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 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Switzerland
Frontiers Media S.A
09-05-2024
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| Subjects: | |
| Online Access: | Get full text |
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| Summary: | 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. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Charles Courchaine, National University, United States Edited by: Ricky J. Sethi, Fitchburg State University, United States Reviewed by: Zhenkun Liu, Nanjing University of Posts and Telecommunications, China |
| ISSN: | 2624-909X 2624-909X |
| DOI: | 10.3389/fdata.2024.1369895 |