The rapid evolution of financial markets has necessitated advanced methodologies in high-frequency trading (HFT) to maintain competitive advantage and optimize performance. This chapter delves into the integration of edge computing within HFT systems, a transformative approach designed to enhance trading efficiency and reduce latency. By processing data closer to its source, edge computing significantly accelerates decision-making and execution processes, addressing the critical challenge of millisecond-level delays that can impact trading outcomes. The chapter explores key aspects of edge computing implementation, including its role in real-time data processing, system reliability, and security enhancements. It highlights the advantages of localized data handling, which not only improves computational speed but also offers robust fault tolerance and reduced risk of data breaches. Through detailed examination of edge computing technologies and their applications in HFT, this chapter provides valuable insights into achieving superior trading performance in high-speed financial environments.
The complexity of trading techniques and the requirement for quick decisions are driving constant change in the financial markets environment [1]. HFT, which focuses on taking advantage of small market inefficiencies and quickly executing trades, has become a popular strategy in this high-stakes environment [2]. The reduction of latency, or the interval between the start of a transaction and its execution, was critical to HFT's efficacy [3,4]. The need for technology solutions that can process large volumes of data quickly has increased as trading algorithms become more complex [5-8]. In this perspective, edge computing represents a significant breakthrough as it provides a remedy for the latency issues that come with conventional centralized computing designs [9,10].
Processing data closer to its source as opposed to depending on centralized data centers was known as edge computing [11,12]. This strategy was especially helpful in high-frequency trading, because trading success was directly impacted by the speed at which data was processed and executed. Edge computing drastically cuts down on the amount of time needed for data transmission by locating computational resources close to trading venues and data sources at the edge of the network. By reducing latency, this localized processing makes it possible to analyze and implement trading strategies more quickly. Improved operational efficiency and performance in HFT systems are the result of the fundamental shift in trade data handling from centralized to edge computing [13].
