Optimal Execution with Reinforcement Learning

Yadh Hafsi and Edoardo Vittori

Papers from arXiv.org

Abstract: 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.

Date: 2024-11, Revised 2025-11
New Economics Papers: this item is included in nep-cmp and nep-mst
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