The new course is working on a momentum strategy, and naturally covers the long/short portfolio. Here one takes a group of similar assets, goes long on the ones that have performed the best recently, and short on the ones that have performed the worst.
Trouble is, things change. The last time I tried that (using returns over the past year as the measure of performance), there was a downturn in the market. All the best performers crashed a lot more than the poorer performers did. I guess they had further to fall. I lost quite a bit of money with that strategy. Not sure that enhancing it with ML will help. I’ve already tried clustering algorithms on crypto assets, without much success. I’ve used K-Means and DBScan. This new course is recommending Heirarchical Clustering. I guess I’ll give ti a try. Probably won’t actually commit any funds to the result though. Not sure what it will take to change my mind on that.