Computational Investment in Python - I

Hello and welcome to the first article about 'Computational Investment in Python'. The purpose of this course for me is to learn-while-teaching so the approach is going to be done from a very low level. You will need, however, a solid understanding of the stock market principles and mechanisms that make it work. A good starting point for this is the 'Computational Investment' course on Coursera taught by Professor Tucker Balch.

We are going to get our daily market data from Quandl. For those of you that don't know what it is, Quandl is (quoting their website):

Designed for professionals, Quandl delivers financial, economic and alternative data to over 200,000 people worldwide. Our customers include the world’s top hedge funds, asset managers and investment banks.

We are going to use pandas and numpy quite extensively along the lectures so if you are new to python I recommend you learning a bit of them both before moving on. They are really easy to use and there are a lot of information about them. In my humble opinion, your best choice would be Sentdex tutorials in the pythonprogramming.net because it is really easy to follow.

For every chapter we are also going to follow along a jupyter notebook that you can get from github. That is going to be the base of our hypothesis and sketches. for those of you that don't know what a jupyter notebook is, think of it as an enclosed python environment. You can have an incredible amount of material about it in just a web search away.

In this first example we are just demonstrating how to load the basic data from Quandl and perform a basic plot from the data. So dont waste any more time, grab the notebook and happy hacking! See you on the next chapter in which we are going to explain a basic trading strategy and how it works in the real world.