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.

该研究论文探讨了将强化学习应用于统计套利策略中的高频交易场景，通过利用强化学习的自适应学习能力，发现其可以揭示传统方法可能忽略的模式并设计交易策略，同时解决在金融市场中这一非稳态环境中应用强化学习所面临的挑战，并研究缓解相关风险的方法。通过广泛的模拟和回测，研究结果表明，强化学习不仅提升了交易策略的适应性，而且显示了改善盈利指标和风险调整回报的潜力，从而将其定位为下一代基于高频交易的统计套利的关键工具，为该领域的研究人员和从业者提供了洞察。