Artificial Intelligence in Enhancing High Frequency Trading Strategies

Sanjana Ahmed Chaity¹*, Md. Ahsan Shoishob¹

This research underscores the capacity of Artificial Intelligence-facilitated Deep Reinforcement Learning to enhance high-frequency trading, thereby facilitating accelerated, more intelligent, and financially advantageous decision-making.

Abstract

Background: The digital transformation of global financial markets has revolutionized trading operations, with High-Frequency Trading (HFT) emerging as a critical component of modern finance. HFT relies on advanced algorithms to execute thousands of trades in milliseconds, exploiting minimal price variations. Yet, conventional Machine Learning (ML) models such as LSTM and Random Forest struggle to manage dynamic, unlabeled, and high-volume financial data effectively.

Methods: This study proposes a Deep Reinforcement Learning (DRL) framework to enhance HFT performance by learning directly from real-time order book data. The DRL model integrates Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to process both high- and low-frequency market signals, allowing adaptive and data-driven decision-making without the need for pre-labeled datasets.

Results: Through comparative analysis with traditional ML models, the DRL framework achieved superior outcomes, demonstrating a 62.8%-win rate and a profit factor of 2.4. The results indicate enhanced prediction accuracy, improved market adaptability, and greater trading profitability compared to existing algorithms.

Conclusion: The findings confirm that DRL can autonomously optimize HFT strategies by efficiently interpreting complex market dynamics. Although implementation demands substantial technological investment, the model’s success underscores its potential to transform automated trading systems, particularly in emerging markets like Bangladesh, fostering smarter, faster, and more resilient financial ecosystems.

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

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