Optimizing Portfolio Allocation with Deep Q-Learning: A Reinforcement Learning Approach

The evolving financial market requires strategies for their evaluation to incorporate a framework that gives substantive attention to returns and the risks they bear. This study proposes a unified model for portfolio performance assessment using quantitative financial metrics like daily returns, cumulative returns, and the Sharpe Ratio. We model the optimization of portfolio allocations in a reinforcement-learning environment by using a Deep Q-Network agent, thus dynamically striking a balance between investment returns and the volatility of the market itself. Our empirical findings indicate that the designed portfolio produces a Sharpe ratio 2.61, which suggests the performance was appreciably risk-adjusted. Furthermore, the benchmark portfolio against a representative market index Sharpe ratio of 2.65 is further evidence of the capabilities of the model in achieving competitive risk-adjusted returns. The above findings make one cognizant of the importance of risk assessment in optimizing trading strategy, adding to the body of quantitative finance by providing New York evidence into a more structured data-driven means of portfolio evaluation. This study will allow investors or analysts to make better decisions by applying advanced algorithmic trading methodology.