Open IanQS opened 6 years ago
Plan
1) we'll try a bunch of linear models whatever we can get our hands on. We should exhaust sklearn's provided methods 2) combinations through a controller which is some ensemble model 3) hierarchical learning 4) portfolio management methods
Resources
The base example will be done in the next hour or so. After which I'll explain add more commentary.
Tasks: 1) @bryantpq @wrhm populate a bunch of different models
statsmodels
or wherever else), but I specifically want lightboost, xgboost, and catboost. LMK when you're done or if you ever need help rebasin
After we've got like 10 models or so lemme know and we'll talk about
2) @ianqs write a coordinator that takes a bunch of models, and depending on the state of the market decides: i) what models to retrain based on some cost function ii) how to weight the other models (based on some notion of momentum in the market?)
Validation Scheme
The validation scheme will be across a different pair from what the thing was trained on.
Bookkeeping questions/ideas for tracking the models we implement:
Is there a way for us to maintain something like a spreadsheet within GitHub? Or should create and link a Google Sheet or something?
Can be done on github projects board
I think it makes the most sense to create a new issue for each new model being worked on, to make code review as efficient as possible. Thoughts?
I'd rather not - a lot of things like utils
will be common among models and rebasing constantly will be a PITA
Skeleton for early work. In the future, we'll split off all the models from here so that we don't have to PR to get new
linear_models
into the system