An automatic trading strategy for the Colombian government bonds in R

The objective of this work is to make advances in the execution of an algorithm that performs a trading strategy, based on the academic support of the classic portfolio theory, macroeconomic theory of economic policy and some precepts of implementation with financial machine learning (ML) applications. Regarding ML, despite the fact that there are several authors, this work is based on the techniques and advances that have emerged from the studies of the author Marcos Lopez de Prado, since it is well known not only in the academic field, but also in investment industry. These characteristics make the methodological development of this work be applied to the related data, which is real data from the Colombian market, public debt bonds hereafter referred TES. The advances in ML by Professor Lopez de Prado will serve as a point of reference to backtest a very simple trading strategy, but looking beyond, the work will serve to leave a guide on how to implement through steps the development of an algorithm that performs any strategy, taking into account each stage for its later execution.

• Autores: Rey álvarez, Karen Estefanía
• Fecha de publicación: 2020
• Institución: Universidad de los Andes
• Repositorio: Séneca: repositorio Uniandes
• Idioma: eng
• OAI Identifier: oai:repositorio.uniandes.edu.co:1992/51443
• Acceso en línea: http://hdl.handle.net/1992/51443
• Palabra clave: Bonos; Deuda pública; Aprendizaje automático (Inteligencia artificial); Administración
• Rights: openAccess
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/