You are currently viewing #1: Introduction – LearnMLTrading Series

#1: Introduction – LearnMLTrading Series

Welcome to the first post of the LearnMLTrading Series!

In this short post, we will set out the purpose and the goals of this series.


We would like to build an automated machine learning trading algorithm that will be deployed in the cloud to make live trades (using either real or paper money). The emphasis for this series is primarily on the software engineering, but also on the time-series / machine learning aspect. We will use Python 3.

At the end of the series, I will produce a condensed tutorial summarizing how to build an automated trading algorithm and how to deploy it.


I expect to produce weekly / bi-weekly posts and videos to document my progress and to share my findings with you. This includes any changes to the code-base, new features, and the ideas I am working on. This series is interactive, so if you have any ideas or suggestions, please leave a comment!

Setting up the Project

We will be using some tools to help us manage our project – some of these you may be familiar with.

  • Pyenv: manage Python version
  • Poetry: Dependency management (alternative to venv, virtualenv)
  • GitHub: Version control
  • OANDA: Brokerage (but we will try to keep this as a free parameter)
  • AWS: Deployment
  • The rest we will figure out as we go along.

The corresponding video for this post:


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