Optimal Execution Using Reinforcement Learning
This work addresses optimal execution for cryptocurrency traders by introducing cross-exchange data, but it is incremental as it builds on existing reinforcement learning methods.
The paper tackled optimal order execution in cryptocurrency markets by using cross-exchange signals for the first time, showing that these signals provide additional information to facilitate the process.
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.