AI Assistant for Simulating Arbitrage Strategies in Finance: Architecture, Mathematical Model, and Didactic Potential
DOI:
https://doi.org/10.52575/2687-0932-2025-52-4-887-896Keywords:
AI Assistant, modular architecture, arbitrage strategy, financial modeling, adaptive learning systemAbstract
This research addresses the critical gap between theoretical knowledge and practical skills in modern financial education by designing a specialized AI assistant for simulating arbitrage strategies.
The study aims to develop a comprehensive solution that moves beyond traditional static teaching methods. The proposed methodology is based on a modular system architecture that integrates real-time data collection, an analytical core for identifying opportunities, a trading simulator, and an AI-powered dialog interface for feedback. A key innovation is the application of a reinforcement learning model, where the AI assistant acts as a meta-agent, dynamically adapting the complexity of the market simulation to the student's proficiency level. The main results include the formalized architecture of the system and a set of criteria for evaluating its pedagogical effectiveness. The study concludes that the implemented AI assistant creates an intelligent learning environment capable of providing personalized, hands-on training, thereby significantly enhancing the quality of future financiers' preparation for the digital economy.
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Memarian B., Doleck T. 2024. A scoping review of reinforcement learning in education. Computers and Education Open, 6: 100175. https://doi.org/10.1016/j.caeo.2024.100175
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Thaiya M.S., Julia K., Mbugua S. 2022. On software modular architecture: concepts, metrics and trends. International Journal of Computer and Organization Trends, 12(1): 3–10.
Tzirides A.O.O. 2024. Combining human and artificial intelligence for enhanced AI literacy in higher education. Computers and Education Open, 6: 100184. https://doi.org/10.1016/j.caeo.2024.100184
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