Python project to maintain and analyse commodity prices.
In this series of posts, I am going to describe the full setup, design and operation of a Python Django application to acquire and analyse commodity futures with the aim of finding a winning strategy for predicting a [pretend] winning strategy.
Note that this is just for a bit of fun and to demonstrate the power of Python.
In this post I will describe setting up a development environment on a Mac machine. If you are following along and are using Windows I will indicate any differences to the procedure.
I suggest you allocate yourself a directory somewhere on your computer (Mac, Windows, Linux) and then create a sub-directory within for each project your embark on.
To prevent problems with some packages, I suggest you avoid directory names containing spaces.
On my Mac I have a directory on my desktop called Python and within that I have created a sub-directory called commodities.
(base) peter@Peters-MacBook-Pro-3 commodities % pwd
/Users/peter/Desktop/Python/commodities
(base) peter@Peters-MacBook-Pro-3 commodities % I will be using the ta-lib package which sometimes won’t install in pip created virtual environments so I am going to use conda to create my environment instead.
(commodities) peter@Peters-MacBook-Pro-3 commodities % conda install conda-forge::ta-lib