tools/news_tool.py

# from crewai.tools import tool
# from langchain_community.tools.yahoo_finance_news import YahooFinanceNewsTool
# import json

# @tool("get_stock_news")
# def get_stock_news(company_ticker: str) -> str:
#     """
#     Retrieves the latest news and market sentiment for a given stock ticker 
#     from Yahoo Finance News.

#     Args:
#     company_ticker: str -> the ticker symbol of the company (e.g., 'AAPL', 'GOOGL')
#     """
#     try:
#         # Initialize Yahoo Finance News tool
#         yahoo_news_tool = YahooFinanceNewsTool()
        
#         # Get news for the company
#         news_result = yahoo_news_tool.run(company_ticker)
        
#         if not news_result:
#             return f"No news found for ticker: {company_ticker}"
        
#         # If the result is already a string, return it
#         if isinstance(news_result, str):
#             return news_result
        
#         # If it's a dict or list, format it as JSON
#         return json.dumps(news_result, indent=2, ensure_ascii=False)
        
#     except Exception as e:
#         return f"Error retrieving news for {company_ticker}: {str(e)}"
    

from crewai.tools import tool
import finnhub
import os
import json
from datetime import datetime, timedelta

@tool("get_stock_news")
def get_stock_news(company_ticker: str) -> str:
    """
    Retrieves the latest company news for a given stock ticker from Finnhub.
    Gets news from the last 30 days to capture recent developments and market sentiment.

    Args:
    company_ticker: str -> the ticker symbol of the company (e.g., 'AAPL', 'GOOGL')
    """
    try:
        # Initialize Finnhub client
        finnhub_client = finnhub.Client(api_key=os.getenv("FINNHUB_API_KEY"))
        
        # Calculate date range (last 30 days)
        end_date = datetime.now()
        start_date = end_date - timedelta(days=30)
        
        # Format dates as required by Finnhub API
        from_date = start_date.strftime("%Y-%m-%d")
        to_date = end_date.strftime("%Y-%m-%d")
        
        # Get company news
        news_data = finnhub_client.company_news(
            symbol=company_ticker, 
            _from=from_date, 
            to=to_date
        )
        
        if not news_data:
            return f"No recent news found for ticker: {company_ticker} in the last 30 days"
        
        # Format the news data using all available Finnhub fields
        formatted_news = []
        for article in news_data[:20]:  # Analyze top 15 most recent articles
            formatted_article = {
                "id": article.get("id", "unknown"),
                "headline": article.get("headline", "No headline available"),
                "summary": article.get("summary", "No summary available"),
                "category": article.get("category", "General"),
                "source": article.get("source", "Unknown source"),
                "url": article.get("url", ""),
                "image": article.get("image", ""),
                "datetime": datetime.fromtimestamp(article.get("datetime", 0)).strftime("%Y-%m-%d %H:%M:%S") if article.get("datetime") else "Unknown date",
                "unix_timestamp": article.get("datetime", 0),
                "related_stocks": article.get("related", []) if article.get("related") else []
            }
            formatted_news.append(formatted_article)
        
        # Sort by datetime (most recent first)
        formatted_news.sort(key=lambda x: x["unix_timestamp"], reverse=True)
        
        # Analyze news categories and sources
        categories = {}
        sources = {}
        related_companies = set()
        
        for article in formatted_news:
            # Count categories
            category = article["category"]
            categories[category] = categories.get(category, 0) + 1
            
            # Count sources
            source = article["source"]
            sources[source] = sources.get(source, 0) + 1
            
            # Collect related companies/stocks
            if article["related_stocks"]:
                related_companies.update(article["related_stocks"])
        
        # Create comprehensive news analysis
        news_analysis = {
            "company_ticker": company_ticker,
            "analysis_period": {
                "from_date": from_date,
                "to_date": to_date,
                "days_analyzed": 30
            },
            "news_metrics": {
                "total_articles_found": len(news_data),
                "articles_analyzed": len(formatted_news),
                "average_articles_per_day": round(len(news_data) / 30, 2)
            },
            "news_breakdown": {
                "categories": dict(sorted(categories.items(), key=lambda x: x[1], reverse=True)),
                "top_sources": dict(sorted(sources.items(), key=lambda x: x[1], reverse=True)[:5]),
                "related_companies_mentioned": list(related_companies)[:10] if related_companies else []
            },
            "recent_articles": formatted_news
        }
        
        return json.dumps(news_analysis, indent=2, ensure_ascii=False)
        
    except Exception as e:
        return f"Error retrieving news for {company_ticker}: {str(e)}\nPlease ensure FINNHUB_API_KEY is properly set in .env file"