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Optimal Execution Using Reinforcement Learning

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  • Cong Zheng
  • Jiafa He
  • Can Yang

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.

Suggested Citation

  • Cong Zheng & Jiafa He & Can Yang, 2023. "Optimal Execution Using Reinforcement Learning," Papers 2306.17178, arXiv.org.
    • Handle: RePEc:arx:papers:2306.17178
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    References listed on IDEAS

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