Enhancing Crypto Success via Heatmap Visualization of Big Data Analytics for Numerous Variable Moving Average Strategies
This study employed variable moving average (VMA) trading rules and heatmap visualization because the flexibility advantage of the VMA technique and the presentation of numerous outcomes using the heatmap visualization technique may not have been thoroughly considered in prior financial research. We...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/23/12805 |
| _version_ | 1797400441485524992 |
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| author | Chien-Liang Chiu Yensen Ni Hung-Ching Hu Min-Yuh Day Yuhsin Chen |
| author_facet | Chien-Liang Chiu Yensen Ni Hung-Ching Hu Min-Yuh Day Yuhsin Chen |
| author_sort | Chien-Liang Chiu |
| collection | DOAJ |
| description | This study employed variable moving average (VMA) trading rules and heatmap visualization because the flexibility advantage of the VMA technique and the presentation of numerous outcomes using the heatmap visualization technique may not have been thoroughly considered in prior financial research. We not only employ multiple VMA trading rules in trading crypto futures but also present our overall results through heatmap visualization, which will aid investors in selecting an appropriate VMA trading rule, thereby likely generating profits after screening the results generated from various VMA trading rules. Unexpectedly, we demonstrate in this study that our results may impress Ethereum futures traders by disclosing a heatmap matrix that displays multiple geometric average returns (GARs) exceeding 40%, in accordance with various VMA trading rules. Thus, we argue that this study extracted the diverse trading performance of various VMA trading rules, utilized a big data analytics technique for knowledge extraction to observe and evaluate numerous results via heatmap visualization, and then employed this knowledge for investments, thereby contributing to the extant literature. Consequently, this study may cast light on the significance of decision making via big data analytics. |
| first_indexed | 2024-03-09T01:54:37Z |
| format | Article |
| id | doaj.art-339ae8e08c834497a81a28761f877625 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T01:54:37Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-339ae8e08c834497a81a28761f8776252023-12-08T15:11:47ZengMDPI AGApplied Sciences2076-34172023-11-0113231280510.3390/app132312805Enhancing Crypto Success via Heatmap Visualization of Big Data Analytics for Numerous Variable Moving Average StrategiesChien-Liang Chiu0Yensen Ni1Hung-Ching Hu2Min-Yuh Day3Yuhsin Chen4Department of Banking and Finance, Tamkang University, New Taipei City 25137, TaiwanDepartment of Management Sciences, Tamkang University, New Taipei City 25137, TaiwanDepartment of Management Sciences, Tamkang University, New Taipei City 25137, TaiwanGraduate Institute of Information Management, National Taipei University, New Taipei City 23741, TaiwanDepartment of Accounting, Chung Yuan Christian University, Taoyuan 320314, TaiwanThis study employed variable moving average (VMA) trading rules and heatmap visualization because the flexibility advantage of the VMA technique and the presentation of numerous outcomes using the heatmap visualization technique may not have been thoroughly considered in prior financial research. We not only employ multiple VMA trading rules in trading crypto futures but also present our overall results through heatmap visualization, which will aid investors in selecting an appropriate VMA trading rule, thereby likely generating profits after screening the results generated from various VMA trading rules. Unexpectedly, we demonstrate in this study that our results may impress Ethereum futures traders by disclosing a heatmap matrix that displays multiple geometric average returns (GARs) exceeding 40%, in accordance with various VMA trading rules. Thus, we argue that this study extracted the diverse trading performance of various VMA trading rules, utilized a big data analytics technique for knowledge extraction to observe and evaluate numerous results via heatmap visualization, and then employed this knowledge for investments, thereby contributing to the extant literature. Consequently, this study may cast light on the significance of decision making via big data analytics.https://www.mdpi.com/2076-3417/13/23/12805VMA trading rulescryptocurrenciesEthereum (ETH)investing strategiesheatmap visualizationbig data analytics |
| spellingShingle | Chien-Liang Chiu Yensen Ni Hung-Ching Hu Min-Yuh Day Yuhsin Chen Enhancing Crypto Success via Heatmap Visualization of Big Data Analytics for Numerous Variable Moving Average Strategies Applied Sciences VMA trading rules cryptocurrencies Ethereum (ETH) investing strategies heatmap visualization big data analytics |
| title | Enhancing Crypto Success via Heatmap Visualization of Big Data Analytics for Numerous Variable Moving Average Strategies |
| title_full | Enhancing Crypto Success via Heatmap Visualization of Big Data Analytics for Numerous Variable Moving Average Strategies |
| title_fullStr | Enhancing Crypto Success via Heatmap Visualization of Big Data Analytics for Numerous Variable Moving Average Strategies |
| title_full_unstemmed | Enhancing Crypto Success via Heatmap Visualization of Big Data Analytics for Numerous Variable Moving Average Strategies |
| title_short | Enhancing Crypto Success via Heatmap Visualization of Big Data Analytics for Numerous Variable Moving Average Strategies |
| title_sort | enhancing crypto success via heatmap visualization of big data analytics for numerous variable moving average strategies |
| topic | VMA trading rules cryptocurrencies Ethereum (ETH) investing strategies heatmap visualization big data analytics |
| url | https://www.mdpi.com/2076-3417/13/23/12805 |
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