oficcejo
745 lines
24 KiB
Python
745 lines
24 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 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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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_scheduler import sector_strategy_scheduler
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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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# 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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# 模型选择
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col1, col2, col3 = st.columns([2, 2, 2])
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with col1:
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selected_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="Reasoner模型提供更强的推理能力"
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)
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with col2:
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st.write("")
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st.write("")
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analyze_button = st.button("🚀 开始智策分析", type="primary", use_container_width=True)
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with col3:
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st.write("")
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st.write("")
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if st.button("🔄 清除结果", use_container_width=True):
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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
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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(selected_model)
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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 run_sector_strategy_analysis(model="deepseek-chat"):
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"""运行智策分析"""
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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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data = fetcher.get_all_sector_data()
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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
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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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engine = SectorStrategyEngine(model=model)
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result = engine.run_comprehensive_analysis(data)
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progress_bar.progress(90)
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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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progress_bar.progress(100)
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status_text.text("✅ 分析完成！")
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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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# 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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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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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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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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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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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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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_analysis_results(result):
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"""显示分析结果"""
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st.success("✅ 智策分析完成！")
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st.info(f"📅 分析时间: {result.get('timestamp', 'N/A')}")
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# PDF导出功能
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display_pdf_export_section(result)
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st.markdown("---")
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# 创建标签页
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tab1, tab2, tab3, tab4 = st.tabs([
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"📋 核心预测",
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"🤖 智能体分析",
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"📊 综合研判",
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"📈 数据可视化"
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])
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# Tab 1: 核心预测
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with tab1:
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display_predictions(result.get("final_predictions", {}))
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# Tab 2: 智能体分析
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with tab2:
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display_agents_reports(result.get("agents_analysis", {}))
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# Tab 3: 综合研判
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with tab3:
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display_comprehensive_report(result.get("comprehensive_report", ""))
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# Tab 4: 数据可视化
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with tab4:
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display_visualizations(result.get("final_predictions", {}))
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def display_predictions(predictions):
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"""显示核心预测"""
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st.subheader("🎯 智策核心预测")
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if not predictions or predictions.get("prediction_text"):
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# 文本格式
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st.markdown("### 预测报告")
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st.write(predictions.get("prediction_text", "暂无预测"))
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return
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# JSON格式预测
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# 1. 板块多空
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st.markdown("### 📊 板块多空预测")
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col1, col2 = st.columns(2)
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with col1:
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st.markdown("#### 🟢 看多板块")
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bullish = predictions.get("long_short", {}).get("bullish", [])
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if bullish:
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for item in bullish:
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st.markdown(f"""
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<div class="agent-card" style="border-left-color: #4caf50;">
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<h4>{item.get('sector', 'N/A')} <span style="color: #4caf50;">↑</span></h4>
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<p><strong>信心度:</strong> {item.get('confidence', 0)}/10</p>
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<p><strong>理由:</strong> {item.get('reason', '')}</p>
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<p><strong>风险:</strong> {item.get('risk', '')}</p>
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</div>
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""", unsafe_allow_html=True)
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else:
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st.info("暂无看多板块")
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with col2:
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st.markdown("#### 🔴 看空板块")
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bearish = predictions.get("long_short", {}).get("bearish", [])
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if bearish:
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for item in bearish:
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st.markdown(f"""
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<div class="agent-card" style="border-left-color: #f44336;">
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<h4>{item.get('sector', 'N/A')} <span style="color: #f44336;">↓</span></h4>
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<p><strong>信心度:</strong> {item.get('confidence', 0)}/10</p>
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<p><strong>理由:</strong> {item.get('reason', '')}</p>
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<p><strong>风险:</strong> {item.get('risk', '')}</p>
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</div>
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""", unsafe_allow_html=True)
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else:
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st.info("暂无看空板块")
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st.markdown("---")
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# 2. 板块轮动
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st.markdown("### 🔄 板块轮动预测")
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rotation = predictions.get("rotation", {})
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col1, col2, col3 = st.columns(3)
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with col1:
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st.markdown("#### 💪 当前强势")
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current_strong = rotation.get("current_strong", [])
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for item in current_strong:
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st.markdown(f"""
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**{item.get('sector', 'N/A')}**
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- 时间窗口: {item.get('time_window', 'N/A')}
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- 逻辑: {item.get('logic', '')[:50]}...
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- 建议: {item.get('advice', '')}
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""")
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with col2:
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st.markdown("#### 🌱 潜力接力")
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potential = rotation.get("potential", [])
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for item in potential:
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st.markdown(f"""
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**{item.get('sector', 'N/A')}**
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- 时间窗口: {item.get('time_window', 'N/A')}
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- 逻辑: {item.get('logic', '')[:50]}...
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- 建议: {item.get('advice', '')}
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""")
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with col3:
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st.markdown("#### 📉 衰退板块")
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declining = rotation.get("declining", [])
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for item in declining:
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st.markdown(f"""
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**{item.get('sector', 'N/A')}**
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- 时间窗口: {item.get('time_window', 'N/A')}
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- 逻辑: {item.get('logic', '')[:50]}...
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- 建议: {item.get('advice', '')}
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""")
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st.markdown("---")
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# 3. 板块热度
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st.markdown("### 🔥 板块热度排行")
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heat = predictions.get("heat", {})
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col1, col2, col3 = st.columns(3)
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with col1:
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st.markdown("#### 🔥 最热板块")
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hottest = heat.get("hottest", [])
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for idx, item in enumerate(hottest, 1):
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st.metric(
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f"{idx}. {item.get('sector', 'N/A')}",
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f"{item.get('score', 0)}分",
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f"{item.get('trend', 'N/A')}"
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)
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with col2:
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st.markdown("#### 📈 升温板块")
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heating = heat.get("heating", [])
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for idx, item in enumerate(heating, 1):
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st.metric(
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f"{idx}. {item.get('sector', 'N/A')}",
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f"{item.get('score', 0)}分",
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"↗️ 升温"
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)
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with col3:
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st.markdown("#### 📉 降温板块")
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cooling = heat.get("cooling", [])
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for idx, item in enumerate(cooling, 1):
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st.metric(
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f"{idx}. {item.get('sector', 'N/A')}",
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f"{item.get('score', 0)}分",
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"↘️ 降温"
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)
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st.markdown("---")
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# 4. 总结建议
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summary = predictions.get("summary", {})
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if summary:
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st.markdown("### 📝 策略总结")
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col1, col2 = st.columns(2)
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with col1:
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st.markdown(f"""
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<div class="decision-card">
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<h4>💡 市场观点</h4>
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<p>{summary.get('market_view', 'N/A')}</p>
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</div>
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""", unsafe_allow_html=True)
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st.markdown(f"""
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<div class="agent-card" style="border-left-color: #2196f3;">
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<h4>🎯 核心机会</h4>
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<p>{summary.get('key_opportunity', 'N/A')}</p>
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</div>
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""", unsafe_allow_html=True)
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with col2:
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st.markdown(f"""
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<div class="warning-card">
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<h4>⚠️ 主要风险</h4>
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<p>{summary.get('major_risk', 'N/A')}</p>
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</div>
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""", unsafe_allow_html=True)
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st.markdown(f"""
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<div class="agent-card" style="border-left-color: #ff9800;">
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<h4>📋 整体策略</h4>
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<p>{summary.get('strategy', 'N/A')}</p>
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</div>
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""", unsafe_allow_html=True)
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def display_agents_reports(agents_analysis):
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"""显示智能体分析报告"""
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st.subheader("🤖 AI智能体分析报告")
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if not agents_analysis:
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st.info("暂无智能体分析数据")
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return
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# 创建子标签页
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agent_names = []
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agent_data = []
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for key, value in agents_analysis.items():
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agent_names.append(value.get("agent_name", "未知分析师"))
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agent_data.append(value)
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tabs = st.tabs(agent_names)
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for idx, tab in enumerate(tabs):
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with tab:
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agent = agent_data[idx]
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st.markdown(f"""
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<div class="agent-card">
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<h3>👨💼 {agent.get('agent_name', '未知')}</h3>
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<p><strong>职责:</strong> {agent.get('agent_role', '未知')}</p>
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<p><strong>关注领域:</strong> {', '.join(agent.get('focus_areas', []))}</p>
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<p><strong>分析时间:</strong> {agent.get('timestamp', '未知')}</p>
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</div>
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""", unsafe_allow_html=True)
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st.markdown("---")
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st.markdown("### 📄 分析报告")
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st.write(agent.get("analysis", "暂无分析"))
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def display_comprehensive_report(report):
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"""显示综合研判报告"""
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st.subheader("📊 综合研判报告")
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if not report:
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st.info("暂无综合研判数据")
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return
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st.markdown("""
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<div class="decision-card">
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<h4>🎯 智策综合研判</h4>
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<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>
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</div>
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""", unsafe_allow_html=True)
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st.markdown("---")
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st.write(report)
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def display_visualizations(predictions):
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"""显示数据可视化"""
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st.subheader("📈 数据可视化")
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if not predictions or predictions.get("prediction_text"):
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st.info("暂无可视化数据")
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return
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# 1. 板块多空雷达图
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st.markdown("### 📊 板块多空信心度对比")
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bullish = predictions.get("long_short", {}).get("bullish", [])
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bearish = predictions.get("long_short", {}).get("bearish", [])
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if bullish or bearish:
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# 准备数据
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sectors = []
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confidence = []
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types = []
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for item in bullish[:5]:
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sectors.append(item.get('sector', 'N/A'))
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confidence.append(item.get('confidence', 0))
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types.append('看多')
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for item in bearish[:5]:
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sectors.append(item.get('sector', 'N/A'))
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confidence.append(-item.get('confidence', 0)) # 负值表示看空
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types.append('看空')
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# 创建条形图
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df = pd.DataFrame({
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'板块': sectors,
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'信心度': confidence,
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'类型': types
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})
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fig = px.bar(df, x='板块', y='信心度', color='类型',
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color_discrete_map={'看多': '#4caf50', '看空': '#f44336'},
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title='板块多空信心度对比')
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fig.update_layout(height=400)
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st.plotly_chart(fig, use_container_width=True, key="sector_confidence")
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st.markdown("---")
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# 2. 板块热度分布
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st.markdown("### 🔥 板块热度分布")
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|
|
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heat = predictions.get("heat", {})
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hottest = heat.get("hottest", [])
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heating = heat.get("heating", [])
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|
|
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if hottest or heating:
|
|
sectors = []
|
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scores = []
|
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trends = []
|
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|
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for item in hottest:
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sectors.append(item.get('sector', 'N/A'))
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scores.append(item.get('score', 0))
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trends.append('最热')
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for item in heating:
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sectors.append(item.get('sector', 'N/A'))
|
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scores.append(item.get('score', 0))
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trends.append('升温')
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|
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df = pd.DataFrame({
|
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'板块': sectors,
|
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'热度': scores,
|
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'趋势': trends
|
|
})
|
|
|
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fig = px.scatter(df, x='板块', y='热度', size='热度', color='趋势',
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color_discrete_map={'最热': '#ff5722', '升温': '#ff9800'},
|
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title='板块热度分布图')
|
|
|
|
fig.update_layout(height=400)
|
|
st.plotly_chart(fig, use_container_width=True, key="sector_heat")
|
|
|
|
|
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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])
|
|
|
|
with col1:
|
|
st.write("将分析报告导出为PDF文件，方便保存和分享")
|
|
|
|
with col2:
|
|
if st.button("📥 生成PDF报告", type="primary", use_container_width=True):
|
|
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:
|
|
# 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",
|
|
use_container_width=True
|
|
)
|
|
|
|
|
|
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", use_container_width=True, 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", use_container_width=True):
|
|
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", use_container_width=True):
|
|
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", use_container_width=True):
|
|
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()
|
|
|