Deep Deterministic Portfolio Optimization

Deep Deterministic Portfolio Optimization

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...

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Hauptverfasser: Chaouki, Ayman, Hardiman, Stephen, Schmidt, Christian, Sérié, Emmanuel, de Lataillade, Joachim
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Computer Science - Learning
Quantitative Finance - Mathematical Finance
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creator Chaouki, Ayman
Hardiman, Stephen
Schmidt, Christian
Sérié, Emmanuel
de Lataillade, Joachim
description 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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subjects Computer Science - Learning
Quantitative Finance - Mathematical Finance
title Deep Deterministic Portfolio Optimization
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