TradeR: Practical Deep Hierarchical Reinforcement Learning for Trade Execution
This work addresses practical challenges in financial trading for investors, though it appears incremental as it builds on existing hierarchical RL methods.
The paper tackled the problem of trade execution in high-frequency markets with abrupt price changes, such as during the COVID-19 stock market crash, by developing a hierarchical reinforcement learning framework called TradeR, which demonstrated robustness and profitability across 35 S&P500 stock symbols.
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.