Universal Trading for Order Execution with Oracle Policy Distillation

TL;DR

This paper introduces a universal trading policy framework using oracle policy distillation to improve order execution efficiency under noisy market conditions, outperforming existing methods.

Contribution

It presents a novel reinforcement learning framework that distills an oracle policy to enhance order execution strategies in noisy market environments.

Findings

Significant performance improvements over strong baselines.

Effective guidance of policy learning via oracle distillation.

Robustness to noisy and imperfect market information.

Abstract

As a fundamental problem in algorithmic trading, order execution aims at fulfilling a specific trading order, either liquidation or acquirement, for a given instrument. Towards effective execution strategy, recent years have witnessed the shift from the analytical view with model-based market assumptions to model-free perspective, i.e., reinforcement learning, due to its nature of sequential decision optimization. However, the noisy and yet imperfect market information that can be leveraged by the policy has made it quite challenging to build up sample efficient reinforcement learning methods to achieve effective order execution. In this paper, we propose a novel universal trading policy optimization framework to bridge the gap between the noisy yet imperfect market states and the optimal action sequences for order execution. Particularly, this framework leverages a policy distillation method that can better guide the learning of the common policy towards practically optimal execution by an oracle teacher with perfect information to approximate the optimal trading strategy. The extensive experiments have shown significant improvements of our method over various strong baselines, with reasonable trading actions.