论文标题
在基于霍克斯流程的限制订单簿模型下进行市场建设的深入强化学习
Deep Reinforcement Learning for Market Making Under a Hawkes Process-Based Limit Order Book Model
论文作者
论文摘要
最佳市场制造的随机控制问题是定量融资的核心问题之一。在本文中，对基于强化的学习控制器进行了培训，该控制器受到弱一致的多元鹰队基于过程的限制订单模拟器的培训，以获得市场制作控制。所提出的方法利用了蒙特卡洛进行回测的优势，并有助于在弱一致的限制订单模型下对市场建设的研究。随后的深入增强学习控制器与多个市场制作基准进行了比较，结果表明，即使在巨大的交易成本下，它在各种风险奖励指标方面都具有出色的性能。
The stochastic control problem of optimal market making is among the central problems in quantitative finance. In this paper, a deep reinforcement learning-based controller is trained on a weakly consistent, multivariate Hawkes process-based limit order book simulator to obtain market making controls. The proposed approach leverages the advantages of Monte Carlo backtesting and contributes to the line of research on market making under weakly consistent limit order book models. The ensuing deep reinforcement learning controller is compared to multiple market making benchmarks, with the results indicating its superior performance with respect to various risk-reward metrics, even under significant transaction costs.
