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Stock Market Index Data and indicators for Day Trading as a Binary Classification problem

Classification is the attribution of labels to records according to a criterion automatically learned from a training set of labeled records. This task is needed in a huge number of practical applications, and consequently it has been studied intensively and several classification algorithms are ava...

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Autor principal: Bruni, Renato
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5219605/
https://www.ncbi.nlm.nih.gov/pubmed/28070548
http://dx.doi.org/10.1016/j.dib.2016.12.044
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author Bruni, Renato
author_facet Bruni, Renato
author_sort Bruni, Renato
collection PubMed
description Classification is the attribution of labels to records according to a criterion automatically learned from a training set of labeled records. This task is needed in a huge number of practical applications, and consequently it has been studied intensively and several classification algorithms are available today. In finance, a stock market index is a measurement of value of a section of the stock market. It is often used to describe the aggregate trend of a market. One basic financial issue would be forecasting this trend. Clearly, such a stochastic value is very difficult to predict. However, technical analysis is a security analysis methodology developed to forecast the direction of prices through the study of past market data. Day trading consists in buying and selling financial instruments within the same trading day. In this case, one interesting problem is the automatic individuation of favorable days for trading. We model this problem as a binary classification problem, and we provide datasets containing daily index values, the corresponding values of a selection of technical indicators, and the class label, which is 1 if the subsequent time period is favorable for day trading and 0 otherwise. These datasets can be used to test the behavior of different approaches in solving the day trading problem.
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spelling pubmed-52196052017-01-09 Stock Market Index Data and indicators for Day Trading as a Binary Classification problem Bruni, Renato Data Brief Data Article Classification is the attribution of labels to records according to a criterion automatically learned from a training set of labeled records. This task is needed in a huge number of practical applications, and consequently it has been studied intensively and several classification algorithms are available today. In finance, a stock market index is a measurement of value of a section of the stock market. It is often used to describe the aggregate trend of a market. One basic financial issue would be forecasting this trend. Clearly, such a stochastic value is very difficult to predict. However, technical analysis is a security analysis methodology developed to forecast the direction of prices through the study of past market data. Day trading consists in buying and selling financial instruments within the same trading day. In this case, one interesting problem is the automatic individuation of favorable days for trading. We model this problem as a binary classification problem, and we provide datasets containing daily index values, the corresponding values of a selection of technical indicators, and the class label, which is 1 if the subsequent time period is favorable for day trading and 0 otherwise. These datasets can be used to test the behavior of different approaches in solving the day trading problem. Elsevier 2016-12-29 /pmc/articles/PMC5219605/ /pubmed/28070548 http://dx.doi.org/10.1016/j.dib.2016.12.044 Text en © 2017 The Author http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Bruni, Renato
Stock Market Index Data and indicators for Day Trading as a Binary Classification problem
title Stock Market Index Data and indicators for Day Trading as a Binary Classification problem
title_full Stock Market Index Data and indicators for Day Trading as a Binary Classification problem
title_fullStr Stock Market Index Data and indicators for Day Trading as a Binary Classification problem
title_full_unstemmed Stock Market Index Data and indicators for Day Trading as a Binary Classification problem
title_short Stock Market Index Data and indicators for Day Trading as a Binary Classification problem
title_sort stock market index data and indicators for day trading as a binary classification problem
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5219605/
https://www.ncbi.nlm.nih.gov/pubmed/28070548
http://dx.doi.org/10.1016/j.dib.2016.12.044
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