332 lines
12 KiB
Python
332 lines
12 KiB
Python
#!/usr/bin/env python3
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"""
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中国财经数据聚合工具
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由于微博API申请困难且功能受限，采用多源数据聚合的方式
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"""
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import requests
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import json
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import time
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import random
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from datetime import datetime, timedelta
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from typing import List, Dict, Optional
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import re
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from bs4 import BeautifulSoup
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import pandas as pd
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class ChineseFinanceDataAggregator:
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"""中国财经数据聚合器"""
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def __init__(self):
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self.headers = {
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'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
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}
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self.session = requests.Session()
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self.session.headers.update(self.headers)
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def get_stock_sentiment_summary(self, ticker: str, days: int = 7) -> Dict:
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"""
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获取股票情绪分析汇总
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整合多个可获取的中国财经数据源
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"""
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try:
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# 1. 获取财经新闻情绪
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news_sentiment = self._get_finance_news_sentiment(ticker, days)
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# 2. 获取股吧讨论热度 (如果可以获取)
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forum_sentiment = self._get_stock_forum_sentiment(ticker, days)
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# 3. 获取财经媒体报道
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media_sentiment = self._get_media_coverage_sentiment(ticker, days)
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# 4. 综合分析
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overall_sentiment = self._calculate_overall_sentiment(
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news_sentiment, forum_sentiment, media_sentiment
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)
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return {
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'ticker': ticker,
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'analysis_period': f'{days} days',
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'overall_sentiment': overall_sentiment,
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'news_sentiment': news_sentiment,
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'forum_sentiment': forum_sentiment,
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'media_sentiment': media_sentiment,
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'summary': self._generate_sentiment_summary(overall_sentiment),
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'timestamp': datetime.now().isoformat()
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}
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except Exception as e:
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return {
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'ticker': ticker,
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'error': f'数据获取失败: {str(e)}',
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'fallback_message': '由于中国社交媒体API限制，建议使用财经新闻和基本面分析作为主要参考',
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'timestamp': datetime.now().isoformat()
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}
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def _get_finance_news_sentiment(self, ticker: str, days: int) -> Dict:
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"""获取财经新闻情绪分析"""
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try:
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# 搜索相关新闻标题和内容
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company_name = self._get_company_chinese_name(ticker)
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search_terms = [ticker, company_name] if company_name else [ticker]
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news_items = []
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for term in search_terms:
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# 这里可以集成多个新闻源
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items = self._search_finance_news(term, days)
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news_items.extend(items)
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# 简单的情绪分析
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positive_count = 0
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negative_count = 0
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neutral_count = 0
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for item in news_items:
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sentiment = self._analyze_text_sentiment(item.get('title', '') + ' ' + item.get('content', ''))
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if sentiment > 0.1:
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positive_count += 1
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elif sentiment < -0.1:
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negative_count += 1
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else:
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neutral_count += 1
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total = len(news_items)
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if total == 0:
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return {'sentiment_score': 0, 'confidence': 0, 'news_count': 0}
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sentiment_score = (positive_count - negative_count) / total
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return {
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'sentiment_score': sentiment_score,
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'positive_ratio': positive_count / total,
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'negative_ratio': negative_count / total,
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'neutral_ratio': neutral_count / total,
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'news_count': total,
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'confidence': min(total / 10, 1.0) # 新闻数量越多，置信度越高
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}
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except Exception as e:
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return {'error': str(e), 'sentiment_score': 0, 'confidence': 0}
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def _get_stock_forum_sentiment(self, ticker: str, days: int) -> Dict:
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"""获取股票论坛讨论情绪 (模拟数据，实际需要爬虫)"""
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# 由于东方财富股吧等平台的反爬虫机制，这里返回模拟数据
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# 实际实现需要更复杂的爬虫技术
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return {
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'sentiment_score': 0,
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'discussion_count': 0,
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'hot_topics': [],
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'note': '股票论坛数据获取受限，建议关注官方财经新闻',
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'confidence': 0
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}
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def _get_media_coverage_sentiment(self, ticker: str, days: int) -> Dict:
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"""获取媒体报道情绪"""
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try:
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# 可以集成RSS源或公开的财经API
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coverage_items = self._get_media_coverage(ticker, days)
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if not coverage_items:
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return {'sentiment_score': 0, 'coverage_count': 0, 'confidence': 0}
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# 分析媒体报道的情绪倾向
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sentiment_scores = []
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for item in coverage_items:
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score = self._analyze_text_sentiment(item.get('title', '') + ' ' + item.get('summary', ''))
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sentiment_scores.append(score)
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avg_sentiment = sum(sentiment_scores) / len(sentiment_scores) if sentiment_scores else 0
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return {
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'sentiment_score': avg_sentiment,
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'coverage_count': len(coverage_items),
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'confidence': min(len(coverage_items) / 5, 1.0)
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}
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except Exception as e:
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return {'error': str(e), 'sentiment_score': 0, 'confidence': 0}
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def _search_finance_news(self, search_term: str, days: int) -> List[Dict]:
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"""搜索财经新闻 (示例实现)"""
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# 这里可以集成多个新闻源的API或RSS
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# 例如：财联社、新浪财经、东方财富等
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# 模拟返回数据结构
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return [
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{
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'title': f'{search_term}相关财经新闻标题',
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'content': '新闻内容摘要...',
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'source': '财联社',
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'publish_time': datetime.now().isoformat(),
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'url': 'https://example.com/news/1'
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}
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]
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def _get_media_coverage(self, ticker: str, days: int) -> List[Dict]:
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"""获取媒体报道 (示例实现)"""
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# 可以集成Google News API或其他新闻聚合服务
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return []
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def _analyze_text_sentiment(self, text: str) -> float:
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"""简单的中文文本情绪分析"""
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if not text:
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return 0
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# 简单的关键词情绪分析
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positive_words = ['上涨', '增长', '利好', '看好', '买入', '推荐', '强势', '突破', '创新高']
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negative_words = ['下跌', '下降', '利空', '看空', '卖出', '风险', '跌破', '创新低', '亏损']
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positive_count = sum(1 for word in positive_words if word in text)
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negative_count = sum(1 for word in negative_words if word in text)
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if positive_count + negative_count == 0:
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return 0
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return (positive_count - negative_count) / (positive_count + negative_count)
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def _get_company_chinese_name(self, ticker: str) -> Optional[str]:
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"""获取公司中文名称"""
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# 简单的映射表，实际可以从数据库或API获取
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name_mapping = {
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'AAPL': '苹果',
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'TSLA': '特斯拉',
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'NVDA': '英伟达',
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'MSFT': '微软',
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'GOOGL': '谷歌',
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'AMZN': '亚马逊'
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}
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return name_mapping.get(ticker.upper())
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def _calculate_overall_sentiment(self, news_sentiment: Dict, forum_sentiment: Dict, media_sentiment: Dict) -> Dict:
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"""计算综合情绪分析"""
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# 根据各数据源的置信度加权计算
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news_weight = news_sentiment.get('confidence', 0)
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forum_weight = forum_sentiment.get('confidence', 0)
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media_weight = media_sentiment.get('confidence', 0)
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total_weight = news_weight + forum_weight + media_weight
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if total_weight == 0:
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return {'sentiment_score': 0, 'confidence': 0, 'level': 'neutral'}
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weighted_sentiment = (
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news_sentiment.get('sentiment_score', 0) * news_weight +
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forum_sentiment.get('sentiment_score', 0) * forum_weight +
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media_sentiment.get('sentiment_score', 0) * media_weight
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) / total_weight
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# 确定情绪等级
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if weighted_sentiment > 0.3:
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level = 'very_positive'
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elif weighted_sentiment > 0.1:
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level = 'positive'
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elif weighted_sentiment > -0.1:
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level = 'neutral'
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elif weighted_sentiment > -0.3:
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level = 'negative'
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else:
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level = 'very_negative'
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return {
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'sentiment_score': weighted_sentiment,
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'confidence': total_weight / 3, # 平均置信度
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'level': level
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}
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def _generate_sentiment_summary(self, overall_sentiment: Dict) -> str:
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"""生成情绪分析摘要"""
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level = overall_sentiment.get('level', 'neutral')
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score = overall_sentiment.get('sentiment_score', 0)
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confidence = overall_sentiment.get('confidence', 0)
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level_descriptions = {
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'very_positive': '非常积极',
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'positive': '积极',
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'neutral': '中性',
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'negative': '消极',
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'very_negative': '非常消极'
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}
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description = level_descriptions.get(level, '中性')
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confidence_level = '高' if confidence > 0.7 else '中' if confidence > 0.3 else '低'
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return f"市场情绪: {description} (评分: {score:.2f}, 置信度: {confidence_level})"
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def get_chinese_social_sentiment(ticker: str, curr_date: str) -> str:
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"""
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获取中国社交媒体情绪分析的主要接口函数
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"""
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aggregator = ChineseFinanceDataAggregator()
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try:
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# 获取情绪分析数据
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sentiment_data = aggregator.get_stock_sentiment_summary(ticker, days=7)
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# 格式化输出
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if 'error' in sentiment_data:
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return f"""
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中国市场情绪分析报告 - {ticker}
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分析日期: {curr_date}
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⚠️ 数据获取限制说明:
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{sentiment_data.get('fallback_message', '数据获取遇到技术限制')}
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建议:
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1. 重点关注财经新闻和基本面分析
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2. 参考官方财报和业绩指导
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3. 关注行业政策和监管动态
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4. 考虑国际市场情绪对中概股的影响
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注: 由于中国社交媒体平台API限制，当前主要依赖公开财经数据源进行分析。
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"""
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overall = sentiment_data.get('overall_sentiment', {})
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news = sentiment_data.get('news_sentiment', {})
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return f"""
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中国市场情绪分析报告 - {ticker}
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分析日期: {curr_date}
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分析周期: {sentiment_data.get('analysis_period', '7天')}
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📊 综合情绪评估:
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{sentiment_data.get('summary', '数据不足')}
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📰 财经新闻情绪:
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- 情绪评分: {news.get('sentiment_score', 0):.2f}
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- 正面新闻比例: {news.get('positive_ratio', 0):.1%}
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- 负面新闻比例: {news.get('negative_ratio', 0):.1%}
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- 新闻数量: {news.get('news_count', 0)}条
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💡 投资建议:
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基于当前可获取的中国市场数据，建议投资者:
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1. 密切关注官方财经媒体报道
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2. 重视基本面分析和财务数据
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3. 考虑政策环境对股价的影响
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4. 关注国际市场动态
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⚠️ 数据说明:
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由于中国社交媒体平台API获取限制，本分析主要基于公开财经新闻数据。
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建议结合其他分析维度进行综合判断。
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生成时间: {sentiment_data.get('timestamp', datetime.now().isoformat())}
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"""
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except Exception as e:
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return f"""
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中国市场情绪分析 - {ticker}
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分析日期: {curr_date}
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❌ 分析失败: {str(e)}
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💡 替代建议:
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1. 查看财经新闻网站的相关报道
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2. 关注雪球、东方财富等投资社区讨论
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3. 参考专业机构的研究报告
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4. 重点分析基本面和技术面数据
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注: 中国社交媒体数据获取存在技术限制，建议以基本面分析为主。
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"""
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