Evolution of Algorithmic Trading Research: A Systematic Literature Review and Identification of Future Research Directions

Tuhin Mukherjee, Anirban Sarkar

Abstract

Algorithmic trading has transformed modern financial markets through the integration of advanced computational techniques, quantitative models, and automated execution systems. The rapid growth of electronic trading platforms, increasing market data availability, and significant advancements in artificial intelligence (AI), machine learning (ML), deep learning (DL), and high-frequency trading (HFT) have stimulated extensive academic and industry research over the past two decades. Despite this expanding body of literature, existing studies remain fragmented across diverse research themes, methodologies, financial markets, and asset classes, creating a need for a comprehensive synthesis of current knowledge and identification of future research opportunities. This paper presents a comprehensive literature review of algorithmic trading research published with a special emphasize between 2000 and 2026. The review systematically examines the evolution of algorithmic trading, major algorithmic trading strategies, theoretical foundations, data sources, research methodologies, and performance evaluation metrics employed in prior studies. The review further categorizes existing research into key thematic areas, including trend-following strategies, statistical arbitrage, market making, portfolio optimization, high-frequency trading, sentiment-based trading, and cryptocurrency algorithmic trading. The analysis identifies several significant research gaps that continue to limit the practical implementation and academic advancement of algorithmic trading. Based on these findings, the paper proposes a comprehensive future research agenda emphasizing hybrid AI-driven trading frameworks. By synthesizing existing knowledge and identifying critical research gaps, this review provides a valuable reference for researchers, practitioners, financial institutions, and policymakers seeking to advance the theory and practice of algorithmic trading. 

Source: semanticscholar · PDF

Read the AI summary and key takeaways for traders on WOBR Quant Research.