oficcejo
703 lines
28 KiB
Python
703 lines
28 KiB
Python
"""
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新闻流量数据获取模块
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用于获取各大平台的热点新闻和流量数据
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支持22个平台，包含排名、K值计算等功能
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"""
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import requests
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import logging
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from datetime import datetime, timedelta
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from typing import Dict, List, Optional, Tuple
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import time
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from collections import Counter
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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class NewsFlowDataFetcher:
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"""新闻流量数据获取器"""
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def __init__(self):
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self.base_url = "https://newsapi.ws4.cn/api/v1/dailynews/"
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self.timeout = 10
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# 支持的平台配置 - 扩展到22个平台
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self.platforms = {
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# 社交媒体平台（核心流量指标）- 8个
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'weibo': {'name': '微博热搜', 'category': 'social', 'weight': 10, 'influence': 'high'},
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'douyin': {'name': '抖音热点', 'category': 'social', 'weight': 9, 'influence': 'high'},
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'zhihu': {'name': '知乎热榜', 'category': 'social', 'weight': 7, 'influence': 'medium'},
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'bilibili': {'name': '哔哩哔哩', 'category': 'social', 'weight': 6, 'influence': 'medium'},
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'xiaohongshu': {'name': '小红书', 'category': 'social', 'weight': 7, 'influence': 'medium'},
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'kuaishou': {'name': '快手', 'category': 'social', 'weight': 6, 'influence': 'medium'},
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'tieba': {'name': '百度贴吧', 'category': 'social', 'weight': 5, 'influence': 'low'},
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'weixin': {'name': '微信热点', 'category': 'social', 'weight': 8, 'influence': 'high'},
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# 新闻媒体平台 - 6个
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'baidu': {'name': '百度热搜', 'category': 'news', 'weight': 8, 'influence': 'high'},
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'jinritoutiao': {'name': '今日头条', 'category': 'news', 'weight': 7, 'influence': 'high'},
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'tenxunwang': {'name': '腾讯网', 'category': 'news', 'weight': 6, 'influence': 'medium'},
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'netease': {'name': '网易新闻', 'category': 'news', 'weight': 6, 'influence': 'medium'},
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'ifeng': {'name': '凤凰网', 'category': 'news', 'weight': 5, 'influence': 'medium'},
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'sina': {'name': '新浪新闻', 'category': 'news', 'weight': 6, 'influence': 'medium'},
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# 财经平台（股市相关）- 5个
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'sina_finance': {'name': '新浪财经', 'category': 'finance', 'weight': 9, 'influence': 'high'},
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'eastmoney': {'name': '东方财富', 'category': 'finance', 'weight': 9, 'influence': 'high'},
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'xueqiu': {'name': '雪球', 'category': 'finance', 'weight': 8, 'influence': 'high'},
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'cls': {'name': '财联社', 'category': 'finance', 'weight': 8, 'influence': 'high'},
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'wallstreetcn': {'name': '华尔街见闻', 'category': 'finance', 'weight': 7, 'influence': 'medium'},
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# 科技平台 - 3个
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'tskr': {'name': '36氪', 'category': 'tech', 'weight': 6, 'influence': 'medium'},
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'sspai': {'name': '少数派', 'category': 'tech', 'weight': 5, 'influence': 'low'},
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'juejin': {'name': '掘金', 'category': 'tech', 'weight': 5, 'influence': 'low'},
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}
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# 平台类别权重（用于转化率计算）
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self.category_weights = {
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'finance': 1.5, # 财经平台转化率高
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'social': 1.2, # 社交媒体传播快
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'news': 1.0, # 新闻媒体正常
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'tech': 0.8, # 科技平台相关性低
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}
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# 停用词（过滤无意义的词）
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self.stop_words = {
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'的', '是', '在', '了', '和', '与', '等', '为', '将', '被',
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'有', '一', '个', '上', '下', '中', '大', '新', '年', '月', '日',
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'这', '那', '其', '之', '也', '要', '就', '不', '我', '你', '他',
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'来', '去', '到', '说', '会', '能', '都', '对', '着', '让',
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'从', '以', '及', '或', '如', '还', '没', '很', '更', '最',
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}
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def get_platform_news(self, platform: str) -> Dict:
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"""
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获取单个平台的新闻数据
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Args:
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platform: 平台代码（如 'weibo', 'baidu'等）
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Returns:
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{
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'success': bool,
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'platform': str,
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'platform_name': str,
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'category': str,
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'weight': int,
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'influence': str,
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'data': List[Dict],
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'count': int,
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'fetch_time': str,
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'error': str (如果失败)
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}
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"""
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try:
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url = f"{self.base_url}?platform={platform}"
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logger.info(f"正在获取 {platform} 平台数据...")
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response = requests.get(url, timeout=self.timeout)
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response.raise_for_status()
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data = response.json()
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if data.get('status') == '200':
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news_list = data.get('data', [])
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platform_info = self.platforms.get(platform, {})
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# 为每条新闻添加排名信息
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for i, news in enumerate(news_list):
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news['rank'] = i + 1
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news['platform'] = platform
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return {
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'success': True,
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'platform': platform,
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'platform_name': platform_info.get('name', platform),
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'category': platform_info.get('category', 'other'),
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'weight': platform_info.get('weight', 5),
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'influence': platform_info.get('influence', 'medium'),
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'data': news_list,
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'count': len(news_list),
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'fetch_time': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
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}
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else:
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return {
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'success': False,
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'platform': platform,
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'error': f"API返回错误: {data.get('msg', '未知错误')}"
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}
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except requests.exceptions.Timeout:
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return {
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'success': False,
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'platform': platform,
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'error': f"请求超时（{self.timeout}秒）"
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}
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except requests.exceptions.ConnectionError:
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return {
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'success': False,
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'platform': platform,
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'error': "网络连接失败"
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}
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except Exception as e:
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return {
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'success': False,
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'platform': platform,
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'error': f"获取数据失败: {str(e)}"
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}
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def get_multi_platform_news(self, platforms: List[str] = None,
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category: str = None) -> Dict:
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"""
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获取多个平台的新闻数据
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Args:
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platforms: 平台列表，None表示获取所有
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category: 按类别筛选（'social', 'news', 'finance', 'tech'）
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Returns:
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{
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'success': bool,
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'total_platforms': int,
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'success_count': int,
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'failed_count': int,
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'platforms_data': List[Dict],
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'fetch_time': str
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}
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"""
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# 确定要获取的平台列表
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if platforms is None:
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if category:
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target_platforms = [
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p for p, info in self.platforms.items()
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if info.get('category') == category
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]
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else:
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target_platforms = list(self.platforms.keys())
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else:
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target_platforms = platforms
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results = []
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success_count = 0
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failed_count = 0
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for platform in target_platforms:
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result = self.get_platform_news(platform)
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results.append(result)
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if result['success']:
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success_count += 1
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else:
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failed_count += 1
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# 避免请求过快，休息0.3秒
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time.sleep(0.3)
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return {
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'success': success_count > 0,
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'total_platforms': len(target_platforms),
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'success_count': success_count,
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'failed_count': failed_count,
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'platforms_data': results,
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'fetch_time': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
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}
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def extract_stock_related_news(self, platforms_data: List[Dict],
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keywords: List[str] = None) -> List[Dict]:
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"""
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从新闻数据中提取股票相关的新闻
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Args:
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platforms_data: 平台数据列表
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keywords: 股票相关关键词列表
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Returns:
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List[Dict]: 股票相关新闻列表
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"""
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if keywords is None:
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keywords = [
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# 股市基础词汇
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'股', '股市', '股票', 'A股', '港股', '美股', '创业板', '科创板', '北交所',
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'涨停', '跌停', '大涨', '暴涨', '飙升', '暴跌', '涨幅', '跌幅', '翻倍',
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'概念股', '龙头股', '妖股', '题材股', '白马股', '蓝筹股', '成长股',
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'上市', 'IPO', '重组', '并购', '收购', '增发', '回购', '减持', '增持',
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'业绩', '财报', '利好', '利空', '预增', '预减', '盈利', '亏损',
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'牛市', '熊市', '反弹', '回调', '震荡', '突破', '新高',
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'主力', '游资', '北向资金', '外资', '机构', '资金流入', '资金流出',
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'板块', '行业', '赛道', '题材', '轮动', '热点',
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# 热门板块关键词
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'芯片', '半导体', '光刻机', '封装', '存储',
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'新能源', '锂电', '光伏', '储能', '风电', '氢能',
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'AI', '人工智能', '大模型', 'ChatGPT', 'DeepSeek', '算力', 'GPU',
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'机器人', '人形机器人', '工业机器人', '减速器', '伺服电机',
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'低空经济', '无人机', 'eVTOL', '飞行汽车',
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'数据要素', '数字经济', '信创', '国产替代',
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'医药', '创新药', 'CXO', '医疗器械', '中药',
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'消费', '白酒', '食品', '旅游', '免税',
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'军工', '国防', '航空', '航天', '船舶',
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'汽车', '新能源车', '智能驾驶', '无人驾驶', '充电桩',
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'地产', '房地产', '楼市', '房价',
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'金融', '银行', '保险', '券商', '证券',
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# 政策相关
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'政策', '利率', '降息', '降准', '货币政策', '财政政策',
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'国常会', '证监会', '央行', '发改委', '工信部',
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]
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stock_related = []
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for platform_data in platforms_data:
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if not platform_data.get('success'):
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continue
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platform = platform_data['platform']
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platform_name = platform_data['platform_name']
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category = platform_data['category']
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weight = platform_data['weight']
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influence = platform_data.get('influence', 'medium')
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for news in platform_data.get('data', []):
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title = news.get('title') or ''
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content = news.get('content') or ''
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rank = news.get('rank', 99)
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# 检查是否包含股票关键词
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text = f"{title} {content}"
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matched_keywords = [kw for kw in keywords if kw in text]
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if matched_keywords:
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# 计算综合得分（排名越靠前、权重越高得分越高）
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rank_score = max(0, 100 - rank * 2) # 排名1得98分，排名50得0分
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weight_score = weight * 10
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keyword_score = len(matched_keywords) * 5
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total_score = rank_score + weight_score + keyword_score
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stock_related.append({
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'platform': platform,
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'platform_name': platform_name,
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'category': category,
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'weight': weight,
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'influence': influence,
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'rank': rank,
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'title': title,
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'content': content,
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'url': news.get('url') or '',
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'source': news.get('source') or platform_name,
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'publish_time': news.get('publish_time') or '',
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'matched_keywords': matched_keywords,
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'keyword_count': len(matched_keywords),
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'score': total_score,
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})
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# 按综合得分排序
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stock_related.sort(key=lambda x: x['score'], reverse=True)
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return stock_related
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def calculate_flow_score(self, platforms_data: List[Dict]) -> Dict:
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"""
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计算流量得分
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根据"流量为王"理念，评估当前市场热度
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Returns:
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{
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'total_score': int, # 总流量得分（0-1000）
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'social_score': int, # 社交媒体流量得分
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'news_score': int, # 新闻媒体流量得分
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'finance_score': int, # 财经平台流量得分
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'tech_score': int, # 科技平台流量得分
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'level': str, # 流量等级（低/中/高/极高）
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'analysis': str, # 流量分析
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'platform_details': List[Dict], # 各平台详情
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}
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"""
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scores = {
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'social': 0,
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'news': 0,
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'finance': 0,
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'tech': 0,
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}
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platform_details = []
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for platform_data in platforms_data:
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if not platform_data.get('success'):
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continue
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category = platform_data['category']
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weight = platform_data['weight']
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count = platform_data['count']
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platform_name = platform_data['platform_name']
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# 计算得分：权重 × 新闻数量
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score = weight * count
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scores[category] = scores.get(category, 0) + score
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platform_details.append({
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'platform': platform_data['platform'],
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'platform_name': platform_name,
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'category': category,
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'count': count,
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'score': score,
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})
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# 计算总分
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total_score = sum(scores.values())
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# 归一化到0-1000
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if total_score > 0:
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normalized_score = min(int(total_score / 50), 1000)
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else:
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normalized_score = 0
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# 确定流量等级
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if normalized_score >= 800:
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level = "极高"
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analysis = "流量爆发！市场情绪极度活跃，大量新闻热点，存在热点题材炒作机会。建议：密切关注龙头股，注意追高风险。"
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elif normalized_score >= 500:
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level = "高"
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analysis = "流量较高。市场有明确热点，资金活跃度较好。建议：关注热点板块，注意节奏把握。"
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elif normalized_score >= 200:
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level = "中"
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analysis = "流量正常。市场处于常态，有一定热点但不突出。建议：观望为主，等待明确信号。"
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else:
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level = "低"
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analysis = "流量较低。市场情绪低迷，缺乏热点。建议：控制仓位，等待市场转暖。"
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return {
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'total_score': normalized_score,
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'social_score': scores.get('social', 0),
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'news_score': scores.get('news', 0),
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'finance_score': scores.get('finance', 0),
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'tech_score': scores.get('tech', 0),
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'level': level,
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'analysis': analysis,
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'platform_details': platform_details,
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}
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def get_hot_topics(self, platforms_data: List[Dict], top_n: int = 20) -> List[Dict]:
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"""
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获取热门话题（基于标题词频分析）
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Returns:
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List[Dict]: 热门话题列表
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"""
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import jieba
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# 收集所有标题及其来源信息
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all_titles = []
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title_sources = {} # 记录每个标题来自哪些平台
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for platform_data in platforms_data:
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if platform_data.get('success'):
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platform_name = platform_data['platform_name']
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for news in platform_data.get('data', []):
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title = news.get('title') or ''
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if title:
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all_titles.append(title)
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if title not in title_sources:
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title_sources[title] = []
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title_sources[title].append(platform_name)
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# 分词并统计
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word_counter = Counter()
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word_sources = {} # 记录每个词出现在哪些平台
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for title in all_titles:
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if title:
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words = jieba.cut(title)
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for word in words:
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if len(word) >= 2 and word not in self.stop_words:
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word_counter[word] += 1
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if word not in word_sources:
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word_sources[word] = set()
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for source in title_sources.get(title, []):
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word_sources[word].add(source)
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# 获取TOP N
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hot_topics = []
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total_titles = len(all_titles) if all_titles else 1
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for word, count in word_counter.most_common(top_n):
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sources = list(word_sources.get(word, []))
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cross_platform = len(sources) # 跨平台数
|
||
heat = min(int(count / total_titles * 1000), 100)
|
||
|
||
# 跨平台加成
|
||
if cross_platform >= 5:
|
||
heat = min(heat + 20, 100)
|
||
elif cross_platform >= 3:
|
||
heat = min(heat + 10, 100)
|
||
|
||
hot_topics.append({
|
||
'topic': word,
|
||
'count': count,
|
||
'heat': heat,
|
||
'cross_platform': cross_platform,
|
||
'sources': sources[:5], # 最多显示5个来源
|
||
})
|
||
|
||
return hot_topics
|
||
|
||
def get_platform_ranking(self, platforms_data: List[Dict]) -> List[Dict]:
|
||
"""
|
||
获取跨平台热度排名
|
||
|
||
合并所有平台的新闻，按热度排序
|
||
|
||
Returns:
|
||
List[Dict]: 排名列表
|
||
"""
|
||
all_news = []
|
||
|
||
for platform_data in platforms_data:
|
||
if not platform_data.get('success'):
|
||
continue
|
||
|
||
platform = platform_data['platform']
|
||
platform_name = platform_data['platform_name']
|
||
weight = platform_data['weight']
|
||
|
||
for news in platform_data.get('data', []):
|
||
title = news.get('title') or ''
|
||
rank = news.get('rank', 99)
|
||
|
||
if not title:
|
||
continue
|
||
|
||
# 计算综合热度分数
|
||
# 排名越靠前分数越高，平台权重越高分数越高
|
||
heat_score = (100 - rank) * weight
|
||
|
||
all_news.append({
|
||
'title': title,
|
||
'platform': platform,
|
||
'platform_name': platform_name,
|
||
'original_rank': rank,
|
||
'heat_score': heat_score,
|
||
'url': news.get('url') or '',
|
||
'content': news.get('content') or '',
|
||
})
|
||
|
||
# 按热度分数排序
|
||
all_news.sort(key=lambda x: x['heat_score'], reverse=True)
|
||
|
||
# 添加全局排名
|
||
for i, news in enumerate(all_news):
|
||
news['global_rank'] = i + 1
|
||
|
||
return all_news
|
||
|
||
def calculate_viral_coefficient(self, current_data: Dict,
|
||
previous_data: Dict) -> Dict:
|
||
"""
|
||
计算病毒系数K值
|
||
|
||
K值 = 当前传播量 / 上期传播量
|
||
|
||
Args:
|
||
current_data: 当前数据
|
||
previous_data: 上期数据
|
||
|
||
Returns:
|
||
{
|
||
'k_value': float, # K值
|
||
'trend': str, # 趋势（指数型/线性/衰减）
|
||
'analysis': str, # 分析
|
||
}
|
||
"""
|
||
current_score = current_data.get('total_score', 0)
|
||
previous_score = previous_data.get('total_score', 0)
|
||
|
||
if previous_score == 0:
|
||
k_value = 1.0
|
||
trend = "无历史数据"
|
||
analysis = "首次采集，无法计算K值"
|
||
else:
|
||
k_value = round(current_score / previous_score, 2)
|
||
|
||
if k_value > 1.5:
|
||
trend = "指数型爆发"
|
||
analysis = f"K值={k_value}，流量正在指数型增长！这是病毒式传播的特征，题材可能进入加速期。"
|
||
elif k_value > 1.0:
|
||
trend = "线性增长"
|
||
analysis = f"K值={k_value}，流量稳步增长，题材正在发酵中。"
|
||
elif k_value == 1.0:
|
||
trend = "平稳"
|
||
analysis = f"K值={k_value}，流量保持稳定，市场处于平衡状态。"
|
||
else:
|
||
trend = "衰减"
|
||
analysis = f"K值={k_value}，流量正在衰减，题材热度下降，注意风险。"
|
||
|
||
return {
|
||
'k_value': k_value,
|
||
'current_score': current_score,
|
||
'previous_score': previous_score,
|
||
'trend': trend,
|
||
'analysis': analysis,
|
||
}
|
||
|
||
def detect_flow_type(self, history_scores: List[int],
|
||
current_score: int) -> Dict:
|
||
"""
|
||
识别流量类型（存量流量型 vs 增量流量型）
|
||
|
||
存量流量型：出生自带顶流，流量快速到位（政策/大事件）
|
||
增量流量型：初始流量小，具备病毒传播能力（话题发酵）
|
||
|
||
Args:
|
||
history_scores: 历史得分列表（从旧到新）
|
||
current_score: 当前得分
|
||
|
||
Returns:
|
||
{
|
||
'flow_type': str, # 存量流量型/增量流量型/未知
|
||
'characteristics': List[str], # 特征
|
||
'time_window': str, # 时间窗口建议
|
||
'operation': str, # 操作建议
|
||
}
|
||
"""
|
||
if len(history_scores) < 2:
|
||
return {
|
||
'flow_type': '未知',
|
||
'characteristics': ['历史数据不足'],
|
||
'time_window': '无法判断',
|
||
'operation': '继续观察',
|
||
}
|
||
|
||
# 计算初始得分和增长率
|
||
initial_score = history_scores[0] if history_scores else 0
|
||
avg_score = sum(history_scores) / len(history_scores)
|
||
|
||
# 计算增长趋势
|
||
growth_rates = []
|
||
for i in range(1, len(history_scores)):
|
||
if history_scores[i-1] > 0:
|
||
rate = (history_scores[i] - history_scores[i-1]) / history_scores[i-1]
|
||
growth_rates.append(rate)
|
||
|
||
avg_growth = sum(growth_rates) / len(growth_rates) if growth_rates else 0
|
||
|
||
# 判断流量类型
|
||
if initial_score >= 500:
|
||
# 初始热度高 -> 存量流量型
|
||
flow_type = "存量流量型"
|
||
characteristics = [
|
||
"初始热度高",
|
||
"流量快速到位",
|
||
"可能与政策/大事件相关",
|
||
]
|
||
time_window = "时间窗口短（2-3天）"
|
||
operation = "快进快出，密切关注热度变化"
|
||
elif avg_growth > 0.2 and len([r for r in growth_rates if r > 0]) >= 2:
|
||
# 持续增长 -> 增量流量型
|
||
flow_type = "增量流量型"
|
||
characteristics = [
|
||
"初始热度低",
|
||
"逐步攀升",
|
||
"具备病毒传播特征",
|
||
]
|
||
time_window = "时间窗口长（5-10天）"
|
||
operation = "可以埋伏，等待加速"
|
||
else:
|
||
flow_type = "常规流量"
|
||
characteristics = [
|
||
"热度波动正常",
|
||
"无明显趋势",
|
||
]
|
||
time_window = "无特定窗口"
|
||
operation = "保持观望"
|
||
|
||
return {
|
||
'flow_type': flow_type,
|
||
'characteristics': characteristics,
|
||
'time_window': time_window,
|
||
'operation': operation,
|
||
'initial_score': initial_score,
|
||
'current_score': current_score,
|
||
'avg_growth': round(avg_growth * 100, 1), # 百分比
|
||
}
|
||
|
||
def get_platform_list(self) -> List[Dict]:
|
||
"""
|
||
获取所有支持的平台列表
|
||
|
||
Returns:
|
||
List[Dict]: 平台列表
|
||
"""
|
||
result = []
|
||
for code, info in self.platforms.items():
|
||
result.append({
|
||
'code': code,
|
||
'name': info['name'],
|
||
'category': info['category'],
|
||
'weight': info['weight'],
|
||
'influence': info.get('influence', 'medium'),
|
||
})
|
||
|
||
# 按权重排序
|
||
result.sort(key=lambda x: x['weight'], reverse=True)
|
||
return result
|
||
|
||
def get_platforms_by_category(self) -> Dict[str, List[str]]:
|
||
"""
|
||
按类别获取平台列表
|
||
|
||
Returns:
|
||
Dict[str, List[str]]: 类别 -> 平台代码列表
|
||
"""
|
||
categories = {}
|
||
for code, info in self.platforms.items():
|
||
category = info['category']
|
||
if category not in categories:
|
||
categories[category] = []
|
||
categories[category].append(code)
|
||
return categories
|
||
|
||
|
||
# 测试代码
|
||
if __name__ == "__main__":
|
||
fetcher = NewsFlowDataFetcher()
|
||
|
||
# 测试获取平台列表
|
||
print("=== 支持的平台列表 ===")
|
||
platforms = fetcher.get_platform_list()
|
||
print(f"共支持 {len(platforms)} 个平台：")
|
||
for p in platforms[:5]:
|
||
print(f" - {p['name']} ({p['code']}) - 权重:{p['weight']}")
|
||
|
||
# 测试获取单个平台
|
||
print("\n=== 测试获取微博热搜 ===")
|
||
result = fetcher.get_platform_news('weibo')
|
||
if result['success']:
|
||
print(f"✅ 成功获取 {result['count']} 条新闻")
|
||
print(f"前3条标题：")
|
||
for news in result['data'][:3]:
|
||
print(f" {news['rank']}. {news['title']}")
|
||
else:
|
||
print(f"❌ 失败: {result['error']}")
|
||
|
||
print("\n=== 测试获取财经平台 ===")
|
||
result = fetcher.get_multi_platform_news(category='finance')
|
||
print(f"总共 {result['total_platforms']} 个平台")
|
||
print(f"成功 {result['success_count']} 个")
|
||
print(f"失败 {result['failed_count']} 个")
|
||
|
||
# 提取股票相关新闻
|
||
stock_news = fetcher.extract_stock_related_news(result['platforms_data'])
|
||
print(f"\n✅ 提取到 {len(stock_news)} 条股票相关新闻")
|
||
|
||
# 计算流量得分
|
||
flow_score = fetcher.calculate_flow_score(result['platforms_data'])
|
||
print(f"\n流量得分: {flow_score['total_score']}")
|
||
print(f"流量等级: {flow_score['level']}")
|
||
|
||
# 获取热门话题
|
||
hot_topics = fetcher.get_hot_topics(result['platforms_data'])
|
||
print(f"\n热门话题TOP5:")
|
||
for i, topic in enumerate(hot_topics[:5], 1):
|
||
print(f" {i}. {topic['topic']} (热度:{topic['heat']}, 跨{topic['cross_platform']}平台)")
|