This paper presents a stock trading recommendation system that integrates news sentiment analysis with the relative strength index (RSI) to provide informed buy–sell decisions. The system uses a rule-based natural language processing (NLP) approach to analyze recent news articles and combines the resulting sentiment scores with the RSI, which tracks stock momentum. By evaluating seven days of news data, the system assigns a sentiment score (1 to 100) that reflects market sentiment, while the RSI identifies overbought or oversold conditions. This combined approach allows traders to make data-driven buy, sell, or hold decisions in real time. In this study, we conducted a comparative study with benchmark indices across various subsets of stocks to evaluate their relative performance, highlighting our system’s competitive edge in terms of accuracy, profitability, and lightweight design with low computational cost. The results showed the system’s adaptability across different market segments and its potential to enhance trading outcomes. By integrating real-time sentiment analysis with technical indicators, the system offers a practical and actionable investment strategy.