Hey Chris,

K-Means can indeed be slow. I think there are some clever things you can do involving distance heuristics that avoid you having to recompute distances (https://arxiv.org/abs/1108.1351). K-Modes may be faster as you can avoid having to do extra distance computation steps whenever you pick a new synthetic mean. Instead you pick existing observations as centroids. https://shapeofdata.wordpress.com/2014/03/04/k-modes/

When I worked in a computational genetics lab we had to deal with complexity a lot as genetics datasets tend to have a high number of features and a lower number of observations. Some algorithms that worked there involved filtering down to a lower number of features first, and then running a computationally complex search like k-means, clustering, or even a brute force method. You might try the relief methods, they're quite fast. https://en.wikipedia.org/wiki/Relief_(feature_selection)

It looks like feature selection is exactly what I was looking for, I'll see how I can implement it with sklearn. Thanks for the tips! Didn't know there were k-mode, k-median, k-prototype, and other k algorithms out there.

Using sklearn selectkbest, I'd like to pick the top 20 predictors in my data set, but I'm not quite sure how to determine what makes a predictor effective. I'm using a list of lists(in the form [[price1, price change1], [price2, price change2], up to [price180, price change 180]]) with each list interval of 180 being a predictor.

Should i use f_regression(http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.f_regression.html#sklearn.feature_selection.f_regression) to select, or mutual_info_regression, or possibly even f_classif? And for whichever function I'm using, what other arrays do i need to create in order to fill the arguments in said function?

My apologies for the novice questions, I'm an undergrad college student who's trying to learn quant finance on his own, and a lot of this material takes some time for me to wrap my head around.

You want to select features by ability to predict an outcome. Usually the outcome is forward returns, so you'd put forward returns in as your exogenous variable in any analysis. Take a look at how alphalens works:

http://quantopian.github.io/alphalens/

In general be weary of trying to predict prices with historical prices. It's very easy to overfit and often you'll get models that don't really mean anything. You can generally explain most predictive power in prices by momentum or mean reversion. What's usually more interesting is to try to select features from alternative datasets like those found here.