||
return
|
||
|
||
# 创建子标签页
|
||
agent_names = []
|
||
agent_data = []
|
||
|
||
for key, value in agents_analysis.items():
|
||
agent_names.append(value.get("agent_name", "未知分析师"))
|
||
agent_data.append(value)
|
||
|
||
tabs = st.tabs(agent_names)
|
||
|
||
for idx, tab in enumerate(tabs):
|
||
with tab:
|
||
agent = agent_data[idx]
|
||
|
||
st.markdown(f"""
|
||
<div class="agent-card">
|
||
<h3>👨💼 {agent.get('agent_name', '未知')}</h3>
|
||
<p><strong>职责:</strong> {agent.get('agent_role', '未知')}</p>
|
||
<p><strong>关注领域:</strong> {', '.join(agent.get('focus_areas', []))}</p>
|
||
<p><strong>分析时间:</strong> {agent.get('timestamp', '未知')}</p>
|
||
</div>
|
||
""", unsafe_allow_html=True)
|
||
|
||
st.markdown("---")
|
||
|
||
st.markdown("### 📄 分析报告")
|
||
st.write(agent.get("analysis", "暂无分析"))
|
||
|
||
|
||
def display_comprehensive_report(report):
|
||
"""显示综合研判报告"""
|
||
|
||
st.subheader("📊 综合研判报告")
|
||
|
||
if not report:
|
||
st.info("暂无综合研判数据")
|
||
return
|
||
|
||
st.markdown("""
|
||
<div class="decision-card">
|
||
<h4>🎯 智策综合研判</h4>
|
||
<p>基于四位专业分析师的深度分析，形成的全面市场和板块研判</p>
|
||
</div>
|
||
""", unsafe_allow_html=True)
|
||
|
||
st.markdown("---")
|
||
|
||
st.write(report)
|
||
|
||
|
||
def display_visualizations(predictions):
|
||
"""显示数据可视化"""
|
||
|
||
st.subheader("📈 数据可视化")
|
||
|
||
if not predictions or predictions.get("prediction_text"):
|
||
st.info("暂无可视化数据")
|
||
return
|
||
|
||
# 1. 板块多空雷达图
|
||
st.markdown("### 📊 板块多空信心度对比")
|
||
|
||
bullish = predictions.get("long_short", {}).get("bullish", [])
|
||
bearish = predictions.get("long_short", {}).get("bearish", [])
|
||
|
||
if bullish or bearish:
|
||
# 准备数据
|
||
sectors = []
|
||
confidence = []
|
||
types = []
|
||
|
||
for item in bullish[:5]:
|
||
sectors.append(item.get('sector', 'N/A'))
|
||
confidence.append(item.get('confidence', 0))
|
||
types.append('看多')
|
||
|
||
for item in bearish[:5]:
|
||
sectors.append(item.get('sector', 'N/A'))
|
||
confidence.append(-item.get('confidence', 0)) # 负值表示看空
|
||
types.append('看空')
|
||
|
||
# 创建条形图
|
||
df = pd.DataFrame({
|
||
'板块': sectors,
|
||
'信心度': confidence,
|
||
'类型': types
|
||
})
|
||
|
||
fig = px.bar(df, x='板块', y='信心度', color='类型',
|
||
color_discrete_map={'看多': '#4caf50', '看空': '#f44336'},
|
||
title='板块多空信心度对比')
|
||
|
||
fig.update_layout(height=400)
|
||
st.plotly_chart(fig, use_container_width=True, config={'responsive': True}, key="sector_confidence")
|
||
|
||
st.markdown("---")
|
||
|
||
# 2. 板块热度分布
|
||
st.markdown("### 🔥 板块热度分布")
|
||
|
||
heat = predictions.get("heat", {})
|
||
hottest = heat.get("hottest", [])
|
||
heating = heat.get("heating", [])
|
||
|
||
if hottest or heating:
|
||
sectors = []
|
||
scores = []
|
||
trends = []
|
||
|
||
for item in hottest:
|
||
sectors.append(item.get('sector', 'N/A'))
|
||
scores.append(item.get('score', 0))
|
||
trends.append('最热')
|
||
|
||
for item in heating:
|
||
sectors.append(item.get('sector', 'N/A'))
|
||
scores.append(item.get('score', 0))
|
||
trends.append('升温')
|
||
|
||
df = pd.DataFrame({
|
||
'板块': sectors,
|
||
'热度': scores,
|
||
'趋势': trends
|
||
})
|
||
|
||
fig = px.scatter(df, x='板块', y='热度', size='热度', color='趋势',
|
||
color_discrete_map={'最热': '#ff5722', '升温': '#ff9800'},
|
||
title='板块热度分布图')
|
||
|
||
fig.update_layout(height=400)
|
||
st.plotly_chart(fig, use_container_width=True, config={'responsive': True}, key="sector_heat")
|
||
|
||
|
||
def display_pdf_export_section(result):
|
||
"""显示PDF导出部分"""
|
||
st.subheader("📄 导出报告")
|
||
|
||
col1, col2, col3, col4 = st.columns([2, 1, 1, 1])
|
||
|
||
with col1:
|
||
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'):
|
||
with st.spinner("正在生成PDF报告..."):
|
||
try:
|
||
# 生成PDF
|
||
generator = SectorStrategyPDFGenerator()
|
||
pdf_path = generator.generate_pdf(result)
|
||
|
||
# 读取PDF文件
|
||
with open(pdf_path, "rb") as f:
|
||
pdf_bytes = f.read()
|
||
|
||
# 保存到session_state
|
||
st.session_state.sector_pdf_data = pdf_bytes
|
||
st.session_state.sector_pdf_filename = f"智策报告_{result.get('timestamp', datetime.now().strftime('%Y%m%d_%H%M%S')).replace(':', '').replace(' ', '_')}.pdf"
|
||
|
||
st.success("✅ PDF报告生成成功！")
|
||
st.rerun()
|
||
|
||
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='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(
|
||
label="💾 下载PDF",
|
||
data=st.session_state.sector_pdf_data,
|
||
file_name=st.session_state.sector_pdf_filename,
|
||
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():
|
||
"""显示定时任务设置"""
|
||
with st.expander("⏰ 定时分析设置", expanded=False):
|
||
st.markdown("""
|
||
**定时分析功能**
|
||
|
||
开启后，系统将在每天指定时间自动运行智策分析，并将核心结果通过邮件发送。
|
||
|
||
**前提条件：**
|
||
- 需要在 `.env` 文件中配置邮件设置
|
||
- 配置项：`EMAIL_ENABLED`, `SMTP_SERVER`, `EMAIL_FROM`, `EMAIL_PASSWORD`, `EMAIL_TO`
|
||
""")
|
||
|
||
# 获取当前状态
|
||
status = sector_strategy_scheduler.get_status()
|
||
|
||
col1, col2 = st.columns([1, 1])
|
||
|
||
with col1:
|
||
# 显示当前状态
|
||
if status['running']:
|
||
st.success(f"✅ 定时任务运行中")
|
||
st.info(f"⏰ 定时时间: {status['schedule_time']}")
|
||
if status['next_run_time']:
|
||
st.info(f"📅 下次运行: {status['next_run_time']}")
|
||
if status['last_run_time']:
|
||
st.info(f"📊 上次运行: {status['last_run_time']}")
|
||
else:
|
||
st.warning("⏸️ 定时任务未运行")
|
||
|
||
with col2:
|
||
# 时间设置
|
||
schedule_time = st.time_input(
|
||
"设置定时时间",
|
||
value=dt_time(9, 0), # 默认9:00
|
||
help="系统将在每天此时间自动运行分析"
|
||
)
|
||
|
||
schedule_time_str = schedule_time.strftime("%H:%M")
|
||
|
||
# 控制按钮
|
||
col_a, col_b, col_c = st.columns(3)
|
||
|
||
with col_a:
|
||
if not status['running']:
|
||
if st.button("▶️ 启动", width='content', type="primary"):
|
||
if sector_strategy_scheduler.start(schedule_time_str):
|
||
st.success(f"✅ 定时任务已启动！每天 {schedule_time_str} 运行")
|
||
time.sleep(1)
|
||
st.rerun()
|
||
else:
|
||
st.error("❌ 启动失败")
|
||
else:
|
||
if st.button("⏹️ 停止", width='content'):
|
||
if sector_strategy_scheduler.stop():
|
||
st.success("✅ 定时任务已停止")
|
||
time.sleep(1)
|
||
st.rerun()
|
||
else:
|
||
st.error("❌ 停止失败")
|
||
|
||
with col_b:
|
||
if st.button("🔄 立即运行", width='content'):
|
||
with st.spinner("正在运行分析..."):
|
||
sector_strategy_scheduler.manual_run()
|
||
st.success("✅ 手动分析完成！")
|
||
|
||
with col_c:
|
||
if st.button("📧 测试邮件", width='content'):
|
||
test_email_notification()
|
||
|
||
# 邮件配置检查
|
||
st.markdown("---")
|
||
check_email_config()
|
||
|
||
|
||
def check_email_config():
|
||
"""检查邮件配置"""
|
||
st.markdown("**📧 邮件配置检查**")
|
||
|
||
import os
|
||
from dotenv import load_dotenv
|
||
load_dotenv()
|
||
|
||
email_enabled = os.getenv('EMAIL_ENABLED', 'false').lower() == 'true'
|
||
smtp_server = os.getenv('SMTP_SERVER', '')
|
||
email_from = os.getenv('EMAIL_FROM', '')
|
||
email_password = os.getenv('EMAIL_PASSWORD', '')
|
||
email_to = os.getenv('EMAIL_TO', '')
|
||
|
||
col1, col2 = st.columns(2)
|
||
|
||
with col1:
|
||
st.write("**配置项**")
|
||
st.write(f"✅ 邮件功能: {'已启用' if email_enabled else '❌ 未启用'}")
|
||
st.write(f"{'✅' if smtp_server else '❌'} SMTP服务器: {smtp_server or '未配置'}")
|
||
st.write(f"{'✅' if email_from else '❌'} 发件邮箱: {email_from or '未配置'}")
|
||
|
||
with col2:
|
||
st.write("**状态**")
|
||
st.write(f"{'✅' if email_password else '❌'} 邮箱密码: {'已配置' if email_password else '未配置'}")
|
||
st.write(f"{'✅' if email_to else '❌'} 收件邮箱: {email_to or '未配置'}")
|
||
|
||
config_complete = all([email_enabled, smtp_server, email_from, email_password, email_to])
|
||
if config_complete:
|
||
st.success("✅ 邮件配置完整")
|
||
else:
|
||
st.warning("⚠️ 邮件配置不完整，请在 .env 文件中配置")
|
||
|
||
|
||
def test_email_notification():
|
||
"""测试邮件通知"""
|
||
try:
|
||
from notification_service import notification_service
|
||
|
||
# 使用notification_service的send_test_email方法
|
||
success, message = notification_service.send_test_email()
|
||
|
||
if success:
|
||
st.success(f"✅ {message}")
|
||
st.balloons()
|
||
else:
|
||
st.error(f"❌ {message}")
|
||
|
||
except Exception as e:
|
||
st.error(f"❌ 发送测试邮件时出错: {str(e)}")
|
||
import traceback
|
||
st.code(traceback.format_exc())
|
||
|
||
|
||
# 主入口
|
||
if __name__ == "__main__":
|
||
display_sector_strategy()
|
||
|