Skip to Main content Skip to Navigation
Conference papers

A Deep Reinforcement Learning Approach for Automated Cryptocurrency Trading

Abstract : Nowadays, Artificial Intelligence (AI) is changing our daily life in many application fields. Automatic trading has inspired a large number of field experts and scientists in developing innovative techniques and deploying cutting-edge technologies to trade different markets. In this context, cryptocurrency has given new interest in the application of AI techniques for predicting the future price of a financial asset. In this work Deep Reinforcement Learning is applied to trade bitcoin. More precisely, Double and Dueling Double Deep Q-learning Networks are compared over a period of almost four years. Two reward functions are also tested: Sharpe ratio and profit reward functions. The Double Deep Q-learning trading system based on Sharpe ratio reward function demonstrated to be the most profitable approach for trading bitcoin.
Document type :
Conference papers
Complete list of metadata

Cited literature [25 references]  Display  Hide  Download

https://hal.inria.fr/hal-02331326
Contributor : Hal Ifip <>
Submitted on : Thursday, October 24, 2019 - 12:51:18 PM
Last modification on : Friday, June 11, 2021 - 9:26:03 AM
Long-term archiving on: : Saturday, January 25, 2020 - 3:48:38 PM

File

 Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed until : 2022-01-01

Please log in to resquest access to the document

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Giorgio Lucarelli, Matteo Borrotti. A Deep Reinforcement Learning Approach for Automated Cryptocurrency Trading. 15th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), May 2019, Hersonissos, Greece. pp.247-258, ⟨10.1007/978-3-030-19823-7_20⟩. ⟨hal-02331326⟩

Share

Metrics

Record views

489