Algorithmic trading with cryptocurrencies

Detalhes bibliográficos
Autor(a) principal: Röhnelt, Tobias
Data de Publicação: 2021
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/151986
Resumo: Since its inception in 2009, Bitcoin has gained popularity and importance in financial markets. The Bitcoin price is highly volatile entailing high risk and chances of high returns for traders. We define a holistic approach to build an intraday Bitcoin trading algorithm based on predictive analysis of ML models. The results show that our trading algorithm generates positive returns and to outperform its benchmark strategies after considerations for feasibility and profitability.
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spelling Algorithmic trading with cryptocurrenciesForecastingBusiness analyticsCryptocurrencyTrading strategyBitcoinAlgorithmic tradingDomínio/Área Científica::Ciências Sociais::Economia e GestãoSince its inception in 2009, Bitcoin has gained popularity and importance in financial markets. The Bitcoin price is highly volatile entailing high risk and chances of high returns for traders. We define a holistic approach to build an intraday Bitcoin trading algorithm based on predictive analysis of ML models. The results show that our trading algorithm generates positive returns and to outperform its benchmark strategies after considerations for feasibility and profitability.Zejnilovic, LeidRUNRöhnelt, Tobias2023-04-21T10:10:06Z2022-09-142021-12-172022-09-14T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/151986TID:203222008enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:34:21Zoai:run.unl.pt:10362/151986Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:54:45.349913Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Algorithmic trading with cryptocurrencies
title Algorithmic trading with cryptocurrencies
spellingShingle Algorithmic trading with cryptocurrencies
Röhnelt, Tobias
Forecasting
Business analytics
Cryptocurrency
Trading strategy
Bitcoin
Algorithmic trading
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
title_short Algorithmic trading with cryptocurrencies
title_full Algorithmic trading with cryptocurrencies
title_fullStr Algorithmic trading with cryptocurrencies
title_full_unstemmed Algorithmic trading with cryptocurrencies
title_sort Algorithmic trading with cryptocurrencies
author Röhnelt, Tobias
author_facet Röhnelt, Tobias
author_role author
dc.contributor.none.fl_str_mv Zejnilovic, Leid
RUN
dc.contributor.author.fl_str_mv Röhnelt, Tobias
dc.subject.por.fl_str_mv Forecasting
Business analytics
Cryptocurrency
Trading strategy
Bitcoin
Algorithmic trading
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
topic Forecasting
Business analytics
Cryptocurrency
Trading strategy
Bitcoin
Algorithmic trading
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
description Since its inception in 2009, Bitcoin has gained popularity and importance in financial markets. The Bitcoin price is highly volatile entailing high risk and chances of high returns for traders. We define a holistic approach to build an intraday Bitcoin trading algorithm based on predictive analysis of ML models. The results show that our trading algorithm generates positive returns and to outperform its benchmark strategies after considerations for feasibility and profitability.
publishDate 2021
dc.date.none.fl_str_mv 2021-12-17
2022-09-14
2022-09-14T00:00:00Z
2023-04-21T10:10:06Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/151986
TID:203222008
url http://hdl.handle.net/10362/151986
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dc.language.iso.fl_str_mv eng
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