Deep deterministic portfolio optimisation

13 March 2020

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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.

Authors

Ayman Chaouki , Stephen J. Hardiman , Christian Schmidt , Emmanuel Sérié , Joachim de Lataillade

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