Use of Patterned Datasets (Minimum and Maximum) to predict Bitcoin and Ethereum price movements
Rizky Parlika ()
Technium, 2023, vol. 16, issue 1, 137-142
Abstract:
Bitcoin is always interesting to keep predicting where the next price movement will go. Bitcoin is the first and most influential Cryptocurrency on cryptocurrency price movements. Bitcoin is traded in many markets, the largest in Indonesia is Indodax. Indodax provides a document sharing API so that third parties can build applications that are able to process data on bitcoin price movements in real-time and continuously. This research shows how patterned datasets can be applied to monitor bitcoin price movements from the indodax market and show their effects on other cryptocurrency assets besides bitcoin. This research shows how data that is patterned and then processed using the minimum and maximum functions can provide 2 important information, namely the potential position of bitcoin when it is at the maximum and minimum points. The results of the patterned dataset formula are then compared to the movements of the 2 cryptocurrencies with the largest capitalization, namely BTC (Bitcoin) and ETH (Ethereum). The results of the comparison show that the use of patterned dataset formulas successfully shows important points in cryptocurrency trading, with simpler instructions.
Date: 2023
References: View complete reference list from CitEc
Citations:
Downloads: (external link)
https://techniumscience.com/index.php/technium/article/view/9972/3781 (application/pdf)
https://techniumscience.com/index.php/technium/article/view/9972 (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:tec:techni:v:16:y:2023:i:1:p:137-142
DOI: 10.47577/technium.v16i.9972
Access Statistics for this article
Technium is currently edited by Scurtu Ionut Cristian
More articles in Technium from Technium Science
Bibliographic data for series maintained by Ana Maria Golita ().