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AI-Powered NSE Stock Paper Trading Web Application

Tarun Jain¹, Nitin Kumar², Vidit Nagar³, Piyush Gupta⁴

Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning)

 Inderprastha Engineering College, Ghaziabad, India

Introduction
Paper trading – simulating trades without real money – is a crucial tool for aspiring traders to practice strategies in a risk-free environment. It allows investors to buy and sell securities without financial risk, helping them learn market mechanics and test strategies before committing capitalIn today’s markets, artificial intelligence (AI) has emerged as a game-changer in finance, revolutionizing how investors analyze and predict stock movements  . Machine learning models can uncover patterns in vast historical data and avoid human biases, often improving prediction accuracy and consistency. Notably, AI-powered funds and algorithms have outperformed traditional methods, indicating that AI can be the decisive factor between seizing opportunities or missing them in volatile markets  . AI techniques like price prediction models and sentiment analysis of news are increasingly used to enhance trading decisions.

This paper presents a web-based stock paper trading application for the National Stock Exchange (NSE) of India that leverages AI for educational and strategy-testing purposes. The platform provides users real- time NSE market data and allows them to execute simulated trades (no real money), while an integrated machine learning module predicts stock prices and an NLP module analyzes market sentiment. Such an application offers a sandbox for beginners and experienced traders alike to learn, practice, and validate trading strategies with guidance from AI insights. The rest of this paper details the system’s design and components: an overview of features, the technical stack employed, data processing and modeling methodology, system architecture (with diagrams), example use cases and benefits, performance results, limitations, and future enhancements.

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