oficcejo
1230 lines
47 KiB
Python
1230 lines
47 KiB
Python
"""
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新闻流量监测UI界面
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为短线炒股提供新闻流量分析和交易指导
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包含：仪表盘、实时监测、预警中心、趋势分析、历史记录、设置
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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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def display_news_flow_monitor():
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"""显示新闻流量监测主界面"""
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st.title("📰 新闻流量监测")
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# 功能说明
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with st.expander("💡 功能说明 - 流量为王理念", expanded=False):
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st.markdown("""
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### 核心公式
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**接盘总量 = 流量 × 转化率 × 客单价**
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### 核心理念
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- **流量高潮 = 价格高潮 = 逃命时刻**
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- 当热搜、媒体报道、KOL转发同时达到高潮时，就是出货时机
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- 短线操作：快进快出，紧跟龙头
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### 流量阶段
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1. **启动** - 刚开始发酵，可关注
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2. **加速** - 增速加快，可参与
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3. **分歧** - 多空分歧，需谨慎
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4. **一致** - 流量高潮，危险！准备跑路
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5. **退潮** - 热度下降，及时止盈止损
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### 流量类型
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- **存量流量型**：出生自带顶流（政策/大事件），时间窗口短（2-3天）
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- **增量流量型**：逐步发酵（话题传播），时间窗口长（5-10天）
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""")
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# 标签页
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tabs = st.tabs([
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"📊 仪表盘",
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"🔥 实时监测",
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"⚠️ 预警中心",
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"📈 趋势分析",
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"📚 历史记录",
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"⚙️ 设置"
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])
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with tabs[0]:
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display_dashboard()
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with tabs[1]:
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display_realtime_monitor()
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with tabs[2]:
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display_alert_center()
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with tabs[3]:
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display_trend_analysis()
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with tabs[4]:
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display_history_records()
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with tabs[5]:
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display_settings()
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def display_dashboard():
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"""显示仪表盘"""
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st.subheader("📊 流量仪表盘")
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try:
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from news_flow_engine import news_flow_engine
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dashboard_data = news_flow_engine.get_dashboard_data()
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except Exception as e:
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st.error(f"获取仪表盘数据失败: {e}")
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return
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# 核心指标卡片
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col1, col2, col3, col4 = st.columns(4)
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latest = dashboard_data.get('latest_snapshot', {})
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sentiment = dashboard_data.get('latest_sentiment', {})
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ai = dashboard_data.get('latest_ai_analysis', {})
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with col1:
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score = latest.get('total_score', 0) if latest else 0
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level = latest.get('flow_level', '无数据') if latest else '无数据'
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st.metric("流量得分", f"{score}", delta=level)
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with col2:
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sent_idx = sentiment.get('sentiment_index', 50) if sentiment else 50
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sent_class = sentiment.get('sentiment_class', '中性') if sentiment else '中性'
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st.metric("情绪指数", f"{sent_idx}", delta=sent_class)
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with col3:
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stage = sentiment.get('flow_stage', '未知') if sentiment else '未知'
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k_val = sentiment.get('viral_k', 1.0) if sentiment else 1.0
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st.metric("流量阶段", stage, delta=f"K值:{k_val}")
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with col4:
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advice = ai.get('advice', '观望') if ai else '观望'
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confidence = ai.get('confidence', 50) if ai else 50
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st.metric("AI建议", advice, delta=f"置信度:{confidence}%")
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st.divider()
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# 两列布局
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left_col, right_col = st.columns([2, 1])
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with left_col:
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# 流量趋势图
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st.markdown("#### 流量趋势（7天）")
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trend = dashboard_data.get('flow_trend', {})
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if trend.get('dates'):
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fig = go.Figure()
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fig.add_trace(go.Scatter(
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x=trend['dates'],
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y=trend['avg_scores'],
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mode='lines+markers',
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name='平均得分',
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line=dict(color='#1f77b4', width=2)
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))
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fig.add_trace(go.Scatter(
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x=trend['dates'],
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y=trend['max_scores'],
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mode='lines',
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name='最高得分',
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line=dict(color='#ff7f0e', dash='dash')
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))
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fig.update_layout(
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height=250,
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margin=dict(l=0, r=0, t=20, b=0),
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legend=dict(orientation="h", yanchor="bottom", y=1.02)
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)
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st.plotly_chart(fig, width='stretch')
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st.caption(f"趋势: {trend.get('trend', '无数据')} - {trend.get('analysis', '')[:50]}...")
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else:
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st.info("暂无趋势数据，请先执行监测")
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with right_col:
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# 最近预警
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st.markdown("#### 最近预警")
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alerts = dashboard_data.get('recent_alerts', [])
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if alerts:
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for alert in alerts[:5]:
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level = alert.get('alert_level', 'info')
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icon = {'danger': '🔴', 'warning': '🟠', 'info': '🔵'}.get(level, '⚪')
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st.markdown(f"{icon} **{alert.get('title', '')[:20]}...**")
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st.caption(alert.get('created_at', '')[:16])
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else:
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st.info("暂无预警")
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st.divider()
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# 热点词云和TOP100新闻
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display_wordcloud_and_top_news()
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st.divider()
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# 调度器状态
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scheduler = dashboard_data.get('scheduler_status', {})
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if scheduler:
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st.markdown("#### ⏰ 定时任务状态")
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cols = st.columns(4)
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cols[0].metric("运行状态", "运行中" if scheduler.get('running') else "已停止")
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next_runs = scheduler.get('next_run_times', {})
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cols[1].caption(f"热点同步: {next_runs.get('sync_hotspots', 'N/A')[:16] if next_runs.get('sync_hotspots') else 'N/A'}")
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cols[2].caption(f"预警生成: {next_runs.get('generate_alerts', 'N/A')[:16] if next_runs.get('generate_alerts') else 'N/A'}")
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cols[3].caption(f"深度分析: {next_runs.get('deep_analysis', 'N/A')[:16] if next_runs.get('deep_analysis') else 'N/A'}")
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def display_wordcloud_and_top_news():
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"""显示热点词云和跨平台TOP100新闻"""
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# 尝试获取最新数据
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try:
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from news_flow_db import news_flow_db
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from news_flow_data import NewsFlowDataFetcher
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# 获取最近的快照
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recent_snapshots = news_flow_db.get_recent_snapshots(limit=1)
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if not recent_snapshots:
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st.info("暂无数据，请先执行实时监测")
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return
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snapshot_id = recent_snapshots[0]['id']
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detail = news_flow_db.get_snapshot_detail(snapshot_id)
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hot_topics = detail.get('hot_topics', [])
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except Exception as e:
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st.warning(f"获取数据失败: {e}")
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return
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# 两列布局
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col1, col2 = st.columns([1, 1])
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with col1:
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st.markdown("#### ☁️ 热点词云")
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if hot_topics:
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# 使用Plotly生成词云效果（气泡图模拟）
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import random
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# 准备数据
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words_data = []
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for i, topic in enumerate(hot_topics[:30]):
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words_data.append({
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'word': topic.get('topic', '')[:10],
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'heat': topic.get('heat', 10),
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'x': random.uniform(0, 100),
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'y': random.uniform(0, 100),
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})
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if words_data:
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df_words = pd.DataFrame(words_data)
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# 创建气泡图模拟词云
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fig = px.scatter(
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df_words,
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x='x',
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y='y',
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size='heat',
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text='word',
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color='heat',
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color_continuous_scale='YlOrRd',
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size_max=60
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)
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fig.update_traces(
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textposition='middle center',
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textfont=dict(size=12),
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marker=dict(opacity=0.7)
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)
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fig.update_layout(
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height=350,
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showlegend=False,
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xaxis=dict(visible=False),
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yaxis=dict(visible=False),
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margin=dict(l=0, r=0, t=0, b=0),
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coloraxis_showscale=False
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)
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st.plotly_chart(fig, width='stretch', key="wordcloud_chart")
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else:
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st.info("暂无热点词云数据")
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with col2:
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st.markdown("#### 📊 热点话题TOP20")
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if hot_topics:
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for i, topic in enumerate(hot_topics[:20], 1):
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heat = topic.get('heat', 0)
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cross = topic.get('cross_platform', 0)
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# 热度条
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heat_pct = min(heat, 100)
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st.markdown(f"**{i}. {topic.get('topic', '')[:15]}**")
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st.progress(heat_pct / 100)
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st.caption(f"热度: {heat} | 跨{cross}个平台")
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else:
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st.info("暂无热点话题")
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st.divider()
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# 跨平台热点新闻TOP100
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st.markdown("#### 📰 跨平台热点新闻TOP100")
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try:
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# 获取所有平台新闻
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fetcher = NewsFlowDataFetcher()
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multi_result = fetcher.get_multi_platform_news()
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if multi_result.get('success'):
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# 汇总所有新闻
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all_news = []
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for platform_data in multi_result.get('platforms_data', []):
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if platform_data.get('success'):
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platform_name = platform_data.get('platform_name', '')
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category = platform_data.get('category', '')
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weight = platform_data.get('weight', 5)
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for news in platform_data.get('data', []):
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rank = news.get('rank', 99)
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# 计算综合得分
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score = (100 - rank) * weight
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all_news.append({
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'排名': len(all_news) + 1,
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'平台': platform_name,
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'类别': {'finance': '财经', 'social': '社交', 'news': '新闻', 'tech': '科技'}.get(category, '其他'),
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'标题': (news.get('title') or '')[:40],
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'平台排名': rank,
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'综合分': score,
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})
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# 按综合分排序
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all_news.sort(key=lambda x: x['综合分'], reverse=True)
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# 取TOP100
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top_100 = all_news[:100]
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# 重新编排名
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for i, news in enumerate(top_100, 1):
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news['排名'] = i
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if top_100:
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df_news = pd.DataFrame(top_100)
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# 添加筛选
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filter_col1, filter_col2 = st.columns([1, 3])
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with filter_col1:
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category_filter = st.selectbox(
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"按类别筛选",
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["全部", "财经", "社交", "新闻", "科技"],
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key="news_category_filter"
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)
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if category_filter != "全部":
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df_news = df_news[df_news['类别'] == category_filter]
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st.dataframe(
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df_news[['排名', '平台', '类别', '标题', '综合分']],
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width='stretch',
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hide_index=True,
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height=400
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)
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st.caption(f"共 {len(df_news)} 条新闻 | 数据来源: {multi_result.get('success_count', 0)} 个平台")
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else:
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st.info("暂无新闻数据")
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else:
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st.warning("获取新闻数据失败")
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except Exception as e:
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st.error(f"加载新闻失败: {e}")
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def display_realtime_monitor():
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"""显示实时监测"""
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st.subheader("🔥 实时监测")
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# 监测参数
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col1, col2, col3 = st.columns([1, 1, 1])
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with col1:
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category = st.selectbox(
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"平台类别",
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["全部平台", "财经平台", "社交媒体", "新闻媒体", "科技媒体"],
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key="monitor_category"
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)
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category_map = {
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"全部平台": None,
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"财经平台": "finance",
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"社交媒体": "social",
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"新闻媒体": "news",
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"科技媒体": "tech",
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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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run_btn = st.button("🚀 开始AI智能分析", type="primary", width='stretch')
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with col3:
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st.write("")
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st.write("")
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# 空占位
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if run_btn:
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with st.spinner("🤖 AI正在分析全网热点新闻..."):
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try:
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from news_flow_engine import news_flow_engine
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cat = category_map.get(category)
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# 显示分析进度
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progress_bar = st.progress(0)
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status_text = st.empty()
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status_text.text("📊 获取多平台新闻数据...")
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progress_bar.progress(10)
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result = news_flow_engine.run_full_analysis(category=cat, include_ai=True)
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progress_bar.progress(100)
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status_text.empty()
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progress_bar.empty()
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if result['success']:
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st.session_state['news_flow_result'] = result
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# 清除之前的PDF缓存
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if 'news_flow_pdf_data' in st.session_state:
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del st.session_state['news_flow_pdf_data']
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st.success(f"✅ AI分析完成！耗时 {result.get('duration', 0):.1f} 秒")
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else:
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st.error(f"❌ 分析失败: {result.get('error')}")
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except Exception as e:
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st.error(f"❌ 分析异常: {e}")
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# 显示结果
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if 'news_flow_result' in st.session_state:
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display_analysis_results(st.session_state['news_flow_result'])
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def display_analysis_results(result: dict):
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"""显示分析结果"""
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st.divider()
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flow_data = result.get('flow_data', {})
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model_data = result.get('model_data', {})
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sentiment_data = result.get('sentiment_data', {})
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ai_analysis = result.get('ai_analysis')
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trading_signals = result.get('trading_signals', {})
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# 核心指标
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st.markdown("### 📊 核心指标")
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cols = st.columns(5)
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with cols[0]:
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score = flow_data.get('total_score', 0)
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level = flow_data.get('level', '中')
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level_color = {'极高': '🔴', '高': '🟠', '中': '🟡', '低': '🔵'}.get(level, '⚪')
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st.metric("流量得分", f"{score}", delta=f"{level_color} {level}")
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with cols[1]:
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if sentiment_data:
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sent = sentiment_data.get('sentiment', {})
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st.metric("情绪指数", f"{sent.get('sentiment_index', 50)}",
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delta=sent.get('sentiment_class', '中性'))
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with cols[2]:
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if sentiment_data:
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stage = sentiment_data.get('flow_stage', {})
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signal = stage.get('signal', '观察')
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st.metric("流量阶段", stage.get('stage_name', '未知'), delta=signal)
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with cols[3]:
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if model_data:
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viral = model_data.get('viral_k', {})
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st.metric("K值", f"{viral.get('k_value', 1.0)}", delta=viral.get('trend', ''))
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with cols[4]:
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signal = trading_signals.get('overall_signal', '观望')
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conf = trading_signals.get('confidence', 50)
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signal_color = {'卖出': '🔴', '观望': '🟡', '买入': '🟢', '关注': '🔵'}.get(signal, '⚪')
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st.metric("交易信号", f"{signal_color} {signal}", delta=f"置信度:{conf}%")
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# 关键提示
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key_msg = trading_signals.get('key_message', '')
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if key_msg:
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if '危险' in key_msg or '逃命' in key_msg:
|
|
st.error(f"⚠️ {key_msg}")
|
|
elif '加速' in key_msg or '买入' in key_msg:
|
|
st.success(f"💡 {key_msg}")
|
|
else:
|
|
st.info(f"💡 {key_msg}")
|
|
|
|
st.divider()
|
|
|
|
# 详细分析（两列）
|
|
left_col, right_col = st.columns(2)
|
|
|
|
with left_col:
|
|
# 流量模型
|
|
st.markdown("#### 🔬 流量模型分析")
|
|
|
|
if model_data:
|
|
# 潜力计算
|
|
potential = model_data.get('potential', {})
|
|
conversion = model_data.get('conversion', {})
|
|
|
|
st.markdown(f"""
|
|
- **接盘潜力**: {potential.get('potential_volume', 0):.1f}亿元 ({potential.get('potential_level', '未知')})
|
|
- **转化率**: {conversion.get('conversion_rate', 0):.4%}
|
|
- **流量类型**: {model_data.get('flow_type', {}).get('flow_type', '未知')}
|
|
""")
|
|
|
|
# 流量类型详情
|
|
flow_type = model_data.get('flow_type', {})
|
|
if flow_type.get('characteristics'):
|
|
st.caption(f"特征: {', '.join(flow_type.get('characteristics', [])[:2])}")
|
|
st.caption(f"时间窗口: {flow_type.get('time_window', 'N/A')}")
|
|
|
|
# 情绪分析
|
|
st.markdown("#### 💭 情绪分析")
|
|
|
|
if sentiment_data:
|
|
sentiment = sentiment_data.get('sentiment', {})
|
|
flow_stage = sentiment_data.get('flow_stage', {})
|
|
momentum = sentiment_data.get('momentum', {})
|
|
|
|
st.markdown(f"""
|
|
- **情绪分类**: {sentiment.get('sentiment_class', '中性')} ({sentiment.get('sentiment_index', 50)}分)
|
|
- **流量阶段**: {flow_stage.get('stage_name', '未知')} - {flow_stage.get('signal', '观察')}
|
|
- **情绪动量**: {momentum.get('momentum', 1.0)} ({momentum.get('trend', '稳定')})
|
|
- **风险等级**: {sentiment_data.get('risk_level', '中等')}
|
|
""")
|
|
|
|
st.caption(sentiment_data.get('advice', ''))
|
|
|
|
with right_col:
|
|
# AI分析（如果有）
|
|
if ai_analysis:
|
|
st.markdown("#### 🤖 AI智能分析")
|
|
|
|
# 投资建议
|
|
advice = ai_analysis.get('investment_advice', {})
|
|
if advice:
|
|
advice_emoji = {'买入': '🟢', '持有': '🔵', '观望': '🟡', '回避': '🔴'}.get(advice.get('advice', '观望'), '⚪')
|
|
st.markdown(f"**{advice_emoji} AI建议**: {advice.get('advice', '观望')} (置信度: {advice.get('confidence', 50)}%)")
|
|
|
|
key_msg = advice.get('key_message', '')
|
|
if key_msg:
|
|
st.info(f"💡 {key_msg}")
|
|
|
|
if advice.get('action_plan'):
|
|
with st.expander("📋 行动计划"):
|
|
for i, plan in enumerate(advice.get('action_plan', [])[:5], 1):
|
|
st.write(f"{i}. {plan}")
|
|
|
|
# 风险评估
|
|
risk = ai_analysis.get('risk_assess', {})
|
|
if risk:
|
|
risk_color = {'低': '🟢', '中': '🟡', '高': '🔴'}.get(risk.get('risk_level', '中'), '🟡')
|
|
st.markdown(f"**{risk_color} 风险等级**: {risk.get('risk_level', '未知')} (分数: {risk.get('risk_score', 50)}/100)")
|
|
|
|
else:
|
|
st.info("未运行AI分析。选择'完整分析（含AI）'模式获取AI智能分析。")
|
|
|
|
st.divider()
|
|
|
|
# AI详细分析区域（如果有AI分析）
|
|
if ai_analysis:
|
|
st.markdown("### 📊 AI深度分析报告")
|
|
|
|
# 热门题材
|
|
sector_analysis = ai_analysis.get('sector_analysis', {})
|
|
hot_themes = sector_analysis.get('hot_themes', [])
|
|
|
|
if hot_themes:
|
|
st.markdown("#### 🔥 今日热门题材")
|
|
theme_cols = st.columns(min(len(hot_themes), 4))
|
|
for i, theme in enumerate(hot_themes[:4]):
|
|
with theme_cols[i]:
|
|
heat_emoji = {'极高': '🔴', '高': '🟠', '中': '🟡'}.get(theme.get('heat_level', '中'), '🟡')
|
|
st.metric(
|
|
theme.get('theme', '未知'),
|
|
f"{heat_emoji} {theme.get('heat_level', '中')}",
|
|
delta=theme.get('sustainability', '')
|
|
)
|
|
st.caption(f"题材来源: {', '.join([t.get('source', '')[:15] for t in hot_themes[:3]])}")
|
|
|
|
# 受益板块详细分析
|
|
benefited_sectors = sector_analysis.get('benefited_sectors', [])
|
|
if benefited_sectors:
|
|
st.markdown("#### 📈 受益板块分析")
|
|
for sector in benefited_sectors[:5]:
|
|
with st.expander(f"**{sector.get('name', '')}** - 置信度 {sector.get('confidence', 0)}%"):
|
|
st.write(f"**分析**: {sector.get('reason', '')}")
|
|
if sector.get('related_concepts'):
|
|
st.write(f"**相关概念**: {', '.join(sector.get('related_concepts', []))}")
|
|
if sector.get('leader_characteristics'):
|
|
st.write(f"**龙头特征**: {sector.get('leader_characteristics', '')}")
|
|
|
|
# 多板块深度分析（多次AI调用结果）
|
|
multi_sector = ai_analysis.get('multi_sector', {})
|
|
sector_analyses = multi_sector.get('sector_analyses', [])
|
|
|
|
if sector_analyses:
|
|
st.markdown("#### 🎯 板块深度分析（多Agent分析）")
|
|
st.caption(f"共分析 {len(sector_analyses)} 个热门板块，耗时 {multi_sector.get('analysis_time', 0)} 秒")
|
|
|
|
# 板块核心指标卡片
|
|
sector_cols = st.columns(min(len(sector_analyses), 4))
|
|
for i, sector in enumerate(sector_analyses[:4]):
|
|
with sector_cols[i]:
|
|
heat_emoji = {'极高': '🔴', '高': '🟠', '中': '🟡', '低': '🔵'}.get(
|
|
sector.get('heat_level', '中'), '🟡'
|
|
)
|
|
outlook_emoji = {'看涨': '📈', '震荡': '📊', '看跌': '📉'}.get(
|
|
sector.get('short_term_outlook', '震荡'), '📊'
|
|
)
|
|
|
|
st.metric(
|
|
sector.get('sector_name', '未知'),
|
|
f"{heat_emoji} 热度{sector.get('heat_score', 50)}",
|
|
delta=f"{outlook_emoji} {sector.get('short_term_outlook', '震荡')}"
|
|
)
|
|
|
|
# 显示关键指标
|
|
indicators = sector.get('key_indicators', {})
|
|
if indicators:
|
|
indicator_text = ' | '.join([f"{k}:{v}" for k, v in list(indicators.items())[:3]])
|
|
st.caption(indicator_text)
|
|
|
|
# 详细分析展开
|
|
for sector in sector_analyses:
|
|
with st.expander(f"📊 {sector.get('sector_name', '')} - 详细分析"):
|
|
# 驱动因素
|
|
drivers = sector.get('drivers', [])
|
|
if drivers:
|
|
st.markdown("**驱动因素**:")
|
|
for driver in drivers[:3]:
|
|
impact_emoji = '✅' if driver.get('impact') == '正面' else '❌'
|
|
st.write(f"- {impact_emoji} [{driver.get('type', '')}] {driver.get('content', '')}")
|
|
|
|
# 预判理由
|
|
st.markdown(f"**短期预判**: {sector.get('short_term_outlook', '震荡')} - {sector.get('outlook_reason', '')}")
|
|
|
|
# 龙头股
|
|
leaders = sector.get('leader_stocks', [])
|
|
if leaders:
|
|
st.markdown("**板块龙头股**:")
|
|
leader_data = []
|
|
for stock in leaders[:5]:
|
|
leader_data.append({
|
|
'代码': stock.get('code', ''),
|
|
'名称': stock.get('name', ''),
|
|
'理由': stock.get('reason', '')[:30],
|
|
'策略': stock.get('strategy', '')[:30],
|
|
})
|
|
if leader_data:
|
|
st.dataframe(pd.DataFrame(leader_data), width='stretch', hide_index=True)
|
|
|
|
# 投资建议
|
|
st.info(f"💡 {sector.get('investment_advice', '')}")
|
|
st.warning(f"⚠️ {sector.get('risk_warning', '')}")
|
|
|
|
# 股票推荐
|
|
stock_recommend = ai_analysis.get('stock_recommend', {})
|
|
recommended_stocks = stock_recommend.get('recommended_stocks', [])
|
|
|
|
if recommended_stocks:
|
|
st.markdown("#### 💰 AI选股推荐")
|
|
st.warning("⚠️ 以下为AI分析结果，仅供参考，不构成投资建议。股市有风险，投资需谨慎！")
|
|
|
|
# 用表格展示推荐股票
|
|
stock_data = []
|
|
for stock in recommended_stocks[:8]:
|
|
stock_data.append({
|
|
'代码': stock.get('code', ''),
|
|
'名称': stock.get('name', ''),
|
|
'板块': stock.get('sector', ''),
|
|
'风险': stock.get('risk_level', '中'),
|
|
'目标空间': stock.get('target_space', '-'),
|
|
'推荐理由': stock.get('reason', '')[:30] + '...' if len(stock.get('reason', '')) > 30 else stock.get('reason', ''),
|
|
})
|
|
|
|
if stock_data:
|
|
df_stocks = pd.DataFrame(stock_data)
|
|
st.dataframe(df_stocks, width='stretch', hide_index=True)
|
|
|
|
# 展开查看详情
|
|
with st.expander("📋 查看详细推荐理由"):
|
|
for stock in recommended_stocks[:8]:
|
|
st.markdown(f"**{stock.get('code', '')} {stock.get('name', '')}**")
|
|
st.write(f"- 板块: {stock.get('sector', '')}")
|
|
st.write(f"- 理由: {stock.get('reason', '')}")
|
|
st.write(f"- 催化剂: {stock.get('catalyst', '')}")
|
|
st.write(f"- 策略: {stock.get('strategy', '')}")
|
|
if stock.get('attention_points'):
|
|
st.write(f"- 注意: {', '.join(stock.get('attention_points', []))}")
|
|
st.divider()
|
|
|
|
# 整体策略
|
|
if stock_recommend.get('overall_strategy'):
|
|
st.info(f"📊 **整体策略**: {stock_recommend.get('overall_strategy', '')}")
|
|
if stock_recommend.get('timing_advice'):
|
|
st.caption(f"⏰ 时机建议: {stock_recommend.get('timing_advice', '')}")
|
|
if stock_recommend.get('risk_warning'):
|
|
st.error(f"⚠️ {stock_recommend.get('risk_warning', '')}")
|
|
|
|
# 机会评估
|
|
opportunity = sector_analysis.get('opportunity_assessment', '')
|
|
trading_suggestion = sector_analysis.get('trading_suggestion', '')
|
|
if opportunity or trading_suggestion:
|
|
st.markdown("#### 💡 综合评估")
|
|
if opportunity:
|
|
st.write(opportunity)
|
|
if trading_suggestion:
|
|
st.success(f"📌 {trading_suggestion}")
|
|
|
|
st.divider()
|
|
|
|
# 热门话题和新闻
|
|
col1, col2 = st.columns(2)
|
|
|
|
with col1:
|
|
st.markdown("#### 🔥 热门话题TOP10")
|
|
hot_topics = result.get('hot_topics', [])[:10]
|
|
|
|
if hot_topics:
|
|
df = pd.DataFrame([{
|
|
'话题': t['topic'],
|
|
'热度': t['heat'],
|
|
'跨平台': t.get('cross_platform', 0),
|
|
} for t in hot_topics])
|
|
|
|
fig = px.bar(df, x='热度', y='话题', orientation='h',
|
|
color='热度', color_continuous_scale='Oranges')
|
|
fig.update_layout(height=300, margin=dict(l=0, r=0, t=0, b=0))
|
|
st.plotly_chart(fig, width='stretch')
|
|
|
|
with col2:
|
|
st.markdown("#### 📰 股票相关新闻TOP5")
|
|
stock_news = result.get('stock_news', [])[:5]
|
|
|
|
if stock_news:
|
|
for news in stock_news:
|
|
st.markdown(f"**[{news.get('platform_name', '')}]** {news.get('title', '')[:40]}...")
|
|
st.caption(f"关键词: {', '.join(news.get('matched_keywords', [])[:3])}")
|
|
else:
|
|
st.info("暂无股票相关新闻")
|
|
|
|
# PDF导出区域
|
|
display_pdf_export_section(result)
|
|
|
|
|
|
def display_pdf_export_section(result: dict):
|
|
"""显示PDF导出区域"""
|
|
st.divider()
|
|
st.markdown("### 📄 导出报告")
|
|
|
|
col1, col2, col3 = st.columns([3, 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", key="gen_pdf_btn"):
|
|
with st.spinner("正在生成PDF报告..."):
|
|
try:
|
|
from news_flow_pdf import NewsFlowPDFGenerator
|
|
generator = NewsFlowPDFGenerator()
|
|
pdf_path = generator.generate_report(result)
|
|
|
|
if pdf_path:
|
|
with open(pdf_path, "rb") as f:
|
|
pdf_bytes = f.read()
|
|
|
|
# 保存到session_state
|
|
st.session_state.news_flow_pdf_data = pdf_bytes
|
|
st.session_state.news_flow_pdf_filename = f"新闻流量分析报告_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pdf"
|
|
|
|
st.success("✅ PDF报告生成成功！")
|
|
st.rerun()
|
|
else:
|
|
st.error("❌ PDF生成失败")
|
|
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 'news_flow_pdf_data' in st.session_state:
|
|
st.download_button(
|
|
label="💾 下载PDF",
|
|
data=st.session_state.news_flow_pdf_data,
|
|
file_name=st.session_state.news_flow_pdf_filename,
|
|
mime="application/pdf",
|
|
key="download_pdf_btn"
|
|
)
|
|
|
|
|
|
def display_alert_center():
|
|
"""显示预警中心"""
|
|
st.subheader("⚠️ 预警中心")
|
|
|
|
try:
|
|
from news_flow_alert import news_flow_alert_system
|
|
from news_flow_db import news_flow_db
|
|
except Exception as e:
|
|
st.error(f"模块加载失败: {e}")
|
|
return
|
|
|
|
# 预警统计
|
|
summary = news_flow_alert_system.get_alert_summary(days=7)
|
|
|
|
cols = st.columns(4)
|
|
cols[0].metric("总预警数", summary.get('total_count', 0))
|
|
cols[1].metric("危险预警", summary.get('danger_count', 0))
|
|
cols[2].metric("警告预警", summary.get('warning_count', 0))
|
|
cols[3].metric("提示预警", summary.get('info_count', 0))
|
|
|
|
st.divider()
|
|
|
|
# 预警列表
|
|
col1, col2 = st.columns([3, 1])
|
|
|
|
with col2:
|
|
days = st.selectbox("时间范围", [1, 3, 7, 30], index=2, key="alert_days")
|
|
alert_type = st.selectbox("预警类型", ["全部", "热度飙升", "流量高潮", "情绪极值", "病毒传播", "流量退潮"], key="alert_type_filter")
|
|
|
|
type_map = {
|
|
"热度飙升": "heat_surge",
|
|
"流量高潮": "flow_peak",
|
|
"情绪极值": "sentiment_extreme",
|
|
"病毒传播": "viral_spread",
|
|
"流量退潮": "flow_decline",
|
|
}
|
|
|
|
filter_type = type_map.get(alert_type) if alert_type != "全部" else None
|
|
alerts = news_flow_alert_system.get_alert_history(days=days, alert_type=filter_type)
|
|
|
|
with col1:
|
|
if alerts:
|
|
for alert in alerts[:20]:
|
|
level = alert.get('alert_level', 'info')
|
|
icon = {'danger': '🔴', 'warning': '🟠', 'info': '🔵'}.get(level, '⚪')
|
|
level_name = {'danger': '危险', 'warning': '警告', 'info': '提示'}.get(level, '未知')
|
|
|
|
with st.expander(f"{icon} [{level_name}] {alert.get('title', '')[:40]}..."):
|
|
st.markdown(alert.get('content', ''))
|
|
st.caption(f"时间: {alert.get('created_at', '')} | 类型: {alert.get('alert_type', '')}")
|
|
else:
|
|
st.info("暂无预警记录")
|
|
|
|
st.divider()
|
|
|
|
# 预警配置
|
|
st.markdown("#### ⚙️ 预警阈值配置")
|
|
|
|
config = news_flow_alert_system.get_threshold_config()
|
|
|
|
col1, col2, col3 = st.columns(3)
|
|
|
|
with col1:
|
|
heat_th = st.number_input("热度阈值", value=int(config.get('heat_threshold', 800)),
|
|
min_value=100, max_value=1000, key="heat_th")
|
|
k_th = st.number_input("K值阈值", value=float(config.get('viral_k_threshold', 1.5)),
|
|
min_value=1.0, max_value=3.0, step=0.1, key="k_th")
|
|
|
|
with col2:
|
|
sent_high = st.number_input("情绪高位阈值", value=int(config.get('sentiment_high_threshold', 90)),
|
|
min_value=70, max_value=100, key="sent_high")
|
|
sent_low = st.number_input("情绪低位阈值", value=int(config.get('sentiment_low_threshold', 20)),
|
|
min_value=0, max_value=30, key="sent_low")
|
|
|
|
with col3:
|
|
rank_th = st.number_input("排名变化阈值", value=int(config.get('rank_change_threshold', 10)),
|
|
min_value=5, max_value=50, key="rank_th")
|
|
|
|
if st.button("保存配置", key="save_alert_config"):
|
|
news_flow_alert_system.set_threshold('heat_threshold', heat_th)
|
|
news_flow_alert_system.set_threshold('viral_k_threshold', k_th)
|
|
news_flow_alert_system.set_threshold('sentiment_high_threshold', sent_high)
|
|
news_flow_alert_system.set_threshold('sentiment_low_threshold', sent_low)
|
|
news_flow_alert_system.set_threshold('rank_change_threshold', rank_th)
|
|
st.success("配置已保存")
|
|
|
|
|
|
def display_trend_analysis():
|
|
"""显示趋势分析"""
|
|
st.subheader("📈 趋势分析")
|
|
|
|
try:
|
|
from news_flow_engine import news_flow_engine
|
|
from news_flow_db import news_flow_db
|
|
except Exception as e:
|
|
st.error(f"模块加载失败: {e}")
|
|
return
|
|
|
|
# 时间范围选择
|
|
days = st.slider("分析天数", min_value=3, max_value=30, value=7, key="trend_days")
|
|
|
|
# 流量趋势
|
|
trend = news_flow_engine.get_flow_trend(days=days)
|
|
|
|
col1, col2 = st.columns([2, 1])
|
|
|
|
with col1:
|
|
st.markdown("#### 流量趋势图")
|
|
|
|
if trend.get('dates'):
|
|
fig = go.Figure()
|
|
|
|
# 平均得分
|
|
fig.add_trace(go.Scatter(
|
|
x=trend['dates'],
|
|
y=trend['avg_scores'],
|
|
mode='lines+markers',
|
|
name='平均得分',
|
|
line=dict(color='#1f77b4', width=2),
|
|
fill='tozeroy',
|
|
fillcolor='rgba(31, 119, 180, 0.1)'
|
|
))
|
|
|
|
# 最高得分
|
|
fig.add_trace(go.Scatter(
|
|
x=trend['dates'],
|
|
y=trend['max_scores'],
|
|
mode='lines',
|
|
name='最高得分',
|
|
line=dict(color='#ff7f0e', dash='dash')
|
|
))
|
|
|
|
# 最低得分
|
|
fig.add_trace(go.Scatter(
|
|
x=trend['dates'],
|
|
y=trend.get('min_scores', []),
|
|
mode='lines',
|
|
name='最低得分',
|
|
line=dict(color='#2ca02c', dash='dot')
|
|
))
|
|
|
|
fig.update_layout(
|
|
height=350,
|
|
margin=dict(l=0, r=0, t=20, b=0),
|
|
legend=dict(orientation="h", yanchor="bottom", y=1.02),
|
|
yaxis_title="流量得分"
|
|
)
|
|
st.plotly_chart(fig, width='stretch')
|
|
else:
|
|
st.info("暂无趋势数据")
|
|
|
|
with col2:
|
|
st.markdown("#### 趋势分析")
|
|
st.markdown(f"**趋势方向**: {trend.get('trend', '无数据')}")
|
|
st.markdown(trend.get('analysis', '暂无分析'))
|
|
|
|
st.divider()
|
|
|
|
# 情绪趋势
|
|
st.markdown("#### 情绪趋势")
|
|
|
|
sentiments = news_flow_db.get_sentiment_history(limit=days * 3)
|
|
|
|
if sentiments:
|
|
df = pd.DataFrame([{
|
|
'时间': s.get('fetch_time', s.get('created_at', ''))[:16],
|
|
'情绪指数': s.get('sentiment_index', 50),
|
|
'K值': s.get('viral_k', 1.0),
|
|
'阶段': s.get('flow_stage', '未知'),
|
|
} for s in sentiments])
|
|
|
|
if not df.empty:
|
|
df = df.sort_values('时间')
|
|
|
|
fig = go.Figure()
|
|
|
|
fig.add_trace(go.Scatter(
|
|
x=df['时间'],
|
|
y=df['情绪指数'],
|
|
mode='lines+markers',
|
|
name='情绪指数',
|
|
line=dict(color='#9467bd', width=2)
|
|
))
|
|
|
|
# 添加阈值线
|
|
fig.add_hline(y=80, line_dash="dash", line_color="red",
|
|
annotation_text="乐观区", annotation_position="right")
|
|
fig.add_hline(y=20, line_dash="dash", line_color="green",
|
|
annotation_text="悲观区", annotation_position="right")
|
|
|
|
fig.update_layout(
|
|
height=250,
|
|
margin=dict(l=0, r=0, t=20, b=0),
|
|
yaxis_title="情绪指数"
|
|
)
|
|
st.plotly_chart(fig, width='stretch')
|
|
else:
|
|
st.info("暂无情绪数据")
|
|
|
|
st.divider()
|
|
|
|
# 每日统计表格
|
|
st.markdown("#### 每日统计")
|
|
|
|
stats = news_flow_db.get_daily_statistics(days)
|
|
|
|
if stats:
|
|
df = pd.DataFrame([{
|
|
'日期': s['date'],
|
|
'平均得分': s['avg_score'],
|
|
'最高得分': s['max_score'],
|
|
'最低得分': s['min_score'],
|
|
'采集次数': s['snapshot_count'],
|
|
'热门话题': ', '.join(s.get('top_topics', [])[:3]),
|
|
} for s in stats])
|
|
|
|
st.dataframe(df, width='stretch', hide_index=True)
|
|
else:
|
|
st.info("暂无统计数据")
|
|
|
|
|
|
def display_history_records():
|
|
"""显示历史记录"""
|
|
st.subheader("📚 历史记录")
|
|
|
|
try:
|
|
from news_flow_db import news_flow_db
|
|
except Exception as e:
|
|
st.error(f"模块加载失败: {e}")
|
|
return
|
|
|
|
# 获取历史快照
|
|
snapshots = news_flow_db.get_history_snapshots(limit=50)
|
|
|
|
if not snapshots:
|
|
st.info("暂无历史记录，请先执行监测")
|
|
return
|
|
|
|
# 快照列表
|
|
st.markdown("#### 分析报告列表")
|
|
|
|
for snapshot in snapshots[:20]:
|
|
score = snapshot.get('total_score', 0)
|
|
level = snapshot.get('flow_level', '中')
|
|
level_icon = {'极高': '🔴', '高': '🟠', '中': '🟡', '低': '🔵'}.get(level, '⚪')
|
|
|
|
with st.expander(f"{level_icon} {snapshot.get('fetch_time', '')} - 流量得分: {score} ({level})"):
|
|
# 获取详情
|
|
detail = news_flow_db.get_snapshot_detail(snapshot['id'])
|
|
|
|
if detail:
|
|
col1, col2 = st.columns(2)
|
|
|
|
with col1:
|
|
st.markdown("**基本信息**")
|
|
st.markdown(f"- 流量得分: {score}")
|
|
st.markdown(f"- 流量等级: {level}")
|
|
st.markdown(f"- 平台数: {snapshot.get('total_platforms', 0)}")
|
|
st.markdown(f"- 成功数: {snapshot.get('success_count', 0)}")
|
|
|
|
# 情绪信息
|
|
sentiment = detail.get('sentiment')
|
|
if sentiment:
|
|
st.markdown("**情绪分析**")
|
|
st.markdown(f"- 情绪指数: {sentiment.get('sentiment_index', 'N/A')}")
|
|
st.markdown(f"- 情绪分类: {sentiment.get('sentiment_class', 'N/A')}")
|
|
st.markdown(f"- 流量阶段: {sentiment.get('flow_stage', 'N/A')}")
|
|
|
|
with col2:
|
|
# AI分析
|
|
ai = detail.get('ai_analysis')
|
|
if ai:
|
|
st.markdown("**AI分析结果**")
|
|
st.markdown(f"- 投资建议: {ai.get('advice', 'N/A')}")
|
|
st.markdown(f"- 置信度: {ai.get('confidence', 'N/A')}%")
|
|
st.markdown(f"- 风险等级: {ai.get('risk_level', 'N/A')}")
|
|
|
|
if ai.get('summary'):
|
|
st.caption(ai['summary'][:100] + "...")
|
|
|
|
# 热门话题
|
|
topics = detail.get('hot_topics', [])[:5]
|
|
if topics:
|
|
st.markdown("**热门话题**")
|
|
for t in topics:
|
|
st.markdown(f"- {t['topic']} (热度:{t['heat']})")
|
|
|
|
st.divider()
|
|
|
|
# AI分析历史
|
|
st.markdown("#### AI分析历史")
|
|
|
|
ai_history = news_flow_db.get_ai_analysis_history(limit=10)
|
|
|
|
if ai_history:
|
|
df = pd.DataFrame([{
|
|
'时间': a.get('created_at', '')[:16],
|
|
'建议': a.get('advice', 'N/A'),
|
|
'置信度': f"{a.get('confidence', 0)}%",
|
|
'风险': a.get('risk_level', 'N/A'),
|
|
'摘要': (a.get('summary', '') or '')[:30] + "...",
|
|
} for a in ai_history])
|
|
|
|
st.dataframe(df, width='stretch', hide_index=True)
|
|
else:
|
|
st.info("暂无AI分析记录")
|
|
|
|
|
|
def display_settings():
|
|
"""显示设置"""
|
|
st.subheader("⚙️ 设置")
|
|
|
|
# 平台配置
|
|
st.markdown("#### 📡 支持的平台")
|
|
|
|
try:
|
|
from news_flow_data import NewsFlowDataFetcher
|
|
fetcher = NewsFlowDataFetcher()
|
|
platforms = fetcher.get_platform_list()
|
|
|
|
# 按类别分组
|
|
categories = {}
|
|
for p in platforms:
|
|
cat = p['category']
|
|
if cat not in categories:
|
|
categories[cat] = []
|
|
categories[cat].append(p)
|
|
|
|
cat_names = {
|
|
'social': '社交媒体',
|
|
'news': '新闻媒体',
|
|
'finance': '财经平台',
|
|
'tech': '科技媒体',
|
|
}
|
|
|
|
cols = st.columns(len(categories))
|
|
for i, (cat, items) in enumerate(categories.items()):
|
|
with cols[i]:
|
|
st.markdown(f"**{cat_names.get(cat, cat)}** ({len(items)}个)")
|
|
for p in items:
|
|
st.caption(f"- {p['name']} (权重:{p['weight']})")
|
|
except:
|
|
st.warning("无法加载平台列表")
|
|
|
|
st.divider()
|
|
|
|
# 定时任务配置
|
|
st.markdown("#### ⏰ 定时任务管理")
|
|
|
|
try:
|
|
from news_flow_scheduler import news_flow_scheduler
|
|
|
|
status = news_flow_scheduler.get_status()
|
|
|
|
col1, col2 = st.columns([1, 2])
|
|
|
|
with col1:
|
|
st.markdown(f"**运行状态**: {'✅ 运行中' if status.get('running') else '⏸️ 已停止'}")
|
|
|
|
if status.get('running'):
|
|
if st.button("停止调度器", key="stop_scheduler"):
|
|
news_flow_scheduler.stop()
|
|
st.success("调度器已停止")
|
|
st.rerun()
|
|
else:
|
|
if st.button("启动调度器", key="start_scheduler"):
|
|
news_flow_scheduler.start()
|
|
st.success("调度器已启动")
|
|
st.rerun()
|
|
|
|
with col2:
|
|
st.markdown("**任务配置**")
|
|
|
|
intervals = status.get('task_intervals', {})
|
|
enabled = status.get('task_enabled', {})
|
|
|
|
tasks = [
|
|
('sync_hotspots', '热点同步', 30),
|
|
('generate_alerts', '预警生成', 60),
|
|
('deep_analysis', '深度分析', 120),
|
|
]
|
|
|
|
for task_id, task_name, default_interval in tasks:
|
|
col_a, col_b, col_c = st.columns([2, 2, 1])
|
|
|
|
with col_a:
|
|
is_enabled = st.checkbox(
|
|
task_name,
|
|
value=enabled.get(task_id, True),
|
|
key=f"task_enabled_{task_id}"
|
|
)
|
|
|
|
with col_b:
|
|
interval = st.number_input(
|
|
"间隔(分钟)",
|
|
value=intervals.get(task_id, default_interval),
|
|
min_value=5,
|
|
max_value=240,
|
|
key=f"task_interval_{task_id}",
|
|
label_visibility="collapsed"
|
|
)
|
|
|
|
with col_c:
|
|
if st.button("立即执行", key=f"run_{task_id}"):
|
|
if task_id == 'sync_hotspots':
|
|
news_flow_scheduler.run_sync_now()
|
|
elif task_id == 'generate_alerts':
|
|
news_flow_scheduler.run_alerts_now()
|
|
else:
|
|
news_flow_scheduler.run_analysis_now()
|
|
st.success(f"{task_name} 已触发")
|
|
|
|
if st.button("保存任务配置", key="save_task_config"):
|
|
for task_id, _, _ in tasks:
|
|
news_flow_scheduler.set_task_enabled(
|
|
task_id,
|
|
st.session_state.get(f"task_enabled_{task_id}", True)
|
|
)
|
|
news_flow_scheduler.set_task_interval(
|
|
task_id,
|
|
st.session_state.get(f"task_interval_{task_id}", 30)
|
|
)
|
|
st.success("任务配置已保存")
|
|
|
|
except Exception as e:
|
|
st.warning(f"定时任务模块加载失败: {e}")
|
|
|
|
st.divider()
|
|
|
|
# 使用建议
|
|
st.markdown("#### 💡 使用建议")
|
|
st.markdown("""
|
|
1. **盘前分析** (09:00前)
|
|
- 运行快速分析，了解当日热点
|
|
- 关注流量阶段和情绪指数
|
|
|
|
2. **盘中监测** (交易时间)
|
|
- 开启定时任务自动监测
|
|
- 关注预警通知
|
|
|
|
3. **盘后复盘** (15:00后)
|
|
- 运行完整分析（含AI）
|
|
- 分析当日流量变化趋势
|
|
|
|
4. **风险提示**
|
|
- 当流量阶段进入"一致"时，立即警惕
|
|
- 当情绪指数>85或K值>1.5时，注意风险
|
|
- 本工具仅供参考，投资决策需谨慎
|
|
""")
|
|
|
|
|
|
# 入口
|
|
if __name__ == "__main__":
|
|
display_news_flow_monitor()
|