Deep Deterministic Portfolio Optimization

Ayman Chaouki, Stephen Hardiman, Christian Schmidt, Emmanuel S\'eri\'e and Joachim de Lataillade

Papers from arXiv.org

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.

Date: 2020-03, Revised 2020-04
New Economics Papers: this item is included in nep-big, nep-cmp and nep-fmk
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