Deep Deterministic Portfolio Optimization

• URL: http://arxiv.org/abs/2003.06497v2
• Date: Thu, 9 Apr 2020 10:56:24 GMT
• Title: Deep Deterministic Portfolio Optimization
• Authors: Ayman Chaouki, Stephen Hardiman, Christian Schmidt, Emmanuel S\'eri\'e, and Joachim de Lataillade
• Abstract summary: This work is to test reinforcement learning algorithms on conceptually simple, but mathematically non-trivial, trading environments. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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