Optimal Execution Using Reinforcement Learning
Cong Zheng Jiafa He Can Yang
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.