Optimal Execution Using Reinforcement Learning

Cong Zheng; Jiafa He; Can Yang

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arXiv · 2306.17178

Optimal Execution Using Reinforcement Learning

Cong Zheng·Jiafa He·Can Yang

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Abstract

This work is about optimal order execution, where a large order is split into several small orders to maximize the implementation shortfall. Based on the diversity of cryptocurrency exchanges, we attempt to extract cross-exchange signals by aligning data from multiple exchanges for the first time. Unlike most previous studies that focused on using single-exchange information, we discuss the impact of cross-exchange signals on the agent's decision-making in the optimal execution problem. Experimental results show that cross-exchange signals can provide additional information for the optimal execution of cryptocurrency to facilitate the optimal execution process.

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BibTeX RIS

Cong Zheng, Jiafa He, Can Yang. 2023-06-19. Optimal Execution Using Reinforcement Learning. https://arxiv.org/abs/2306.17178

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