Trading Strategy of the Cryptocurrency Market Based on Deep Q-Learning Agents
As of December 2021, the cryptocurrency market had a market value of over US$270 billion, and over 5,700 types of cryptocurrencies were circulating among 23,000 online exchanges. Reinforcement learning (RL) has been used to identify the optimal trading strategy. However, most RL-based optimal tradin...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
|
| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2024.2381165 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849220208826253312 |
|---|---|
| author | Chester S. J. Huang Yu-Sheng Su |
| author_facet | Chester S. J. Huang Yu-Sheng Su |
| author_sort | Chester S. J. Huang |
| collection | DOAJ |
| description | As of December 2021, the cryptocurrency market had a market value of over US$270 billion, and over 5,700 types of cryptocurrencies were circulating among 23,000 online exchanges. Reinforcement learning (RL) has been used to identify the optimal trading strategy. However, most RL-based optimal trading strategies adopted in the cryptocurrency market focus on trading one type of cryptocurrency, whereas most traders in the cryptocurrency market often trade multiple cryptocurrencies. Therefore, the present study proposes a method based on deep Q-learning for identifying the optimal trading strategy for multiple cryptocurrencies. The proposed method uses the same training data to train multiple agents repeatedly so that each agent has accumulated learning experiences to improve its prediction of the future market trend and to determine the optimal action. The empirical results obtained with the proposed method are described in the following text. For Ethereum, VeChain, and Ripple, which were considered to have an uptrend, a horizontal trend, and a downtrend, respectively, the annualized rates of return were 725.48%, −14.95%, and − 3.70%, respectively. Regardless of the cryptocurrency market trend, a higher annualized rate of return was achieved when using the proposed method than when using the buy-and-hold strategy. |
| format | Article |
| id | doaj-art-7a713070536d4b34b401504fce9af376 |
| institution | Kabale University |
| issn | 0883-9514 1087-6545 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj-art-7a713070536d4b34b401504fce9af3762024-12-16T16:13:02ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452024-12-0138110.1080/08839514.2024.2381165Trading Strategy of the Cryptocurrency Market Based on Deep Q-Learning AgentsChester S. J. Huang0Yu-Sheng Su1Department of Money and Banking, National Kaohsiung University of Science and Technology, Kaohsiung City, Taiwan (Republic of China)Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan (Republic of China)As of December 2021, the cryptocurrency market had a market value of over US$270 billion, and over 5,700 types of cryptocurrencies were circulating among 23,000 online exchanges. Reinforcement learning (RL) has been used to identify the optimal trading strategy. However, most RL-based optimal trading strategies adopted in the cryptocurrency market focus on trading one type of cryptocurrency, whereas most traders in the cryptocurrency market often trade multiple cryptocurrencies. Therefore, the present study proposes a method based on deep Q-learning for identifying the optimal trading strategy for multiple cryptocurrencies. The proposed method uses the same training data to train multiple agents repeatedly so that each agent has accumulated learning experiences to improve its prediction of the future market trend and to determine the optimal action. The empirical results obtained with the proposed method are described in the following text. For Ethereum, VeChain, and Ripple, which were considered to have an uptrend, a horizontal trend, and a downtrend, respectively, the annualized rates of return were 725.48%, −14.95%, and − 3.70%, respectively. Regardless of the cryptocurrency market trend, a higher annualized rate of return was achieved when using the proposed method than when using the buy-and-hold strategy.https://www.tandfonline.com/doi/10.1080/08839514.2024.2381165 |
| spellingShingle | Chester S. J. Huang Yu-Sheng Su Trading Strategy of the Cryptocurrency Market Based on Deep Q-Learning Agents Applied Artificial Intelligence |
| title | Trading Strategy of the Cryptocurrency Market Based on Deep Q-Learning Agents |
| title_full | Trading Strategy of the Cryptocurrency Market Based on Deep Q-Learning Agents |
| title_fullStr | Trading Strategy of the Cryptocurrency Market Based on Deep Q-Learning Agents |
| title_full_unstemmed | Trading Strategy of the Cryptocurrency Market Based on Deep Q-Learning Agents |
| title_short | Trading Strategy of the Cryptocurrency Market Based on Deep Q-Learning Agents |
| title_sort | trading strategy of the cryptocurrency market based on deep q learning agents |
| url | https://www.tandfonline.com/doi/10.1080/08839514.2024.2381165 |
| work_keys_str_mv | AT chestersjhuang tradingstrategyofthecryptocurrencymarketbasedondeepqlearningagents AT yushengsu tradingstrategyofthecryptocurrencymarketbasedondeepqlearningagents |