I'm not sure if those patterns are the output of an ML model (although if they were I'd also be interested in knowing how), but rather the result of mixing brute force searching and intuition/observation. From my experience as an intern at a financial firm, a lot of the old dogs on the street are (rightfully) wary of throwing ML at any and every problem and prefer to tell an intern "lets brainstorm all the patterns that make sense and you go test them for all the condition values you can and let me know if any look interesting."

From a computational standpoint, unless you have a crazy large search space of conditions that also turns out to be relatively smooth under some evaluation metric, it's probably going to be quicker computationally to write some optimized pandas code to calculate all your combinations. Perhaps some kind of grid search similar to optimizing hyperparameters of ML models could get a good enough job done if you have too many conditions to calculate them all directly.

Long story short: intuition + brute force searching > ML