@inproceedings{7cb864ceb8604cdcb6ec66171c9d0578,
title = "Trading Cryptocurrency with Deep Deterministic Policy Gradients",
abstract = "The volatility incorporated in cryptocurrency prices makes it difficult to earn a profit through day trading. Usually, the best strategy is to buy a cryptocurrency and hold it until the price rises over a long period. This project aims to automate short term trading using Reinforcement Learning (RL), predominantly using the Deep Deterministic Policy Gradient (DDPG) algorithm. The algorithm integrates with the BitMEX cryptocurrency exchange and uses Technical Indicators (TIs) to create an abundance of features. Training on these different features and using diverse environments proved to have mixed results, many of them being exceptionally interesting. The most peculiar model shows that it is possible to create a strategy that can beat a buy and hold strategy relatively effortlessly in terms of profit made.",
keywords = "Cryptocurrency, Reinforcement Learning, Trading",
author = "Evan Tummon and Raja, {Muhammad Adil} and Conor Ryan",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020 ; Conference date: 04-11-2020 Through 06-11-2020",
year = "2020",
doi = "10.1007/978-3-030-62362-3_22",
language = "English",
isbn = "9783030623616",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "245--256",
editor = "Cesar Analide and Paulo Novais and David Camacho and Hujun Yin",
booktitle = "Intelligent Data Engineering and Automated Learning – IDEAL 2020 - 21st International Conference, 2020, Proceedings",
}