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Research Article

Trading Strategy of the Cryptocurrency Market Based on Deep Q-Learning Agents

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Article: 2381165 | Received 07 May 2024, Accepted 09 Jul 2024, Published online: 21 Jul 2024

References

  • Baur, D. G., K. Hong, and A. D. Lee. 2018. Bitcoin: Medium of exchange or speculative assets? Journal of International Financial Markets, Institutions and Money 54:177–22. doi:10.1016/j.intfin.2017.12.004.
  • Booth, A., E. Gerding, and F. McGroarty. 2014. Automated trading with performance weighted random forests and seasonality. Expert Systems with Applications 41 (8):3651–61. doi:10.1016/j.eswa.2013.12.009.
  • Bouoiyour, J., and R. Selmi. 2015. What does bitcoin look like? Annals of Economics and Finance 16 (2):449–492.
  • Bu, S. J., and S. B. Cho. 2018. Learning optimal Q-function using deep Boltzmann machine for reliable trading of cryptocurrency. In Intelligent Data Engineering and Automated Learning–IDEAL 2018, Madrid, Spain, 468–80. Springer International Publishing.
  • Chen, C., L. Liu, and N. Zhao. 2020. Fear sentiment, uncertainty, and bitcoin price dynamics: The case of COVID-19. Emerging Markets Finance & Trade 56 (10):2298–309. doi:10.1080/1540496X.2020.1787150.
  • Ciaian, P., M. Rajcaniova, and D. A. Kancs. 2016. The economics of BitCoin price formation. Applied Economics 48 (19):1799–815. doi:10.1080/00036846.2015.1109038.
  • Corbet, S., A. Meegan, C. Larkin, B. Lucey, and L. Yarovaya. 2018. Exploring the dynamic relationships between cryptocurrencies and other financial assets. Economics Letters 165:28–34. doi:10.1016/j.econlet.2018.01.004.
  • Creamer, G., and Y. Freund. 2010. Automated trading with boosting and expert weighting. Quantitative Finance 10 (4):401–20. doi:10.1080/14697680903104113.
  • D’Amato, V., S. Levantesi, and G. Piscopo. 2022. Deep learning in predicting cryptocurrency volatility. Physica A: Statistical Mechanics and its Applications 596:127158. doi:10.1016/j.physa.2022.127158.
  • Dastgir, S., E. Demir, G. Downing, G. Gozgor, and C. K. M. Lau. 2019. The causal relationship between bitcoin attention and bitcoin returns: Evidence from the copula-based Granger causality test. Finance Research Letters 28:160–64. doi:10.1016/j.frl.2018.04.019.
  • Deng, Y., F. Bao, Y. Kong, Z. Ren, and Q. Dai. 2016. Deep direct reinforcement learning for financial signal representation and trading. IEEE Transactions on Neural Networks and Learning Systems 28 (3):653–64. doi:10.1109/TNNLS.2016.2522401.
  • Fang, F., C. Ventre, M. Basios, L. Kanthan, D. Martinez-Rego, F. Wu, and L. Li. 2022. Cryptocurrency trading: A comprehensive survey. Financial Innovation 8 (1):1–59. doi:10.1186/s40854-021-00321-6.
  • Foley, S., J. R. Karlsen, and T. J. Putniņš. 2019. Sex, drugs, and bitcoin: How much illegal activity is financed through cryptocurrencies? The Review of Financial Studies 32 (5):1798–853. doi:10.1093/rfs/hhz015.
  • Gurdgiev, C., and D. O’Loughlin. 2020. Herding and anchoring in cryptocurrency markets: Investor reaction to fear and uncertainty. Journal of Behavioral and Experimental Finance 25:100271. doi:10.1016/j.jbef.2020.100271.
  • Hasselt, H. 2010. Double Q-learning. Advances in Neural Information Processing Systems 23:1–9.
  • Hessel, M., J. Modayil, H. van Hasselt, T. Schaul, G. Ostrovski, W. Dabney, D. Horgan, B. Piot, M. Azar, and D. Silver. 2018. Rainbow: Combining improvements in deep reinforcement learning. Proceedings of the AAAI Conference on Artificial Intelligence 32 (1). doi:10.1609/aaai.v32i1.11796.
  • Hung, C. C., Y. J. Chen, and J. E. Trinidad Segovia. 2021. DPP: Deep predictor for price movement from candlestick charts. PLoS One 16 (6):e0252404. doi:10.1371/journal.pone.0252404.
  • Jiang, Z., and J. Liang. 2017. Cryptocurrency portfolio management with deep reinforcement learning. In 2017 Intelligent systems conference, London, UK, 905–13. IEEE.
  • Kim, Y. B., J. G. Kim, W. Kim, J. H. Im, T. H. Kim, S. J. Kang, C. H. Kim, and W.-X. Zhou. 2016. Predicting fluctuations in cryptocurrency transactions based on user comments and replies. PLoS One 11 (8):e0161197. doi:10.1371/journal.pone.0161197.
  • Kristoufek, L. 2013. BitCoin meets Google trends and Wikipedia: Quantifying the relationship between phenomena of the internet era. Scientific Reports 3 (1):1–7. doi:10.1038/srep03415.
  • Kristoufek, L., and E. Scalas. 2015. What are the main drivers of the bitcoin price? Evidence from wavelet coherence analysis. PLoS One 10 (4):e0123923. doi:10.1371/journal.pone.0123923.
  • Li, X., and C. A. Wang. 2017. The technology and economic determinants of cryptocurrency exchange rates: The case of bitcoin. Decision Support Systems 95:49–60. doi:10.1016/j.dss.2016.12.001.
  • Li, Y., P. Ni, and V. Chang. 2020. Application of deep reinforcement learning in stock trading strategies and stock forecasting. Computing 102 (6):1305–22. doi:10.1007/s00607-019-00773-w.
  • Liang, Z., H. Chen, J. Zhu, K. Jiang, and Y. Li. 2018. Adversarial deep reinforcement learning in portfolio management. arXiv preprint arXiv:1808.09940.
  • Magdon-Ismail, M., and A. F. Atiya. 2004. Maximum drawdown. The Risk Magazine 17 (10):99–102.
  • Nakamoto, S. 2008. Bitcoin: A peer-to-peer electronic cash system.
  • Phillips, R. C., D. Gorse, and M. Espinosa. 2018. Cryptocurrency price drivers: Wavelet coherence analysis revisited. PLoS One 13 (4):e0195200. doi:10.1371/journal.pone.0195200.
  • Polasik, M., A. I. Piotrowska, T. P. Wisniewski, R. Kotkowski, and G. Lightfoot. 2015. Price fluctuations and the use of bitcoin: An empirical inquiry. International Journal of Electronic Commerce 20 (1):9–49. doi:10.1080/10864415.2016.1061413.
  • Pyo, S., and J. Lee. 2020. Do FOMC and macroeconomic announcements affect bitcoin prices? Finance Research Letters 37:101386. doi:10.1016/j.frl.2019.101386.
  • Schnaubelt, M. 2022. Deep reinforcement learning for the optimal placement of cryptocurrency limit orders. European Journal of Operational Research 296 (3):993–1006. doi:10.1016/j.ejor.2021.04.050.
  • Shavandi, A., and M. Khedmati. 2022. A multi-agent deep reinforcement learning framework for algorithmic trading in financial markets. Expert Systems with Applications 208:118124. doi:10.1016/j.eswa.2022.118124.
  • Sovbetov, Y. 2018. Factors influencing cryptocurrency prices: Evidence from bitcoin, ethereum, dash, litcoin, and monero. Journal of Economics and Financial Analysis 2 (2):1–27.
  • Stavroyiannis, S., V. Babalos, S. Bekiros, S. Lahmiri, and G. S. Uddin. 2019. The high frequency multifractal properties of bitcoin. Physica A: Statistical Mechanics and its Applications 520:62–71. doi:10.1016/j.physa.2018.12.037.
  • Subramanian, H., S. Ramamoorthy, P. Stone, and B. J. Kuipers. 2006. Designing safe, profitable automated stock trading agents using evolutionary algorithms. In Proceedings of the 8th annual conference on Genetic and evolutionary computation, Seattle Washington USA, 1777–84.
  • Sutton, R. S., and A. G. Barto. 2018. Reinforcement learning: An introduction. Cambridge, MA: MIT press.
  • Thrun, S., and A. Schwartz. 1993. Issues in using function approximation for reinforcement learning. In Proceedings of the 1993 connectionist models summer school, San Mateo, California, 255–63. Psychology Press.
  • van Hasselt, H., A. Guez, and D. Silver. 2016. Deep reinforcement learning with double q-learning. Proceedings of the AAAI Conference on Artificial Intelligence 30 (1). doi:10.1609/aaai.v30i1.10295.
  • Wang, Z., T. Schaul, M. Hessel, H. Hasselt, M. Lanctot, and N. Freitas. 2016. Dueling network architectures for deep reinforcement learning. In International conference on machine learning, New York, USA, 1995–2003. PMLR.
  • Yaga, D., P. Mell, N. Roby, and K. Scarfone. 2019. Blockchain technology overview. arXiv preprint arXiv:1906.11078.
  • Yang, H., X. Y. Liu, S. Zhong, and A. Walid. 2020. Deep reinforcement learning for automated stock trading: An ensemble strategy. In Proceedings of the first ACM international conference on AI in finance, New York, USA, 1–8.
  • Żbikowski, K. 2015. Using volume weighted support vector machines with walk forward testing and feature selection for the purpose of creating stock trading strategy. Expert Systems with Applications 42 (4):1797–805. doi:10.1016/j.eswa.2014.10.001.