A Step-by-step Implementation of a Trading Strategy in Python using ARIMA + GARCH models

Make use of a completely functional ARIMA+GARCH python implementation and test it over different markets using a simple framework for visualization and comparisons.

Maurício Cordeiro
Analytics Vidhya

--

Image by author.

Update

For information about the course Introduction to Python for Scientists (available on YouTube) and other articles like this, please visit my website cordmaur.carrd.co.

Introduction

When it comes to financial Time Series (TS) modelling, autoregressive models (models that makes use of previous values to forecast the future) such as ARMA, ARIMA or GARCH and its various variants are usually the preferred ones to explain the foundations of TS modelling. However, practical application of these techniques in real trading strategies and it’s comparison to naïve strategies (like Buy and Hold) are not that common. Moreover, it’s not easy to find a ready to use implementation that could be easily replicated for other markets, assets, etc. Some of the codes that I had run into have failures or are just incomplete and missing something. To make things even more difficult, the good implementations are written in R and the packages to fit the…

--

--

Maurício Cordeiro
Analytics Vidhya

Ph.D. Geospatial Data Scientist and water specialist at Brazilian National Water and Sanitation Agency. To get in touch: https://www.linkedin.com/in/cordmaur/