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main.py
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330
main.py
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"""
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美股低价值公司分析系统 - 主程序
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"""
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import sys
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import logging
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from datetime import datetime
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from typing import Dict, Optional
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# 导入自定义模块
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from data_collector import StockDataCollector
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from analysis_engine import StockAnalyzer
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from report_generator import ReportGenerator
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from database import StockDatabase
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from analysis_storage import AnalysisStorage
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from config import LOW_VALUE_CRITERIA
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# 设置日志
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
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handlers=[
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logging.FileHandler('stock_analysis.log', encoding='utf-8'),
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logging.StreamHandler(sys.stdout)
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]
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)
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logger = logging.getLogger(__name__)
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class StockAnalysisSystem:
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def __init__(self):
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self.data_collector = StockDataCollector()
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self.analyzer = StockAnalyzer()
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self.report_generator = ReportGenerator()
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self.database = StockDatabase()
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self.storage = AnalysisStorage()
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def analyze_stock(self, symbol: str, force_refresh: bool = False) -> Dict:
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"""
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分析单只股票
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Args:
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symbol: 股票代码
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force_refresh: 是否强制刷新数据
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Returns:
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分析结果字典
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"""
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try:
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logger.info(f"开始分析股票: {symbol}")
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# 检查是否需要刷新数据
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if not force_refresh:
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latest_analysis = self.database.get_latest_analysis(symbol)
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if latest_analysis:
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# 检查分析时间是否在24小时内
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analysis_time = datetime.fromisoformat(latest_analysis['analysis_date'])
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if (datetime.now() - analysis_time).total_seconds() < 86400: # 24小时
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logger.info(f"使用缓存的分析结果: {symbol}")
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return latest_analysis
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# 1. 数据收集阶段
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logger.info(f"正在收集数据: {symbol}")
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raw_data = self.data_collector.collect_all_data(symbol)
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if not raw_data.get('company_info'):
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logger.error(f"无法获取公司信息: {symbol}")
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return {'error': '无法获取公司信息'}
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# 保存数据到数据库
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self.database.save_company_info(symbol, raw_data['company_info'])
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if not raw_data['stock_prices'].empty:
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self.database.save_stock_prices(symbol, raw_data['stock_prices'])
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if raw_data['financial_statements']:
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self.database.save_financial_data(symbol, raw_data['financial_statements'])
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# 2. 分析阶段
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logger.info(f"正在进行深度分析: {symbol}")
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# 估值分析
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valuation_results = self.analyzer.calculate_valuation_metrics(raw_data)
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# 财务健康度分析
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health_results = self.analyzer.calculate_financial_health(raw_data)
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# 成长性分析
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growth_results = self.analyzer.calculate_growth_metrics(raw_data)
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# 风险分析
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risk_results = self.analyzer.calculate_risk_metrics(raw_data)
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# DCF估值
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dcf_results = self.analyzer.perform_dcf_valuation(raw_data)
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# 生成投资建议
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investment_recommendation = self.analyzer.generate_investment_recommendation({
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'valuation_score': valuation_results.get('valuation_score', 0),
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'financial_health_score': health_results.get('health_score', 0),
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'growth_score': growth_results.get('growth_score', 0),
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'risk_score': risk_results.get('risk_score', 0)
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})
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# 整合分析结果
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analysis_results = {
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'symbol': symbol,
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'analysis_date': datetime.now().isoformat(),
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'valuation_metrics': valuation_results,
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'financial_health': health_results,
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'growth_metrics': growth_results,
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'risk_metrics': risk_results,
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'dcf_analysis': dcf_results,
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'investment_recommendation': investment_recommendation,
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'overall_score': investment_recommendation.get('overall_score', 0),
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'recommendation': investment_recommendation.get('recommendation', 'N/A')
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}
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# 保存分析结果到数据库
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self.database.save_analysis_result(symbol, analysis_results)
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# 3. 报告生成阶段
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logger.info(f"正在生成报告: {symbol}")
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# 生成详细报告
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report_file = self.report_generator.generate_comprehensive_report(
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symbol, raw_data, analysis_results
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)
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# 生成简要报告
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summary_file = self.report_generator.generate_summary_report(
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symbol, analysis_results
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)
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# 4. 保存标准化分析报告
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logger.info(f"正在保存分析报告: {symbol}")
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storage_result = self.storage.save_analysis_report(symbol, {
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'company_info': raw_data.get('company_info', {}),
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'financial_data': raw_data.get('financial_statements', {}),
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'valuation_analysis': analysis_results.get('valuation_metrics', {}),
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'financial_health': analysis_results.get('financial_health', {}),
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'growth_analysis': analysis_results.get('growth_metrics', {}),
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'risk_analysis': analysis_results.get('risk_metrics', {}),
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'investment_recommendation': analysis_results.get('investment_recommendation', {}),
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'analyst_opinions': raw_data.get('analyst_recommendations', {}),
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'market_data': raw_data.get('stock_prices', {}),
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'raw_data': raw_data
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})
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analysis_results['report_file'] = report_file
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analysis_results['summary_file'] = summary_file
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analysis_results['storage_files'] = storage_result
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logger.info(f"分析完成: {symbol}")
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return analysis_results
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except Exception as e:
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logger.error(f"分析股票失败 {symbol}: {e}")
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return {'error': str(e)}
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def quick_screening(self, symbol: str) -> Dict:
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"""
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快速筛选 - 检查是否符合低价值投资标准
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Args:
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symbol: 股票代码
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Returns:
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筛选结果
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"""
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try:
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logger.info(f"快速筛选: {symbol}")
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# 获取基础数据
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raw_data = self.data_collector.collect_all_data(symbol)
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if not raw_data.get('company_info'):
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return {'error': '无法获取公司信息', 'passes_screening': False}
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# 基础筛选条件
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company_info = raw_data['company_info']
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key_metrics = raw_data.get('key_metrics', {})
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screening_results = {
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'symbol': symbol,
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'company_name': company_info.get('name', ''),
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'market_cap': company_info.get('market_cap', 0),
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'passes_screening': True,
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'criteria_met': [],
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'criteria_failed': []
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}
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# 检查市值
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if company_info.get('market_cap', 0) >= LOW_VALUE_CRITERIA['min_market_cap']:
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screening_results['criteria_met'].append('市值符合要求')
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else:
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screening_results['criteria_failed'].append('市值过小')
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screening_results['passes_screening'] = False
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# 检查PE比率
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pe_ratio = key_metrics.get('pe_ratio', 0)
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if 0 < pe_ratio <= LOW_VALUE_CRITERIA['max_pe_ratio']:
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screening_results['criteria_met'].append(f'PE比率合理 ({pe_ratio:.2f})')
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elif pe_ratio > LOW_VALUE_CRITERIA['max_pe_ratio']:
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screening_results['criteria_failed'].append(f'PE比率过高 ({pe_ratio:.2f})')
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screening_results['passes_screening'] = False
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# 检查PB比率
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pb_ratio = key_metrics.get('pb_ratio', 0)
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if 0 < pb_ratio <= LOW_VALUE_CRITERIA['max_pb_ratio']:
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screening_results['criteria_met'].append(f'PB比率合理 ({pb_ratio:.2f})')
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elif pb_ratio > LOW_VALUE_CRITERIA['max_pb_ratio']:
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screening_results['criteria_failed'].append(f'PB比率过高 ({pb_ratio:.2f})')
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screening_results['passes_screening'] = False
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# 检查PS比率
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ps_ratio = key_metrics.get('ps_ratio', 0)
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if 0 < ps_ratio <= LOW_VALUE_CRITERIA['max_ps_ratio']:
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screening_results['criteria_met'].append(f'PS比率合理 ({ps_ratio:.2f})')
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elif ps_ratio > LOW_VALUE_CRITERIA['max_ps_ratio']:
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screening_results['criteria_failed'].append(f'PS比率过高 ({ps_ratio:.2f})')
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screening_results['passes_screening'] = False
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return screening_results
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except Exception as e:
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logger.error(f"快速筛选失败 {symbol}: {e}")
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return {'error': str(e), 'passes_screening': False}
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def batch_analysis(self, symbols: list) -> Dict:
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"""
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批量分析多只股票
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Args:
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symbols: 股票代码列表
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Returns:
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批量分析结果
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"""
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results = {}
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for symbol in symbols:
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logger.info(f"批量分析: {symbol}")
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results[symbol] = self.analyze_stock(symbol)
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return results
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def main():
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"""主函数 - 命令行接口"""
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if len(sys.argv) < 2:
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print("使用方法:")
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print("python main.py <股票代码> [--refresh]")
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print("python main.py --batch <股票代码1,股票代码2,...>")
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print("python main.py --screen <股票代码>")
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return
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system = StockAnalysisSystem()
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if sys.argv[1] == '--batch':
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# 批量分析
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if len(sys.argv) < 3:
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print("请提供股票代码列表")
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return
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symbols = [s.strip() for s in sys.argv[2].split(',')]
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results = system.batch_analysis(symbols)
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print("\n=== 批量分析结果 ===")
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for symbol, result in results.items():
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if 'error' in result:
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print(f"{symbol}: 分析失败 - {result['error']}")
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else:
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print(f"{symbol}: {result.get('recommendation', 'N/A')} (评分: {result.get('overall_score', 0):.1f})")
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elif sys.argv[1] == '--screen':
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# 快速筛选
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if len(sys.argv) < 3:
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print("请提供股票代码")
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return
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symbol = sys.argv[2].upper()
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result = system.quick_screening(symbol)
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print(f"\n=== {symbol} 快速筛选结果 ===")
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if 'error' in result:
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print(f"筛选失败: {result['error']}")
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else:
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print(f"公司名称: {result.get('company_name', 'N/A')}")
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print(f"市值: ${result.get('market_cap', 0):,.0f}")
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print(f"通过筛选: {'是' if result.get('passes_screening') else '否'}")
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if result.get('criteria_met'):
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print("符合条件:")
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for criteria in result['criteria_met']:
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print(f" ✓ {criteria}")
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if result.get('criteria_failed'):
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print("不符合条件:")
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for criteria in result['criteria_failed']:
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print(f" ✗ {criteria}")
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else:
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# 单只股票分析
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symbol = sys.argv[1].upper()
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force_refresh = '--refresh' in sys.argv
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print(f"正在分析股票: {symbol}")
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result = system.analyze_stock(symbol, force_refresh)
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if 'error' in result:
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print(f"分析失败: {result['error']}")
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else:
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print(f"\n=== {symbol} 分析结果 ===")
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print(f"投资建议: {result.get('recommendation', 'N/A')}")
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print(f"综合评分: {result.get('overall_score', 0):.1f}/100")
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recommendation = result.get('investment_recommendation', {})
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if recommendation.get('key_strengths'):
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print("关键优势:")
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for strength in recommendation['key_strengths']:
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print(f" • {strength}")
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if recommendation.get('key_concerns'):
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print("关键担忧:")
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for concern in recommendation['key_concerns']:
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print(f" • {concern}")
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if result.get('report_file'):
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print(f"\n详细报告已生成: {result['report_file']}")
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if result.get('summary_file'):
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print(f"简要报告已生成: {result['summary_file']}")
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if __name__ == "__main__":
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main()
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