Deep differentiable reinforcement learning and optimal trading

Abstract

In this article we introduce the differentiable reinforcement learning framework. It is based on the fact that in many reinforcement learning applications, the environment reward and transition functions are not black boxes but known differentiable functions. Incorporating deep learning in this framework we find more accurate and stable solutions than more generic actor critic algorithms. We apply this deep differentiable reinforcement learning (DDRL) algorithm to the problem of 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 and 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.