As a developer, I want to use meta-labeling to build a secondary machine learning model, so that I know how much money our trading model can risk for any bet (aka size of bet).
Suppose that you have a model for setting the side of the bet (long or short). You just need to learn the size of that bet, which includes the possibility of no bet at all (zero size). This is a situation that practitioners face regularly. We often know whether we want to buy or sell a product, and the only remaining question is how much money we should risk in such a bet. We do not want the ML algorithm to learn the side, just to tell us what is the appropriate size. At this point, it probably does not surprise you to hear that no book or paper has so far discussed this common problem. Thankfully, that misery ends here. I call this problem meta-labeling because we want to build a secondary ML model that learns how to use a primary exogenous model.
Goal
As a developer, I want to use meta-labeling to build a secondary machine learning model, so that I know how much money our trading model can risk for any bet (aka size of bet).
Consider
Inspiration
Source: Marcos Lopez de Prado (Advances in Financial Machine Learning, 2018)
Source: The 7 Reasons Most Machine Learning Funds Fail (Presentation Slides), 2018
Source: New frontiers: Marcos Lopez de Prado on Machine Learning for finance, 2018