AI-Infused Algorithmic Trading: Genetic Algorithms and Machine Learning in High-Frequency Trading

Abstract

In this research we explore the transformative impact of Artificial Intelligence (AI) and Genetic Algorithms (GAs) in the context of algorithmic trading, with a specific focus on High-Frequency Trading (HFT). Algorithmic trading has gained prominence for its automated execution of predefined strategies, and HFT, with its lightning-fast trades, has reshaped financial markets. Leveraging the power of AI and GAs, traders can now make data-driven decisions and optimize strategies like never before. We delve into the theory and principles of GAs, representing trading strategies as chromosomes and using fitness functions for evaluation. Moreover, we highlight practical applications, including strategy optimization, parameter tuning, and portfolio allocation. The role of AI techniques, such as machine learning and deep learning, is explored in market prediction and risk management, enabling real-time assessment and adaptive trading. Additionally, AI-driven pattern recognition techniques offer insights into market anomalies. We discuss the strategic importance of AI in market making and address challenges, such as latency and ethical considerations. Empirical analysis and case studies provide evidence of GA performance and successful AI-driven trading strategies. Looking ahead, we explore emerging AI techniques and potential advancements, emphasizing the significance of continuous exploration to shape the future of algorithmic trading in financial markets.