Open Access

ARTICLE

On Visualization Analysis of Stock Data

Yue Cai¹, Zeying Song¹, Guang Sun^{1, *}, Jing Wang¹, Ziyi Guo¹, Yi Zuo¹, Xiaoping Fan¹, Jianjun Zhang², Lin Lang¹

1 Hunan University of Finance and Economics, Changsha, 410205, China.
2 Hunan Normal University, Changsha, 410081, China.

* Corresponding Author: Guang Sun. Email: .

Journal on Big Data 2019, 1(3), 135-144. https://doi.org/10.32604/jbd.2019.08274

Abstract

Big data technology is changing with each passing day, generating massive amounts of data every day. These data have large capacity, many types, fast growth, and valuable features. The same is true for the stock investment market. The growth of the amount of stock data generated every day is difficult to predict. The price trend in the stock market is uncertain, and the valuable information hidden in the stock data is difficult to detect. For example, the price trend of stocks, profit trends, how to make a reasonable speculation on the price trend of stocks and profit trends is a major problem that needs to be solved at this stage. This article uses the Python language to visually analyze, calculate, and predict each stock. Realize the integration and calculation of stock data to help people find out the valuable information hidden in stocks. The method proposed in this paper has been tested and proved to be feasible. It can reasonably extract, analyze and calculate the stock data, and predict the stock price trend to a certain extent.

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Keywords

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