Deep differentiable reinforcement learning and optimal trading

Abstract

In many reinforcement learning applications, the underlying environment reward and transition functions are explicitly known differentiable functions. This enables us to use recent research which applies machine learning tools to stochastic control to find optimal action functions. In this paper, we define differentiable reinforcement learning as a particular case of this research. We find that incorporating deep learning in this framework leads to more accurate and stable solutions than those obtained from more generic actor critic algorithms. We apply this deep differentiable reinforcement learning (DDRL) algorithm to the problem of one asset optimal trading strategies in various environments where the market dynamics are known. Thanks to the stability of this method, we are able to efficiently find optimal strategies for complex multi-scale market models. We also extend these methods to simultaneously find optimal action functions for a wide range of environment parameters. This makes it applicable to real life financial signals and portfolio optimization where the expected return has multiple time scales. In the case of a slow and a fast alpha signal, we find that the optimal trading strategy consists in using the fast signal to time the trades associated to the slow signal.

Keywords:

• Reinforcement learning
• Optimal trading
• Differentiable
• Stochastic control
• Dynamic programming
• Deep learning
• Multi-scale signals

Acknowledgments

The author thanks Reda Messikh, Stéphane Daul and Rémy Cottet for useful discussions on the topic and feedbacks on the manuscript and two anonymous referees for their remarks and recommendations. The opinions expressed in this article are solely those of the author.

Disclosure statement

No potential conflict of interest was reported by the author(s).