SMU Data Science Review
Abstract
In this paper, we present a tool that provides trading recommendations for cryptocurrency using a stochastic gradient boost classifier trained from a model labeled by technical indicators. The cryptocurrency market is volatile due to its infancy and limited size making it difficult for investors to know when to enter, exit, or stay in the market. Therefore, a tool is needed to provide investment recommendations for investors. We developed such a tool to support one cryptocurrency, Bitcoin, based on its historical price and volume data to recommend a trading decision for today or past days. This tool is 95.50% accurate with a standard deviation of 0.54%. From our analysis, we conclude that Bitcoin is a unique asset with similarities to gold. As a young asset, it lacks economic fundamentals making it very difficult to predict. By leveraging technical momentum indicators to provide buy, sell, and hold markers or labels, a tool can be developed that performs as good or better than a buy and hold trading strategy in a bear market, bull market or both markets.
Recommended Citation
baldree, matthew; widhalm, paul; hill, brandon; and ortisi, matteo
(2018)
"Cryptovisor: A Cryptocurrency Advisor Tool,"
SMU Data Science Review: Vol. 1:
No.
2, Article 3.
Available at:
https://scholar.smu.edu/datasciencereview/vol1/iss2/3
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Included in
Artificial Intelligence and Robotics Commons, Databases and Information Systems Commons, Finance and Financial Management Commons, Other Computer Sciences Commons
