Bruni Renato
Dip. di Ingegneria Informatica, Automatica e Gestionale, Sapienza Università di Roma, Rome, Italy.
Data Brief. 2016 Dec 29;10:569-575. doi: 10.1016/j.dib.2016.12.044. eCollection 2017 Feb.
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.
分类是根据从带标签记录的训练集中自动学习到的标准为记录赋予标签。在大量实际应用中都需要这项任务，因此它得到了深入研究，如今有几种分类算法可供使用。在金融领域，股票市场指数是对股票市场某一部分价值的衡量。它常被用于描述市场的总体趋势。一个基本的金融问题就是预测这种趋势。显然，这样一个随机值很难预测。然而，技术分析是一种通过研究过去的市场数据来预测价格走势而发展起来的证券分析方法。日内交易是指在同一个交易日内买卖金融工具。在这种情况下，一个有趣的问题是自动识别有利的交易日。我们将这个问题建模为一个二元分类问题，并提供包含每日指数值、一系列技术指标的相应值以及类别标签的数据集，如果后续时间段有利于日内交易，则类别标签为1，否则为0。这些数据集可用于测试不同方法在解决日内交易问题时的表现。
