Cargando…
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...
Autor principal: | |
---|---|
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 |
_version_ | 1782492484325081088 |
---|---|
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. |
format | Online Article Text |
id | pubmed-5219605 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT brunirenato stockmarketindexdataandindicatorsfordaytradingasabinaryclassificationproblem |