mlsanityData to 7 Oct 2026

Profitable Strategy Design by Using Deep Reinforcement Learning for Trades on Cryptocurrency Markets

arXiv 2201.05906 ↗First posted 15 Jan 2022q-fin.TR cs.AI cs.LGcited by 1

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Abstract (arXiv)

Deep Reinforcement Learning solutions have been applied to different control problems with outperforming and promising results. In this research work we have applied Proximal Policy Optimization, Soft Actor-Critic and Generative Adversarial Imitation Learning to strategy design problem of three cryptocurrency markets. Our input data includes price data and technical indicators. We have implemented a Gym environment based on cryptocurrency markets to be used with the algorithms. Our test results on unseen data shows a great potential for this approach in helping investors with an expert system to exploit the market and gain profit. Our highest gain for an unseen 66 day span is 4850 US dollars per 10000 US dollars investment. We also discuss on how a specific hyperparameter in the environment design can be used to adjust risk in the generated strategies.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 13 of its 51 references that are in the map. First posted on arXiv 15 Jan 2022.

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13 of 51 references.

References · 13

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  3. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
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1 more
  1. 2019Model-Free Reinforcement Learning for Financial Portfolios: A Brief Surveycited by 10

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Citing papers per year: 2024: 1.

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  6. 2024Robot See, Robot Do: Imitation Reward for Noisy Financial Environments
  7. 2020Tracking the Race Between Deep Reinforcement Learning and Imitation Learning -- Extended Version
  8. 2023IMM: An Imitative Reinforcement Learning Approach with Predictive Representation Learning for Automatic Market Making