Current Volume 9
Abstract
The rapid evolution of financial technology (FinTech) has transformed stock market participation for retail investors across the globe. Despite the proliferation of digital trading platforms, beginners continue to face significant financial risks due to insufficient practical knowledge and the absence of structured simulation environments. This paper presents the design and implementation of an Automated Algo Trading System with Subscription-Based Paper Trading—a web-based platform enabling users to simulate stock market trades using virtual funds in a completely risk-free environment. The system integrates a subscription-based access model (Free and Premium tiers) to deliver a scalable, monetizable, and beginner-friendly trading education platform. Built using Python (FastAPI backend), React.js (frontend), and Firebase (authentication and real-time database), the system supports real-time market data display, virtual Buy/Sell operations, portfolio management, profit/loss analytics, and structured reporting. Evaluation across multiple functional test cases demonstrates the system's effectiveness in bridging the gap between theoretical financial knowledge and practical trading experience, particularly for novice retail investors in emerging markets such as India.
Keywords
Algorithmic Trading, Paper Trading, FinTech, Subscription Model, Firebase, React.js, Portfolio Management, Stock Market Simulation, FastAPI, Machine Learning
Citations
IRE Journals:
Abha Dhirendra Singh, Dr. Prakash Kene "Automated Algo Trading System with Subscription-Based Paper Trading" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 4428-4436 https://doi.org/10.64388/IREV9I11-1718363
IEEE:
Abha Dhirendra Singh, Dr. Prakash Kene
"Automated Algo Trading System with Subscription-Based Paper Trading" Iconic Research And Engineering Journals, 9(11) https://doi.org/10.64388/IREV9I11-1718363
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