Cryptocurrencies trading algorithms: A review
Author
Abstract
Suggested Citation
DOI: 10.1002/for.2886
Download full text from publisher
References listed on IDEAS
- Donthu, Naveen & Kumar, Satish & Mukherjee, Debmalya & Pandey, Nitesh & Lim, Weng Marc, 2021. "How to conduct a bibliometric analysis: An overview and guidelines," Journal of Business Research, Elsevier, vol. 133(C), pages 285-296.
- Atsalakis, George S. & Atsalaki, Ioanna G. & Pasiouras, Fotios & Zopounidis, Constantin, 2019.
"Bitcoin price forecasting with neuro-fuzzy techniques,"
European Journal of Operational Research, Elsevier, vol. 276(2), pages 770-780.
- George S. Atsalakis & Ioanna G. Atsalaki & Fotios Pasiouras & Constantin Zopounidis, 2019. "Bitcoin price forecasting with neuro-fuzzy techniques," Post-Print hal-02879928, HAL.
- Caporale, Guglielmo Maria & Plastun, Alex, 2019.
"The day of the week effect in the cryptocurrency market,"
Finance Research Letters, Elsevier, vol. 31(C).
- Guglielmo Maria Caporale & Alex Plastun, 2017. "The Day of the Week Effect in the Crypto Currency Market," Discussion Papers of DIW Berlin 1694, DIW Berlin, German Institute for Economic Research.
- Guglielmo Maria Caporale & Alex Plastun, 2017. "The Day of the Week Effect in the Crypto Currency Market," CESifo Working Paper Series 6716, CESifo.
- Aniruddha Dutta & Saket Kumar & Meheli Basu, 2020. "A Gated Recurrent Unit Approach to Bitcoin Price Prediction," JRFM, MDPI, vol. 13(2), pages 1-16, February.
- Laura Alessandretti & Abeer ElBahrawy & Luca Maria Aiello & Andrea Baronchelli, 2018. "Anticipating cryptocurrency prices using machine learning," Papers 1805.08550, arXiv.org, revised Nov 2018.
- Guégan, Dominique & Renault, Thomas, 2021.
"Does investor sentiment on social media provide robust information for Bitcoin returns predictability?,"
Finance Research Letters, Elsevier, vol. 38(C).
- Dominique Guégan & Thomas Renault, 2021. "Does investor sentiment on social media provide robust information for Bitcoin returns predictability?," Post-Print hal-03205154, HAL.
- Dominique Guégan & Thomas Renault, 2021. "Does investor sentiment on social media provide robust information for Bitcoin returns predictability?," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03205154, HAL.
- Thomas E. Koker & Dimitrios Koutmos, 2020. "Cryptocurrency Trading Using Machine Learning," JRFM, MDPI, vol. 13(8), pages 1-7, August.
- Chu, Jeffrey & Chan, Stephen & Zhang, Yuanyuan, 2020. "High frequency momentum trading with cryptocurrencies," Research in International Business and Finance, Elsevier, vol. 52(C).
- Bouri, Elie & Lau, Chi Keung Marco & Lucey, Brian & Roubaud, David, 2019. "Trading volume and the predictability of return and volatility in the cryptocurrency market," Finance Research Letters, Elsevier, vol. 29(C), pages 340-346.
- Suhwan Ji & Jongmin Kim & Hyeonseung Im, 2019. "A Comparative Study of Bitcoin Price Prediction Using Deep Learning," Mathematics, MDPI, vol. 7(10), pages 1-20, September.
- Lahmiri, Salim & Bekiros, Stelios, 2019. "Cryptocurrency forecasting with deep learning chaotic neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 118(C), pages 35-40.
- Laura Alessandretti & Abeer ElBahrawy & Luca Maria Aiello & Andrea Baronchelli, 2018. "Anticipating Cryptocurrency Prices Using Machine Learning," Complexity, Hindawi, vol. 2018, pages 1-16, November.
- Troster, Victor & Tiwari, Aviral Kumar & Shahbaz, Muhammad & Macedo, Demian Nicolás, 2019. "Bitcoin returns and risk: A general GARCH and GAS analysis," Finance Research Letters, Elsevier, vol. 30(C), pages 187-193.
- David Garcia & Frank Schweitzer, 2015. "Social signals and algorithmic trading of Bitcoin," Papers 1506.01513, arXiv.org, revised Sep 2015.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & David Martinez-Rego & Fan Wu & Lingbo Li, 2022. "Cryptocurrency trading: a comprehensive survey," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-59, December.
- Ahmed M. Khedr & Ifra Arif & Pravija Raj P V & Magdi El‐Bannany & Saadat M. Alhashmi & Meenu Sreedharan, 2021. "Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 28(1), pages 3-34, January.
- Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & Lingbo Li & David Martinez-Regoband & Fan Wu, 2020. "Cryptocurrency Trading: A Comprehensive Survey," Papers 2003.11352, arXiv.org, revised Jan 2022.
- Helder Sebastião & Pedro Godinho, 2021. "Forecasting and trading cryptocurrencies with machine learning under changing market conditions," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-30, December.
- Ren, Yi-Shuai & Ma, Chao-Qun & Kong, Xiao-Lin & Baltas, Konstantinos & Zureigat, Qasim, 2022. "Past, present, and future of the application of machine learning in cryptocurrency research," Research in International Business and Finance, Elsevier, vol. 63(C).
- Gil Cohen, 2022. "Algorithmic Trading and Financial Forecasting Using Advanced Artificial Intelligence Methodologies," Mathematics, MDPI, vol. 10(18), pages 1-13, September.
- Rico-Peña, Juan Jesús & Arguedas-Sanz, Raquel & López-Martin, Carmen, 2023. "Models used to characterise blockchain features. A systematic literature review and bibliometric analysis," Technovation, Elsevier, vol. 123(C).
- Pattnaik, Debidutta & Hassan, M. Kabir & Dsouza, Arun & Tiwari, Aviral & Devji, Shridev, 2023. "Ex-post facto analysis of cryptocurrency literature over a decade using bibliometric technique," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
- Chowdhury, Reaz & Rahman, M. Arifur & Rahman, M. Sohel & Mahdy, M.R.C., 2020. "An approach to predict and forecast the price of constituents and index of cryptocurrency using machine learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
- Duygu Ider & Stefan Lessmann, 2022. "Forecasting Cryptocurrency Returns from Sentiment Signals: An Analysis of BERT Classifiers and Weak Supervision," Papers 2204.05781, arXiv.org, revised Mar 2023.
- Ze Shen & Qing Wan & David J. Leatham, 2021. "Bitcoin Return Volatility Forecasting: A Comparative Study between GARCH and RNN," JRFM, MDPI, vol. 14(7), pages 1-18, July.
- Jacopo Fior & Luca Cagliero & Paolo Garza, 2022. "Leveraging Explainable AI to Support Cryptocurrency Investors," Future Internet, MDPI, vol. 14(9), pages 1-19, August.
- Lahmiri, Salim & Bekiros, Stelios, 2020. "Big data analytics using multi-fractal wavelet leaders in high-frequency Bitcoin markets," Chaos, Solitons & Fractals, Elsevier, vol. 131(C).
- Gradojevic, Nikola & Kukolj, Dragan & Adcock, Robert & Djakovic, Vladimir, 2023. "Forecasting Bitcoin with technical analysis: A not-so-random forest?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 1-17.
- Wang, Yaqi & Wang, Chunfeng & Sensoy, Ahmet & Yao, Shouyu & Cheng, Feiyang, 2022. "Can investors’ informed trading predict cryptocurrency returns? Evidence from machine learning," Research in International Business and Finance, Elsevier, vol. 62(C).
- Giacomo di Tollo & Joseph Andria & Gianni Filograsso, 2023. "The Predictive Power of Social Media Sentiment: Evidence from Cryptocurrencies and Stock Markets Using NLP and Stochastic ANNs," Mathematics, MDPI, vol. 11(16), pages 1-18, August.
- Aurelio F. Bariviera & Ignasi Merediz‐Solà, 2021.
"Where Do We Stand In Cryptocurrencies Economic Research? A Survey Based On Hybrid Analysis,"
Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 377-407, April.
- Aurelio F. Bariviera & Ignasi Merediz-Sol`a, 2020. "Where do we stand in cryptocurrencies economic research? A survey based on hybrid analysis," Papers 2003.09723, arXiv.org.
- Mingzhe Wei & Georgios Sermpinis & Charalampos Stasinakis, 2023. "Forecasting and trading Bitcoin with machine learning techniques and a hybrid volatility/sentiment leverage," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 852-871, July.
- Lahmiri, Salim & Bekiros, Stelios, 2019. "Decomposing the persistence structure of Islamic and green crypto-currencies with nonlinear stepwise filtering," Chaos, Solitons & Fractals, Elsevier, vol. 127(C), pages 334-341.
- Gil Cohen, 2021. "Optimizing candlesticks patterns for Bitcoin's trading systems," Review of Quantitative Finance and Accounting, Springer, vol. 57(3), pages 1155-1167, October.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:jforec:v:41:y:2022:i:8:p:1661-1668. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.