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The Power of the “Pursuit” Learning Paradigm in the Partitioning of Data Read chapter Read first chapter

2019 | OriginalPaper | Chapter

A Deep Reinforcement Learning Approach for Automated Cryptocurrency Trading

Authors : Giorgio Lucarelli, Matteo Borrotti

Published in: Artificial Intelligence Applications and Innovations

Publisher: Springer International Publishing

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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.

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Footnotes
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Metadata
Title
A Deep Reinforcement Learning Approach for Automated Cryptocurrency Trading
Authors
Giorgio Lucarelli
Matteo Borrotti
Copyright Year
2019
Book
Artificial Intelligence Applications and Innovations

Print ISBN: 978-3-030-19822-0

Electronic ISBN: 978-3-030-19823-7

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-19823-7_20

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