TradeR: Practical Deep Hierarchical Reinforcement Learning for Trade Execution

Abstract

Advances in Reinforcement Learning (RL) span a wide variety of applications which motivate development in this area. While application tasks serve as suitable benchmarks for real world problems, RL is seldomly used in practical scenarios consisting of abrupt dynamics. This allows one to rethink the problem setup in light of practical challenges. We present Trade Execution using Reinforcement Learning (TradeR) which aims to address two such practical challenges of catastrophy and surprise minimization by formulating trading as a real-world hierarchical RL problem. Through this lens, TradeR makes use of hierarchical RL to execute trade bids on high frequency real market experiences comprising of abrupt price variations during the 2019 fiscal year COVID19 stock market crash. The framework utilizes an energy-based scheme in conjunction with surprise value function for estimating and minimizing surprise. In a large-scale study of 35 stock symbols from the S&P500 index, TradeR demonstrates robustness to abrupt price changes and catastrophic losses while maintaining profitable outcomes. We hope that our work serves as a motivating example for application of RL to practical problems.

References in corpus (9)

• Adam: A Method for Stochastic Optimization
• Continuous control with deep reinforcement learning
• Reinforcement Learning with Deep Energy-Based Policies
• D4RL: Datasets for Deep Data-Driven Reinforcement Learning
• An Application of Deep Reinforcement Learning to Algorithmic Trading
• Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning?
• Maximum Mutation Reinforcement Learning for Scalable Control
• Extending Deep Reinforcement Learning Frameworks in Cryptocurrency Market Making
• Energy-based Surprise Minimization for Multi-Agent Value Factorization