Deep Deterministic Portfolio Optimization

2020-03-13Code Available1· sign in to hype

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

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github.com/CFMTech/Deep-RL-for-Portfolio-Optimization
OfficialIn paperpytorch★ 131

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.

Tasks

Deep Reinforcement Learning Portfolio Optimization reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Deep Deterministic Portfolio Optimization

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Abstract

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