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dc.rights.licensehttps://creativecommons.org/licenses/by-sa/2.5/ar/es_AR
dc.contributor.advisorRoccatagliata, Pablo
dc.contributor.authorAlba Chicar, Agustínes_AR
dc.date.accessioned2023-01-06T16:26:43Z
dc.date.available2023-01-06T16:26:43Z
dc.date.issued2021
dc.identifier.urihttps://repositorio.utdt.edu/handle/20.500.13098/11558
dc.description.abstractFinancial companies from all around the world have started to focus their investments in quantitative and algorithmic funds. Those methods run in server applications that execute automatic trades. It is important to distinguish high frequency trading from machine learning trading. The latter is used and analyzed in detail in the present work. This project explains the development of a trading strategy on Bitcoins based on machine learning techniques. A pipeline proposal is shown which is based on Lopez de Prado's book ([Pra18]). Some modi cations are introduced in the book's pipeline to adjust a momentum primary model on Bitcoins, and to incorporate and study features that would let estimate the size of the primary model bets (secondary model to be trained on top of the rst model). The range of features to analyze goes from nancial metrics derived from Bitcoin prices and volumes, to Bitcoin and blockchain related features and nally social indexes which incorporate interest and animosity towards Bitcoin itself. The pipeline proposed in [Pra18] and implemented in this thesis rigorously handles the dataset, the involved models and nally the posterior backtesting strategies. Details about statistical foundation of the involved methods, algorithm complexity and implementation and domain explanations (such as those related to cryptocurrencies) can be found. The pipeline allows to gather enough information to compare and decide whether a propose strategy is good enough to be implemented. We will use this to compare models that introduce microstructure indexes such as SADF (Supremum Augmented Dickey Fuller) in comparison and conjunction with social indexes.es_AR
dc.format.extent85 p.es_AR
dc.format.mediumapplication/pdfes_AR
dc.languagespaes_AR
dc.rightsinfo:eu-repo/semantics/openAccesses_AR
dc.subjectAlgoritmoses_AR
dc.subjectInversioneses_AR
dc.subjectinversiones financierases_AR
dc.subjectAnálisis de datoses_AR
dc.subjectGestión Financieraes_AR
dc.subjectAlgorithmses_AR
dc.subjectFinancial investmentses_AR
dc.subjectdataes_AR
dc.titleA Quantamental approach to Bitcoin trading: Are we swinging for the Fences?es_AR
dc.typeinfo:eu-repo/semantics/masterThesises_AR
thesis.degree.nameMaster in Management + Analyticsen
thesis.degree.grantorUniversidad Torcuato Di Tellaes_Ar
thesis.degree.grantorEscuela de Negocioses_Ar
dc.subject.keywordBitcoinses_AR
dc.subject.keywordCompraventa de bitcoinses_AR
dc.subject.keywordAprendizaje automáticoes_AR
dc.subject.keywordMachine Learninges_AR
dc.subject.keywordBitcoins Tradinges_AR
dc.type.versioninfo:eu-repo/semantics/acceptedVersiones_AR


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