Optimal Execution Using Reinforcement Learning

TL;DR

This paper explores how cross-exchange signals can enhance optimal order execution in cryptocurrency trading by integrating data from multiple exchanges to improve decision-making and reduce execution costs.

Contribution

It introduces a novel approach of utilizing cross-exchange signals for optimal execution, unlike previous methods that relied on single-exchange data.

Findings

Cross-exchange signals improve execution performance.

Data alignment across exchanges provides valuable trading insights.

Enhanced decision-making leads to better execution outcomes.

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.