Wong JiaJie
National University of Singapore, Singapore
Publications
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Research Article
Portfolio Optimization through a Multi-modal Deep Reinforcement Learning Framework
Author(s): Wong JiaJie and LIU LiLi*
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 Multi- modal 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 Indu.. Read More»