Tools
Tools
Murugan, Girish Kandan (2021) Creation of a recommendation system to recommend cryptocurrency portfolio using Association rule mining. Masters thesis, Dublin, National College of Ireland.
Preview |
PDF (Master of Science)
Download (1MB) | Preview |
Preview |
PDF (Configuration manual)
Download (681kB) | Preview |
Abstract
Cryptocurrencies have emerged well in the past decade starting from bitcoin to very latest ones. Although there are certain inconsistencies people have started using cryptocurrency for trading and transaction purposes. There are asset managers, individual traders, stock managers who add cryptocurrencies to their portfolios. Hence, we propose a cryptocurrency portfolio recommender system using association rule mining (ARM) which analyses the cryptocurrency dataset with the suggestion of cryptocurrencies ranked in a basket. The main objective of this research is to help the traders and the managers involved in crypto market in making decisions to invest in group of cryptocurrencies when maximum profit transaction evidence is available for investment.
Our recommender system we propose is not the same as other systems as it determines the correlation between the cryptocurrencies and recommends a portfolio. Existing research in cryptocurrency mostly concentrates on portfolio management, portfolio optimization, Cryptocurrency price prediction, Crypto price trend forecasting etc. We have used the association rule technique which was never used in cryptocurrency portfolio recommender system creation. It’s not always feasible to use the traditional ARM technique as the rules created will be exponential and getting the most relevant rule will be difficult to choose.
We have done a thorough research on our cryptocurrency historical dataset that we got from Kaggle. We have compared our portfolio we created with the ranking of the crypto currency available in the actual crypto market data and the CRIX (Crypto Index) values. The result of our research shows the top 10 cryptocurrency portfolios for short selling(Intraday trading) purpose which has Return of interest of more than 99 percent.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | Block-chain; Crypto currency; Association rule mining (ARM); Recommendation system; Apriori algorithm; Intraday trading; KDD |
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software T Technology > T Technology (General) > Information Technology > Computer software H Social Sciences > HG Finance > Money > Currency |
| Divisions: | School of Computing > Master of Science in Data Analytics |
| Depositing User: | Clara Chan |
| Date Deposited: | 09 Dec 2021 17:24 |
| Last Modified: | 09 Dec 2021 17:24 |
| URI: | https://norma.ncirl.ie/id/eprint/5195 |
Actions (login required)
 |
View Item |