What?
Clearly these ML models aren't trying hard enough. Correlating stock movements with the weather, geo populations, ... there are potentially infinite patterns buried in the data that a hedge fund could uncover.
Currencies are traded in pairs. Bonds are numerous, if illiquid off the run. Not to mention the bursting menagerie of derivatives our species tends to.
edit: obviously many steps of this process can use machine learning in general and deep learning in particular
Why is that?
Or, from another angle, say you've got 10 ticks of a stock per second for 12 hours = 432,000 data points. This is the same as a single (relatively small) 380x380x3 image. If you assume there are roughly 30,000 instruments like this in the world of this volume, that's probably, I dunno, the number of images facebook sees in a second. Those are all guesses but you can see the scale of differences involved here.
1. This is all observational data - even if you have all the features in the domain it's pretty clear that you won't have seen all of the generative theory. 2. You don't have all the features because the features change, renewable power suddenly makes average wind conditions relevant, and so on. 3. This domain is clearly chaotic; you can induce the various attractors and theories, but you cannot measure the conditions well enough to predict the shift between attractors.
There are many people who are using ML to predict closed parts of the financial world where they have an deep and well founded understanding and excellent data. But there are lots and lots of people who are feeding data into models and getting good results and then going on good runs who are going to get a shock.