Published Paper Details:

In today's volatile financial markets, technology-driven decision-making is becoming increasingly important. Traditional trading platforms often lack personalization, predictive intelligence, and seamless integration with modern technologies. This research paper presents the design and implementation of a modern trading platform that integrates a MongoDB database, machine learning (ML) models in Python, and a full-stack web architecture using React and Node.js. The system is designed to connect with live stock market APIs, process financial data in real time, and generate intelligent predictions that guide users in making trading decisions. The platform includes user authentication, portfolio management, risk assessment, and ML-based stock movement prediction. In addition, modern UI/UX design principles are adopted to ensure a responsive and attractive frontend. The system is unique because it combines real-time financial data, cloud-ready backend architecture, and machine learning services, which makes it both scalable and adaptable. Experimental results demonstrate the system's capability to provide fast, accurate, and user-friendly insights compared to existing solutions. This paper provides a comprehensive analysis of architecture, methodology, implementation, results, and future enhancements.