Optimal Execution with Reinforcement Learning
Yadh Hafsi, Edoardo Vittori
本研究通过强化学习研究最佳执行策略的发展,旨在确定交易者在有限的时间内买卖库存的最有效方法。 我们提出的模型利用从限价单簿当前状态衍生的输入特性,并以高频运行,以最大限度地控制。 为了模拟这种环境并克服与依赖历史数据相关的限制,我们利用多代理市场模拟器ABIDES,该模拟器在限价单簿中提供了多样化的深度水平。 我们提出了自定义的MDP配方,然后是我们的方法的结果,并根据标准执行策略对性能进行基准测试。 结果表明,强化学习代理优于标准策略,并为现实世界的交易应用提供了实际基础。
This study investigates the development of an optimal execution strategy through reinforcement learning, aiming to determine the most effective approach for traders to buy and sell inventory within a finite time horizon. Our proposed model leverages input features derived from the current state of the limit order book and operates at a high frequency to maximize control. To simulate this environment and overcome the limitations associated with relying on historical data, we utilize the multi-age...