TY - GEN
T1 - Cryptocurrency Trading Agent Using Deep Reinforcement Learning
AU - Suliman, Uwais
AU - Van Zyl, Terence L.
AU - Paskaramoorthy, Andrew
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Cryptocurrencies are peer-to-peer digital assets monitored and organised by a blockchain network. Price prediction has been a significant focus point with various machine learning algorithms, especially concerning cryptocurrency. This work addresses the challenge faced by traders of short-term profit maximisation. The study presents a deep reinforcement learning algorithm to trade in cryptocurrency markets, Duelling DQN. The environment has been designed to simulate actual trading behaviour, observing historical price movements and taking action on real-time prices. The proposed algorithm was tested with Bitcoin, Ethereum, and Litecoin. The respective portfolio returns are used as a metric to measure the algorithm's performance against the buy-and-hold benchmark, with the buy-and-hold outperforming the results produced by the Duelling DQN agent.
AB - Cryptocurrencies are peer-to-peer digital assets monitored and organised by a blockchain network. Price prediction has been a significant focus point with various machine learning algorithms, especially concerning cryptocurrency. This work addresses the challenge faced by traders of short-term profit maximisation. The study presents a deep reinforcement learning algorithm to trade in cryptocurrency markets, Duelling DQN. The environment has been designed to simulate actual trading behaviour, observing historical price movements and taking action on real-time prices. The proposed algorithm was tested with Bitcoin, Ethereum, and Litecoin. The respective portfolio returns are used as a metric to measure the algorithm's performance against the buy-and-hold benchmark, with the buy-and-hold outperforming the results produced by the Duelling DQN agent.
KW - Algorithmic Trading
KW - Cryptocurrency
KW - Deep Reinforcement Learning
KW - Machine Learning
KW - Neural Networks
UR - http://www.scopus.com/inward/record.url?scp=85151752744&partnerID=8YFLogxK
U2 - 10.1109/ISCMI56532.2022.10068485
DO - 10.1109/ISCMI56532.2022.10068485
M3 - Conference contribution
AN - SCOPUS:85151752744
T3 - 2022 9th International Conference on Soft Computing and Machine Intelligence, ISCMI 2022
SP - 6
EP - 10
BT - 2022 9th International Conference on Soft Computing and Machine Intelligence, ISCMI 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Conference on Soft Computing and Machine Intelligence, ISCMI 2022
Y2 - 26 November 2022 through 27 November 2022
ER -