Deep Deterministic Portfolio Optimization
Mathematical Finance
2020-04-10 v2 Machine Learning
Abstract
Can deep reinforcement learning algorithms be exploited as solvers for optimal trading strategies? The aim of this work is to test reinforcement learning algorithms on conceptually simple, but mathematically non-trivial, trading environments. The environments are chosen such that an optimal or close-to-optimal trading strategy is known. We study the deep deterministic policy gradient algorithm and show that such a reinforcement learning agent can successfully recover the essential features of the optimal trading strategies and achieve close-to-optimal rewards.
Keywords
Cite
@article{arxiv.2003.06497,
title = {Deep Deterministic Portfolio Optimization},
author = {Ayman Chaouki and Stephen Hardiman and Christian Schmidt and Emmanuel Sérié and Joachim de Lataillade},
journal= {arXiv preprint arXiv:2003.06497},
year = {2020}
}
Comments
Minor typo