Sistema de trading algorítmico utilizando un modelo de machine learning generado por auto machine learning como regla de filtro

ilustraciones, diagramas

Autores:: López Benítez, Edwin José

Tipo de recurso:

Fecha de publicación:: 2023

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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dc.title.spa.fl_str_mv	Sistema de trading algorítmico utilizando un modelo de machine learning generado por auto machine learning como regla de filtro
dc.title.translated.eng.fl_str_mv	Algorithmic trading system using a machine learning model generated by auto machine learning machine learning as a filter rule
title	Sistema de trading algorítmico utilizando un modelo de machine learning generado por auto machine learning como regla de filtro
spellingShingle	Sistema de trading algorítmico utilizando un modelo de machine learning generado por auto machine learning como regla de filtro 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 330 - Economía::332 - Economía financiera Economía industrial Inteligencia artificial Engineering economy Artificial intelligence Trading algorítmico Aprendizaje Automáticos Mercados financieros Auto machine learning Algorithmic trading Machine learning Financial markets
title_short	Sistema de trading algorítmico utilizando un modelo de machine learning generado por auto machine learning como regla de filtro
title_full	Sistema de trading algorítmico utilizando un modelo de machine learning generado por auto machine learning como regla de filtro
title_fullStr	Sistema de trading algorítmico utilizando un modelo de machine learning generado por auto machine learning como regla de filtro
title_full_unstemmed	Sistema de trading algorítmico utilizando un modelo de machine learning generado por auto machine learning como regla de filtro
title_sort	Sistema de trading algorítmico utilizando un modelo de machine learning generado por auto machine learning como regla de filtro
dc.creator.fl_str_mv	López Benítez, Edwin José
dc.contributor.advisor.none.fl_str_mv	Hernandez Perez, German Jairo
dc.contributor.author.none.fl_str_mv	López Benítez, Edwin José
dc.contributor.researchgroup.spa.fl_str_mv	Algoritmos y Combinatoria (Algos-Un)
dc.subject.ddc.spa.fl_str_mv	620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 330 - Economía::332 - Economía financiera
topic	620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 330 - Economía::332 - Economía financiera Economía industrial Inteligencia artificial Engineering economy Artificial intelligence Trading algorítmico Aprendizaje Automáticos Mercados financieros Auto machine learning Algorithmic trading Machine learning Financial markets
dc.subject.lemb.spa.fl_str_mv	Economía industrial Inteligencia artificial
dc.subject.lemb.eng.fl_str_mv	Engineering economy Artificial intelligence
dc.subject.proposal.spa.fl_str_mv	Trading algorítmico Aprendizaje Automáticos Mercados financieros
dc.subject.proposal.eng.fl_str_mv	Auto machine learning Algorithmic trading Machine learning Financial markets
description	ilustraciones, diagramas
publishDate	2023
dc.date.accessioned.none.fl_str_mv	2023-07-18T15:27:04Z
dc.date.available.none.fl_str_mv	2023-07-18T15:27:04Z
dc.date.issued.none.fl_str_mv	2023
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
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status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/84201
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/84201 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
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spelling	Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Hernandez Perez, German Jairo657ce2285e24c2147940ed57ee42ed6dLópez Benítez, Edwin José506955981e0babdc595e18f686e5cb80Algoritmos y Combinatoria (Algos-Un)2023-07-18T15:27:04Z2023-07-18T15:27:04Z2023https://repositorio.unal.edu.co/handle/unal/84201Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramasEn el presente trabajo se mejora el rendimiento de un sistema o estrategia de trading algorítmico, basado en indicadores técnicos, mediante la incorporación de un modelo de clasificación que permite discriminar operaciones potenciales de la estrategia entre ganadoras y perdedoras. Las características utilizadas como entrada del modelo de machine learning son generadas a partir de indicadores técnicos en el instante que se abre una operación, obtenidas mediante una simulación de mercado con datos de en formato Open, High, Low, Close en la divisa del eurodólar. Para el proceso de búsqueda del modelo clasificación adecuado, se plantean dos mecanismos basados en automachine learning y algoritmos evolutivos, utilizando la librería Evaluation of a Tree-Based Pipeline Optimization Tool for Automating Data Science (TPOT) y una propuesta basada en el Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) y TPOT, donde se orienta la búsqueda multiobjetivo con la métrica del accuracy y el System Quality Number (SQN) una métrica para evaluar sistemas de trading. En los experimentos realizados, los modelos de clasificación elegidos por NSGA-II mejoraron significativamente el rendimiento de la estrategia de trading, con un 32.5% de los modelos fuera de muestra que mostraron rendimientos positivos y un comportamiento similar dentro y fuera de muestra. Mientras que con TPOT, los clasificadores encontrados tendieron a tener buen rendimiento dentro de muestra, pero no consistente fuera de muestra. La estrategia final elegida por NSGA-II tuvo un 60-61% de operaciones rentables tanto dentro como fuera de muestra, mientras TPOT tuvo un 98% y un 62% respectivamente. (Texto tomado de la fuente)This paper improves the performance of an algorithmic trading system or strategy, based on technical indicators, by incorporating a classification model that allows discriminating potential trades of the strategy between winners and losers. The characteristics used as input of the machine learning model are generated from technical indicators at the moment a trade is opened, obtained through a market simulation with data in Open, High, Low, Close format in the Eurodollar currency. For the search process of the appropriate classification model, two mechanisms based on automachine learning and evolutionary algorithms are proposed, using the Evaluation of a Tree-Based Pipeline Optimization Tool for Automating Data Science (TPOT) library and a proposal based on the Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and TPOT, where the multi-objective search is oriented with the accuracy metric and the System Quality Number (SQN), a metric to evaluate trading systems. In the experiments conducted, the classification models chosen by NSGA-II significantly improved the performance of the trading strategy, with 32.5% of the out-of-sample models showing positive returns and similar in-sample and out-of-sample behavior. Whereas with TPOT, the classifiers found tended to perform well in-sample, but not consistently out-of-sample. The final strategy chosen by NSGA-II had 60-61% profitable trades both in-sample and out-of-sample, while TPOT had 98% and 62% respectively.MaestríaMagíster en Ingeniería - Ingeniería IndustrialIngeniería Económica94 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería IndustrialFacultad de IngenieríaBogotá,ColombiaUniversidad Nacional de Colombia - Sede Bogotá620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores330 - Economía::332 - Economía financieraEconomía industrialInteligencia artificialEngineering economyArtificial intelligenceTrading algorítmicoAprendizaje AutomáticosMercados financierosAuto machine learningAlgorithmic tradingMachine learningFinancial marketsSistema de trading algorítmico utilizando un modelo de machine learning generado por auto machine learning como regla de filtroAlgorithmic trading system using a machine learning model generated by auto machine learning machine learning as a filter ruleTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAdebiyi, Ayodele A., Aderemi O. Adewumi, and Charles K. Ayo. 2014a. “Stock Price Prediction Using the ARIMA Model.” Pp. 106–12 in Proceedings - UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, UKSim 2014. Institute of Electrical and Electronics Engineers Inc.Adebiyi, Ayodele A., Aderemi O. Adewumi, and Charles K. 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Huber. 2019. “Benchmark and Survey of Automated Machine Learning Frameworks.”LICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/84201/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1032417503.2023.pdf1032417503.2023.pdfTesis de Maestría en Ingeniería - Ingeniería Industrialapplication/pdf2665022https://repositorio.unal.edu.co/bitstream/unal/84201/2/1032417503.2023.pdf151909fb247b8808be081a52248758dbMD52THUMBNAIL1032417503.2023.pdf.jpg1032417503.2023.pdf.jpgGenerated Thumbnailimage/jpeg4972https://repositorio.unal.edu.co/bitstream/unal/84201/3/1032417503.2023.pdf.jpg951db3e61f1f7e3e113cf5596c543b3bMD53unal/84201oai:repositorio.unal.edu.co:unal/842012023-08-12 23:04:04.084Repositorio Institucional Universidad Nacional de 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Sistema de trading algorítmico utilizando un modelo de machine learning generado por auto machine learning como regla de filtro

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