oficcejo
457 lines
14 KiB
Python
457 lines
14 KiB
Python
import os
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import base64
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import re
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from datetime import datetime
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import streamlit as st
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import pandas as pd
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def generate_main_force_markdown_report(analyzer, result):
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"""生成主力选股Markdown格式的分析报告"""
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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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# 获取分析参数
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params = result.get('params', {})
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start_date = params.get('start_date', 'N/A')
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min_cap = params.get('min_market_cap', 50)
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max_cap = params.get('max_market_cap', 5000)
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max_change = params.get('max_range_change', 50)
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markdown_content = f"""
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# 主力选股AI分析报告
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**生成时间**: {current_time}
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---
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## 📊 选股参数
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| 项目 | 值 |
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|------|-----|
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| **起始日期** | {start_date} |
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| **市值范围** | {min_cap}亿 - {max_cap}亿 |
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| **最大涨跌幅** | {max_change}% |
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| **初始数据量** | {result.get('total_fetched', 0)}只 |
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| **筛选后数量** | {result.get('filtered_count', 0)}只 |
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| **最终推荐** | {len(result.get('final_recommendations', []))}只 |
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---
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## 🤖 AI分析师团队报告
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"""
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# 添加资金流向分析
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if hasattr(analyzer, 'fund_flow_analysis') and analyzer.fund_flow_analysis:
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markdown_content += f"""
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### 💰 资金流向分析师
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{analyzer.fund_flow_analysis}
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---
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"""
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# 添加行业板块分析
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if hasattr(analyzer, 'industry_analysis') and analyzer.industry_analysis:
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markdown_content += f"""
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### 📊 行业板块及市场热点分析师
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{analyzer.industry_analysis}
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---
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"""
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# 添加财务基本面分析
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if hasattr(analyzer, 'fundamental_analysis') and analyzer.fundamental_analysis:
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markdown_content += f"""
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### 📈 财务基本面分析师
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{analyzer.fundamental_analysis}
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---
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"""
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# 添加精选推荐
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markdown_content += """
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## ⭐ 精选推荐股票
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"""
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final_recommendations = result.get('final_recommendations', [])
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if final_recommendations:
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for rec in final_recommendations:
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markdown_content += f"""
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### 【第{rec['rank']}名】{rec['symbol']} - {rec['name']}
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**推荐理由**:
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{rec.get('reason', '暂无')}
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**关键指标**:
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"""
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if 'stock_data' in rec:
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stock_data = rec['stock_data']
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markdown_content += f"""
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- **所属行业**: {stock_data.get('industry', 'N/A')}
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- **市值**: {stock_data.get('market_cap', 'N/A')}
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- **主力资金流向**: {stock_data.get('main_fund_inflow', 'N/A')}
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- **区间涨跌幅**: {stock_data.get('range_change', 'N/A')}%
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- **市盈率**: {stock_data.get('pe_ratio', 'N/A')}
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- **市净率**: {stock_data.get('pb_ratio', 'N/A')}
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"""
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if 'scores' in rec.get('stock_data', {}):
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scores = rec['stock_data']['scores']
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if scores:
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markdown_content += "**能力评分**:\n"
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for score_name, score_value in scores.items():
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markdown_content += f"- {score_name}: {score_value}\n"
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markdown_content += "\n"
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markdown_content += "---\n\n"
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else:
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markdown_content += "暂无推荐股票\n\n---\n\n"
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# 添加候选股票列表（前100名，按主力资金排序）
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if analyzer and analyzer.raw_stocks is not None and not analyzer.raw_stocks.empty:
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markdown_content += """
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## 📋 候选股票完整列表（按主力资金净流入排序）
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"""
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# 获取主力资金列名
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df = analyzer.raw_stocks
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main_fund_col = None
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main_fund_patterns = [
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'区间主力资金流向', '区间主力资金净流入',
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'主力资金流向', '主力资金净流入', '主力净流入'
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]
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for pattern in main_fund_patterns:
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matching = [col for col in df.columns if pattern in col]
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if matching:
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main_fund_col = matching[0]
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break
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# 按主力资金排序
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if main_fund_col:
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df_sorted = df.copy()
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df_sorted[main_fund_col] = pd.to_numeric(df_sorted[main_fund_col], errors='coerce')
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df_sorted = df_sorted.sort_values(by=main_fund_col, ascending=False).head(100)
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else:
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df_sorted = df.head(100)
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# 选择要显示的列
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display_cols = []
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if '股票代码' in df_sorted.columns:
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display_cols.append('股票代码')
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if '股票简称' in df_sorted.columns:
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display_cols.append('股票简称')
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# 行业
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industry_cols = [col for col in df_sorted.columns if '行业' in col]
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if industry_cols:
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display_cols.append(industry_cols[0])
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# 主力资金
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if main_fund_col:
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display_cols.append(main_fund_col)
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# 涨跌幅
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change_cols = [col for col in df_sorted.columns if '涨跌幅' in col]
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if change_cols:
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display_cols.append(change_cols[0])
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# 市值、市盈率、市净率
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for col_name in ['总市值', '市盈率', '市净率']:
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matching_cols = [col for col in df_sorted.columns if col_name in col]
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if matching_cols:
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display_cols.append(matching_cols[0])
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# 生成表格
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if display_cols:
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final_display_cols = [col for col in display_cols if col in df_sorted.columns]
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markdown_content += "| 序号 | " + " | ".join(final_display_cols) + " |\n"
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markdown_content += "|------|" + "|".join(['-----' for _ in final_display_cols]) + "|\n"
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for idx, (_, row) in enumerate(df_sorted[final_display_cols].iterrows(), 1):
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row_data = [str(idx)]
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for col in final_display_cols:
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value = row[col]
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if pd.isna(value):
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row_data.append('N/A')
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else:
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row_data.append(str(value))
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markdown_content += "| " + " | ".join(row_data) + " |\n"
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markdown_content += "\n"
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# 添加免责声明
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markdown_content += f"""
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---
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## 📝 免责声明
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本报告由AI系统生成，仅供参考，不构成投资建议。投资有风险，入市需谨慎。请在做出投资决策前咨询专业的投资顾问。
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---
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*报告生成时间: {current_time}*
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*主力选股AI分析系统 v1.0*
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"""
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return markdown_content
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def generate_html_content(markdown_content):
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"""将Markdown转换为HTML"""
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html_content = f"""
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<!DOCTYPE html>
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<html>
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<head>
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<meta charset="UTF-8">
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<title>主力选股AI分析报告</title>
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<style>
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body {{
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font-family: 'Microsoft YaHei', Arial, sans-serif;
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line-height: 1.6;
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max-width: 1200px;
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margin: 0 auto;
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padding: 20px;
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background-color: #f5f5f5;
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}}
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.container {{
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background-color: white;
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padding: 30px;
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border-radius: 10px;
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box-shadow: 0 2px 10px rgba(0,0,0,0.1);
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}}
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h1 {{
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color: #2c3e50;
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border-bottom: 3px solid #3498db;
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padding-bottom: 10px;
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}}
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h2 {{
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color: #34495e;
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border-left: 4px solid #3498db;
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padding-left: 15px;
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margin-top: 30px;
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}}
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h3 {{
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color: #2980b9;
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margin-top: 25px;
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}}
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table {{
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width: 100%;
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border-collapse: collapse;
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margin: 20px 0;
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font-size: 14px;
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}}
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th, td {{
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border: 1px solid #ddd;
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padding: 8px;
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text-align: left;
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}}
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th {{
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background-color: #3498db;
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color: white;
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font-weight: bold;
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}}
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tr:nth-child(even) {{
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background-color: #f9f9f9;
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}}
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tr:hover {{
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background-color: #f0f0f0;
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}}
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.disclaimer {{
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background-color: #fff3cd;
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border: 1px solid #ffeaa7;
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border-radius: 5px;
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padding: 15px;
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margin-top: 30px;
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}}
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.footer {{
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text-align: center;
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margin-top: 30px;
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color: #7f8c8d;
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font-style: italic;
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}}
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hr {{
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border: none;
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height: 2px;
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background-color: #ecf0f1;
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margin: 20px 0;
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}}
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strong {{
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color: #2c3e50;
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}}
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ul, ol {{
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margin: 10px 0;
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padding-left: 30px;
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}}
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li {{
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margin: 5px 0;
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}}
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</style>
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</head>
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<body>
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<div class="container">
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"""
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# 简单的Markdown到HTML转换
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html_body = markdown_content
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html_body = html_body.replace('\n# ', '\n<h1>').replace('\n## ', '\n<h2>').replace('\n### ', '\n<h3>')
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html_body = html_body.replace('# ', '<h1>').replace('## ', '<h2>').replace('### ', '<h3>')
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html_body = html_body.replace('\n---\n', '\n<hr>\n')
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# 处理粗体文本
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html_body = re.sub(r'\*\*(.*?)\*\*', r'<strong>\1</strong>', html_body)
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# 处理表格
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lines = html_body.split('\n')
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in_table = False
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processed_lines = []
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for line in lines:
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if '|' in line and not in_table and line.strip().startswith('|'):
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processed_lines.append('<table>')
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in_table = True
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cells = [cell.strip() for cell in line.split('|')[1:-1]]
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processed_lines.append('<tr>')
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for cell in cells:
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processed_lines.append(f'<th>{cell}</th>')
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processed_lines.append('</tr>')
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elif '|' in line and in_table:
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if '---' not in line:
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cells = [cell.strip() for cell in line.split('|')[1:-1]]
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processed_lines.append('<tr>')
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for cell in cells:
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processed_lines.append(f'<td>{cell}</td>')
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processed_lines.append('</tr>')
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elif in_table and '|' not in line:
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processed_lines.append('</table>')
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in_table = False
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processed_lines.append(line)
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else:
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processed_lines.append(line)
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if in_table:
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processed_lines.append('</table>')
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html_body = '\n'.join(processed_lines)
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# 处理列表
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html_body = re.sub(r'\n- (.*)', r'\n<li>\1</li>', html_body)
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html_body = re.sub(r'(<li>.*</li>)\n(?!<li>)', r'<ul>\1</ul>\n', html_body)
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html_body = re.sub(r'(<li>.*</li>\n)+', lambda m: '<ul>\n' + m.group(0) + '</ul>\n', html_body)
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# 处理换行
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html_body = html_body.replace('\n\n', '</p><p>')
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html_body = '<p>' + html_body + '</p>'
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html_content += html_body
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html_content += """
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</div>
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</body>
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</html>
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"""
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return html_content
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def create_download_link(content, filename, link_text):
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"""创建下载链接"""
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b64 = base64.b64encode(content.encode()).decode()
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href = f'<a href="data:text/markdown;base64,{b64}" download="{filename}" style="display: inline-block; padding: 10px 20px; background-color: #4CAF50; color: white; text-decoration: none; border-radius: 5px; margin: 5px;">{link_text}</a>'
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return href
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def create_html_download_link(content, filename, link_text):
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"""创建HTML下载链接"""
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b64 = base64.b64encode(content.encode('utf-8')).decode()
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href = f'<a href="data:text/html;base64,{b64}" download="{filename}" style="display: inline-block; padding: 10px 20px; background-color: #2196F3; color: white; text-decoration: none; border-radius: 5px; margin: 5px;">{link_text}</a>'
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return href
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def display_report_download_section(analyzer, result):
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"""显示报告下载区域"""
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st.markdown("---")
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st.markdown("### 📥 下载分析报告")
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col1, col2 = st.columns(2)
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with col1:
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st.markdown("#### 📄 Markdown格式")
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st.caption("适合编辑和进一步处理")
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# 生成Markdown报告
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markdown_content = generate_main_force_markdown_report(analyzer, result)
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# 生成文件名
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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md_filename = f"主力选股分析报告_{timestamp}.md"
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# 创建下载链接
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md_link = create_download_link(markdown_content, md_filename, "📥 下载Markdown报告")
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st.markdown(md_link, unsafe_allow_html=True)
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# 显示预览
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with st.expander("👀 预览Markdown内容"):
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st.code(markdown_content[:2000] + "..." if len(markdown_content) > 2000 else markdown_content)
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with col2:
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st.markdown("#### 🌐 HTML格式")
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st.caption("可在浏览器中打开查看")
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# 生成HTML报告
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html_content = generate_html_content(markdown_content)
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# 生成文件名
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html_filename = f"主力选股分析报告_{timestamp}.html"
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# 创建下载链接
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html_link = create_html_download_link(html_content, html_filename, "📥 下载HTML报告")
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st.markdown(html_link, unsafe_allow_html=True)
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# 显示说明
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st.info("💡 HTML报告可以直接在浏览器中打开，格式美观易读")
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# 添加CSV下载（候选股票列表）
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if analyzer and analyzer.raw_stocks is not None and not analyzer.raw_stocks.empty:
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st.markdown("---")
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st.markdown("#### 📊 候选股票数据")
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# 按主力资金排序
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df = analyzer.raw_stocks.copy()
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main_fund_col = None
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main_fund_patterns = [
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'区间主力资金流向', '区间主力资金净流入',
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'主力资金流向', '主力资金净流入', '主力净流入'
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]
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for pattern in main_fund_patterns:
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matching = [col for col in df.columns if pattern in col]
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if matching:
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main_fund_col = matching[0]
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break
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if main_fund_col:
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df[main_fund_col] = pd.to_numeric(df[main_fund_col], errors='coerce')
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df = df.sort_values(by=main_fund_col, ascending=False)
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# 导出为CSV
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csv = df.to_csv(index=False, encoding='utf-8-sig')
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csv_filename = f"主力选股候选列表_{timestamp}.csv"
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st.download_button(
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label="📥 下载候选股票CSV",
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data=csv,
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file_name=csv_filename,
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mime="text/csv",
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use_container_width=True
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)
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