制約を課した深層強化学習による最適注文執行手法の検討

抄録

For traders, it is important to minimize execution costs and achieve more efficient order execution. Since the mechanisms for incurring costs are unclear, being able to properly account for them will lead to lower execution costs and higher revenues. In order to achieve order execution with minimal costs, methods that model and infer market principles have been used. In recent years, model-free offline reinforcement learning methods have widely been utilized. However, the data on financial instruments contains a lot of noise, which makes learning hard and makes it difficult to converge to the optimal trading method. In this paper, we propose an optimal order execution method that improves performance by imposing constraints on the model. Through experiments, we have found that by imposing appropriate constraints, we can improve the performance of the optimal order execution method. We show that by setting appropriate constraints, we can achieve improved order execution compared to conventional methods.