# Deep Deterministic Portfolio Optimization

arXiv: https://arxiv.org/abs/2003.06497 · HTML: https://mlsanity.com/paper/2003.06497/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2020.

## Built on by (most cited first, 4 of 4)

- [Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning](https://mlsanity.com/paper/2407.07631/) (2024)
- [End-to-End Large Portfolio Optimization for Variance Minimization with Neural Networks through Covariance Cleaning](https://mlsanity.com/paper/2507.01918/) (2025)
- [Decision by Supervised Learning with Deep Ensembles: A Practical Framework for Robust Portfolio Optimization](https://mlsanity.com/paper/2503.13544/) (2025)
- [TradeR: Practical Deep Hierarchical Reinforcement Learning for Trade Execution](https://mlsanity.com/paper/2104.00620/) (2021)

## Abstract (arXiv)

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.