Implementation of Python-Based Topsis Method for Best Stock Selection Analysis Using Yahoo Finance

Abstract

This study developed a web-based application implementing the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method for stock investment analysis and recommendations. The application was built using the Flask framework and integrated with the Yahoo Finance API for real-time stock data retrieval. The TOPSIS method evaluated stock alternatives based on criteria such as closing price, P/E ratio, revenue growth, and dividend per share. Testing included functional evaluation, response time analysis, and simulations of three investor scenarios: High Risk-High Return, Low Risk-Low Return, and Balanced. Results indicate that the application effectively delivers stock recommendations aligned with investor preferences, achieving an average response time of 1–4 seconds per feature. Simulations highlight its adaptability in adjusting criteria weights to match different risk profiles. Despite limitations due to external API dependencies, the application demonstrates effectiveness as a decision support tool for stock investment, offering accessibility and flexibility to investors.