Deep differentiable reinforcement learning and optimal trading

TL;DR

This paper introduces deep differentiable reinforcement learning (DDRL) for optimal trading, leveraging known differentiable market models to achieve more stable and accurate solutions than traditional methods, and demonstrates its effectiveness in complex financial environments.

Contribution

The paper develops DDRL, a novel approach combining deep learning with differentiable stochastic control, specifically applied to optimal trading strategies in known market models.

Findings

DDRL outperforms actor-critic algorithms in stability and accuracy.

Efficiently finds optimal strategies in complex multi-scale market models.

Identifies that fast signals can be used to time trades of slow signals.

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.