Investigating the Performance of Quantum Support Vector Machines for High-Frequency Trading Strategies

Abstract

High-frequency trading (HFT) has emerged as one of the most transformative paradigms in contemporary financial markets, driven by the proliferation of algorithmic trading systems, electronic exchanges, and high-speed communication networks. HFT strategies exploit millisecond-to-microsecond market inefficiencies by executing large volumes of trades at extremely low latencies, seeking incremental profit opportunities that are often invisible to traditional traders (Aldridge, 2013; Fatunmbi et al., 2022). These strategies rely on continuous monitoring of multiple financial instruments, rapid order book analysis, and the integration of diverse market signals, including price movements, trade volumes, order imbalances, and momentum indicators. The real-time, high-dimensional nature of these datasets presents significant computational and analytical challenges, particularly when attempting to maintain predictive accuracy while ensuring sub-millisecond decision-making speeds.