It seems like building a context tree with a convex branch cross attention estimator then using branch and bound to prune the tree while descending to get exact cross attention when it's above a threshold would work pretty well, assuming the cross attention matrix actually is very sparse and the trouble is just accurately guessing the non-sparse elements.
this sounds to me like a dollar cost averaging strategy - only buy in when the current price falls below an n-day moving average.
I doubt there is any risk adjusted alpha to the strategy - in practice it's my, newbie, understanding that the only thing that differentiates such strategies in the broader scheme of things is tax efficiency.
