Exploring Leading Practices in Algorithmic Trading: Insights From Survey and Case Studies on Machine Learning Techniques and Market Dynamics

This chapter explores algorithmic trading strategies, their applications, and the role of emerging AI and machine learning technologies in optimizing trading performance. Using a mixed-methods approach, it combines quantitative survey data from 65 high-frequency trading professionals with qualitative case studies to provide unique insights into real-world trading practices. The survey reveals key factors influencing algorithmic trading decisions, such as risk management and data quality. Through case studies, we further examine the effectiveness of various strategies and their impact on market dynamics, including liquidity, volatility, and price discovery. The research contributes valuable professional perspectives that are often underrepresented in quantitative studies, offering a comprehensive analysis of the current landscape, challenges, and ethical concerns surrounding algorithmic trading. Findings and recommendations for future research are discussed in the final sections.