wsx180808
+215
-4
@@ -116,9 +116,12 @@ def display_analysis_tab():
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col1, col2, col3 = st.columns([2, 2, 2])
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with col1:
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# 导入model_config.py中定义的model_options
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from model_config import model_options as app_model_options
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selected_model = st.selectbox(
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"选择AI模型",
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["deepseek-chat", "deepseek-reasoner"],
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"AI模型",
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list(app_model_options.keys()),
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format_func=lambda x: app_model_options[x],
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help="Reasoner模型提供更强的推理能力"
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)
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@@ -774,10 +777,10 @@ def display_pdf_export_section(result):
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"""显示PDF导出部分"""
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st.subheader("📄 导出报告")
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col1, col2, col3 = st.columns([2, 1, 1])
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col1, col2, col3, col4 = st.columns([2, 1, 1, 1])
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with col1:
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st.write("将分析报告导出为PDF文件，方便保存和分享")
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st.write("将分析报告导出为PDF或Markdown文件，方便保存和分享")
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with col2:
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if st.button("📥 生成PDF报告", type="primary", width='content'):
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@@ -802,6 +805,23 @@ def display_pdf_export_section(result):
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st.error(f"❌ PDF生成失败: {str(e)}")
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with col3:
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if st.button("📝 生成Markdown", type="secondary", width='content'):
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with st.spinner("正在生成Markdown报告..."):
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try:
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# 生成Markdown内容
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markdown_content = generate_sector_markdown_report(result)
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# 保存到session_state
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st.session_state.sector_markdown_data = markdown_content
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st.session_state.sector_markdown_filename = f"智策报告_{result.get('timestamp', datetime.now().strftime('%Y%m%d_%H%M%S')).replace(':', '').replace(' ', '_')}.md"
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st.success("✅ Markdown报告生成成功！")
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st.rerun()
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except Exception as e:
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st.error(f"❌ Markdown生成失败: {str(e)}")
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with col4:
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# 如果已经生成了PDF，显示下载按钮
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if 'sector_pdf_data' in st.session_state:
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st.download_button(
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@@ -811,6 +831,197 @@ def display_pdf_export_section(result):
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mime="application/pdf",
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width='content'
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)
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# 如果已经生成了Markdown，显示下载按钮
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if 'sector_markdown_data' in st.session_state:
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st.download_button(
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label="💾 下载Markdown",
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data=st.session_state.sector_markdown_data,
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file_name=st.session_state.sector_markdown_filename,
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mime="text/markdown",
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width='content'
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)
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def generate_sector_markdown_report(result_data: dict) -> str:
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"""生成智策分析Markdown报告"""
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# 获取当前时间
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current_time = datetime.now().strftime("%Y年%m月%d日 %H:%M:%S")
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# 标题页
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markdown_content = f"""# 智策板块策略分析报告
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**AI驱动的多维度板块投资决策支持系统**
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---
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||||
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||||
## 📊 报告信息
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||||
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||||
- **生成时间**: {current_time}
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||||
- **分析周期**: 当日市场数据
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- **AI模型**: DeepSeek Multi-Agent System
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- **分析维度**: 宏观·板块·资金·情绪
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> ⚠️ 本报告由AI系统自动生成，仅供参考，不构成投资建议。投资有风险，决策需谨慎。
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---
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## 📈 市场概况
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||||
本报告基于{result_data.get('timestamp', 'N/A')}的实时市场数据，
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通过四位AI智能体的多维度分析，为您提供板块投资策略建议。
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### 分析师团队:
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- **宏观策略师** - 分析宏观经济、政策导向、新闻事件
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- **板块诊断师** - 分析板块走势、估值水平、轮动特征
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- **资金流向分析师** - 分析主力资金、北向资金流向
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- **市场情绪解码员** - 分析市场情绪、热度、赚钱效应
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"""
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# 核心预测
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predictions = result_data.get('final_predictions', {})
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if predictions.get('prediction_text'):
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# 文本格式预测
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markdown_content += f"""
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## 🎯 核心预测
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{predictions.get('prediction_text', '')}
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"""
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else:
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# JSON格式预测
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markdown_content += "## 🎯 核心预测\n\n"
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# 1. 板块多空预测
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long_short = predictions.get('long_short', {})
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bullish = long_short.get('bullish', [])
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bearish = long_short.get('bearish', [])
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markdown_content += "### 📊 板块多空预测\n\n"
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if bullish:
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markdown_content += "#### 🟢 看多板块\n\n"
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for idx, item in enumerate(bullish, 1):
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markdown_content += f"{idx}. **{item.get('sector', 'N/A')}** (信心度: {item.get('confidence', 0)}/10)\n"
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markdown_content += f" - 理由: {item.get('reason', 'N/A')}\n"
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markdown_content += f" - 风险: {item.get('risk', 'N/A')}\n\n"
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if bearish:
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markdown_content += "#### 🔴 看空板块\n\n"
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for idx, item in enumerate(bearish, 1):
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markdown_content += f"{idx}. **{item.get('sector', 'N/A')}** (信心度: {item.get('confidence', 0)}/10)\n"
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markdown_content += f" - 理由: {item.get('reason', 'N/A')}\n"
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markdown_content += f" - 风险: {item.get('risk', 'N/A')}\n\n"
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# 2. 板块轮动预测
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rotation = predictions.get('rotation', {})
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current_strong = rotation.get('current_strong', [])
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potential = rotation.get('potential', [])
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declining = rotation.get('declining', [])
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markdown_content += "### 🔄 板块轮动预测\n\n"
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if current_strong:
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markdown_content += "#### 💪 当前强势板块\n\n"
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for item in current_strong:
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markdown_content += f"- **{item.get('sector', 'N/A')}**\n"
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markdown_content += f" - 轮动逻辑: {item.get('logic', 'N/A')}\n"
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markdown_content += f" - 时间窗口: {item.get('time_window', 'N/A')}\n"
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markdown_content += f" - 操作建议: {item.get('advice', 'N/A')}\n\n"
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if potential:
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markdown_content += "#### 🌱 潜力接力板块\n\n"
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for item in potential:
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markdown_content += f"- **{item.get('sector', 'N/A')}**\n"
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markdown_content += f" - 轮动逻辑: {item.get('logic', 'N/A')}\n"
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markdown_content += f" - 时间窗口: {item.get('time_window', 'N/A')}\n"
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markdown_content += f" - 操作建议: {item.get('advice', 'N/A')}\n\n"
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if declining:
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markdown_content += "#### 📉 衰退板块\n\n"
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for item in declining:
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markdown_content += f"- **{item.get('sector', 'N/A')}**\n"
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markdown_content += f" - 轮动逻辑: {item.get('logic', 'N/A')}\n"
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markdown_content += f" - 时间窗口: {item.get('time_window', 'N/A')}\n"
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markdown_content += f" - 操作建议: {item.get('advice', 'N/A')}\n\n"
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# 3. 板块热度排行
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heat = predictions.get('heat', {})
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hottest = heat.get('hottest', [])
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heating = heat.get('heating', [])
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cooling = heat.get('cooling', [])
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markdown_content += "### 🔥 板块热度排行\n\n"
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if hottest:
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markdown_content += "#### 最热板块\n\n| 排名 | 板块 | 热度评分 | 趋势 | 持续性 |\n|------|------|----------|------|--------|\n"
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for idx, item in enumerate(hottest[:10], 1):
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markdown_content += f"| {idx} | {item.get('sector', 'N/A')} | {item.get('score', 0)} | {item.get('trend', 'N/A')} | {item.get('sustainability', 'N/A')} |\n"
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markdown_content += "\n"
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if heating:
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markdown_content += "#### 升温板块\n\n"
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for idx, item in enumerate(heating[:5], 1):
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markdown_content += f"{idx}. {item.get('sector', 'N/A')} (评分: {item.get('score', 0)})\n"
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markdown_content += "\n"
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if cooling:
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markdown_content += "#### 降温板块\n\n"
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for idx, item in enumerate(cooling[:5], 1):
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markdown_content += f"{idx}. {item.get('sector', 'N/A')} (评分: {item.get('score', 0)})\n"
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markdown_content += "\n"
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# 4. 策略总结
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summary = predictions.get('summary', {})
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if summary:
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markdown_content += "### 📝 策略总结\n\n"
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if summary.get('market_view'):
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markdown_content += f"**市场观点:** {summary.get('market_view', '')}\n\n"
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|
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if summary.get('key_opportunity'):
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markdown_content += f"**核心机会:** {summary.get('key_opportunity', '')}\n\n"
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|
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if summary.get('major_risk'):
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markdown_content += f"**主要风险:** {summary.get('major_risk', '')}\n\n"
|
||||
|
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if summary.get('strategy'):
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markdown_content += f"**整体策略:** {summary.get('strategy', '')}\n\n"
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# AI智能体分析
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agents_analysis = result_data.get('agents_analysis', {})
|
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if agents_analysis:
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markdown_content += "## 🤖 AI智能体分析\n\n"
|
||||
|
||||
for key, agent_data in agents_analysis.items():
|
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agent_name = agent_data.get('agent_name', '未知分析师')
|
||||
agent_role = agent_data.get('agent_role', '')
|
||||
focus_areas = ', '.join(agent_data.get('focus_areas', []))
|
||||
analysis = agent_data.get('analysis', '')
|
||||
|
||||
markdown_content += f"### {agent_name}\n\n"
|
||||
markdown_content += f"- **职责**: {agent_role}\n"
|
||||
markdown_content += f"- **关注领域**: {focus_areas}\n\n"
|
||||
markdown_content += f"{analysis}\n\n"
|
||||
markdown_content += "---\n\n"
|
||||
|
||||
# 综合研判
|
||||
comprehensive_report = result_data.get('comprehensive_report', '')
|
||||
if comprehensive_report:
|
||||
markdown_content += "## 📊 综合研判\n\n"
|
||||
markdown_content += f"{comprehensive_report}\n\n"
|
||||
|
||||
markdown_content += """
|
||||
---
|
||||
|
||||
*报告由智策AI系统自动生成*
|
||||
"""
|
||||
|
||||
return markdown_content
|
||||
|
||||
|
||||
def display_scheduler_settings():
|
||||
|
||||
Reference in New Issue
Block a user