Machine learning para previsão de preços em high-frequency trading.

Detalhes bibliográficos
Autor(a) principal: Marco, Acácio Bonifácio
Data de Publicação: 2023
Tipo de documento: Trabalho de conclusão de curso
Idioma: por
Título da fonte: Biblioteca Digital de Teses e Dissertações do Mackenzie
Texto Completo: https://dspace.mackenzie.br/handle/10899/32811
Resumo: TCC
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spelling Marco, Acácio BonifácioOliveira, Rogério2023-07-03T14:08:35Z2023-07-03T14:08:35Z2023-06-16TCCThis study utilizes the Deep Learning LSTM technique in conjunction with data filtering techniques such as the Percentile Method and the Hampel Filter to enhance high-frequency stock price prediction. The results show that LSTM models are capable of capturing patterns and trends in price data, and the inclusion of indicators such as Bollinger Bands and Order Flow Imbalance does not demonstrate significant differences in the results. The tested configurations yielded similar results, indicating the accuracy of LSTM models in high-frequency price prediction.Este estudo utiliza a técnica de Deep Learning LSTM em conjunto com técnicas de filtragem de dados, como o Método Percentil e o Filtro de Hampel, para aprimorar a previsão de preços de ações em alta frequência. Os resultados mostram que os modelos LSTM são capazes de capturar padrões e tendências nos dados de preços, e a inclusão de indicadores como Bandas de Bollinger e Order Flow Imbalance não demonstra diferenças significativas nos resultados. As configurações testadas apresentaram resultados próximos, indicando a precisão dos modelos LSTM na previsão de preços em alta frequência.https://dspace.mackenzie.br/handle/10899/32811Universidade Presbiteriana MackenzieFaculdade de Computação e Informática (FCI)Attribution-NonCommercial-NoDerivs 3.0 Brazilhttp://creativecommons.org/licenses/by-nc-nd/3.0/br/info:eu-repo/semantics/openAccessdeep learninglstmhftprevisão de preçoindicadoresMachine learning para previsão de preços em high-frequency trading.info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bachelorThesisporreponame:Biblioteca Digital de Teses e Dissertações do Mackenzieinstname:Universidade Presbiteriana Mackenzie (MACKENZIE)instacron:MACKENZIEORIGINAL1195-Artigo Final-5404-1-4-20230616 (1).pdf1195-Artigo Final-5404-1-4-20230616 (1).pdfAcácio Bonifácio de Marcoapplication/pdf1497098https://dspace.mackenzie.br/bitstreams/877809d8-d00b-4915-aa39-41229dbdaa8b/download245e3d0590298e8bfb18da8252ae91caMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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dc.title.pt_BR.fl_str_mv Machine learning para previsão de preços em high-frequency trading.
title Machine learning para previsão de preços em high-frequency trading.
spellingShingle Machine learning para previsão de preços em high-frequency trading.
Marco, Acácio Bonifácio
deep learning
lstm
hft
previsão de preço
indicadores
title_short Machine learning para previsão de preços em high-frequency trading.
title_full Machine learning para previsão de preços em high-frequency trading.
title_fullStr Machine learning para previsão de preços em high-frequency trading.
title_full_unstemmed Machine learning para previsão de preços em high-frequency trading.
title_sort Machine learning para previsão de preços em high-frequency trading.
author Marco, Acácio Bonifácio
author_facet Marco, Acácio Bonifácio
author_role author
dc.contributor.author.fl_str_mv Marco, Acácio Bonifácio
dc.contributor.advisor1.fl_str_mv Oliveira, Rogério
contributor_str_mv Oliveira, Rogério
dc.subject.por.fl_str_mv deep learning
lstm
hft
previsão de preço
indicadores
topic deep learning
lstm
hft
previsão de preço
indicadores
description TCC
publishDate 2023
dc.date.accessioned.fl_str_mv 2023-07-03T14:08:35Z
dc.date.available.fl_str_mv 2023-07-03T14:08:35Z
dc.date.issued.fl_str_mv 2023-06-16
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dc.publisher.department.fl_str_mv Faculdade de Computação e Informática (FCI)
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