{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T09:53:43Z","timestamp":1774950823632,"version":"3.50.1"},"reference-count":110,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,11,14]],"date-time":"2022-11-14T00:00:00Z","timestamp":1668384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>The total capital in cryptocurrency markets is around two trillion dollars in 2022, which is almost the same as Apple\u2019s market capitalisation at the same time. Increasingly, cryptocurrencies have become established in financial markets with an enormous number of transactions and trades happening every day. Similar to other financial systems, price prediction is one of the main challenges in cryptocurrency trading. Therefore, the application of artificial intelligence, as one of the tools of prediction, has emerged as a recently popular subject of investigation in the cryptocurrency domain. Since machine learning models, as opposed to traditional financial models, demonstrate satisfactory performance in quantitative finance, they seem ideal for coping with the price prediction problem in the complex and volatile cryptocurrency market. There have been several studies that have focused on applying machine learning for price and movement prediction and portfolio management in cryptocurrency markets, though these methods and models are in their early stages. This survey paper aims to review the current research trends in applications of supervised and reinforcement learning models in cryptocurrency price prediction. This study also highlights potential research gaps and possible areas for improvement. In addition, it emphasises potential challenges and research directions that will be of interest in the artificial intelligence and machine learning communities focusing on cryptocurrencies.<\/jats:p>","DOI":"10.3390\/a15110428","type":"journal-article","created":{"date-parts":[[2022,11,15]],"date-time":"2022-11-15T02:31:15Z","timestamp":1668479475000},"page":"428","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Applying Artificial Intelligence in Cryptocurrency Markets: A Survey"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4548-4745","authenticated-orcid":false,"given":"Rasoul","family":"Amirzadeh","sequence":"first","affiliation":[{"name":"School of Information Technology, Deakin University, Geelong, VIC 3216, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4955-9684","authenticated-orcid":false,"given":"Asef","family":"Nazari","sequence":"additional","affiliation":[{"name":"School of Information Technology, Deakin University, Geelong, VIC 3216, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8011-933X","authenticated-orcid":false,"given":"Dhananjay","family":"Thiruvady","sequence":"additional","affiliation":[{"name":"School of Information Technology, Deakin University, Geelong, VIC 3216, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1799","DOI":"10.1080\/00036846.2015.1109038","article-title":"The economics of BitCoin price formation","volume":"48","author":"Ciaian","year":"2016","journal-title":"Appl. Econ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.qref.2018.05.016","article-title":"Network causality structures among Bitcoin and other financial assets: A directed acyclic graph approach","volume":"70","author":"Ji","year":"2018","journal-title":"Q. Rev. Econ. Financ."},{"key":"ref_3","first-page":"1","article-title":"Cryptocurrency price prediction using tweet volumes and sentiment analysis","volume":"1","author":"Abraham","year":"2018","journal-title":"SMU Data Sci. Rev."},{"key":"ref_4","first-page":"2229","article-title":"Automated cryptocurrencies prices prediction using machine learning","volume":"8","author":"Mittal","year":"2018","journal-title":"Div. Comput. Eng. Netaji Subhas Inst. Technol. India"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.ins.2020.05.066","article-title":"Adaptive stock trading strategies with deep reinforcement learning methods","volume":"538","author":"Wu","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/s12204-017-1821-9","article-title":"Big data framework for quantitative trading system","volume":"22","author":"Dai","year":"2017","journal-title":"J. Shanghai Jiaotong Univ. (Sci.)"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1080\/17517575.2018.1493145","article-title":"Automated trading systems statistical and machine learning methods and hardware implementation: A survey","volume":"13","author":"Huang","year":"2019","journal-title":"Enterp. Inf. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1080\/0015198X.2019.1596678","article-title":"Machine learning for stock selection","volume":"75","author":"Rasekhschaffe","year":"2019","journal-title":"Financ. Anal. J."},{"key":"ref_9","unstructured":"Lee, D., and Deng, R.H. (2017). Handbook of Blockchain, Digital Finance, and Inclusion: Cryptocurrency, FinTech, InsurTech, Regulation, ChinaTech, Mobile Security, and Distributed Ledger, Academic Press."},{"key":"ref_10","first-page":"74","article-title":"FinTech (Financial Technology): What is it and how to use technologies to create business value in fintech way?","volume":"9","author":"Leong","year":"2018","journal-title":"Int. J. Innov. Manag. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1111\/jacf.12378","article-title":"Fintech, bigtech, and the future of banks","volume":"31","author":"Stulz","year":"2019","journal-title":"J. Appl. Corp. Financ."},{"key":"ref_12","unstructured":"Strobel, V. (2018). Pold87\/academic-keyword-occurrence: First release. Zenodo."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1186\/s40854-021-00321-6","article-title":"Cryptocurrency trading: A comprehensive survey","volume":"8","author":"Fang","year":"2022","journal-title":"Financ. Innov."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Mosavi, A., Faghan, Y., Ghamisi, P., Duan, P., Ardabili, S.F., Salwana, E., and Band, S.S. (2020). Comprehensive review of deep reinforcement learning methods and applications in economics. Mathematics, 8.","DOI":"10.31226\/osf.io\/53esy"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"175840","DOI":"10.1109\/ACCESS.2020.3025211","article-title":"Cryptocurrencies and Artificial Intelligence: Challenges and Opportunities","volume":"8","author":"Sabry","year":"2020","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Murat Ozbayoglu, A., Ugur Gudelek, M., and Berat Sezer, O. (2020). Deep Learning for Financial Applications: A Survey. arXiv.","DOI":"10.1016\/j.asoc.2020.106384"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"36500","DOI":"10.1109\/ACCESS.2019.2903554","article-title":"Blockchain in industries: A survey","volume":"7","author":"Mohamed","year":"2019","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yli-Huumo, J., Ko, D., Choi, S., Park, S., and Smolander, K. (2016). Where is current research on blockchain technology?\u2014A systematic review. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0163477"},{"key":"ref_19","first-page":"286","article-title":"Blockchain: Properties and misconceptions","volume":"11","author":"Stalick","year":"2017","journal-title":"Asia Pac. J. Innov. Entrep."},{"key":"ref_20","unstructured":"Nakamoto, S., and Bitcoin, A. (2008). A peer-to-peer electronic cash system. Bitcoin, 4, Available online: https:\/\/bitcoin.org\/bitcoin.pdf."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.qref.2018.05.010","article-title":"Can cryptocurrencies fulfil the functions of money?","volume":"70","author":"Ammous","year":"2018","journal-title":"Q. Rev. Econ. Financ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1093\/ijlit\/eaz008","article-title":"How to regulate bitcoin? Decentralized regulation for a decentralized cryptocurrency","volume":"27","author":"Nabilou","year":"2019","journal-title":"Int. J. Law Inf. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Chen, Y., and Bellavitis, C. (2019). Decentralized finance: Blockchain technology and the quest for an open financial system. SSRN J.","DOI":"10.2139\/ssrn.3418557"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"19","DOI":"10.20470\/jsi.v9i1.335","article-title":"Possible state approaches to cryptocurrencies","volume":"9","author":"Lansky","year":"2018","journal-title":"J. Syst. Integr."},{"key":"ref_25","unstructured":"Bank, E.C. (2012). Virtual Currency Schemes, European Central Bank. EC Bank, Virtual Currency Schemes (h. 13\u201314)."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"117134","DOI":"10.1109\/ACCESS.2019.2936094","article-title":"A survey of blockchain from the perspectives of applications, challenges, and opportunities","volume":"7","author":"Monrat","year":"2019","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.intfin.2017.11.001","article-title":"Virtual relationships: Short-and long-run evidence from BitCoin and altcoin markets","volume":"52","author":"Ciaian","year":"2018","journal-title":"J. Int. Financ. Mark. Inst. Money"},{"key":"ref_28","first-page":"102583","article-title":"A Deep Learning-based Cryptocurrency Price Prediction Scheme for Financial Institutions","volume":"55","author":"Patel","year":"2020","journal-title":"J. Inf. Secur. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"101993","DOI":"10.1016\/j.cose.2020.101993","article-title":"Characterizing cryptocurrency exchange scams","volume":"98","author":"Xia","year":"2020","journal-title":"Comput. Secur."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.econlet.2018.04.003","article-title":"Liquidity and market efficiency in cryptocurrencies","volume":"168","author":"Wei","year":"2018","journal-title":"Econ. Lett."},{"key":"ref_31","first-page":"1293","article-title":"An analysis of cryptocurrency, bitcoin, and the future","volume":"1","author":"DeVries","year":"2016","journal-title":"Int. J. Bus. Manag. Commer."},{"key":"ref_32","first-page":"43","article-title":"Identity theft and social media","volume":"18","author":"Irshad","year":"2018","journal-title":"Int. J. Comput. Sci. Netw. Secur."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.econlet.2018.07.039","article-title":"Bitcoin risk modeling with blockchain graphs","volume":"173","author":"Akcora","year":"2018","journal-title":"Econ. Lett."},{"key":"ref_34","first-page":"02001","article-title":"Forecasting cryptocurrency prices time series using machine learning approach","volume":"Volume 65","author":"Derbentsev","year":"2019","journal-title":"Proceedings of the SHS Web of Conferences, the 8th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2 2019)"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1007\/s40822-018-0108-2","article-title":"Exploring the dynamics of Bitcoin\u2019s price: A Bayesian structural time series approach","volume":"9","author":"Poyser","year":"2019","journal-title":"Eurasian Econ. Rev."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5427","DOI":"10.1109\/ACCESS.2017.2779181","article-title":"An empirical study on modeling and prediction of bitcoin prices with bayesian neural networks based on blockchain information","volume":"6","author":"Jang","year":"2017","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.econmod.2020.05.003","article-title":"Fancy Bitcoin and conventional financial assets: Measuring market integration based on connectedness networks","volume":"90","author":"Zeng","year":"2020","journal-title":"Econ. Model."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.econlet.2018.01.004","article-title":"Exploring the dynamic relationships between cryptocurrencies and other financial assets","volume":"165","author":"Corbet","year":"2018","journal-title":"Econ. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Gkillas, K., Bekiros, S., and Siriopoulos, C. (2018). Extreme correlation in cryptocurrency markets. SSRN.","DOI":"10.2139\/ssrn.3180934"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1016\/j.physa.2018.05.050","article-title":"Collective behavior of cryptocurrency price changes","volume":"507","author":"Stosic","year":"2018","journal-title":"Phys. A Stat. Mech. Its Appl."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.frl.2017.12.006","article-title":"Datestamping the Bitcoin and Ethereum bubbles","volume":"26","author":"Corbet","year":"2018","journal-title":"Financ. Res. Lett."},{"key":"ref_42","first-page":"12","article-title":"A proposal for the dartmouth summer research project on artificial intelligence, August 31, 1955","volume":"27","author":"McCarthy","year":"2006","journal-title":"AI Mag."},{"key":"ref_43","unstructured":"Dick, S. (2022, November 01). Artificial Intelligence. HDSR, 1 July 2019. Available online: https:\/\/hdsr.duqduq.org\/pub\/0aytgrau."},{"key":"ref_44","unstructured":"(2022, November 01). Artificial Intelligence the Next Digital Frontier. Available online: https:\/\/apo.org.au\/node\/210501."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13347-020-00396-6","article-title":"AI and its new winter: From myths to realities","volume":"33","author":"Floridi","year":"2020","journal-title":"Philos. Technol."},{"key":"ref_46","unstructured":"Whittaker, M., Crawford, K., Dobbe, R., Fried, G., Kaziunas, E., Mathur, V., West, S.M., Richardson, R., Schultz, J., and Schwartz, O. (2018). AI Now Report 2018, AI Now Institute at New York University."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/s12178-020-09600-8","article-title":"Machine learning and artificial intelligence: Definitions, applications, and future directions","volume":"13","author":"Helm","year":"2020","journal-title":"Curr. Rev. Musculoskelet. Med."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1007\/s12525-021-00475-2","article-title":"Machine learning and deep learning","volume":"31","author":"Janiesch","year":"2021","journal-title":"Electron. Mark."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"5633","DOI":"10.1007\/s10462-021-09967-1","article-title":"Application of deep learning algorithms in geotechnical engineering: A short critical review","volume":"54","author":"Zhang","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2098","DOI":"10.1007\/s11227-017-2228-y","article-title":"An innovative neural network approach for stock market prediction","volume":"76","author":"Pang","year":"2020","journal-title":"J. Supercomput."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"124569","DOI":"10.1016\/j.physa.2020.124569","article-title":"An approach to predict and forecast the price of constituents and index of cryptocurrency using machine learning","volume":"551","author":"Chowdhury","year":"2020","journal-title":"Phys. A Stat. Mech. Its Appl."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"112395","DOI":"10.1016\/j.cam.2019.112395","article-title":"Bitcoin price prediction using machine learning: An approach to sample dimension engineering","volume":"365","author":"Chen","year":"2020","journal-title":"J. Comput. Appl. Math."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"106527","DOI":"10.1016\/j.compeleceng.2019.106527","article-title":"Prediction of the price of Ethereum blockchain cryptocurrency in an industrial finance system","volume":"81","author":"Poongodi","year":"2020","journal-title":"Comput. Electr. Eng."},{"key":"ref_54","unstructured":"McNally, S. (2016). Predicting the price of Bitcoin using Machine Learning. [Ph.D. Thesis, National College of Ireland]."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Kim, Y.B., Kim, J.G., Kim, W., Im, J.H., Kim, T.H., Kang, S.J., and Kim, C.H. (2016). Predicting fluctuations in cryptocurrency transactions based on user comments and replies. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0161197"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"8983590","DOI":"10.1155\/2018\/8983590","article-title":"Anticipating cryptocurrency prices using machine learning","volume":"2018","author":"Alessandretti","year":"2018","journal-title":"Complexity"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.eswa.2017.12.004","article-title":"The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with Support Vector Regression","volume":"97","author":"Peng","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_58","first-page":"1","article-title":"Cryptocurrency price prediction using news and social media sentiment","volume":"1","author":"Lamon","year":"2017","journal-title":"SMU Data Sci. Rev."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"101084","DOI":"10.1016\/j.frl.2018.12.032","article-title":"A novel cryptocurrency price trend forecasting model based on LightGBM","volume":"32","author":"Sun","year":"2020","journal-title":"Financ. Res. Lett."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"109641","DOI":"10.1016\/j.chaos.2020.109641","article-title":"Intelligent forecasting with machine learning trading systems in chaotic intraday Bitcoin market","volume":"133","author":"Lahmiri","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Kotu, V., and Deshpande, B. (2015). Chapter 2: Data Mining Process. Predictive Analytics and Data Mining, Elsevier.","DOI":"10.1016\/B978-0-12-801460-8.00002-1"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1007\/s00779-018-1121-x","article-title":"A novel stock recommendation system using Guba sentiment analysis","volume":"22","author":"Sun","year":"2018","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.knosys.2018.10.034","article-title":"Deep learning-based feature engineering for stock price movement prediction","volume":"164","author":"Long","year":"2019","journal-title":"Knowl.-Based Syst."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.ejor.2009.04.015","article-title":"Fuzzy adaptive decision-making for boundedly rational traders in speculative stock markets","volume":"202","author":"Bekiros","year":"2010","journal-title":"Eur. J. Oper. Res."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.asoc.2017.02.006","article-title":"An intelligent hybrid trading system for discovering trading rules for the futures market using rough sets and genetic algorithms","volume":"55","author":"Kim","year":"2017","journal-title":"Appl. Soft Comput."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Yang, H., Liu, X.Y., Zhong, S., and Walid, A. (2020, January 15\u201316). Deep reinforcement learning for automated stock trading: An ensemble strategy. Proceedings of the First ACM International Conference on AI in Finance (ICAIF \u201920), New York, NY, USA.","DOI":"10.1145\/3383455.3422540"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1109\/MSP.2017.2743240","article-title":"Deep reinforcement learning: A brief survey","volume":"34","author":"Arulkumaran","year":"2017","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_68","unstructured":"Li, Y. (2017). Deep reinforcement learning: An overview. arXiv."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Sutton, R.S., and Barto, A.G. (1998). Introduction to Reinforcement Learning, MIT Press.","DOI":"10.1109\/TNN.1998.712192"},{"key":"ref_70","unstructured":"Rosenstein, M.T., Barto, A.G., Si, J., Barto, A., Powell, W., and Wunsch, D. (2004). Supervised actor-critic reinforcement learning. Learning and Approximate Dynamic Programming: Scaling Up to the Real World, IEEE."},{"key":"ref_71","unstructured":"Chinnamgari, S.K. (2019). R Machine Learning Projects: Implement Supervised, Unsupervised, and Reinforcement Learning Techniques Using R 3.5, Packt Publishing Ltd."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1016\/j.tics.2019.02.006","article-title":"Reinforcement learning, fast and slow","volume":"23","author":"Botvinick","year":"2019","journal-title":"Trends Cogn. Sci."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"Mnih","year":"2015","journal-title":"Nature"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1038\/nature16961","article-title":"Mastering the game of Go with deep neural networks and tree search","volume":"529","author":"Silver","year":"2016","journal-title":"Nature"},{"key":"ref_75","unstructured":"Sutton, R.S., and Barto, A.G. (2018). Reinforcement Learning: An Introduction, MIT Press."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Zhang, H., and Yu, T. (2020). Taxonomy of Reinforcement Learning Algorithms. Deep Reinforcement Learning, Springer.","DOI":"10.1007\/978-981-15-4095-0_3"},{"key":"ref_77","unstructured":"Feinberg, V., Wan, A., Stoica, I., Jordan, M.I., Gonzalez, J.E., and Levine, S. (2018). Model-based value estimation for efficient model-free reinforcement learning. arXiv."},{"key":"ref_78","unstructured":"Kaiser, L., Babaeizadeh, M., Milos, P., Osinski, B., Campbell, R.H., Czechowski, K., Erhan, D., Finn, C., Kozakowski, P., and Levine, S. (2019). Model-based reinforcement learning for atari. arXiv."},{"key":"ref_79","unstructured":"Nachum, O., Norouzi, M., Xu, K., and Schuurmans, D. (2017). Bridging the gap between value and policy based reinforcement learning. arXiv."},{"key":"ref_80","unstructured":"Kumar, A., Zhou, A., Tucker, G., and Levine, S. (2020). Conservative q-learning for offline reinforcement learning. arXiv."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Masson, W., Ranchod, P., and Konidaris, G. (2016, January 12\u201317). Reinforcement learning with parameterized actions. Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA.","DOI":"10.1609\/aaai.v30i1.10226"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., and Meger, D. (2018, January 2\u20137). Deep reinforcement learning that matters. Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.11694"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"2223","DOI":"10.1109\/TNNLS.2020.3044196","article-title":"An Off-Policy Trust Region Policy Optimization Method with Monotonic Improvement Guarantee for Deep Reinforcement Learning","volume":"33","author":"Meng","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_84","unstructured":"Nachum, O., Norouzi, M., Xu, K., and Schuurmans, D. (2017). Trust-pcl: An off-policy trust region method for continuous control. arXiv."},{"key":"ref_85","unstructured":"Jaderberg, M., Mnih, V., Czarnecki, W.M., Schaul, T., Leibo, J.Z., Silver, D., and Kavukcuoglu, K. (2016). Reinforcement learning with unsupervised auxiliary tasks. arXiv."},{"key":"ref_86","unstructured":"Mirowski, P., Pascanu, R., Viola, F., Soyer, H., Ballard, A.J., Banino, A., Denil, M., Goroshin, R., Sifre, L., and Kavukcuoglu, K. (2016). Learning to navigate in complex environments. arXiv."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Liang, C., Berant, J., Le, Q., Forbus, K.D., and Lao, N. (2016). Neural symbolic machines: Learning semantic parsers on freebase with weak supervision. arXiv.","DOI":"10.18653\/v1\/P17-1003"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Cao, Q., Lin, L., Shi, Y., Liang, X., and Li, G. (2017, January 21\u201326). Attention-aware face hallucination via deep reinforcement learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.180"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Koker, T.E., and Koutmos, D. (2020). Cryptocurrency Trading Using Machine Learning. J. Risk Financ. Manag., 13.","DOI":"10.3390\/jrfm13080178"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.jfineco.2019.07.001","article-title":"Trading and arbitrage in cryptocurrency markets","volume":"135","author":"Makarov","year":"2020","journal-title":"J. Financ. Econ."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.ejor.2019.04.013","article-title":"Large data sets and machine learning: Applications to statistical arbitrage","volume":"278","author":"Huck","year":"2019","journal-title":"Eur. J. Oper. Res."},{"key":"ref_92","unstructured":"Sadighian, J. (2019). Deep Reinforcement Learning in Cryptocurrency Market Making. arXiv."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Sattarov, O., Muminov, A., Lee, C.W., Kang, H.K., Oh, R., Ahn, J., Oh, H.J., and Jeon, H.S. (2020). Recommending Cryptocurrency Trading Points with Deep Reinforcement Learning Approach. Appl. Sci., 10.","DOI":"10.3390\/app10041506"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"5","DOI":"10.18564\/jasss.3733","article-title":"Generating synthetic Bitcoin transactions and predicting market price movement via inverse reinforcement learning and agent-based modeling","volume":"21","author":"Lee","year":"2018","journal-title":"J. Artif. Soc. Soc. Simul."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1016\/j.ejor.2021.04.050","article-title":"Deep reinforcement learning for the optimal placement of cryptocurrency limit orders","volume":"296","author":"Schnaubelt","year":"2022","journal-title":"Eur. J. Oper. Res."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"17229","DOI":"10.1007\/s00521-020-05359-8","article-title":"A deep Q-learning portfolio management framework for the cryptocurrency market","volume":"32","author":"Lucarelli","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Ye, Y., Pei, H., Wang, B., Chen, P.Y., Zhu, Y., Xiao, J., and Li, B. (2020, January 7\u201312). Reinforcement-learning based portfolio management with augmented asset movement prediction states. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i01.5462"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Jiang, Z., and Liang, J. (2017, January 7\u20138). Cryptocurrency portfolio management with deep reinforcement learning. Proceedings of the 2017 Intelligent Systems Conference (IntelliSys), London, UK.","DOI":"10.1109\/IntelliSys.2017.8324237"},{"key":"ref_99","unstructured":"Hegazy, K., and Mumford, S. (2022, November 01). Comparitive Automated Bitcoin Trading Strategies. Available online: http:\/\/cs229.stanford.edu\/proj2016\/report\/MumfordHegazy-ComparitiveAutomatedBitcoinTradingStrategies-report.pdf."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"114002","DOI":"10.1016\/j.eswa.2020.114002","article-title":"Deep reinforcement learning for portfolio management of markets with a dynamic number of assets","volume":"164","author":"Betancourt","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1007\/s10207-019-00434-1","article-title":"DDoS attack detection with feature engineering and machine learning: The framework and performance evaluation","volume":"18","author":"Aamir","year":"2019","journal-title":"Int. J. Inf. Secur."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.neucom.2020.04.004","article-title":"Portfolio trading system of digital currencies: A deep reinforcement learning with multidimensional attention gating mechanism","volume":"402","author":"Weng","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1016\/j.envsoft.2018.09.016","article-title":"Advances in Bayesian network modelling: Integration of modelling technologies","volume":"111","author":"Marcot","year":"2019","journal-title":"Environ. Model. Softw."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"109377","DOI":"10.1016\/j.econlet.2020.109377","article-title":"Any port in a storm: Cryptocurrency safe-havens during the COVID-19 pandemic","volume":"194","author":"Corbet","year":"2020","journal-title":"Econ. Lett."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"101981","DOI":"10.1016\/j.frl.2021.101981","article-title":"Transitions in the cryptocurrency market during the COVID-19 pandemic: A network analysis","volume":"43","year":"2021","journal-title":"Financ. Res. Lett."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Surden, H. (2021). Machine learning and law: An overview. Research Handbook on Big Data Law, Edward Elgar Publishing.","DOI":"10.4337\/9781788972826.00014"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1016\/j.ribaf.2019.02.001","article-title":"Bitcoin return: Impacts from the introduction of new altcoins","volume":"48","author":"Nguyen","year":"2019","journal-title":"Res. Int. Bus. Financ."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"1920150","DOI":"10.1080\/23322039.2021.1920150","article-title":"Measurement of extreme market risk: Insights from a comprehensive literature review","volume":"9","author":"Chakraborty","year":"2021","journal-title":"Cogent Econ. Financ."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.jeconom.2008.12.013","article-title":"Granger causality in risk and detection of extreme risk spillover between financial markets","volume":"150","author":"Hong","year":"2009","journal-title":"J. Econom."},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Ahmed, M., Choudhury, N., and Uddin, S. (August, January 31). Anomaly detection on big data in financial markets. Proceedings of the 2017 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Sydney, Australia.","DOI":"10.1145\/3110025.3119402"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/15\/11\/428\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:17:42Z","timestamp":1760145462000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/15\/11\/428"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,14]]},"references-count":110,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["a15110428"],"URL":"https:\/\/doi.org\/10.3390\/a15110428","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,14]]}}}