Stock Market Analytics with ML — Resources

Tyler Hackbart
3 min readMar 24, 2021

Machine learning in the last decade has defined the next version of computer processes and opened doors to more personal, more persist and more reliable experiences with the use of technology.

Included in the last decade is a huge onset of cyrpto currencies and stock market trading for the masses. The new markets are no longer restrited by walled gardens, massive fees and big organization control. Everyday people are trading and trying to make some extra money in the process.

As we view both these massive sectors coming in, there is no question about how both could be joined together to create a powerhouse of money generation and reliable market predictions.

Now, if you are a developer stumbling onto this page you might think this is going to be a tutorial to do all the things I have mentioned above. In reality, its not, I am sorry. I am sorry for letting a bit of my Canadian out there too, sorry… again.

Now the two reasons why I am not going the tutorial route is because:

  1. Writing and fitting a use case for me, might not be the use case for you. I have been down the path of hours learning a way to do something in a tutorial and it ended up being completely different than what I want / need out of it. Which just ends up frustrating me.
  2. Technology changes, version of languages and functionality change. Writing something today could be obsolete tomorrow. (As of Mar 21, 2021) There is 3,520,000 million…. MILLION results on Google when searching “stock market machine learning tutorials” — Find the one that works for you, don’t let me box you in.

However I will pass on some helpful tips / resources I have came to use and love over the years as I build out my own machine learning models and use stock market data.

Notes for Resources

  • Don’t pay for ONLY one course, pay into a service like LinkedIn Learning that gives you a range of resources. They usually give you the first month free too so if you play your cards fast it might even be free to learn.
  • When searching tutorials add in timeframe of past 2 years. There are a lot of resources out there but a lot of old stuff too. Make sure you are searching for recent and relative things.
Search with refined timeframe, so you get more recent results
Custom search past 2 years, don’t use “Any time”


  • Python
  • Javascript

Great breakdown of machine learning languages

Packages / Frameworks


Data Sources

Resources Used in the Past

<h1>Happy Coding </h1>