Despite all my complaining about ‘problems’ I’m actually making solid progress. I think I understand conceptually how reinforcement algorithms work, and I can even read those pesky mathematical equations I complained about a few posts ago. I’m getting a better grip on the jargon, and the concepts generally. I’ve even managed to get a few examples of code up and running.
So that’s Phase 1. Phase 2 will be to consolidate my programming these algorithms and not just getting other peoples code to actually work on my computer. I’ve encountered quite a few numpy functions recently that I haven’t seen in the past couple of years of programming in quantitative finance. Most of them have to do with reshaping matrices in various ways. In the past the pandas DataFrame has been my goto data structure. In machine learning it’s a bit more basic, numpy arrays and the tensor library equivalents (either torch or tensorflow).
Phase 3 will be trying out different neural network architectures to work on this general problem. So far I’ve been staying with multi layer perceptrons but they’re pretty basic. Lots of other options to explore
And finally Phase 4 will concentrate on determining exactly what kind of input data will give the best results.