Basically given X non stationary stock prices, it finds out weights such that the combined portfolio is stationary.
Hugely valuable explanation and thank you. I have cloned the algo, for which more thanks and will turn my attention to it once I have got a little further forward in my foray into machine learning (using Noddy textbooks that even I can understand).
I am still trying to answer the following question which I have been asking myself for a while now:
Does increased statistical and ML sophistication translate into better trading? Does it (can it) enhance or replace "traditional" technical analysis solutions converted to systematic strategies such as the simple momentum system I posted here.
Will my research overcome my scepticism and lead me to withdraw my accusations of "mathturbation" ( a phrase famously used by sometime physicist and trader Ed Seykota).
Will I manage to verify the following academic paper? And if so, will it stand up to further out of sample training/testing? Will it achieve a high CAGR in actual trading or will it turn out to have been over fitted to the data?
45% CAGR from ML enhanced Momentum Trading