We focus on the problem of market making in high-frequency trading. Market making is a critical function in financial markets that involves providing liquidity by buying and selling assets. However, the increasing complexity of financial markets and the high volume of data generated by tick-level trading makes it challenging to develop effective market making strategies. To address this challenge, we propose a deep reinforcement learning approach that fuses tick-level data with periodic prediction signals to develop a more accurate and robust market making strategy. Our results of market making strategies based on different deep reinforcement learning algorithms under the simulation scenarios and real data experiments in the cryptocurrency markets show that the proposed framework outperforms existing methods in terms of profitability and risk management.

翻译：本研究聚焦于高频交易中的做市问题。做市作为金融市场的重要功能，通过买卖资产为市场提供流动性。然而，金融市场的日益复杂性以及逐笔交易产生的高数据量，使得开发有效的做市策略面临挑战。为应对这一挑战，我们提出一种深度融合逐笔数据与周期预测信号的深度强化学习方法，旨在构建更精确、更稳健的做市策略。基于不同深度强化学习算法构建的做市策略在模拟场景及加密货币市场真实数据实验中的结果表明，本文提出的框架在盈利能力和风险管理方面均优于现有方法。