INTEGRATING DEEP LEARNING IN ALGORITHMIC TRADING: A NEW ERA OF DATA-DRIVEN FINANCIAL DECISION-MAKING

Abstract

Due to its ability to improve financial market prediction trading efficiency and risk management, deep learning has greatly revolutionised algorithmic trading. However, deep learning such as recurrent neural networks (RNN), long short-term memory (LSTM), convolutional neural networks (CNN), and transformers are different from traditional algorithmic trading models because they are capable of analysing large amounts of datasets, discovering patterns of signals and optimise execution strategy. In this paper, we study the application of deep learning in algorithmic trading, first by calibrating its performance classical models and then addressing the most important challenges in this field such as quality of the data, high computational requirements, and the lack of transparency of the model. This implies that AI-driven strategies significantly improve profitability as well as market liquidity. However, it still suffers from issues of regulatory compliance as well as explainability. There are a few other challenges for future work, such as inducing data reliability, incorporating quantum computing, and building interpretable AI models.