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 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.

• 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