Portfolio Optimization through a Multi-modal Deep Reinforcement Learning Framework

In today’s increasingly complex and volatile stock markets, leveraging advanced machine learning and quantitative techniques is becoming indispensable for enhancing trading strategies and optimizing returns. This study introduces a so- phisticated Multimodal framework that combines Deep Rein- forcement Learning (DRL) with Algorithmic Trading Signals and Price Forecasts to improve risk-adjusted returns in equity trad- ing. Utilizing the Proximal Policy Optimization (PPO) algorithm within a custom trading environment built on the FinRL library, our approach integrates advanced algorithmic signals−such as moving average crossovers and oscillator divergence−and incorporates enriched price forecasts from Long Short-Term Memory (LSTM) networks. The proposed framework was rigorously evaluated using a diverse set of 29 out of 30 constituent stocks within the Dow Jones Industrial Average (DJI). The empirical results highlight the effectiveness of the Multi-modal DRL approach, demonstrating significant outperformance over traditional benchmarks, with an annualized return of 16.24%, an annualized standard deviation of 17.49%, a Sharpe Ratio of 0.86, and a Sortino Ratio of 1.27. These findings underscore the potential of Multi-modal DRL frameworks to offer consistent, robust performance and contribute to advancing trading strategies in dynamic market environments.