#!/usr/bin/env python3
"""
中国财经数据聚合工具
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
"""

import requests
import json
import time
import random
from datetime import datetime, timedelta
from typing import List, Dict, Optional
import re
from bs4 import BeautifulSoup
import pandas as pd


class ChineseFinanceDataAggregator:
    """中国财经数据聚合器"""
    
    def __init__(self):
        self.headers = {
            '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'
        }
        self.session = requests.Session()
        self.session.headers.update(self.headers)
    
    def get_stock_sentiment_summary(self, ticker: str, days: int = 7) -> Dict:
        """
        获取股票情绪分析汇总
        整合多个可获取的中国财经数据源
        """
        try:
            # 1. 获取财经新闻情绪
            news_sentiment = self._get_finance_news_sentiment(ticker, days)
            
            # 2. 获取股吧讨论热度 (如果可以获取)
            forum_sentiment = self._get_stock_forum_sentiment(ticker, days)
            
            # 3. 获取财经媒体报道
            media_sentiment = self._get_media_coverage_sentiment(ticker, days)
            
            # 4. 综合分析
            overall_sentiment = self._calculate_overall_sentiment(
                news_sentiment, forum_sentiment, media_sentiment
            )
            
            return {
                'ticker': ticker,
                'analysis_period': f'{days} days',
                'overall_sentiment': overall_sentiment,
                'news_sentiment': news_sentiment,
                'forum_sentiment': forum_sentiment,
                'media_sentiment': media_sentiment,
                'summary': self._generate_sentiment_summary(overall_sentiment),
                'timestamp': datetime.now().isoformat()
            }
            
        except Exception as e:
            return {
                'ticker': ticker,
                'error': f'数据获取失败: {str(e)}',
                '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',
                'timestamp': datetime.now().isoformat()
            }
    
    def _get_finance_news_sentiment(self, ticker: str, days: int) -> Dict:
        """获取财经新闻情绪分析"""
        try:
            # 搜索相关新闻标题和内容
            company_name = self._get_company_chinese_name(ticker)
            search_terms = [ticker, company_name] if company_name else [ticker]
            
            news_items = []
            for term in search_terms:
                # 这里可以集成多个新闻源
                items = self._search_finance_news(term, days)
                news_items.extend(items)
            
            # 简单的情绪分析
            positive_count = 0
            negative_count = 0
            neutral_count = 0
            
            for item in news_items:
                sentiment = self._analyze_text_sentiment(item.get('title', '') + ' ' + item.get('content', ''))
                if sentiment > 0.1:
                    positive_count += 1
                elif sentiment < -0.1:
                    negative_count += 1
                else:
                    neutral_count += 1
            
            total = len(news_items)
            if total == 0:
                return {'sentiment_score': 0, 'confidence': 0, 'news_count': 0}
            
            sentiment_score = (positive_count - negative_count) / total
            
            return {
                'sentiment_score': sentiment_score,
                'positive_ratio': positive_count / total,
                'negative_ratio': negative_count / total,
                'neutral_ratio': neutral_count / total,
                'news_count': total,
                'confidence': min(total / 10, 1.0)  # 新闻数量越多，置信度越高
            }
            
        except Exception as e:
            return {'error': str(e), 'sentiment_score': 0, 'confidence': 0}
    
    def _get_stock_forum_sentiment(self, ticker: str, days: int) -> Dict:
        """获取股票论坛讨论情绪 (模拟数据，实际需要爬虫)"""
        # 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
        # 实际实现需要更复杂的爬虫技术
        
        return {
            'sentiment_score': 0,
            'discussion_count': 0,
            'hot_topics': [],
            'note': '股票论坛数据获取受限，建议关注官方财经新闻',
            'confidence': 0
        }
    
    def _get_media_coverage_sentiment(self, ticker: str, days: int) -> Dict:
        """获取媒体报道情绪"""
        try:
            # 可以集成RSS源或公开的财经API
            coverage_items = self._get_media_coverage(ticker, days)
            
            if not coverage_items:
                return {'sentiment_score': 0, 'coverage_count': 0, 'confidence': 0}
            
            # 分析媒体报道的情绪倾向
            sentiment_scores = []
            for item in coverage_items:
                score = self._analyze_text_sentiment(item.get('title', '') + ' ' + item.get('summary', ''))
                sentiment_scores.append(score)
            
            avg_sentiment = sum(sentiment_scores) / len(sentiment_scores) if sentiment_scores else 0
            
            return {
                'sentiment_score': avg_sentiment,
                'coverage_count': len(coverage_items),
                'confidence': min(len(coverage_items) / 5, 1.0)
            }
            
        except Exception as e:
            return {'error': str(e), 'sentiment_score': 0, 'confidence': 0}
    
    def _search_finance_news(self, search_term: str, days: int) -> List[Dict]:
        """搜索财经新闻 (示例实现)"""
        # 这里可以集成多个新闻源的API或RSS
        # 例如：财联社、新浪财经、东方财富等
        
        # 模拟返回数据结构
        return [
            {
                'title': f'{search_term}相关财经新闻标题',
                'content': '新闻内容摘要...',
                'source': '财联社',
                'publish_time': datetime.now().isoformat(),
                'url': 'https://example.com/news/1'
            }
        ]
    
    def _get_media_coverage(self, ticker: str, days: int) -> List[Dict]:
        """获取媒体报道 (示例实现)"""
        # 可以集成Google News API或其他新闻聚合服务
        return []
    
    def _analyze_text_sentiment(self, text: str) -> float:
        """简单的中文文本情绪分析"""
        if not text:
            return 0
        
        # 简单的关键词情绪分析
        positive_words = ['上涨', '增长', '利好', '看好', '买入', '推荐', '强势', '突破', '创新高']
        negative_words = ['下跌', '下降', '利空', '看空', '卖出', '风险', '跌破', '创新低', '亏损']
        
        positive_count = sum(1 for word in positive_words if word in text)
        negative_count = sum(1 for word in negative_words if word in text)
        
        if positive_count + negative_count == 0:
            return 0
        
        return (positive_count - negative_count) / (positive_count + negative_count)
    
    def _get_company_chinese_name(self, ticker: str) -> Optional[str]:
        """获取公司中文名称"""
        # 简单的映射表，实际可以从数据库或API获取
        name_mapping = {
            'AAPL': '苹果',
            'TSLA': '特斯拉',
            'NVDA': '英伟达',
            'MSFT': '微软',
            'GOOGL': '谷歌',
            'AMZN': '亚马逊'
        }
        return name_mapping.get(ticker.upper())
    
    def _calculate_overall_sentiment(self, news_sentiment: Dict, forum_sentiment: Dict, media_sentiment: Dict) -> Dict:
        """计算综合情绪分析"""
        # 根据各数据源的置信度加权计算
        news_weight = news_sentiment.get('confidence', 0)
        forum_weight = forum_sentiment.get('confidence', 0)
        media_weight = media_sentiment.get('confidence', 0)
        
        total_weight = news_weight + forum_weight + media_weight
        
        if total_weight == 0:
            return {'sentiment_score': 0, 'confidence': 0, 'level': 'neutral'}
        
        weighted_sentiment = (
            news_sentiment.get('sentiment_score', 0) * news_weight +
            forum_sentiment.get('sentiment_score', 0) * forum_weight +
            media_sentiment.get('sentiment_score', 0) * media_weight
        ) / total_weight
        
        # 确定情绪等级
        if weighted_sentiment > 0.3:
            level = 'very_positive'
        elif weighted_sentiment > 0.1:
            level = 'positive'
        elif weighted_sentiment > -0.1:
            level = 'neutral'
        elif weighted_sentiment > -0.3:
            level = 'negative'
        else:
            level = 'very_negative'
        
        return {
            'sentiment_score': weighted_sentiment,
            'confidence': total_weight / 3,  # 平均置信度
            'level': level
        }
    
    def _generate_sentiment_summary(self, overall_sentiment: Dict) -> str:
        """生成情绪分析摘要"""
        level = overall_sentiment.get('level', 'neutral')
        score = overall_sentiment.get('sentiment_score', 0)
        confidence = overall_sentiment.get('confidence', 0)
        
        level_descriptions = {
            'very_positive': '非常积极',
            'positive': '积极',
            'neutral': '中性',
            'negative': '消极',
            'very_negative': '非常消极'
        }
        
        description = level_descriptions.get(level, '中性')
        confidence_level = '高' if confidence > 0.7 else '中' if confidence > 0.3 else '低'
        
        return f"市场情绪: {description} (评分: {score:.2f}, 置信度: {confidence_level})"


def get_chinese_social_sentiment(ticker: str, curr_date: str) -> str:
    """
    获取中国社交媒体情绪分析的主要接口函数
    """
    aggregator = ChineseFinanceDataAggregator()
    
    try:
        # 获取情绪分析数据
        sentiment_data = aggregator.get_stock_sentiment_summary(ticker, days=7)
        
        # 格式化输出
        if 'error' in sentiment_data:
            return f"""
中国市场情绪分析报告 - {ticker}
分析日期: {curr_date}

⚠️ 数据获取限制说明:
{sentiment_data.get('fallback_message', '数据获取遇到技术限制')}

建议:
1. 重点关注财经新闻和基本面分析
2. 参考官方财报和业绩指导
3. 关注行业政策和监管动态
4. 考虑国际市场情绪对中概股的影响

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。
"""
        
        overall = sentiment_data.get('overall_sentiment', {})
        news = sentiment_data.get('news_sentiment', {})
        
        return f"""
中国市场情绪分析报告 - {ticker}
分析日期: {curr_date}
分析周期: {sentiment_data.get('analysis_period', '7天')}

📊 综合情绪评估:
{sentiment_data.get('summary', '数据不足')}

📰 财经新闻情绪:
- 情绪评分: {news.get('sentiment_score', 0):.2f}
- 正面新闻比例: {news.get('positive_ratio', 0):.1%}
- 负面新闻比例: {news.get('negative_ratio', 0):.1%}
- 新闻数量: {news.get('news_count', 0)}条

💡 投资建议:
基于当前可获取的中国市场数据，建议投资者:
1. 密切关注官方财经媒体报道
2. 重视基本面分析和财务数据
3. 考虑政策环境对股价的影响
4. 关注国际市场动态

⚠️ 数据说明:
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。
建议结合其他分析维度进行综合判断。

生成时间: {sentiment_data.get('timestamp', datetime.now().isoformat())}
"""
        
    except Exception as e:
        return f"""
中国市场情绪分析 - {ticker}
分析日期: {curr_date}

❌ 分析失败: {str(e)}

💡 替代建议:
1. 查看财经新闻网站的相关报道
2. 关注雪球、东方财富等投资社区讨论
3. 参考专业机构的研究报告
4. 重点分析基本面和技术面数据

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。
"""
