Artificial intelligence techniques applied to cryptocurrency market prediction
The emerging cryptocurrency market is undergoing rapid evolution and presenting numerous novel technologies aimed at addressing problems and capitalizing on opportunities for societal improvement. Despite substantial growth over the past decade, this market remains immature, resulting in high volati...
- Autores:
-
Sánchez Ardila, Juan Diego
Oliveros Forero, Julián Esteban
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2023
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/67952
- Acceso en línea:
- http://hdl.handle.net/1992/67952
- Palabra clave:
- Neural Networks
Artificial Intelligence
Machine Learning
Cryptocurrency
Long Short-Term Memory (LSTM) models
Full-stack application
Ingeniería
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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Artificial intelligence techniques applied to cryptocurrency market prediction |
| title |
Artificial intelligence techniques applied to cryptocurrency market prediction |
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Artificial intelligence techniques applied to cryptocurrency market prediction Neural Networks Artificial Intelligence Machine Learning Cryptocurrency Long Short-Term Memory (LSTM) models Full-stack application Ingeniería |
| title_short |
Artificial intelligence techniques applied to cryptocurrency market prediction |
| title_full |
Artificial intelligence techniques applied to cryptocurrency market prediction |
| title_fullStr |
Artificial intelligence techniques applied to cryptocurrency market prediction |
| title_full_unstemmed |
Artificial intelligence techniques applied to cryptocurrency market prediction |
| title_sort |
Artificial intelligence techniques applied to cryptocurrency market prediction |
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Sánchez Ardila, Juan Diego Oliveros Forero, Julián Esteban |
| dc.contributor.advisor.none.fl_str_mv |
Manrique Piramanrique, Rubén Francisco |
| dc.contributor.author.none.fl_str_mv |
Sánchez Ardila, Juan Diego Oliveros Forero, Julián Esteban |
| dc.contributor.jury.none.fl_str_mv |
Manrique Piramanrique, Rubén Francisco |
| dc.contributor.researchgroup.es_CO.fl_str_mv |
Grupo de investigación Flag (https://flaglab.github.io/) |
| dc.subject.keyword.none.fl_str_mv |
Neural Networks Artificial Intelligence Machine Learning Cryptocurrency Long Short-Term Memory (LSTM) models Full-stack application |
| topic |
Neural Networks Artificial Intelligence Machine Learning Cryptocurrency Long Short-Term Memory (LSTM) models Full-stack application Ingeniería |
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Ingeniería |
| description |
The emerging cryptocurrency market is undergoing rapid evolution and presenting numerous novel technologies aimed at addressing problems and capitalizing on opportunities for societal improvement. Despite substantial growth over the past decade, this market remains immature, resulting in high volatility and risk for all stakeholders involved. The primary issue lies in the market's lack of stability and predictability. The objective of this project is to enhance comprehension of market predictability by employing machine learning techniques. This endeavor aims to assist cryptocurrency investors and stakeholders in making informed decisions by predicting market prices and trends, focusing specifically on Bitcoin, Ethereum, and Cardano. Machine learning models will be trained using historical market data to achieve this goal. Furthermore, the project includes a significant ancillary objective, as relying solely on models can be ineffective. The aim is to develop a robust platform that automatically collects real-time data, incorporates an API for integrating data sources with models, and features a user interface designed to present model results to cryptocurrency stakeholders. Lastly, it is worth noting the intrinsic value of this project, which originates from a shared personal goal among the authors. As an undergraduate degree thesis, this endeavor enables the authors to gain practical knowledge in applied blockchain technology and artificial intelligence, while fostering an integrated approach that consolidates their academic formation. |
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2023 |
| dc.date.accessioned.none.fl_str_mv |
2023-06-28T19:27:55Z |
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2023-06-28T19:27:55Z |
| dc.date.issued.none.fl_str_mv |
2023-05-29 |
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Trabajo de grado - Pregrado |
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info:eu-repo/semantics/bachelorThesis |
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eng |
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eng |
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A Brief History of Cryptocurrency - CryptoVantage. (n.d.). Retrieved February 15, 2023, from https://www.cryptovantage.com/guides/a-brief-history-of-cryptocurrency/ Bitcoin Exchange | Exchange de Criptomonedas | Binance. (n.d.). Retrieved February 16, 2023, from https://www.binance.com/es/binance-api Callaghan, Nathan. (2022). Using an LSTM- Recurrent Neural Network to forecast the trend in bitcoin prices. Cryptocurrency Explained With Pros and Cons for Investment. (n.d.). Investopedia. Retrieved February 15, 2023, from https://www.investopedia.com/terms/c/cryptocurrency.asp DeFi vs. CeFi: Comparing decentralized to centralized finance. (n.d.). Retrieved February 15, 2023, from https://cointelegraph.com/defi-101/defi-vs-cefi-comparing-decentralized-to-centralized-finance Idell, A. G. (2021, July 21). Intro to Crypto Philosophy. CodeX. https://medium.com/codex/intro-to-crypto-philosophy-93b5b5525a1f Nakamoto, S. (2008) Bitcoin: A Peer-to-Peer Electronic Cash System. https://bitcoin.org/bitcoin.pdf What Is A White Paper And How To Write It | Cointelegraph. (n.d.). Retrieved February 15, 2023, from https://cointelegraph.com/ico-101/what-is-a-white-paper-and-how-to-write-it%20 WTF Happened In 1971? (n.d.). Retrieved February 15, 2023, from https://wtfhappenedin1971.com/ |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdf |
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38 páginas |
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Universidad de los Andes |
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Ingeniería de Sistemas y Computación |
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Facultad de Ingeniería |
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Departamento de Ingeniería Sistemas y Computación |
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Universidad de los Andes |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacionalhttps://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdfinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Manrique Piramanrique, Rubén Francisco9d0fa5a3-24b8-419f-b898-8e128bc9e42a600Sánchez Ardila, Juan Diego4217876c-06b9-4fbf-8432-0ad697b05943600Oliveros Forero, Julián Estebandc497e2e-df74-4c1f-85b6-933f9f37fae0600Manrique Piramanrique, Rubén FranciscoGrupo de investigación Flag (https://flaglab.github.io/)2023-06-28T19:27:55Z2023-06-28T19:27:55Z2023-05-29http://hdl.handle.net/1992/67952instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/The emerging cryptocurrency market is undergoing rapid evolution and presenting numerous novel technologies aimed at addressing problems and capitalizing on opportunities for societal improvement. Despite substantial growth over the past decade, this market remains immature, resulting in high volatility and risk for all stakeholders involved. The primary issue lies in the market's lack of stability and predictability. The objective of this project is to enhance comprehension of market predictability by employing machine learning techniques. This endeavor aims to assist cryptocurrency investors and stakeholders in making informed decisions by predicting market prices and trends, focusing specifically on Bitcoin, Ethereum, and Cardano. Machine learning models will be trained using historical market data to achieve this goal. Furthermore, the project includes a significant ancillary objective, as relying solely on models can be ineffective. The aim is to develop a robust platform that automatically collects real-time data, incorporates an API for integrating data sources with models, and features a user interface designed to present model results to cryptocurrency stakeholders. Lastly, it is worth noting the intrinsic value of this project, which originates from a shared personal goal among the authors. As an undergraduate degree thesis, this endeavor enables the authors to gain practical knowledge in applied blockchain technology and artificial intelligence, while fostering an integrated approach that consolidates their academic formation.El mercado emergente de criptomonedas está experimentando una rápida evolución y presenta numerosas tecnologías novedosas destinadas a abordar problemas y capitalizar oportunidades para la mejora social. A pesar del crecimiento sustancial durante la última década, este mercado sigue siendo inmaduro, lo que resulta en una alta volatilidad y riesgo para todas las partes interesadas involucradas. El problema principal radica en la falta de estabilidad y previsibilidad del mercado. El objetivo de este proyecto es mejorar la comprensión de la previsibilidad del mercado mediante el empleo de técnicas de aprendizaje automático. Este esfuerzo tiene como objetivo ayudar a los inversores y partes interesadas en criptomonedas a tomar decisiones informadas al predecir los precios y las tendencias del mercado, centrándose específicamente en Bitcoin, Ethereum y Cardano. Los modelos de aprendizaje automático se entrenarán utilizando datos históricos del mercado para lograr este objetivo. Además, el proyecto incluye un objetivo secundario importante, ya que depender únicamente de modelos puede resultar ineficaz. El objetivo es desarrollar una plataforma sólida que recopile automáticamente datos en tiempo real, incorpore una API para integrar fuentes de datos con modelos y presente una interfaz de usuario diseñada para presentar los resultados del modelo a las partes interesadas en criptomonedas. Por último, cabe señalar el valor intrínseco de este proyecto, que parte de un objetivo personal compartido entre los autores. Como tesis de grado, este esfuerzo permite a los autores adquirir conocimientos prácticos en tecnología blockchain aplicada e inteligencia artificial, al mismo tiempo que fomenta un enfoque integrado que consolida su formación académica.Ingeniero de Sistemas y ComputaciónPregradoInteligencia Artificial38 páginasapplication/pdfengUniversidad de los AndesIngeniería de Sistemas y ComputaciónFacultad de IngenieríaDepartamento de Ingeniería Sistemas y ComputaciónArtificial intelligence techniques applied to cryptocurrency market predictionTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fTexthttp://purl.org/redcol/resource_type/TPNeural NetworksArtificial IntelligenceMachine LearningCryptocurrencyLong Short-Term Memory (LSTM) modelsFull-stack applicationIngenieríaA Brief History of Cryptocurrency - CryptoVantage. (n.d.). Retrieved February 15, 2023, from https://www.cryptovantage.com/guides/a-brief-history-of-cryptocurrency/Bitcoin Exchange | Exchange de Criptomonedas | Binance. (n.d.). Retrieved February 16, 2023, from https://www.binance.com/es/binance-apiCallaghan, Nathan. (2022). Using an LSTM- Recurrent Neural Network to forecast the trend in bitcoin prices.Cryptocurrency Explained With Pros and Cons for Investment. (n.d.). Investopedia. Retrieved February 15, 2023, from https://www.investopedia.com/terms/c/cryptocurrency.aspDeFi vs. CeFi: Comparing decentralized to centralized finance. (n.d.). Retrieved February 15, 2023, from https://cointelegraph.com/defi-101/defi-vs-cefi-comparing-decentralized-to-centralized-financeIdell, A. G. (2021, July 21). Intro to Crypto Philosophy. CodeX. https://medium.com/codex/intro-to-crypto-philosophy-93b5b5525a1fNakamoto, S. (2008) Bitcoin: A Peer-to-Peer Electronic Cash System. https://bitcoin.org/bitcoin.pdfWhat Is A White Paper And How To Write It | Cointelegraph. (n.d.). Retrieved February 15, 2023, from https://cointelegraph.com/ico-101/what-is-a-white-paper-and-how-to-write-it%20WTF Happened In 1971? (n.d.). 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