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Title:Analiza uspešnosti lastnega programa za avtomatizirano trgovanje kriptovalut v primerjavi s konkurenčnim : diplomsko delo
Authors:ID Gojkošek, Alen (Author)
ID Močnik, Dijana (Mentor) More about this mentor... New window
ID Šumak, Boštjan (Comentor)
Files:.pdf UN_Gojkosek_Alen_2022.pdf (1,08 MB)
MD5: D504B3A6137A691F62C37AAEA64042D7
 
Language:Slovenian
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V diplomskem delu smo predstavili kripto trg in njegove udeležence, predvsem kripto kite. Razvili smo programsko rešitev za avtomatizirano trgovanje kriptovalute bitcoin, ki se je na podlagi podatkov o transakcijah kripto kitov odločala, kdaj bo kriptovaluto bitcoin kupila oziroma prodala. Cilj programa je bil visoka donosnost in neodvisnost od nihanja cen kriptovalut. Rezultate simulacije trgovanja z resničnimi zgodovinskimi podatki kriptovalut iz leta 2022 smo primerjali z rezultati konkurenčnega programa. Dosegli smo vse zastavljene cilje in v primerjalni analizi obeh programov ugotovili, da je naša programska rešitev uspešnejša in ima na dolgi rok manjše naložbeno tveganje.
Keywords:Avtomatizirano trgovanje, bitcoin, kripto kiti, Python
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[Al. Gojkošek]
Year of publishing:2022
Number of pages:1 spletni vir (1 datoteka PDF (VI, 32 f.))
PID:20.500.12556/DKUM-82633 New window
UDC:004.7:336.745(043.2)
COBISS.SI-ID:130767875 New window
Publication date in DKUM:21.10.2022
Views:717
Downloads:107
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:28.08.2022

Secondary language

Language:English
Title:Performance analysis of our own automated software for cryptocurrency trading compared to a competitor's
Abstract:In our thesis, we presented the crypto market and its participants, especially crypto whales. We developed automated cryptocurrency trading program that decided when to buy and sell bitcoin, based on the crypto whales transactions. The goal of the program was high profitability and independence from the crypto market's volatility. We compared the results of simulated trading with real historical data of the cryptocurrencies from 2022 with the competitors program. All the goals we set were achieved. The comparative analysis of the two programs showed that our trading program was more successful and had lower investment risk in the long run.
Keywords:Automated trading, bitcoin, crypto whales, Python


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