Cryptocurrecy market forecasting: Technical Analysis with Convolutional Neural Networks

Detalhes bibliográficos
Autor(a) principal: Farias, Marcos da Costa
Data de Publicação: 2022
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/145561
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and Management
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spelling Cryptocurrecy market forecasting: Technical Analysis with Convolutional Neural NetworksDeep LearningConvolutional Neural NetworksTechnical AnalysisCandlesticksCryptocurrencyDissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and ManagementThe cryptocurrency market recently experienced a growth in capitalization from US$1.2 Trillion to US$3.0 Trillion – and this happened in the second half of 2021, from July to November. Three months later, it dropped to US$1.6 Trillion. Undoubtedly, the cryptocurrency market is among the fastest growing (and most volatile) financial markets. This research combines the strengths of two domains to recognize future cryptocurrency price movements. The first domain is the financial technical analysis – candlesticks, Relative Strength Indicator (RSI), Moving Averages, and Bollinger Bands – all in a single layout resulting in an image dataset with 863,910 charts. The second domain is Deep Learning, specifically the Convolutional Neural Networks applied to image classification – one of the fields of computer vision where this architecture is proven to be a powerful classifier. The challenge is to capture the underlying signals of price movements in the pixels of the chart. This study aims to combine these two approaches in a system and assess the profitability and robustness of its buy and sell signals.Castelli, MauroRUNFarias, Marcos da Costa2022-11-16T12:06:11Z2022-10-252022-10-25T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/145561TID:203098005enginfo: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:RCAAP2024-03-11T05:26:04Zoai:run.unl.pt:10362/145561Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:52:09.118636Repositório 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 Cryptocurrecy market forecasting: Technical Analysis with Convolutional Neural Networks
title Cryptocurrecy market forecasting: Technical Analysis with Convolutional Neural Networks
spellingShingle Cryptocurrecy market forecasting: Technical Analysis with Convolutional Neural Networks
Farias, Marcos da Costa
Deep Learning
Convolutional Neural Networks
Technical Analysis
Candlesticks
Cryptocurrency
title_short Cryptocurrecy market forecasting: Technical Analysis with Convolutional Neural Networks
title_full Cryptocurrecy market forecasting: Technical Analysis with Convolutional Neural Networks
title_fullStr Cryptocurrecy market forecasting: Technical Analysis with Convolutional Neural Networks
title_full_unstemmed Cryptocurrecy market forecasting: Technical Analysis with Convolutional Neural Networks
title_sort Cryptocurrecy market forecasting: Technical Analysis with Convolutional Neural Networks
author Farias, Marcos da Costa
author_facet Farias, Marcos da Costa
author_role author
dc.contributor.none.fl_str_mv Castelli, Mauro
RUN
dc.contributor.author.fl_str_mv Farias, Marcos da Costa
dc.subject.por.fl_str_mv Deep Learning
Convolutional Neural Networks
Technical Analysis
Candlesticks
Cryptocurrency
topic Deep Learning
Convolutional Neural Networks
Technical Analysis
Candlesticks
Cryptocurrency
description Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and Management
publishDate 2022
dc.date.none.fl_str_mv 2022-11-16T12:06:11Z
2022-10-25
2022-10-25T00:00:00Z
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/10362/145561
TID:203098005
url http://hdl.handle.net/10362/145561
identifier_str_mv TID:203098005
dc.language.iso.fl_str_mv eng
language eng
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repository.mail.fl_str_mv
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