Trading Strategy of the Cryptocurrency Market Based on Deep Q-Learning Agents
| Autor: | Chester S. J. Huang, Yu-Sheng Su |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Applied Artificial Intelligence, Vol 38, Iss 1 (2024) |
| Druh dokumentu: | article |
| ISSN: | 08839514 1087-6545 0883-9514 |
| DOI: | 10.1080/08839514.2024.2381165 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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