216 lines
9.2 KiB
Python
216 lines
9.2 KiB
Python
"""
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数据收集模块 - 从各种API获取股票和财务数据
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"""
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import yfinance as yf
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import pandas as pd
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import requests
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from typing import Dict, List, Optional, Tuple
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import time
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from datetime import datetime, timedelta
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import logging
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# 设置日志
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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class StockDataCollector:
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def __init__(self):
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self.session = requests.Session()
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self.session.headers.update({
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'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
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})
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def get_company_info(self, symbol: str) -> Dict:
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"""获取公司基本信息"""
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try:
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ticker = yf.Ticker(symbol)
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info = ticker.info
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return {
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'symbol': symbol,
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'name': info.get('longName', ''),
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'sector': info.get('sector', ''),
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'industry': info.get('industry', ''),
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'market_cap': info.get('marketCap', 0),
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'employees': info.get('fullTimeEmployees', 0),
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'website': info.get('website', ''),
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'description': info.get('longBusinessSummary', ''),
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'country': info.get('country', ''),
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'currency': info.get('currency', 'USD')
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}
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except Exception as e:
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logger.error(f"获取公司信息失败 {symbol}: {e}")
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return {}
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def get_stock_prices(self, symbol: str, period: str = "1y") -> pd.DataFrame:
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"""获取股价数据"""
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try:
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ticker = yf.Ticker(symbol)
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data = ticker.history(period=period)
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if data.empty:
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logger.warning(f"未找到股价数据: {symbol}")
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return pd.DataFrame()
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# 重命名列以匹配数据库结构
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data = data.reset_index()
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data.columns = ['date', 'open', 'high', 'low', 'close', 'volume', 'dividends', 'stock_splits']
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data = data.drop(['dividends', 'stock_splits'], axis=1)
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return data
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except Exception as e:
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logger.error(f"获取股价数据失败 {symbol}: {e}")
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return pd.DataFrame()
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def get_financial_statements(self, symbol: str) -> Dict:
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"""获取财务报表数据"""
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try:
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ticker = yf.Ticker(symbol)
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# 获取季度和年度财务数据
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quarterly_data = {}
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annual_data = {}
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# 季度数据
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try:
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quarterly_financials = ticker.quarterly_financials
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if not quarterly_financials.empty:
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for i, (date, row) in enumerate(quarterly_financials.iterrows()):
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quarterly_data[f"Q{i+1}_{date.year}"] = {
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'year': date.year,
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'quarter': (i % 4) + 1,
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'revenue': row.get('Total Revenue', 0),
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'net_income': row.get('Net Income', 0),
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'total_assets': row.get('Total Assets', 0),
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'total_liabilities': row.get('Total Liabilities', 0),
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'shareholders_equity': row.get('Stockholders Equity', 0),
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'cash': row.get('Cash And Cash Equivalents', 0),
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'debt': row.get('Total Debt', 0)
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}
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except Exception as e:
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logger.warning(f"获取季度财务数据失败 {symbol}: {e}")
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# 年度数据
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try:
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annual_financials = ticker.financials
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if not annual_financials.empty:
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for i, (date, row) in enumerate(annual_financials.iterrows()):
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annual_data[f"Annual_{date.year}"] = {
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'year': date.year,
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'quarter': 0,
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'revenue': row.get('Total Revenue', 0),
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'net_income': row.get('Net Income', 0),
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'total_assets': row.get('Total Assets', 0),
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'total_liabilities': row.get('Total Liabilities', 0),
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'shareholders_equity': row.get('Stockholders Equity', 0),
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'cash': row.get('Cash And Cash Equivalents', 0),
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'debt': row.get('Total Debt', 0)
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}
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except Exception as e:
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logger.warning(f"获取年度财务数据失败 {symbol}: {e}")
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return {**quarterly_data, **annual_data}
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except Exception as e:
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logger.error(f"获取财务数据失败 {symbol}: {e}")
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return {}
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def get_key_metrics(self, symbol: str) -> Dict:
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"""获取关键财务指标"""
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try:
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ticker = yf.Ticker(symbol)
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info = ticker.info
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return {
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'pe_ratio': info.get('trailingPE', 0),
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'pb_ratio': info.get('priceToBook', 0),
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'ps_ratio': info.get('priceToSalesTrailing12Months', 0),
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'peg_ratio': info.get('pegRatio', 0),
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'debt_to_equity': info.get('debtToEquity', 0),
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'current_ratio': info.get('currentRatio', 0),
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'quick_ratio': info.get('quickRatio', 0),
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'return_on_equity': info.get('returnOnEquity', 0),
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'return_on_assets': info.get('returnOnAssets', 0),
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'profit_margin': info.get('profitMargins', 0),
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'operating_margin': info.get('operatingMargins', 0),
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'revenue_growth': info.get('revenueGrowth', 0),
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'earnings_growth': info.get('earningsGrowth', 0),
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'beta': info.get('beta', 0),
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'dividend_yield': info.get('dividendYield', 0),
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'payout_ratio': info.get('payoutRatio', 0)
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}
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except Exception as e:
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logger.error(f"获取关键指标失败 {symbol}: {e}")
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return {}
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def get_analyst_recommendations(self, symbol: str) -> Dict:
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"""获取分析师推荐"""
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try:
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ticker = yf.Ticker(symbol)
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recommendations = ticker.recommendations
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if recommendations is None or recommendations.empty:
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return {}
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# 获取最新的推荐
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latest_rec = recommendations.iloc[-1] if not recommendations.empty else None
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return {
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'latest_recommendation': latest_rec.get('To Grade', '') if latest_rec is not None else '',
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'latest_firm': latest_rec.get('Firm', '') if latest_rec is not None else '',
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'latest_date': latest_rec.get('Date', '') if latest_rec is not None else '',
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'total_recommendations': len(recommendations)
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}
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except Exception as e:
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logger.warning(f"获取分析师推荐失败 {symbol}: {e}")
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return {}
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def get_news_sentiment(self, symbol: str) -> Dict:
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"""获取新闻情绪分析（简化版）"""
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try:
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ticker = yf.Ticker(symbol)
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news = ticker.news
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if not news:
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return {'sentiment_score': 0, 'news_count': 0}
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# 简单的情绪分析（实际应用中可以使用更复杂的NLP模型）
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positive_keywords = ['growth', 'profit', 'increase', 'strong', 'positive', 'beat', 'exceed']
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negative_keywords = ['loss', 'decline', 'weak', 'negative', 'miss', 'fall', 'drop']
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sentiment_score = 0
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for article in news[:10]: # 只分析最近10条新闻
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title = article.get('title', '').lower()
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summary = article.get('summary', '').lower()
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text = title + ' ' + summary
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positive_count = sum(1 for word in positive_keywords if word in text)
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negative_count = sum(1 for word in negative_keywords if word in text)
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sentiment_score += (positive_count - negative_count)
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return {
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'sentiment_score': sentiment_score,
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'news_count': len(news),
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'recent_news': news[:5] # 最近5条新闻
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}
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except Exception as e:
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logger.warning(f"获取新闻情绪失败 {symbol}: {e}")
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return {'sentiment_score': 0, 'news_count': 0}
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def collect_all_data(self, symbol: str) -> Dict:
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"""收集所有相关数据"""
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logger.info(f"开始收集数据: {symbol}")
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all_data = {
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'symbol': symbol,
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'collection_time': datetime.now().isoformat(),
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'company_info': self.get_company_info(symbol),
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'stock_prices': self.get_stock_prices(symbol),
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'financial_statements': self.get_financial_statements(symbol),
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'key_metrics': self.get_key_metrics(symbol),
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'analyst_recommendations': self.get_analyst_recommendations(symbol),
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'news_sentiment': self.get_news_sentiment(symbol)
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}
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logger.info(f"数据收集完成: {symbol}")
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return all_data |