oficcejo
1630 lines
63 KiB
Python
1630 lines
63 KiB
Python
"""
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智瞰龙虎UI界面模块
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展示龙虎榜分析结果和推荐股票
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"""
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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, timedelta
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import time
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import base64
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from longhubang_engine import LonghubangEngine
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from longhubang_pdf import LonghubangPDFGenerator
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import config
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def display_longhubang():
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"""显示智瞰龙虎主界面"""
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st.markdown("""
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<div class="top-nav">
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<h1 class="nav-title">🎯 智瞰龙虎 - AI驱动的龙虎榜分析</h1>
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<p class="nav-subtitle">Multi-Agent Dragon Tiger Analysis | 游资·个股·题材·风险多维分析</p>
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</div>
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""", unsafe_allow_html=True)
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st.markdown("---")
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# 功能说明
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with st.expander("💡 智瞰龙虎系统介绍", expanded=False):
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st.markdown("""
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### 🌟 系统特色
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**智瞰龙虎**是基于多AI智能体的龙虎榜深度分析系统，通过5位专业分析师的协同工作，
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为您挖掘次日大概率上涨的潜力股票。
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### 🤖 AI分析师团队
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1. **🎯 游资行为分析师**
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- 识别活跃游资及其操作风格
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- 分析游资席位的进出特征
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- 研判游资对个股的态度
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2. **📈 个股潜力分析师**
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- 从龙虎榜数据挖掘潜力股
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- 识别次日大概率上涨的股票
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- 分析资金动向和技术形态
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3. **🔥 题材追踪分析师**
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- 识别当前热点题材和概念
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- 分析题材的炒作周期
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- 预判题材的持续性
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4. **⚠️ 风险控制专家**
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- 识别高风险股票和陷阱
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- 分析游资出货信号
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- 提供风险管理建议
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5. **👔 首席策略师**
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- 综合所有分析师意见
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- 给出最终推荐股票清单
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- 提供具体操作策略
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### 📊 数据来源
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数据来自**StockAPI龙虎榜接口**，包括：
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- 游资上榜交割单历史数据
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- 股票买卖金额和净流入
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- 热门概念和题材
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- 更新时间：交易日下午5点40
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### 🎯 核心功能
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- ✅ **潜力股挖掘** - AI识别次日大概率上涨股票
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- ✅ **游资追踪** - 跟踪活跃游资的操作
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- ✅ **题材识别** - 发现热点题材和龙头股
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- ✅ **风险提示** - 识别高风险股票和陷阱
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- ✅ **历史记录** - 存储所有龙虎榜数据
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- ✅ **PDF报告** - 生成专业分析报告
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""")
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st.markdown("---")
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||
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# 创建标签页
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tab1, tab2, tab3 = st.tabs([
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"📊 龙虎榜分析",
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"📚 历史报告",
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"📈 数据统计"
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])
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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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with tab3:
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display_statistics_tab()
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def display_analysis_tab():
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"""显示分析标签页"""
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||
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# 检查是否触发批量分析（不立即删除标志）
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if st.session_state.get('longhubang_batch_trigger'):
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run_longhubang_batch_analysis()
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return
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st.subheader("🔍 龙虎榜综合分析")
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|
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# 参数设置
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col1, col2 = st.columns([2, 2])
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with col1:
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analysis_mode = st.selectbox(
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"分析模式",
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["指定日期", "最近N天"],
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help="选择分析特定日期还是最近几天的数据"
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)
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with col2:
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if analysis_mode == "指定日期":
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selected_date = st.date_input(
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"选择日期",
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value=datetime.now() - timedelta(days=1),
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help="选择要分析的龙虎榜日期"
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)
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else:
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days = st.number_input(
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"最近天数",
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min_value=1,
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max_value=10,
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value=1,
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help="分析最近N天的龙虎榜数据"
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)
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# 分析按钮
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col1, col2 = st.columns([2, 2])
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with col1:
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analyze_button = st.button("🚀 开始分析", type="primary", width='stretch')
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with col2:
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if st.button("🔄 清除结果", width='stretch'):
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if 'longhubang_result' in st.session_state:
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del st.session_state.longhubang_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 'longhubang_result' in st.session_state:
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del st.session_state.longhubang_result
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# 准备参数（使用.env中配置的默认模型）
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if analysis_mode == "指定日期":
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date_str = selected_date.strftime('%Y-%m-%d')
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run_longhubang_analysis(date=date_str)
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else:
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run_longhubang_analysis(days=days)
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# 显示分析结果
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if 'longhubang_result' in st.session_state:
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result = st.session_state.longhubang_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_longhubang_analysis(model=None, date=None, days=1):
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"""运行龙虎榜分析"""
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import config
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model = model or config.DEFAULT_MODEL_NAME
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|
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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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status_text.text("🚀 初始化分析引擎...")
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progress_bar.progress(5)
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engine = LonghubangEngine(model=model)
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status_text.text("📊 正在获取龙虎榜数据...")
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progress_bar.progress(15)
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||
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# 运行分析
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result = engine.run_comprehensive_analysis(date=date, days=days)
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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.longhubang_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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||
|
||
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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||
|
||
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_analysis_results(result):
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"""显示分析结果"""
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||
|
||
st.success("✅ 龙虎榜分析完成！")
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||
st.info(f"📅 分析时间: {result.get('timestamp', 'N/A')}")
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||
|
||
# 数据概况
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||
data_info = result.get('data_info', {})
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col1, col2, col3, col4 = st.columns(4)
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||
|
||
with col1:
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||
st.metric("龙虎榜记录", f"{data_info.get('total_records', 0)} 条")
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||
|
||
with col2:
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||
st.metric("涉及股票", f"{data_info.get('total_stocks', 0)} 只")
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||
|
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with col3:
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st.metric("涉及游资", f"{data_info.get('total_youzi', 0)} 个")
|
||
|
||
with col4:
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recommended = result.get('recommended_stocks', [])
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st.metric("推荐股票", f"{len(recommended)} 只", delta="AI筛选")
|
||
|
||
# PDF导出功能
|
||
display_pdf_export_section(result)
|
||
|
||
st.markdown("---")
|
||
|
||
# 创建子标签页
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||
tab1, tab2, tab3, tab4, tab5 = st.tabs([
|
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"🏆 AI评分排名",
|
||
"🎯 推荐股票",
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||
"🤖 AI分析师报告",
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||
"📊 数据详情",
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||
"📈 可视化图表"
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||
])
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||
|
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with tab1:
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display_scoring_ranking(result)
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||
|
||
with tab2:
|
||
display_recommended_stocks(result)
|
||
|
||
with tab3:
|
||
display_agents_reports(result)
|
||
|
||
with tab4:
|
||
display_data_details(result)
|
||
|
||
with tab5:
|
||
display_visualizations(result)
|
||
|
||
|
||
def display_scoring_ranking(result):
|
||
"""显示AI智能评分排名"""
|
||
|
||
st.subheader("🏆 AI智能评分排名")
|
||
|
||
scoring_df = result.get('scoring_ranking')
|
||
|
||
if scoring_df is None or (hasattr(scoring_df, 'empty') and scoring_df.empty):
|
||
st.warning("暂无评分数据")
|
||
return
|
||
|
||
# 评分说明
|
||
with st.expander("📖 评分维度说明", expanded=False):
|
||
st.markdown("""
|
||
### 📊 AI智能评分体系 (总分100分)
|
||
|
||
#### 1️⃣ 买入资金含金量 (0-30分)
|
||
- **顶级游资**（赵老哥、章盟主、92科比等）：每个 +10分
|
||
- **知名游资**（深股通、中信证券等）：每个 +5分
|
||
- **普通游资**：每个 +1.5分
|
||
|
||
#### 2️⃣ 净买入额评分 (0-25分)
|
||
- 净流入 < 1000万：0-10分
|
||
- 净流入 1000-5000万：10-18分
|
||
- 净流入 5000万-1亿：18-22分
|
||
- 净流入 > 1亿：22-25分
|
||
|
||
#### 3️⃣ 卖出压力评分 (0-20分)
|
||
- 卖出比例 0-10%：20分 ✨（压力极小）
|
||
- 卖出比例 10-30%：15-20分（压力较小）
|
||
- 卖出比例 30-50%：10-15分（压力中等）
|
||
- 卖出比例 50-80%：5-10分（压力较大）
|
||
- 卖出比例 > 80%：0-5分（压力极大）
|
||
|
||
#### 4️⃣ 机构共振评分 (0-15分)
|
||
- **机构+游资共振**：15分 ⭐（最强信号）
|
||
- 仅机构买入：8-12分
|
||
- 仅游资买入：5-10分
|
||
|
||
#### 5️⃣ 其他加分项 (0-10分)
|
||
- **主力集中度**：席位越少越集中 (+1-3分)
|
||
- **热门概念**：AI、新能源、芯片等 (+0-3分)
|
||
- **连续上榜**：连续多日上榜 (+0-2分)
|
||
- **买卖比例优秀**：买入远大于卖出 (+0-2分)
|
||
|
||
---
|
||
|
||
💡 **评分越高，表示该股票受到资金青睐程度越高！**
|
||
⚠️ **但仍需结合市场环境、技术面等因素综合判断！**
|
||
""")
|
||
|
||
st.markdown("---")
|
||
|
||
# 显示TOP10评分表格
|
||
st.markdown("### 🥇 TOP10 综合评分排名")
|
||
|
||
# 兼容历史数据与类型统一，避免 Arrow 序列化错误
|
||
if isinstance(scoring_df, list):
|
||
scoring_df = pd.DataFrame(scoring_df)
|
||
|
||
numeric_cols = ['排名','综合评分','资金含金量','净买入额','卖出压力','机构共振','加分项','顶级游资','买方数','净流入']
|
||
for col in numeric_cols:
|
||
if col in scoring_df.columns:
|
||
scoring_df[col] = pd.to_numeric(scoring_df[col], errors='coerce')
|
||
|
||
text_cols = ['股票名称','股票代码','机构参与']
|
||
for col in text_cols:
|
||
if col in scoring_df.columns:
|
||
scoring_df[col] = scoring_df[col].astype(str)
|
||
|
||
top10_df = scoring_df.head(10).copy()
|
||
if '排名' in top10_df.columns:
|
||
top10_df['排名'] = pd.to_numeric(top10_df['排名'], errors='coerce').fillna(0).astype(int)
|
||
|
||
# 格式化显示
|
||
st.dataframe(
|
||
top10_df,
|
||
column_config={
|
||
"排名": st.column_config.NumberColumn("排名", format="%d", width="small"),
|
||
"股票名称": st.column_config.TextColumn("股票名称", width="medium"),
|
||
"股票代码": st.column_config.TextColumn("代码", width="small"),
|
||
"综合评分": st.column_config.NumberColumn(
|
||
"综合评分",
|
||
format="%.1f",
|
||
help="总分100分"
|
||
),
|
||
"资金含金量": st.column_config.ProgressColumn(
|
||
"资金含金量",
|
||
format="%d分",
|
||
min_value=0,
|
||
max_value=30
|
||
),
|
||
"净买入额": st.column_config.ProgressColumn(
|
||
"净买入额",
|
||
format="%d分",
|
||
min_value=0,
|
||
max_value=25
|
||
),
|
||
"卖出压力": st.column_config.ProgressColumn(
|
||
"卖出压力",
|
||
format="%d分",
|
||
min_value=0,
|
||
max_value=20
|
||
),
|
||
"机构共振": st.column_config.ProgressColumn(
|
||
"机构共振",
|
||
format="%d分",
|
||
min_value=0,
|
||
max_value=15
|
||
),
|
||
"加分项": st.column_config.ProgressColumn(
|
||
"加分项",
|
||
format="%d分",
|
||
min_value=0,
|
||
max_value=10
|
||
),
|
||
"顶级游资": st.column_config.NumberColumn("顶级游资", format="%d家"),
|
||
"买方数": st.column_config.NumberColumn("买方数", format="%d家"),
|
||
"机构参与": st.column_config.TextColumn("机构参与"),
|
||
"净流入": st.column_config.NumberColumn("净流入(元)", format="%.2f")
|
||
},
|
||
hide_index=True,
|
||
width='stretch'
|
||
)
|
||
|
||
# 一键批量分析功能
|
||
st.markdown("---")
|
||
|
||
col_batch1, col_batch2, col_batch3 = st.columns([2, 1, 1])
|
||
with col_batch1:
|
||
st.markdown("#### 🚀 批量深度分析")
|
||
st.caption("对TOP10股票进行完整的AI团队分析，获取投资评级和关键价位")
|
||
|
||
with col_batch2:
|
||
batch_count = st.selectbox(
|
||
"分析数量",
|
||
options=[3, 5, 10],
|
||
index=0,
|
||
help="选择分析前N只股票",
|
||
key="batch_count_selector"
|
||
)
|
||
# 同步更新session_state中的batch_count
|
||
st.session_state.batch_count = batch_count
|
||
|
||
with col_batch3:
|
||
st.write("") # 占位
|
||
if st.button("🚀 开始批量分析", type="primary", width='stretch'):
|
||
# 提取股票代码
|
||
stock_codes = top10_df.head(batch_count)['股票代码'].tolist()
|
||
|
||
# 存储到session_state，触发批量分析
|
||
st.session_state.longhubang_batch_codes = stock_codes
|
||
st.session_state.longhubang_batch_trigger = True
|
||
st.rerun()
|
||
|
||
st.markdown("---")
|
||
|
||
# 评分分布图表
|
||
st.markdown("### 📊 评分分布可视化")
|
||
|
||
col1, col2 = st.columns(2)
|
||
|
||
with col1:
|
||
# 综合评分柱状图
|
||
fig1 = px.bar(
|
||
top10_df,
|
||
x='股票名称',
|
||
y='综合评分',
|
||
title='TOP10 综合评分对比',
|
||
text='综合评分',
|
||
color='综合评分',
|
||
color_continuous_scale='RdYlGn'
|
||
)
|
||
fig1.update_traces(texttemplate='%{text:.1f}分', textposition='outside')
|
||
fig1.update_layout(
|
||
xaxis_tickangle=-45,
|
||
showlegend=False,
|
||
height=400
|
||
)
|
||
st.plotly_chart(fig1, config={'displayModeBar': False}, use_container_width=True)
|
||
|
||
with col2:
|
||
# 五维评分雷达图（显示批量分析数量的股票）
|
||
if len(top10_df) > 0:
|
||
display_count = min(5, len(top10_df))
|
||
|
||
fig2 = go.Figure()
|
||
|
||
# 为每只股票添加雷达图
|
||
colors = ['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4', '#FFEAA7']
|
||
for i in range(display_count):
|
||
stock = top10_df.iloc[i]
|
||
|
||
fig2.add_trace(go.Scatterpolar(
|
||
r=[
|
||
stock['资金含金量'] / 30 * 100,
|
||
stock['净买入额'] / 25 * 100,
|
||
stock['卖出压力'] / 20 * 100,
|
||
stock['机构共振'] / 15 * 100,
|
||
stock['加分项'] / 10 * 100
|
||
],
|
||
theta=['资金含金量', '净买入额', '卖出压力', '机构共振', '加分项'],
|
||
fill='toself',
|
||
name=f"{stock['股票名称']}",
|
||
line_color=colors[i % len(colors)],
|
||
fillcolor=colors[i % len(colors)],
|
||
opacity=0.6
|
||
))
|
||
|
||
fig2.update_layout(
|
||
polar=dict(
|
||
radialaxis=dict(
|
||
visible=True,
|
||
range=[0, 100]
|
||
)
|
||
),
|
||
showlegend=True,
|
||
title=f"🏆 TOP{display_count} 五维评分对比",
|
||
height=400,
|
||
legend=dict(
|
||
orientation="h",
|
||
yanchor="auto",
|
||
y=-0.2,
|
||
xanchor="center",
|
||
x=0.5
|
||
)
|
||
)
|
||
st.plotly_chart(fig2, config={'displayModeBar': False}, use_container_width=True)
|
||
|
||
st.markdown("---")
|
||
|
||
# 完整排名表格
|
||
st.markdown("### 📋 完整评分排名")
|
||
|
||
st.dataframe(
|
||
scoring_df,
|
||
column_config={
|
||
"排名": st.column_config.NumberColumn("排名", format="%d", width="small"),
|
||
"股票名称": st.column_config.TextColumn("股票名称"),
|
||
"股票代码": st.column_config.TextColumn("代码"),
|
||
"综合评分": st.column_config.NumberColumn("综合评分", format="%.1f"),
|
||
"顶级游资": st.column_config.NumberColumn("顶级游资", format="%d家"),
|
||
"买方数": st.column_config.NumberColumn("买方数", format="%d家"),
|
||
"机构参与": st.column_config.TextColumn("机构"),
|
||
"净流入": st.column_config.NumberColumn("净流入(元)", format="%.2f")
|
||
},
|
||
hide_index=True,
|
||
width='stretch'
|
||
)
|
||
|
||
|
||
def display_recommended_stocks(result):
|
||
"""显示推荐股票"""
|
||
|
||
st.subheader("🎯 AI推荐股票")
|
||
|
||
recommended = result.get('recommended_stocks', [])
|
||
|
||
if not recommended:
|
||
st.warning("暂无推荐股票")
|
||
return
|
||
|
||
st.info(f"💡 基于5位AI分析师的综合分析，系统识别出以下 **{len(recommended)}** 只潜力股票")
|
||
|
||
# 创建DataFrame
|
||
df_recommended = pd.DataFrame(recommended)
|
||
|
||
# 显示表格
|
||
st.dataframe(
|
||
df_recommended,
|
||
column_config={
|
||
"rank": st.column_config.NumberColumn("排名", format="%d"),
|
||
"code": st.column_config.TextColumn("股票代码"),
|
||
"name": st.column_config.TextColumn("股票名称"),
|
||
"net_inflow": st.column_config.NumberColumn("净流入金额", format="%.2f"),
|
||
"confidence": st.column_config.TextColumn("确定性"),
|
||
"hold_period": st.column_config.TextColumn("持有周期"),
|
||
"reason": st.column_config.TextColumn("推荐理由")
|
||
},
|
||
hide_index=True,
|
||
width='stretch'
|
||
)
|
||
|
||
# 详细推荐理由
|
||
st.markdown("### 📝 详细推荐理由")
|
||
|
||
for stock in recommended[:5]: # 只显示前5只
|
||
with st.expander(f"**{stock.get('rank', '-')}. {stock.get('name', '-')} ({stock.get('code', '-')})**"):
|
||
col1, col2 = st.columns([2, 1])
|
||
|
||
with col1:
|
||
st.markdown(f"**推荐理由:** {stock.get('reason', '暂无')}")
|
||
st.markdown(f"**净流入:** {stock.get('net_inflow', 0):,.2f} 元")
|
||
|
||
with col2:
|
||
st.markdown(f"**确定性:** {stock.get('confidence', '-')}")
|
||
st.markdown(f"**持有周期:** {stock.get('hold_period', '-')}")
|
||
|
||
|
||
def display_agents_reports(result):
|
||
"""显示AI分析师报告"""
|
||
|
||
st.subheader("🤖 AI分析师团队报告")
|
||
|
||
agents_analysis = result.get('agents_analysis', {})
|
||
|
||
if not agents_analysis:
|
||
st.warning("暂无分析报告")
|
||
return
|
||
|
||
# 各分析师报告
|
||
agent_info = {
|
||
'youzi': {'title': '🎯 游资行为分析师', 'icon': '🎯'},
|
||
'stock': {'title': '📈 个股潜力分析师', 'icon': '📈'},
|
||
'theme': {'title': '🔥 题材追踪分析师', 'icon': '🔥'},
|
||
'risk': {'title': '⚠️ 风险控制专家', 'icon': '⚠️'},
|
||
'chief': {'title': '👔 首席策略师综合研判', 'icon': '👔'}
|
||
}
|
||
|
||
for agent_key, info in agent_info.items():
|
||
agent_data = agents_analysis.get(agent_key, {})
|
||
if agent_data:
|
||
with st.expander(f"{info['icon']} {info['title']}", expanded=(agent_key == 'chief')):
|
||
analysis = agent_data.get('analysis', '暂无分析')
|
||
st.markdown(analysis)
|
||
|
||
st.markdown(f"*{agent_data.get('agent_role', '')}*")
|
||
st.caption(f"分析时间: {agent_data.get('timestamp', 'N/A')}")
|
||
|
||
|
||
def display_data_details(result):
|
||
"""显示数据详情"""
|
||
|
||
st.subheader("📊 龙虎榜数据详情")
|
||
|
||
data_info = result.get('data_info', {})
|
||
summary = data_info.get('summary', {})
|
||
|
||
# TOP游资
|
||
if summary.get('top_youzi'):
|
||
st.markdown("### 🏆 活跃游资 TOP10")
|
||
|
||
youzi_data = [
|
||
{'排名': idx, '游资名称': name, '净流入金额': amount}
|
||
for idx, (name, amount) in enumerate(list(summary['top_youzi'].items())[:10], 1)
|
||
]
|
||
df_youzi = pd.DataFrame(youzi_data)
|
||
|
||
st.dataframe(
|
||
df_youzi,
|
||
column_config={
|
||
"排名": st.column_config.NumberColumn("排名", format="%d"),
|
||
"游资名称": st.column_config.TextColumn("游资名称"),
|
||
"净流入金额": st.column_config.NumberColumn("净流入金额(元)", format="%.2f")
|
||
},
|
||
hide_index=True,
|
||
width='stretch'
|
||
)
|
||
|
||
# TOP股票
|
||
if summary.get('top_stocks'):
|
||
st.markdown("### 📈 资金净流入 TOP20 股票")
|
||
|
||
df_stocks = pd.DataFrame(summary['top_stocks'][:20])
|
||
|
||
st.dataframe(
|
||
df_stocks,
|
||
column_config={
|
||
"code": st.column_config.TextColumn("股票代码"),
|
||
"name": st.column_config.TextColumn("股票名称"),
|
||
"net_inflow": st.column_config.NumberColumn("净流入金额(元)", format="%.2f")
|
||
},
|
||
hide_index=True,
|
||
width='stretch'
|
||
)
|
||
|
||
# 热门概念
|
||
if summary.get('hot_concepts'):
|
||
st.markdown("### 🔥 热门概念 TOP20")
|
||
|
||
concepts_data = [
|
||
{'排名': idx, '概念名称': concept, '出现次数': count}
|
||
for idx, (concept, count) in enumerate(list(summary['hot_concepts'].items())[:20], 1)
|
||
]
|
||
df_concepts = pd.DataFrame(concepts_data)
|
||
|
||
st.dataframe(
|
||
df_concepts,
|
||
column_config={
|
||
"排名": st.column_config.NumberColumn("排名", format="%d"),
|
||
"概念名称": st.column_config.TextColumn("概念名称"),
|
||
"出现次数": st.column_config.NumberColumn("出现次数", format="%d")
|
||
},
|
||
hide_index=True,
|
||
width='stretch'
|
||
)
|
||
|
||
|
||
def display_visualizations(result):
|
||
"""显示可视化图表"""
|
||
|
||
st.subheader("📈 数据可视化")
|
||
|
||
data_info = result.get('data_info', {})
|
||
summary = data_info.get('summary', {})
|
||
|
||
# 资金流向图表
|
||
if summary.get('top_stocks'):
|
||
st.markdown("### 💰 TOP20 股票资金净流入")
|
||
|
||
stocks = summary['top_stocks'][:20]
|
||
df_chart = pd.DataFrame(stocks)
|
||
|
||
fig = px.bar(
|
||
df_chart,
|
||
x='name',
|
||
y='net_inflow',
|
||
title='TOP20 股票资金净流入金额',
|
||
labels={'name': '股票名称', 'net_inflow': '净流入金额(元)'}
|
||
)
|
||
fig.update_layout(xaxis_tickangle=-45)
|
||
st.plotly_chart(fig, config={'displayModeBar': False}, use_container_width=True)
|
||
|
||
# 热门概念图表
|
||
if summary.get('hot_concepts'):
|
||
st.markdown("### 🔥 热门概念分布")
|
||
|
||
concepts = list(summary['hot_concepts'].items())[:15]
|
||
df_concepts = pd.DataFrame(concepts, columns=['概念', '次数'])
|
||
|
||
fig = px.pie(
|
||
df_concepts,
|
||
values='次数',
|
||
names='概念',
|
||
title='热门概念出现次数分布'
|
||
)
|
||
st.plotly_chart(fig, config={'displayModeBar': False}, use_container_width=True)
|
||
|
||
|
||
def display_pdf_export_section(result):
|
||
"""显示PDF导出功能"""
|
||
|
||
st.markdown("### 📄 导出报告")
|
||
|
||
col1, col2, col3 = st.columns([2, 1, 1])
|
||
|
||
with col1:
|
||
st.info("💡 点击按钮生成并下载专业分析报告")
|
||
|
||
with col2:
|
||
if st.button("📥 生成PDF", type="primary", width='stretch'):
|
||
with st.spinner("正在生成PDF报告..."):
|
||
try:
|
||
generator = LonghubangPDFGenerator()
|
||
pdf_path = generator.generate_pdf(result)
|
||
|
||
# 读取PDF文件
|
||
with open(pdf_path, "rb") as f:
|
||
pdf_bytes = f.read()
|
||
|
||
# 提供下载
|
||
st.download_button(
|
||
label="📥 下载PDF报告",
|
||
data=pdf_bytes,
|
||
file_name=f"智瞰龙虎报告_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pdf",
|
||
mime="application/pdf",
|
||
width='stretch'
|
||
)
|
||
|
||
st.success("✅ PDF报告生成成功！")
|
||
|
||
except Exception as e:
|
||
st.error(f"❌ PDF生成失败: {str(e)}")
|
||
|
||
with col3:
|
||
if st.button("📝 生成Markdown", type="secondary", width='stretch'):
|
||
with st.spinner("正在生成Markdown报告..."):
|
||
try:
|
||
# 生成Markdown内容
|
||
markdown_content = generate_markdown_report(result)
|
||
|
||
# 提供下载
|
||
st.download_button(
|
||
label="📥 下载Markdown报告",
|
||
data=markdown_content,
|
||
file_name=f"智瞰龙虎报告_{datetime.now().strftime('%Y%m%d_%H%M%S')}.md",
|
||
mime="text/markdown",
|
||
width='stretch'
|
||
)
|
||
|
||
st.success("✅ Markdown报告生成成功！")
|
||
|
||
except Exception as e:
|
||
st.error(f"❌ Markdown生成失败: {str(e)}")
|
||
|
||
|
||
def generate_markdown_report(result_data: dict) -> str:
|
||
"""生成龙虎榜分析Markdown报告"""
|
||
|
||
# 获取当前时间
|
||
current_time = datetime.now().strftime("%Y年%m月%d日 %H:%M:%S")
|
||
|
||
# 标题页
|
||
markdown_content = f"""# 智瞰龙虎榜分析报告
|
||
|
||
**AI驱动的龙虎榜多维度分析系统**
|
||
|
||
---
|
||
|
||
## 📊 报告概览
|
||
|
||
- **生成时间**: {current_time}
|
||
- **数据记录**: {result_data.get('data_info', {}).get('total_records', 0)} 条
|
||
- **涉及股票**: {result_data.get('data_info', {}).get('total_stocks', 0)} 只
|
||
- **涉及游资**: {result_data.get('data_info', {}).get('total_youzi', 0)} 个
|
||
- **AI分析师**: 5位专业分析师团队
|
||
- **分析模型**: DeepSeek AI Multi-Agent System
|
||
|
||
> ⚠️ 本报告由AI系统基于龙虎榜公开数据自动生成，仅供参考，不构成投资建议。市场有风险，投资需谨慎。
|
||
|
||
---
|
||
|
||
## 📈 数据概况
|
||
|
||
本次分析共涵盖 **{result_data.get('data_info', {}).get('total_records', 0)}** 条龙虎榜记录，
|
||
涉及 **{result_data.get('data_info', {}).get('total_stocks', 0)}** 只股票和
|
||
**{result_data.get('data_info', {}).get('total_youzi', 0)}** 个游资席位。
|
||
|
||
"""
|
||
|
||
# 资金概况
|
||
summary = result_data.get('data_info', {}).get('summary', {})
|
||
markdown_content += f"""
|
||
### 💰 资金概况
|
||
|
||
- **总买入金额**: {summary.get('total_buy_amount', 0):,.2f} 元
|
||
- **总卖出金额**: {summary.get('total_sell_amount', 0):,.2f} 元
|
||
- **净流入金额**: {summary.get('total_net_inflow', 0):,.2f} 元
|
||
|
||
"""
|
||
|
||
# TOP游资
|
||
if summary.get('top_youzi'):
|
||
markdown_content += "### 🏆 活跃游资 TOP10\n\n| 排名 | 游资名称 | 净流入金额(元) |\n|------|----------|---------------|\n"
|
||
for idx, (name, amount) in enumerate(list(summary['top_youzi'].items())[:10], 1):
|
||
markdown_content += f"| {idx} | {name} | {amount:,.2f} |\n"
|
||
markdown_content += "\n"
|
||
|
||
# TOP股票
|
||
if summary.get('top_stocks'):
|
||
markdown_content += "### 📈 资金净流入 TOP20 股票\n\n| 排名 | 股票代码 | 股票名称 | 净流入金额(元) |\n|------|----------|----------|---------------|\n"
|
||
for idx, stock in enumerate(summary['top_stocks'][:20], 1):
|
||
markdown_content += f"| {idx} | {stock['code']} | {stock['name']} | {stock['net_inflow']:,.2f} |\n"
|
||
markdown_content += "\n"
|
||
|
||
# 热门概念
|
||
if summary.get('hot_concepts'):
|
||
markdown_content += "### 🔥 热门概念 TOP15\n\n"
|
||
for idx, (concept, count) in enumerate(list(summary['hot_concepts'].items())[:15], 1):
|
||
markdown_content += f"{idx}. {concept} ({count}次) \n"
|
||
markdown_content += "\n"
|
||
|
||
# 推荐股票
|
||
recommended = result_data.get('recommended_stocks', [])
|
||
if recommended:
|
||
markdown_content += f"""
|
||
## 🎯 AI推荐股票
|
||
|
||
基于5位AI分析师的综合分析，系统识别出以下 **{len(recommended)}** 只潜力股票，
|
||
这些股票在资金流向、游资关注度、题材热度等多个维度表现突出。
|
||
|
||
### 推荐股票清单
|
||
|
||
| 排名 | 股票代码 | 股票名称 | 净流入金额 | 确定性 | 持有周期 |
|
||
|------|----------|----------|------------|--------|----------|
|
||
"""
|
||
for stock in recommended[:10]:
|
||
markdown_content += f"| {stock.get('rank', '-')} | {stock.get('code', '-')} | {stock.get('name', '-')} | {stock.get('net_inflow', 0):,.0f} | {stock.get('confidence', '-')} | {stock.get('hold_period', '-')} |\n"
|
||
|
||
markdown_content += "\n### 推荐理由详解\n\n"
|
||
for stock in recommended[:5]: # 只详细展示前5只
|
||
markdown_content += f"**{stock.get('rank', '-')}. {stock.get('name', '-')} ({stock.get('code', '-')})**\n\n"
|
||
markdown_content += f"- 推荐理由: {stock.get('reason', '暂无')}\n"
|
||
markdown_content += f"- 确定性: {stock.get('confidence', '-')}\n"
|
||
markdown_content += f"- 持有周期: {stock.get('hold_period', '-')}\n\n"
|
||
|
||
# AI分析师报告
|
||
agents_analysis = result_data.get('agents_analysis', {})
|
||
if agents_analysis:
|
||
markdown_content += "## 🤖 AI分析师报告\n\n"
|
||
markdown_content += "本报告由5位AI专业分析师从不同维度进行分析，综合形成投资建议：\n\n"
|
||
markdown_content += "- **游资行为分析师** - 分析游资操作特征和意图\n"
|
||
markdown_content += "- **个股潜力分析师** - 挖掘次日大概率上涨的股票\n"
|
||
markdown_content += "- **题材追踪分析师** - 识别热点题材和轮动机会\n"
|
||
markdown_content += "- **风险控制专家** - 识别高风险股票和市场陷阱\n"
|
||
markdown_content += "- **首席策略师** - 综合研判并给出最终建议\n\n"
|
||
|
||
agent_titles = {
|
||
'youzi': '游资行为分析师',
|
||
'stock': '个股潜力分析师',
|
||
'theme': '题材追踪分析师',
|
||
'risk': '风险控制专家',
|
||
'chief': '首席策略师综合研判'
|
||
}
|
||
|
||
for agent_key, agent_title in agent_titles.items():
|
||
agent_data = agents_analysis.get(agent_key, {})
|
||
if agent_data:
|
||
markdown_content += f"### {agent_title}\n\n"
|
||
analysis_text = agent_data.get('analysis', '暂无分析')
|
||
# 处理文本中的换行
|
||
analysis_text = analysis_text.replace('\n', '\n\n')
|
||
markdown_content += f"{analysis_text}\n\n"
|
||
|
||
markdown_content += """
|
||
---
|
||
|
||
*报告由智瞰龙虎AI系统自动生成*
|
||
"""
|
||
|
||
return markdown_content
|
||
|
||
|
||
def display_history_tab():
|
||
"""显示历史报告标签页（增强版）"""
|
||
|
||
st.subheader("📚 历史分析报告")
|
||
|
||
try:
|
||
engine = LonghubangEngine()
|
||
reports_df = engine.get_historical_reports(limit=50)
|
||
|
||
if reports_df.empty:
|
||
st.info("暂无历史报告")
|
||
return
|
||
|
||
st.info(f"💾 共有 {len(reports_df)} 条历史报告")
|
||
|
||
# 显示报告列表
|
||
st.markdown("### 📋 报告列表")
|
||
|
||
# 为每条报告创建展开面板
|
||
for idx, row in reports_df.iterrows():
|
||
report_id = row['id']
|
||
analysis_date = row['analysis_date']
|
||
data_date_range = row['data_date_range']
|
||
summary = row['summary']
|
||
|
||
# 创建展开面板
|
||
with st.expander(
|
||
f"📄 报告 #{report_id} | {analysis_date} | 数据范围: {data_date_range}",
|
||
expanded=False
|
||
):
|
||
# 获取完整报告详情
|
||
report_detail = engine.get_report_detail(report_id)
|
||
|
||
if not report_detail:
|
||
st.warning("无法加载报告详情")
|
||
continue
|
||
|
||
# 显示摘要
|
||
st.markdown("#### 📝 报告摘要")
|
||
st.info(summary)
|
||
|
||
st.markdown("---")
|
||
|
||
# 显示推荐股票
|
||
recommended_stocks = report_detail.get('recommended_stocks', [])
|
||
if recommended_stocks:
|
||
st.markdown(f"#### 🎯 推荐股票 ({len(recommended_stocks)}只)")
|
||
|
||
# 创建DataFrame显示
|
||
df_stocks = pd.DataFrame(recommended_stocks)
|
||
st.dataframe(
|
||
df_stocks,
|
||
column_config={
|
||
"rank": st.column_config.NumberColumn("排名", format="%d"),
|
||
"code": st.column_config.TextColumn("代码"),
|
||
"name": st.column_config.TextColumn("名称"),
|
||
"net_inflow": st.column_config.NumberColumn("净流入", format="%.2f"),
|
||
"reason": st.column_config.TextColumn("推荐理由"),
|
||
"confidence": st.column_config.TextColumn("确定性"),
|
||
"hold_period": st.column_config.TextColumn("持有周期")
|
||
},
|
||
hide_index=True,
|
||
width='stretch'
|
||
)
|
||
|
||
st.markdown("---")
|
||
|
||
# 尝试解析完整分析内容
|
||
analysis_content_parsed = report_detail.get('analysis_content_parsed')
|
||
|
||
if analysis_content_parsed and isinstance(analysis_content_parsed, dict):
|
||
# 显示AI分析师团队报告
|
||
agents_analysis = analysis_content_parsed.get('agents_analysis', {})
|
||
|
||
if agents_analysis:
|
||
st.markdown("#### 🤖 AI分析师团队报告")
|
||
|
||
agent_info = {
|
||
'youzi': {'title': '🎯 游资行为分析师', 'icon': '🎯'},
|
||
'stock': {'title': '📈 个股潜力分析师', 'icon': '📈'},
|
||
'theme': {'title': '🔥 题材追踪分析师', 'icon': '🔥'},
|
||
'risk': {'title': '⚠️ 风险控制专家', 'icon': '⚠️'},
|
||
'chief': {'title': '👔 首席策略师', 'icon': '👔'}
|
||
}
|
||
|
||
for agent_key, info in agent_info.items():
|
||
agent_data = agents_analysis.get(agent_key, {})
|
||
if agent_data:
|
||
with st.expander(f"{info['icon']} {info['title']}", expanded=False):
|
||
analysis = agent_data.get('analysis', '暂无分析')
|
||
st.markdown(analysis)
|
||
st.caption(f"分析时间: {agent_data.get('timestamp', 'N/A')}")
|
||
|
||
# 显示AI评分排名
|
||
scoring_ranking = analysis_content_parsed.get('scoring_ranking', [])
|
||
if scoring_ranking:
|
||
st.markdown("---")
|
||
st.markdown("#### 🏆 AI智能评分排名 (TOP10)")
|
||
|
||
df_scoring = pd.DataFrame(scoring_ranking[:10])
|
||
# 类型统一，避免Arrow序列化错误
|
||
numeric_cols = ['排名','综合评分','资金含金量','净买入额','卖出压力','机构共振','加分项','顶级游资','买方数','净流入']
|
||
for col in numeric_cols:
|
||
if col in df_scoring.columns:
|
||
df_scoring[col] = pd.to_numeric(df_scoring[col], errors='coerce')
|
||
text_cols = ['股票名称','股票代码','机构参与']
|
||
for col in text_cols:
|
||
if col in df_scoring.columns:
|
||
df_scoring[col] = df_scoring[col].astype(str)
|
||
if '排名' in df_scoring.columns:
|
||
df_scoring['排名'] = pd.to_numeric(df_scoring['排名'], errors='coerce').fillna(0).astype(int)
|
||
|
||
# 显示完整的评分表格
|
||
st.dataframe(
|
||
df_scoring,
|
||
column_config={
|
||
"排名": st.column_config.NumberColumn("排名", format="%d"),
|
||
"股票名称": st.column_config.TextColumn("股票名称", width="medium"),
|
||
"股票代码": st.column_config.TextColumn("代码", width="small"),
|
||
"综合评分": st.column_config.NumberColumn(
|
||
"综合评分",
|
||
format="%.1f",
|
||
help="总分100分"
|
||
),
|
||
"资金含金量": st.column_config.ProgressColumn(
|
||
"资金含金量",
|
||
format="%d分",
|
||
min_value=0,
|
||
max_value=30
|
||
),
|
||
"净买入额": st.column_config.ProgressColumn(
|
||
"净买入额",
|
||
format="%d分",
|
||
min_value=0,
|
||
max_value=25
|
||
),
|
||
"卖出压力": st.column_config.ProgressColumn(
|
||
"卖出压力",
|
||
format="%d分",
|
||
min_value=0,
|
||
max_value=20
|
||
),
|
||
"机构共振": st.column_config.ProgressColumn(
|
||
"机构共振",
|
||
format="%d分",
|
||
min_value=0,
|
||
max_value=15
|
||
),
|
||
"加分项": st.column_config.ProgressColumn(
|
||
"加分项",
|
||
format="%d分",
|
||
min_value=0,
|
||
max_value=10
|
||
),
|
||
"顶级游资": st.column_config.NumberColumn("顶级游资", format="%d家"),
|
||
"买方数": st.column_config.NumberColumn("买方数", format="%d家"),
|
||
"机构参与": st.column_config.TextColumn("机构参与"),
|
||
"净流入": st.column_config.NumberColumn("净流入(元)", format="%.2f")
|
||
},
|
||
hide_index=True,
|
||
width='stretch'
|
||
)
|
||
|
||
# 显示评分说明
|
||
with st.expander("📖 评分维度说明", expanded=False):
|
||
st.markdown("""
|
||
**AI智能评分体系 (总分100分)**
|
||
|
||
- **资金含金量** (0-30分)：顶级游资+10分，知名游资+5分，普通游资+1.5分
|
||
- **净买入额** (0-25分)：根据净流入金额大小评分
|
||
- **卖出压力** (0-20分)：卖出比例越低得分越高
|
||
- **机构共振** (0-15分)：机构+游资共振15分最高
|
||
- **加分项** (0-10分)：主力集中度、热门概念、连续上榜等
|
||
|
||
💡 评分越高，表示该股票受到资金青睐程度越高！
|
||
""")
|
||
|
||
# 显示数据概况
|
||
data_info = analysis_content_parsed.get('data_info', {})
|
||
if data_info:
|
||
st.markdown("---")
|
||
st.markdown("#### 📊 数据概况")
|
||
|
||
col1, col2, col3 = st.columns(3)
|
||
with col1:
|
||
st.metric("龙虎榜记录", f"{data_info.get('total_records', 0)} 条")
|
||
with col2:
|
||
st.metric("涉及股票", f"{data_info.get('total_stocks', 0)} 只")
|
||
with col3:
|
||
st.metric("涉及游资", f"{data_info.get('total_youzi', 0)} 个")
|
||
|
||
else:
|
||
# 如果无法解析，显示原始内容
|
||
st.markdown("#### 📄 原始分析内容")
|
||
analysis_content = report_detail.get('analysis_content', '')
|
||
if analysis_content:
|
||
st.text_area("原始分析内容", value=analysis_content[:2000], height=200, disabled=True)
|
||
if len(analysis_content) > 2000:
|
||
st.caption("(内容过长，仅显示前2000字符)")
|
||
|
||
# 操作按钮
|
||
st.markdown("---")
|
||
col_export1, col_export2, col_export3 = st.columns(3)
|
||
|
||
with col_export1:
|
||
if st.button(f"📥 导出为PDF", key=f"export_pdf_{report_id}"):
|
||
st.info("PDF导出功能开发中...")
|
||
|
||
with col_export2:
|
||
# 使用session_state来管理按钮状态，避免需要点击两次的问题
|
||
load_key = f"load_report_{report_id}"
|
||
if st.button(f"📋 加载到分析页", key=load_key):
|
||
# 将历史报告加载到当前分析结果中
|
||
if analysis_content_parsed:
|
||
# 重建完整的result结构
|
||
scoring_data = analysis_content_parsed.get('scoring_ranking', [])
|
||
if scoring_data:
|
||
df_scoring = pd.DataFrame(scoring_data)
|
||
# 类型统一，避免Arrow序列化错误
|
||
numeric_cols = ['排名','综合评分','资金含金量','净买入额','卖出压力','机构共振','加分项','顶级游资','买方数','净流入']
|
||
for col in numeric_cols:
|
||
if col in df_scoring.columns:
|
||
df_scoring[col] = pd.to_numeric(df_scoring[col], errors='coerce')
|
||
text_cols = ['股票名称','股票代码','机构参与']
|
||
for col in text_cols:
|
||
if col in df_scoring.columns:
|
||
df_scoring[col] = df_scoring[col].astype(str)
|
||
if '排名' in df_scoring.columns:
|
||
df_scoring['排名'] = pd.to_numeric(df_scoring['排名'], errors='coerce').fillna(0).astype(int)
|
||
else:
|
||
df_scoring = None
|
||
|
||
loaded_result = {
|
||
"success": True,
|
||
"timestamp": report_detail.get('analysis_date', ''),
|
||
"data_info": analysis_content_parsed.get('data_info', {}),
|
||
"agents_analysis": analysis_content_parsed.get('agents_analysis', {}),
|
||
"scoring_ranking": df_scoring,
|
||
"final_report": analysis_content_parsed.get('final_report', {}),
|
||
"recommended_stocks": report_detail.get('recommended_stocks', [])
|
||
}
|
||
st.session_state.longhubang_result = loaded_result
|
||
# 使用rerun来立即刷新页面状态
|
||
st.success('✅ 报告已加载到分析页面，请切换到"龙虎榜分析"标签查看')
|
||
st.rerun()
|
||
|
||
with col_export3:
|
||
# 删除按钮
|
||
delete_key = f"delete_report_{report_id}"
|
||
if st.button(f"🗑️ 删除报告", key=delete_key, type="secondary"):
|
||
# 使用session_state来管理删除确认状态
|
||
st.session_state[f"confirm_delete_{report_id}"] = True
|
||
st.rerun()
|
||
|
||
# 删除确认对话框
|
||
if st.session_state.get(f"confirm_delete_{report_id}", False):
|
||
st.warning(f"⚠️ 确认删除报告 #{report_id}？此操作不可撤销！")
|
||
col_confirm1, col_confirm2 = st.columns(2)
|
||
|
||
with col_confirm1:
|
||
if st.button(f"✅ 确认删除", key=f"confirm_delete_yes_{report_id}", type="primary"):
|
||
try:
|
||
# 调用数据库删除方法 - 修复属性名
|
||
engine.database.delete_analysis_report(report_id)
|
||
st.success(f"✅ 报告 #{report_id} 已成功删除")
|
||
# 清除确认状态并刷新页面
|
||
if f"confirm_delete_{report_id}" in st.session_state:
|
||
del st.session_state[f"confirm_delete_{report_id}"]
|
||
st.rerun()
|
||
except Exception as e:
|
||
st.error(f"❌ 删除失败: {str(e)}")
|
||
|
||
with col_confirm2:
|
||
if st.button(f"❌ 取消", key=f"confirm_delete_no_{report_id}"):
|
||
# 清除确认状态
|
||
if f"confirm_delete_{report_id}" in st.session_state:
|
||
del st.session_state[f"confirm_delete_{report_id}"]
|
||
st.rerun()
|
||
|
||
except Exception as e:
|
||
st.error(f"❌ 加载历史报告失败: {str(e)}")
|
||
import traceback
|
||
st.code(traceback.format_exc())
|
||
|
||
|
||
def display_statistics_tab():
|
||
"""显示数据统计标签页"""
|
||
|
||
st.subheader("📈 数据统计")
|
||
|
||
try:
|
||
engine = LonghubangEngine()
|
||
stats = engine.get_statistics()
|
||
|
||
# 基本统计
|
||
col1, col2, col3, col4 = st.columns(4)
|
||
|
||
with col1:
|
||
st.metric("总记录数", f"{stats.get('total_records', 0):,}")
|
||
|
||
with col2:
|
||
st.metric("股票总数", f"{stats.get('total_stocks', 0):,}")
|
||
|
||
with col3:
|
||
st.metric("游资总数", f"{stats.get('total_youzi', 0):,}")
|
||
|
||
with col4:
|
||
st.metric("分析报告", f"{stats.get('total_reports', 0):,}")
|
||
|
||
# 日期范围
|
||
date_range = stats.get('date_range', {})
|
||
if date_range:
|
||
st.info(f"📅 数据日期范围: {date_range.get('start', 'N/A')} 至 {date_range.get('end', 'N/A')}")
|
||
|
||
st.markdown("---")
|
||
|
||
# 活跃游资排名
|
||
st.markdown("### 🏆 历史活跃游资排名 (近30天)")
|
||
|
||
end_date = datetime.now().strftime('%Y-%m-%d')
|
||
start_date = (datetime.now() - timedelta(days=30)).strftime('%Y-%m-%d')
|
||
|
||
top_youzi_df = engine.get_top_youzi(start_date, end_date, limit=20)
|
||
|
||
if not top_youzi_df.empty:
|
||
st.dataframe(
|
||
top_youzi_df,
|
||
column_config={
|
||
"youzi_name": st.column_config.TextColumn("游资名称"),
|
||
"trade_count": st.column_config.NumberColumn("交易次数", format="%d"),
|
||
"total_net_inflow": st.column_config.NumberColumn("总净流入(元)", format="%.2f")
|
||
},
|
||
hide_index=True,
|
||
width='stretch'
|
||
)
|
||
|
||
st.markdown("---")
|
||
|
||
# 热门股票排名
|
||
st.markdown("### 📈 历史热门股票排名 (近30天)")
|
||
|
||
top_stocks_df = engine.get_top_stocks(start_date, end_date, limit=20)
|
||
|
||
if not top_stocks_df.empty:
|
||
st.dataframe(
|
||
top_stocks_df,
|
||
column_config={
|
||
"stock_code": st.column_config.TextColumn("股票代码"),
|
||
"stock_name": st.column_config.TextColumn("股票名称"),
|
||
"youzi_count": st.column_config.NumberColumn("游资数量", format="%d"),
|
||
"total_net_inflow": st.column_config.NumberColumn("总净流入(元)", format="%.2f")
|
||
},
|
||
hide_index=True,
|
||
width='stretch'
|
||
)
|
||
|
||
except Exception as e:
|
||
st.error(f"❌ 加载统计数据失败: {str(e)}")
|
||
|
||
|
||
def run_longhubang_batch_analysis():
|
||
"""执行龙虎榜TOP股票批量分析（遵循统一调用规范）"""
|
||
|
||
st.markdown("## 🚀 龙虎榜TOP股票批量分析")
|
||
st.markdown("---")
|
||
|
||
# 检查是否已有分析结果
|
||
if st.session_state.get('longhubang_batch_results'):
|
||
display_longhubang_batch_results(st.session_state.longhubang_batch_results)
|
||
|
||
# 返回按钮
|
||
col_back, col_clear = st.columns(2)
|
||
with col_back:
|
||
if st.button("🔙 返回龙虎榜分析", width='stretch'):
|
||
# 清除所有批量分析相关状态
|
||
if 'longhubang_batch_trigger' in st.session_state:
|
||
del st.session_state.longhubang_batch_trigger
|
||
if 'longhubang_batch_codes' in st.session_state:
|
||
del st.session_state.longhubang_batch_codes
|
||
if 'longhubang_batch_results' in st.session_state:
|
||
del st.session_state.longhubang_batch_results
|
||
st.rerun()
|
||
|
||
with col_clear:
|
||
if st.button("🔄 重新分析", width='stretch'):
|
||
# 清除结果，保留触发标志和代码
|
||
if 'longhubang_batch_results' in st.session_state:
|
||
del st.session_state.longhubang_batch_results
|
||
st.rerun()
|
||
|
||
return
|
||
|
||
# 获取股票代码列表
|
||
stock_codes = st.session_state.get('longhubang_batch_codes', [])
|
||
|
||
if not stock_codes:
|
||
st.error("未找到股票代码列表")
|
||
# 清除触发标志
|
||
if 'longhubang_batch_trigger' in st.session_state:
|
||
del st.session_state.longhubang_batch_trigger
|
||
return
|
||
|
||
st.info(f"即将分析 {len(stock_codes)} 只股票：{', '.join(stock_codes)}")
|
||
|
||
# 返回按钮
|
||
if st.button("🔙 取消返回", type="secondary"):
|
||
# 清除所有批量分析相关状态
|
||
if 'longhubang_batch_trigger' in st.session_state:
|
||
del st.session_state.longhubang_batch_trigger
|
||
if 'longhubang_batch_codes' in st.session_state:
|
||
del st.session_state.longhubang_batch_codes
|
||
st.rerun()
|
||
|
||
st.markdown("---")
|
||
|
||
# 分析选项
|
||
col1, col2 = st.columns(2)
|
||
|
||
with col1:
|
||
analysis_mode = st.selectbox(
|
||
"分析模式",
|
||
options=["sequential", "parallel"],
|
||
format_func=lambda x: "顺序分析（稳定）" if x == "sequential" else "并行分析（快速）",
|
||
help="顺序分析较慢但稳定，并行分析更快但消耗更多资源"
|
||
)
|
||
|
||
with col2:
|
||
if analysis_mode == "parallel":
|
||
max_workers = st.number_input(
|
||
"并行线程数",
|
||
min_value=2,
|
||
max_value=5,
|
||
value=3,
|
||
help="同时分析的股票数量"
|
||
)
|
||
else:
|
||
max_workers = 1
|
||
|
||
st.markdown("---")
|
||
|
||
# 开始分析按钮
|
||
col_confirm, col_cancel = st.columns(2)
|
||
|
||
start_analysis = False
|
||
with col_confirm:
|
||
if st.button("🚀 确认开始分析", type="primary", width='stretch'):
|
||
start_analysis = True
|
||
|
||
with col_cancel:
|
||
if st.button("❌ 取消", type="secondary", width='stretch'):
|
||
# 清除所有批量分析相关状态
|
||
if 'longhubang_batch_trigger' in st.session_state:
|
||
del st.session_state.longhubang_batch_trigger
|
||
if 'longhubang_batch_codes' in st.session_state:
|
||
del st.session_state.longhubang_batch_codes
|
||
st.rerun()
|
||
|
||
if start_analysis:
|
||
# 导入统一分析函数（遵循统一规范）
|
||
from app import analyze_single_stock_for_batch
|
||
import concurrent.futures
|
||
import time
|
||
|
||
st.markdown("---")
|
||
st.info("⏳ 正在执行批量分析，请稍候...")
|
||
|
||
# 进度显示
|
||
progress_bar = st.progress(0)
|
||
status_text = st.empty()
|
||
|
||
results = []
|
||
start_time = time.time()
|
||
|
||
if analysis_mode == "sequential":
|
||
# 顺序分析
|
||
for i, code in enumerate(stock_codes):
|
||
status_text.text(f"正在分析 {code} ({i+1}/{len(stock_codes)})")
|
||
progress_bar.progress((i + 1) / len(stock_codes))
|
||
|
||
try:
|
||
# 调用统一分析函数
|
||
result = analyze_single_stock_for_batch(
|
||
symbol=code,
|
||
period="1y",
|
||
enabled_analysts_config={
|
||
'technical': True,
|
||
'fundamental': True,
|
||
'fund_flow': True,
|
||
'risk': True,
|
||
'sentiment': False,
|
||
'news': False
|
||
},
|
||
selected_model=config.DEFAULT_MODEL_NAME
|
||
)
|
||
|
||
results.append({
|
||
"code": code,
|
||
"result": result
|
||
})
|
||
|
||
except Exception as e:
|
||
results.append({
|
||
"code": code,
|
||
"result": {"success": False, "error": str(e)}
|
||
})
|
||
|
||
else:
|
||
# 并行分析
|
||
status_text.text(f"并行分析 {len(stock_codes)} 只股票...")
|
||
|
||
def analyze_one(code):
|
||
try:
|
||
result = analyze_single_stock_for_batch(
|
||
symbol=code,
|
||
period="1y",
|
||
enabled_analysts_config={
|
||
'technical': True,
|
||
'fundamental': True,
|
||
'fund_flow': True,
|
||
'risk': True,
|
||
'sentiment': False,
|
||
'news': False
|
||
},
|
||
selected_model=config.DEFAULT_MODEL_NAME
|
||
)
|
||
return {"code": code, "result": result}
|
||
except Exception as e:
|
||
return {"code": code, "result": {"success": False, "error": str(e)}}
|
||
|
||
with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
|
||
futures = {executor.submit(analyze_one, code): code for code in stock_codes}
|
||
|
||
completed = 0
|
||
for future in concurrent.futures.as_completed(futures):
|
||
completed += 1
|
||
progress_bar.progress(completed / len(stock_codes))
|
||
status_text.text(f"已完成 {completed}/{len(stock_codes)}")
|
||
results.append(future.result())
|
||
|
||
# 清除进度
|
||
progress_bar.empty()
|
||
status_text.empty()
|
||
|
||
# 计算统计
|
||
elapsed_time = time.time() - start_time
|
||
success_count = sum(1 for r in results if r.get("result", {}).get("success"))
|
||
failed_count = len(results) - success_count
|
||
|
||
st.success(f"✅ 批量分析完成！成功 {success_count} 只，失败 {failed_count} 只，耗时 {elapsed_time:.1f}秒")
|
||
|
||
# 保存结果到session_state
|
||
st.session_state.longhubang_batch_results = {
|
||
"results": results,
|
||
"total": len(results),
|
||
"success": success_count,
|
||
"failed": failed_count,
|
||
"elapsed_time": elapsed_time
|
||
}
|
||
|
||
time.sleep(0.5)
|
||
st.rerun()
|
||
|
||
|
||
def display_longhubang_batch_results(batch_results: dict):
|
||
"""显示龙虎榜批量分析结果"""
|
||
|
||
st.markdown("### 📊 批量分析结果")
|
||
|
||
results = batch_results.get("results", [])
|
||
total = batch_results.get("total", 0)
|
||
success = batch_results.get("success", 0)
|
||
failed = batch_results.get("failed", 0)
|
||
elapsed_time = batch_results.get("elapsed_time", 0)
|
||
|
||
# 统计信息
|
||
col1, col2, col3, col4 = st.columns(4)
|
||
with col1:
|
||
st.metric("总计", total)
|
||
with col2:
|
||
st.metric("成功", success)
|
||
with col3:
|
||
st.metric("失败", failed)
|
||
with col4:
|
||
st.metric("耗时", f"{elapsed_time:.1f}秒")
|
||
|
||
st.markdown("---")
|
||
|
||
# 失败的股票
|
||
failed_results = [r for r in results if not r.get("result", {}).get("success")]
|
||
if failed_results:
|
||
with st.expander(f"❌ 失败股票 ({len(failed_results)}只)", expanded=False):
|
||
for item in failed_results:
|
||
code = item.get("code", "")
|
||
error = item.get("result", {}).get("error", "未知错误")
|
||
st.error(f"**{code}**: {error}")
|
||
|
||
# 成功的股票
|
||
success_results = [r for r in results if r.get("result", {}).get("success")]
|
||
|
||
if not success_results:
|
||
st.warning("⚠️ 没有成功分析的股票")
|
||
return
|
||
|
||
st.markdown("### 🎯 分析结果详情")
|
||
|
||
# 显示每只股票的分析结果（使用统一字段名）
|
||
for item in success_results:
|
||
code = item.get("code", "")
|
||
result = item.get("result", {})
|
||
final_decision = result.get("final_decision", {})
|
||
stock_info = result.get("stock_info", {})
|
||
|
||
# 使用统一字段名
|
||
rating = final_decision.get("rating", "未知")
|
||
confidence = final_decision.get("confidence_level", "N/A")
|
||
entry_range = final_decision.get("entry_range", "N/A")
|
||
take_profit = final_decision.get("take_profit", "N/A")
|
||
stop_loss = final_decision.get("stop_loss", "N/A")
|
||
target_price = final_decision.get("target_price", "N/A")
|
||
advice = final_decision.get("advice", "")
|
||
|
||
# 评级颜色
|
||
if "强烈买入" in rating or "买入" in rating:
|
||
rating_color = "🟢"
|
||
elif "卖出" in rating:
|
||
rating_color = "🔴"
|
||
else:
|
||
rating_color = "🟡"
|
||
|
||
with st.expander(f"{rating_color} {code} {stock_info.get('name', '')} - {rating} (信心度: {confidence})", expanded=False):
|
||
col1, col2, col3 = st.columns(3)
|
||
|
||
with col1:
|
||
st.markdown("**基本信息**")
|
||
st.write(f"当前价: {stock_info.get('current_price', 'N/A')}")
|
||
st.write(f"目标价: {target_price}")
|
||
|
||
with col2:
|
||
st.markdown("**进出场位置**")
|
||
st.write(f"进场区间: {entry_range}")
|
||
st.write(f"止盈位: {take_profit}")
|
||
|
||
with col3:
|
||
st.markdown("**风控**")
|
||
st.write(f"止损位: {stop_loss}")
|
||
st.write(f"评级: {rating}")
|
||
|
||
if advice:
|
||
st.markdown("**投资建议**")
|
||
st.info(advice)
|
||
|
||
# 添加到监测按钮
|
||
if st.button(f"➕ 加入监测", key=f"add_monitor_{code}"):
|
||
add_to_monitor_from_longhubang(code, stock_info.get('name', ''), final_decision)
|
||
|
||
|
||
def add_to_monitor_from_longhubang(code: str, name: str, final_decision: dict):
|
||
"""从龙虎榜分析结果添加到监测列表"""
|
||
try:
|
||
from monitor_db import monitor_db
|
||
import re
|
||
|
||
# 提取数据（使用统一字段名和解析逻辑）
|
||
rating = final_decision.get("rating", "持有")
|
||
entry_range = final_decision.get("entry_range", "")
|
||
take_profit_str = final_decision.get("take_profit", "")
|
||
stop_loss_str = final_decision.get("stop_loss", "")
|
||
|
||
# 解析进场区间
|
||
entry_min, entry_max = None, None
|
||
if entry_range and isinstance(entry_range, str) and "-" in entry_range:
|
||
try:
|
||
parts = entry_range.split("-")
|
||
entry_min = float(parts[0].strip())
|
||
entry_max = float(parts[1].strip())
|
||
except:
|
||
pass
|
||
|
||
# 解析止盈止损
|
||
take_profit, stop_loss = None, None
|
||
if take_profit_str:
|
||
try:
|
||
numbers = re.findall(r'\d+\.?\d*', str(take_profit_str))
|
||
if numbers:
|
||
take_profit = float(numbers[0])
|
||
except:
|
||
pass
|
||
|
||
if stop_loss_str:
|
||
try:
|
||
numbers = re.findall(r'\d+\.?\d*', str(stop_loss_str))
|
||
if numbers:
|
||
stop_loss = float(numbers[0])
|
||
except:
|
||
pass
|
||
|
||
# 验证必需参数
|
||
if not all([entry_min, entry_max, take_profit, stop_loss]):
|
||
st.error("❌ 分析结果缺少完整的进场区间和止盈止损信息")
|
||
return
|
||
|
||
# 添加到监测
|
||
monitor_db.add_monitored_stock(
|
||
symbol=code,
|
||
name=name,
|
||
rating=rating,
|
||
entry_range={"min": entry_min, "max": entry_max},
|
||
take_profit=take_profit,
|
||
stop_loss=stop_loss,
|
||
check_interval=60,
|
||
notification_enabled=True
|
||
)
|
||
|
||
st.success(f"✅ {code} 已成功加入监测列表！")
|
||
|
||
except Exception as e:
|
||
st.error(f"❌ 添加监测失败: {str(e)}")
|
||
|
||
|
||
# 测试函数
|
||
if __name__ == "__main__":
|
||
st.set_page_config(
|
||
page_title="智瞰龙虎",
|
||
page_icon="🎯",
|
||
layout="wide"
|
||
)
|
||
|
||
display_longhubang()
|
||
|