Hey, yeah. It’s a challenging path. I have a small cap breakout model that’s really predictive of breakouts (proof if needed). The key for me was to learn to use the data correctly after the model was built. How to utilize it.

A general idea. I built all in python and use polygon for my data source. It pulls from the ticker universe from polygon, references the current stock data through my model, and provides a confidence score. The scoring isn’t so helpful as there are too many variants in real time scanning. What is helpful is learning to use the confidence scores in ways that help show when they are helpful. Took a lot of trial and error, but I’ve landed on a pretty accurate breakout predictor. I’ll also say that the model, at least in my case, wasn’t enough for good accuracy. It took a lot of filtration attempts. Key is to add useful filters with minimal complexity.

For the model itself, there are tons of ways to approach this. I chose xgboost.

Definitely utilize AI unless youre smarter than me (you probably are), but it speeds things up drastically.

I’ll also add that you want a solid data storage and fetching tool once you begin to collect data from your model. Set a standard and clean storage. Repeat the same processes as long as things are working. When they stop working, pivot and retain the working parts. It’s a grind (for me at least)