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Volume 3 Number 1 2025

Article Contents

REVIEWS   (Open Access)
Contents Vol 3 (1)

Artificial Intelligence in Enhancing High Frequency Trading Strategies

Sanjana Ahmed Chaity1*, Md. Ahsan Shoishob1

+ Author Affiliations

Business and Social Sciences 3 (1) 1-9 https://doi.org/10.25163/business.3110311

Submitted: 05 May 2025 Revised: 11 July 2025  Published: 14 July 2025 

Abstract

In the world of trading and complex market transactions, technology has greatly revolutionized and adapted to the fast-growing data and complexities of modern trading strategies. Computers and advanced algorithms have paved the way for a more digitalized world where virtual transactions are common place. Not only can people buy and sell products online, but can also do these transactions automatically. However, thanks to complex AI and ML algorithms; high-frequency trading or HFT strategies can be formulated for highly efficient and optimized automated trading. There are various HFT firms that uses complex ML and AI algorithms to identify and dynamically adjust buying or selling prices according to available market data to maximize profit and save time. Various businesses can take advantage of HFT strategies to optimize selling and buying frequencies. Relevant data is required for executing a HFT strategy properly, along with a sophisticated AI algorithm which is capable of handling vast amounts of data dynamically. The key features of HFT makes it a compelling route for optimized trading and buying. These key features include: lightning-fast transactions, dynamic market data analysis and execution of numerous transactions at once. This research paper aims to understand how AI and ML algorithms can dynamically optimize HFT strategies to further take leverage of available market data. The research paper discusses the implementation of Deep Reinforcement Learning or DLL framework for optimizing HFT strategies. In particular, the implementation discussed adopts a multi-time scale DRL for improved dynamic data processing. This research paper aims to shed light into the effectiveness of an AI-driven solution for improving HFT efficiency for better and faster trading using a simple deep reinforcement learning or DRL machine learning algorithm.

Keywords: Deep Reinforced Learning, Algorithms, Market Trend Analysis, Predictive Analysis, Order Book Data.

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