Skip to content

Latest commit

 

History

History
33 lines (25 loc) · 1.57 KB

README.md

File metadata and controls

33 lines (25 loc) · 1.57 KB

ANALYSIS-SP500

Using pca and transformations to analyze s&p dataset.

  1. General
  2. Installation
  3. Footnote

General

We used s&p database to find outliers. Due the number of variables, we had to take actions to change the dataset to spot ones and to use to database in general.

Background

The data is formed from two files:

  • Prices - Includes stock symbol, volume and for each day open, close and high prices. The data is ranging 2010 to 2016.
  • Securities - Has additional information about the stocks. It includes the stock sector, sub industry, address of headquarters, security, and filling type.

PCA - which stands for Principal Component Analysis is used to represent multivariate data as a new dataset with less variables in order view trades, outliers, and clusters.

Installation

I will use google as an example, but similar process can be performed on other notebook editors

  1. Open google Colab

  2. Clone the project by:

    !git clone https://github.com/elaysason/ANALYSIS-SP500.git

  3. Now the folder is in your files on colab. Simpily download the notebook as showed

Footnote

The exercise is focused on the data from 2016 and includes only stocks which had data for each one of the days in the year.