Strategies for Enhancing Data Engineering for High Frequency Trading Systems

Abstract

This paper examines essential techniques and best practices in data engineering that improve the efficiency and speed of high-frequency trading (HFT) systems. It discusses strategies for optimizing data ingestion, storage architectures, real-time processing, and data analytics, with a strong focus on minimizing latency and maximizing throughput. The role of hardware acceleration, including FPGA and GPU-based solutions, is also explored as a means of achieving performance improvements.

HFT systems demand advanced data engineering methods to process large volumes of market data at ultra-low latencies. Optimizing data pipelines, storage, and processing infrastructure is key to achieving the high performance required for these systems.

By integrating various data engineering principles, this paper offers a comprehensive framework for designing and maintaining high-performance data systems for HFT. It also addresses critical challenges in scalability, fault tolerance, and data integrity—factors essential for ensuring the robustness and reliability of HFT systems. The paper concludes with a set of best practices and actionable recommendations aimed at enhancing the data engineering process within the context of high-frequency trading.