Admittedly I am new and learning, but the way I see it is this… each era can be considered an independent sample of data with many features that result in a target.
Say I want to predict what kind of vehicle is coming down my road next. It is far away so I can only determine some coarse grain properties. I can see its color, I can see how fast it is going, I can see if its exhaust is clear or sooty. Yellow, slow, sooty features suggest the next vehicle will be a school bus (in North America anyway). If those features were red, fast and clear exhaust that would suggest a Ferrari. Now there is no way this data will tell me what the vehicle after the Ferrari is, so no point in trying to model yellow->red and slow->fast and soot->clean.
So, there is probably no pattern in the sequence of vehicles as they come down my road (maybe there is - you can always look for one) - but since Tournament is arranged the way it is my guess is the underlying assumption is stock prices are best predicted by current features, not long term feature patterns. The hedge fund people probably know what they are doing. But then again Signals is now a thing, and something I know nothing about yet, so this is a whole different box I’m not ready to open yet.