Forecasting cryptocurrency's buy signal with a bagged tree learning approach to enhance purchase decisions

The cryptocurrency market is captivating the attention of both retail and institutional investors. While this highly volatile market offers investors substantial profit opportunities, it also entails risks due to its sensitivity to speculative news and the erratic behavior of major investors, both o...

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Published in:	Frontiers in big data Vol. 7; p. 1369895
Main Authors:	Alsini, Raed, Abu Al-Haija, Qasem, Alsulami, Abdulaziz A, Alturki, Badraddin, Alqurashi, Abdulaziz A, Mashat, Mouhamad D, Alqahtani, Ali, Alhebaishi, Nawaf
Format:	Journal Article
Language:	English
Published:	Switzerland Frontiers Media S.A 09-05-2024
Subjects:	Big Data cryptocurrency market data-driven trading machine learning technical indicator trading strategies trading strategies cryptocurrency market machine learning technical indicator data-driven trading
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Abstract	The cryptocurrency market is captivating the attention of both retail and institutional investors. While this highly volatile market offers investors substantial profit opportunities, it also entails risks due to its sensitivity to speculative news and the erratic behavior of major investors, both of which can provoke unexpected price fluctuations. In this study, we contend that extreme and sudden price changes and atypical patterns might compromise the performance of technical signals utilized as the basis for feature extraction in a machine learning-based trading system by either augmenting or diminishing the model's generalization capability. To address this issue, this research uses a bagged tree (BT) model to forecast the buy signal for the cryptocurrency market. To achieve this, traders must acquire knowledge about the cryptocurrency market and modify their strategies accordingly. To make an informed decision, we depended on the most prevalently utilized oscillators, namely, the buy signal in the cryptocurrency market, comprising the Relative Strength Index (RSI), Bollinger Bands (BB), and the Moving Average Convergence/Divergence (MACD) indicator. Also, the research evaluates how accurately a model can predict the performance of different cryptocurrencies such as Bitcoin (BTC), Ethereum (ETH), Cardano (ADA), and Binance Coin (BNB). Furthermore, the efficacy of the most popular machine learning model in precisely forecasting outcomes within the cryptocurrency market is examined. Notably, predicting buy signal values using a BT model provides promising results.
AbstractList	IntroductionThe cryptocurrency market is captivating the attention of both retail and institutional investors. While this highly volatile market offers investors substantial profit opportunities, it also entails risks due to its sensitivity to speculative news and the erratic behavior of major investors, both of which can provoke unexpected price fluctuations.MethodsIn this study, we contend that extreme and sudden price changes and atypical patterns might compromise the performance of technical signals utilized as the basis for feature extraction in a machine learning-based trading system by either augmenting or diminishing the model's generalization capability. To address this issue, this research uses a bagged tree (BT) model to forecast the buy signal for the cryptocurrency market. To achieve this, traders must acquire knowledge about the cryptocurrency market and modify their strategies accordingly.Results and discussionTo make an informed decision, we depended on the most prevalently utilized oscillators, namely, the buy signal in the cryptocurrency market, comprising the Relative Strength Index (RSI), Bollinger Bands (BB), and the Moving Average Convergence/Divergence (MACD) indicator. Also, the research evaluates how accurately a model can predict the performance of different cryptocurrencies such as Bitcoin (BTC), Ethereum (ETH), Cardano (ADA), and Binance Coin (BNB). Furthermore, the efficacy of the most popular machine learning model in precisely forecasting outcomes within the cryptocurrency market is examined. Notably, predicting buy signal values using a BT model provides promising results. The cryptocurrency market is captivating the attention of both retail and institutional investors. While this highly volatile market offers investors substantial profit opportunities, it also entails risks due to its sensitivity to speculative news and the erratic behavior of major investors, both of which can provoke unexpected price fluctuations. In this study, we contend that extreme and sudden price changes and atypical patterns might compromise the performance of technical signals utilized as the basis for feature extraction in a machine learning-based trading system by either augmenting or diminishing the model's generalization capability. To address this issue, this research uses a bagged tree (BT) model to forecast the buy signal for the cryptocurrency market. To achieve this, traders must acquire knowledge about the cryptocurrency market and modify their strategies accordingly. To make an informed decision, we depended on the most prevalently utilized oscillators, namely, the buy signal in the cryptocurrency market, comprising the Relative Strength Index (RSI), Bollinger Bands (BB), and the Moving Average Convergence/Divergence (MACD) indicator. Also, the research evaluates how accurately a model can predict the performance of different cryptocurrencies such as Bitcoin (BTC), Ethereum (ETH), Cardano (ADA), and Binance Coin (BNB). Furthermore, the efficacy of the most popular machine learning model in precisely forecasting outcomes within the cryptocurrency market is examined. Notably, predicting buy signal values using a BT model provides promising results. Introduction The cryptocurrency market is captivating the attention of both retail and institutional investors. While this highly volatile market offers investors substantial profit opportunities, it also entails risks due to its sensitivity to speculative news and the erratic behavior of major investors, both of which can provoke unexpected price fluctuations. Methods In this study, we contend that extreme and sudden price changes and atypical patterns might compromise the performance of technical signals utilized as the basis for feature extraction in a machine learning-based trading system by either augmenting or diminishing the model's generalization capability. To address this issue, this research uses a bagged tree (BT) model to forecast the buy signal for the cryptocurrency market. To achieve this, traders must acquire knowledge about the cryptocurrency market and modify their strategies accordingly. Results and discussion To make an informed decision, we depended on the most prevalently utilized oscillators, namely, the buy signal in the cryptocurrency market, comprising the Relative Strength Index (RSI), Bollinger Bands (BB), and the Moving Average Convergence/Divergence (MACD) indicator. Also, the research evaluates how accurately a model can predict the performance of different cryptocurrencies such as Bitcoin (BTC), Ethereum (ETH), Cardano (ADA), and Binance Coin (BNB). Furthermore, the efficacy of the most popular machine learning model in precisely forecasting outcomes within the cryptocurrency market is examined. Notably, predicting buy signal values using a BT model provides promising results.
Author	Alsulami, Abdulaziz A Alhebaishi, Nawaf Alqurashi, Abdulaziz A Abu Al-Haija, Qasem Alturki, Badraddin Alsini, Raed Alqahtani, Ali Mashat, Mouhamad D
AuthorAffiliation	4 Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University , Jeddah , Saudi Arabia 5 Department of Networks and Communications Engineering, College of Computer Science and Information Systems, Najran University , Najran , Saudi Arabia 3 Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University , Jeddah , Saudi Arabia 1 Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University , Jeddah , Saudi Arabia 2 Department of Cybersecurity, Faculty of Computer and Information Technology, Jordan University of Science and Technology , Irbid , Jordan
AuthorAffiliation_xml	– name: 3 Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University , Jeddah , Saudi Arabia – name: 1 Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University , Jeddah , Saudi Arabia – name: 2 Department of Cybersecurity, Faculty of Computer and Information Technology, Jordan University of Science and Technology , Irbid , Jordan – name: 4 Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University , Jeddah , Saudi Arabia – name: 5 Department of Networks and Communications Engineering, College of Computer Science and Information Systems, Najran University , Najran , Saudi Arabia
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Cites_doi	10.3390/electronics9020255 10.3390/app122312336 10.1186/s40854-021-00321-6 10.1108/IJCHM-06-2022-0737 10.1016/j.frl.2020.101655 10.3390/math10122037 10.1109/ICBC56567.2023.10174922 10.1016/j.jisa.2020.102583 10.1016/j.eswa.2022.117409 10.1111/fima.12310 10.1016/j.frl.2019.08.011 10.1016/j.ins.2022.02.015 10.1002/fut.22164 10.1016/j.jfds.2021.03.001 10.1016/j.bushor.2019.08.003 10.3390/jrfm14050198 10.3390/ijfs7020026 10.1016/j.mlwa.2022.100310 10.3390/jrfm14010037 10.2139/ssrn.4422374 10.1016/j.irfa.2021.101972 10.1145/3561300 10.1007/978-3-030-38040-3_2 10.3390/math10183302 10.1007/s11831-020-09426-0 10.1016/j.omega.2024.103034 10.1007/978-3-030-89546-4_7 10.1080/1351847X.2021.1908390 10.22363/2658-4670-2022-30-1-79-87 10.1080/08874417.2018.1552090 10.1080/17520843.2019.1574852 10.1186/s40537-022-00601-7 10.1002/ijfe.2392 10.2139/ssrn.4062476 10.1145/3588686 10.3390/math8081245 10.3390/fi13070165 10.3390/jrfm14020048 10.18178/ijke.2019.5.2.116 10.4108/eai.7-7-2021.170286 10.1109/AICAI.2019.8701353
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References	Hairudin (B23) 2022; 27 Shynkevich (B48) 2021; 41 Wilder (B56) 1978 Jiang (B28) 2024; 125 Alsulami (B1) 2022; 12 Vo (B55) 2019; 5 Fang (B19) 2021; 22 Shah (B47) 2019; 7 Iqbal (B26) 2021; 8 Cohen (B14) 2022; 10 Arowolo (B4) 2022 Liu (B37) 2023; 35 Busayatananphon (B10) 2022 Namasudra (B41) 2021; 28 Gurrib (B22) 2019; 12 Lee (B36) 2019; 62 Smales (B49) 2022; 79 Appel (B3) 1985 Marti (B38) 2023 Gerritsen (B21) 2020; 34 Hansen (B24) 2022; 9 Senthilkumar (B46) 2021 Cohen (B13) 2022; 10 Yu (B57) 2022; 30 B8 Patel (B42) 2020; 55 Sadorsky (B44); 14 B9 Kang (B31) 2021; 14 Bello (B6) 2023 B40 Fang (B20) 2022; 8 Matytsin (B39) 2021 Hu (B25) 2023; 1 Cunha (B16) 2021; 13 Vachhani (B53) 2020 Jaquart (B27) 2021; 7 Joiner (B29) 2022 Bellocca (B7) 2022; 8 La Morgia (B33) 2023; 23 Saxena (B45) 2019 Çelik (B12) 2019 Anghel (B2) 2021; 39 Kumar (B32) 2022 B51 Detzel (B17) 2021; 50 B52 B11 B15 Sun (B50) 2020; 9 Dolatsara (B18) 2022; 203 Sadorsky (B43); 14 Lamothe-Fernández (B34) 2020; 8 Vo (B54) 2018; 60 Lauguico (B35) 2019 Kamara (B30) 2022; 594 Asgari (B5) 2021
References_xml	– volume: 9 start-page: 255 year: 2020 ident: B50 article-title: A formal verification framework for security issues of blockchain smart contracts publication-title: Electronics doi: 10.3390/electronics9020255 contributor: fullname: Sun – volume: 12 start-page: 12336 year: 2022 ident: B1 article-title: An intrusion detection and classification system for IoT traffic with improved data engineering publication-title: Appl. Sci. doi: 10.3390/app122312336 contributor: fullname: Alsulami – volume: 8 start-page: 1 year: 2022 ident: B20 article-title: Cryptocurrency trading: a comprehensive survey publication-title: Financ. Innov. doi: 10.1186/s40854-021-00321-6 contributor: fullname: Fang – volume: 35 start-page: 2084 year: 2023 ident: B37 article-title: Hospitality order cancellation prediction from a profit-driven perspective publication-title: Int. J. Contemp. Hosp. Manage. doi: 10.1108/IJCHM-06-2022-0737 contributor: fullname: Liu – volume: 39 start-page: 101655 year: 2021 ident: B2 article-title: A reality check on trading rule performance in cryptocurrency: machine learning vs. technical analysis publication-title: Financ. Res. Lett. doi: 10.1016/j.frl.2020.101655 contributor: fullname: Anghel – volume: 10 start-page: 2037 year: 2022 ident: B14 article-title: The complexity of cryptocurrencies algorithmic trading publication-title: Mathematics doi: 10.3390/math10122037 contributor: fullname: Cohen – start-page: 1 volume-title: 2023 IEEE International Conference on Blockchain and Cryptocurrency (ICBC) year: 2023 ident: B6 article-title: “LLD: a low latency detection solution to thwart cryptocurrency pump and dumps,” doi: 10.1109/ICBC56567.2023.10174922 contributor: fullname: Bello – volume: 55 start-page: 102583 year: 2020 ident: B42 article-title: A deep learning-based cryptocurrency price prediction scheme for financial institutions publication-title: J. Inf. Secur. Appl. doi: 10.1016/j.jisa.2020.102583 contributor: fullname: Patel – volume: 203 start-page: 117409 year: 2022 ident: B18 article-title: An interpretable decision support systems for daily cryptocurrency trading publication-title: Exp. Syst. Appl. doi: 10.1016/j.eswa.2022.117409 contributor: fullname: Dolatsara – volume: 50 start-page: 107 year: 2021 ident: B17 article-title: Learning and predictability via technical analysis: evidence from bitcoin and stocks with hard-to-value fundamentals publication-title: Financ. Manage. doi: 10.1111/fima.12310 contributor: fullname: Detzel – volume: 34 start-page: 101263 year: 2020 ident: B21 article-title: The profitability of technical trading rules in the Bitcoin market publication-title: Financ. Res. Let. doi: 10.1016/j.frl.2019.08.011 contributor: fullname: Gerritsen – volume: 594 start-page: 1 year: 2022 ident: B30 article-title: An ensemble of a boosted hybrid of deep learning models and technical analysis for forecasting stock prices publication-title: Inf. Sci. doi: 10.1016/j.ins.2022.02.015 contributor: fullname: Kamara – volume: 41 start-page: 115 year: 2021 ident: B48 article-title: Impact of Bitcoin futures on the informational efficiency of the Bitcoin spot market publication-title: J. Fut. Markets doi: 10.1002/fut.22164 contributor: fullname: Shynkevich – volume: 7 start-page: 45 year: 2021 ident: B27 article-title: Short-term bitcoin market prediction via machine learning publication-title: The J. Financ. Data Sci. doi: 10.1016/j.jfds.2021.03.001 contributor: fullname: Jaquart – volume: 62 start-page: 773 year: 2019 ident: B36 article-title: A decentralized token economy: how blockchain and cryptocurrency can revolutionize business publication-title: Bus. Horiz. doi: 10.1016/j.bushor.2019.08.003 contributor: fullname: Lee – ident: B40 – volume: 14 start-page: 198 ident: B44 article-title: Predicting gold and silver price direction using tree-based classifiers publication-title: J. Risk Financ. Manage. doi: 10.3390/jrfm14050198 contributor: fullname: Sadorsky – volume: 7 start-page: 26 year: 2019 ident: B47 article-title: Stock market analysis: a review and taxonomy of prediction techniques publication-title: Int. J. Finan. Stu. doi: 10.3390/ijfs7020026 contributor: fullname: Shah – start-page: 1850 volume-title: Internet Investing as a Remote Algorithm of the Retail Investment Financing. year: 2021 ident: B39 contributor: fullname: Matytsin – volume-title: The Moving Average Convergence-Divergence Trading Method: Advanced Version year: 1985 ident: B3 contributor: fullname: Appel – volume: 8 start-page: 100310 year: 2022 ident: B7 article-title: Leveraging the momentum effect in machine learning-based cryptocurrency trading publication-title: Mach. Learning Appl. doi: 10.1016/j.mlwa.2022.100310 contributor: fullname: Bellocca – volume: 14 start-page: 37 year: 2021 ident: B31 article-title: Improving MACD technical analysis by optimizing parameters and modifying trading rules: evidence from the Japanese Nikkei 225 futures market publication-title: J. Risk Financ. Manage. doi: 10.3390/jrfm14010037 contributor: fullname: Kang – year: 2023 ident: B38 publication-title: Decoding the Quant Market: A Guide to Machine Learning in Trading doi: 10.2139/ssrn.4422374 contributor: fullname: Marti – ident: B51 – ident: B9 – volume: 79 start-page: 101972 year: 2022 ident: B49 article-title: Investor attention in cryptocurrency markets publication-title: Int. Rev. Financ. Anal. doi: 10.1016/j.irfa.2021.101972 contributor: fullname: Smales – volume: 23 start-page: 1 year: 2023 ident: B33 article-title: The doge of wall street: Analysis and detection of pump and dump cryptocurrency manipulations publication-title: ACM Trans. Internet Technol. doi: 10.1145/3561300 contributor: fullname: La Morgia – start-page: 12 volume-title: International Conference on Innovative Data Communication Technologies and Application year: 2020 ident: B53 article-title: “Machine learning based stock market analysis: a short survey,” doi: 10.1007/978-3-030-38040-3_2 contributor: fullname: Vachhani – start-page: 1 volume-title: 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) year: 2019 ident: B35 article-title: “A fuzzy logic-based stock market trading algorithm using bollinger bands,” contributor: fullname: Lauguico – volume: 10 start-page: 3302 year: 2022 ident: B13 article-title: Algorithmic trading and financial forecasting using advanced artificial intelligence methodologies publication-title: Mathematics doi: 10.3390/math10183302 contributor: fullname: Cohen – volume: 28 start-page: 1497 year: 2021 ident: B41 article-title: The revolution of blockchain: State-of-the-art and research challenges publication-title: Arch. Comput. Methods Eng. doi: 10.1007/s11831-020-09426-0 contributor: fullname: Namasudra – volume: 125 start-page: 103034 year: 2024 ident: B28 article-title: Profit-driven weighted classifier with interpretable ability for customer churn prediction publication-title: Omega doi: 10.1016/j.omega.2024.103034 contributor: fullname: Jiang – start-page: 127 volume-title: A Prediction Model for Bitcoin Cryptocurrency Prices. Blockchain Applications in the Smart Era year: 2022 ident: B4 doi: 10.1007/978-3-030-89546-4_7 contributor: fullname: Arowolo – volume-title: New Concepts in Technical Trading Systems year: 1978 ident: B56 contributor: fullname: Wilder – volume: 22 start-page: 1 year: 2021 ident: B19 article-title: Ascertaining price formation in cryptocurrency markets with machine learning publication-title: The Eur. J. Financ. doi: 10.1080/1351847X.2021.1908390 contributor: fullname: Fang – year: 2021 ident: B5 article-title: Profitable strategy design for trades on cryptocurrency markets with machine learning techniques publication-title: arXiv preprint arXiv: contributor: fullname: Asgari – volume: 30 start-page: 79 year: 2022 ident: B57 article-title: On methods of building the trading strategies in the cryptocurrency markets publication-title: Discr. Contin. Models Appl. Comput. Sci. doi: 10.22363/2658-4670-2022-30-1-79-87 contributor: fullname: Yu – volume: 60 start-page: 555 year: 2018 ident: B54 article-title: A high-frequency algorithmic trading strategy for cryptocurrency publication-title: J. Comput. Inf. Syst. doi: 10.1080/08874417.2018.1552090 contributor: fullname: Vo – volume: 12 start-page: 105 year: 2019 ident: B22 article-title: The implementation of an adjusted relative strength index model in foreign currency and energy markets of emerging and developed economies publication-title: Macroecon. Financ. Emerg. Market Econ. doi: 10.1080/17520843.2019.1574852 contributor: fullname: Gurrib – start-page: 1 volume-title: 2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT) year: 2022 ident: B32 article-title: “Ethereum and Binance price forecasting using machine learning,” contributor: fullname: Kumar – volume: 9 start-page: 1 year: 2022 ident: B24 article-title: Multivariate cryptocurrency prediction: Comparative analysis of three recurrent neural networks approaches publication-title: J. Big Data doi: 10.1186/s40537-022-00601-7 contributor: fullname: Hansen – ident: B8 – ident: B11 – volume: 27 start-page: 4633 year: 2022 ident: B23 article-title: Cryptocurrencies: a survey on acceptance, governance, and market dynamics publication-title: Int. J. Financ. Econ. doi: 10.1002/ijfe.2392 contributor: fullname: Hairudin – ident: B52 doi: 10.2139/ssrn.4062476 – volume: 1 start-page: 1 year: 2023 ident: B25 article-title: Sequence-based target coin prediction for cryptocurrency pump-and-dump publication-title: Proc. ACM Manag. Data doi: 10.1145/3588686 contributor: fullname: Hu – volume: 8 start-page: 1245 year: 2020 ident: B34 article-title: Deep learning methods for modeling bitcoin price publication-title: Mathematics doi: 10.3390/math8081245 contributor: fullname: Lamothe-Fernández – volume: 13 start-page: 165 year: 2021 ident: B16 article-title: From Bitcoin to central bank digital currencies: making sense of the digital money revolution publication-title: Fut. Int. doi: 10.3390/fi13070165 contributor: fullname: Cunha – volume: 14 start-page: 48 ident: B43 article-title: A random forests approach to predicting clean energy stock prices publication-title: J. Risk Financ. Manage. doi: 10.3390/jrfm14020048 contributor: fullname: Sadorsky – volume: 5 start-page: 47 year: 2019 ident: B55 article-title: Sentiment analysis of news for effective cryptocurrency price prediction publication-title: Int. J. Know. Eng. doi: 10.18178/ijke.2019.5.2.116 contributor: fullname: Vo – volume: 8 start-page: 28 year: 2021 ident: B26 article-title: Time-series prediction of the cryptocurrency market using machine learning techniques publication-title: EAI Endors. Trans. Creat. Technol. doi: 10.4108/eai.7-7-2021.170286 contributor: fullname: Iqbal – start-page: 267 volume-title: 2022 Joint International Conference on Digital Arts, Media, and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT and NCON) year: 2022 ident: B10 article-title: “Financial technology DeFi protocol: a review,” contributor: fullname: Busayatananphon – volume-title: Data confidentiality, integrity, and authentication. Research Anthology on Blockchain Technology in Business, Healthcare, Education, and Government. year: 2021 ident: B46 contributor: fullname: Senthilkumar – start-page: 756 volume-title: 2019 Amity International Conference on Artificial Intelligence (AICAI) year: 2019 ident: B45 article-title: “Survey on online electronic paymentss security,” doi: 10.1109/AICAI.2019.8701353 contributor: fullname: Saxena – ident: B15 – volume-title: Implementation of Technical Analysis on Selected Cryptocurrencies year: 2019 ident: B12 contributor: fullname: Çelik – start-page: 1 volume-title: 2022 IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE) year: 2022 ident: B29 article-title: “Algorithmic trading and short-term forecast for financial time series with machine learning models; state of the art and perspectives,” contributor: fullname: Joiner
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Snippet	The cryptocurrency market is captivating the attention of both retail and institutional investors. While this highly volatile market offers investors... Introduction The cryptocurrency market is captivating the attention of both retail and institutional investors. While this highly volatile market offers... IntroductionThe cryptocurrency market is captivating the attention of both retail and institutional investors. While this highly volatile market offers...
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Title	Forecasting cryptocurrency's buy signal with a bagged tree learning approach to enhance purchase decisions
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