Harnessing Deep Q-Learning for Enhanced Statistical Arbitrage in High-Frequency Trading: A Comprehensive Exploration. (arXiv:2311.10718v1 [q-fin.TR])

The realm of High-Frequency Trading (HFT) is characterized by rapid
decision-making processes that capitalize on fleeting market inefficiencies. As
the financial markets become increasingly competitive, there is a pressing need
for innovative strategies that can adapt and evolve with changing market
dynamics. Enter Reinforcement Learning (RL), a branch of machine learning where
agents learn by interacting with their environment, making it an intriguing
candidate for HFT applications. This paper dives deep into the integration of
RL in statistical arbitrage strategies tailored for HFT scenarios. By
leveraging the adaptive learning capabilities of RL, we explore its potential
to unearth patterns and devise trading strategies that traditional methods
might overlook. We delve into the intricate exploration-exploitation trade-offs
inherent in RL and how they manifest in the volatile world of HFT. Furthermore,
we confront the challenges of applying RL in non-stationary environments,
typical of financial markets, and investigate methodologies to mitigate
associated risks. Through extensive simulations and backtests, our research
reveals that RL not only enhances the adaptability of trading strategies but
also shows promise in improving profitability metrics and risk-adjusted
returns. This paper, therefore, positions RL as a pivotal tool for the next
generation of HFT-based statistical arbitrage, offering insights for both
researchers and practitioners in the field.