Algorithmic trading using deep learning: Opportunities, challenges and future directions

Algorithmic trading leveraging deep learning presents significant opportunities to enhance the accuracy and efficiency of financial market predictions by capturing complex patterns in vast datasets. This paper investigates the integration of advanced deep learning architectures, such as deep reinforcement learning and recurrent neural networks, to develop adaptive trading strategies capable of dynamic decision-making under market uncertainties. It also explores the challenges related to data quality, model interpretability, and overfitting, proposing future directions to address these issues and improve robustness. Ultimately, this study aims to contribute to the evolution of intelligent, data-driven algorithmic trading systems with superior performance and risk management capabilities.