Data driven

The goal of this notebook is to provide an introduction to stock market portfolio optimisation using Python.

Continuing short series on data analysis applied to finance, this article is a step further into exploring Python’s many functionalities towards stock market diagnosis.

This article is the first of a short series on data analysis applied to finance, in which I’ll seek to demonstrate a few useful artifacts used by stock market professionals, data analysts and traders. The whole approach is done using Python.

In this notebook we will use the data-set, International football results from 1872 to 2019. We will try ANOVA hypothesis testing technique.

The goal of this article is to provide an easy introduction to stock market analysis using Python. In this notebook we will use pandas_datareader module. We will walk through a simple Python script to retrieve, analyze, and visualize data from different markets.

To avoid openssl and TLS version issue we will update openSSL on server and desktop version of Ubuntu 18.0. We will download it manually, install and make necessary file permission changes.

The standard theme that comes with Pelican has one huge disadvantage: it does not display properly on mobile devices.