Developed an innovative application designed to filter and analyze stock-related news, empowering traders with actionable insights. The tool is built on a scalable architecture using Python, PostgreSQL, and Domain-Driven Design (DDD) principles, ensuring efficiency and reliability.

The project involves scraping financial news from trusted sources like Yahoo Finance and Investing.com, categorizing articles, and performing AI-powered sentiment analysis to provide detailed summaries and evaluations. Leveraged tools such as OpenAI, Hugging Face, and VADER Sentiment Analyzer for advanced natural language processing, enabling accurate sentiment scoring and categorization.

This system allows traders to quickly assess market sentiment and trends, delivering reports with clear recommendations based on sentiment scores. By streamlining the process of analyzing vast amounts of financial news, the Stocks News Analyzer saves time and improves decision-making for trading activity.