Optimal Execution with Reinforcement Learning
This addresses the problem of execution optimization for traders, but it appears incremental as it applies existing reinforcement learning methods to a specific domain with benchmark improvements.
This study tackled the problem of developing an optimal execution strategy for traders to buy and sell inventory within a finite time horizon using reinforcement learning, resulting in the agent outperforming standard strategies and providing a practical foundation for real-world trading applications.
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-agent market simulator ABIDES, which provides a diverse range of depth levels within the limit order book. We present a custom MDP formulation followed by the results of our methodology and benchmark the performance against standard execution strategies. Results show that the reinforcement learning agent outperforms standard strategies and offers a practical foundation for real-world trading applications.