Harnessing Reinforcement Learning for Agile Portfolio Management in Nifty 50 Stock Analysis

Abstract

This ground-breaking research paper introduces a novel application of Reinforcement Learning (RL) for portfolio management 
in the context of Nifty 50 stock analysis. Traditional portfolio management methods often suffer from static allocation models 
that lack adaptability to market dynamics, resulting in suboptimal performance and increased risk. In response to this 
limitation, we propose a pioneering approach that harnesses the power of RL to construct an agile and adaptive portfolio 
management system capable of dynamically adjusting stock allocations based on real-time market trends and risk assessments. 
By leveraging the learning capabilities of RL, we demonstrate the system's efficacy in creating resilient and flexible investment 
strategies, ultimately leading to optimized portfolio performance within the Nifty 50 index.