Marcos Lopez de Prado has championed the concept of meta-labelling, which, in the field of quantitative trading, means using an ML model to apply risk management to a different quantitative trading strategy. Ernie Chan has discussed this in various contexts, for example here.
So if I was to use this in my trading, how would it relate to what I’m currently doing? The suggestion seems to be that a deep neural network ‘scrutinizes’ the output of some more shallow ML model, or indeed any other strategy including simple discretionary (not quantitative) trading.
So I’m wondering if perhaps I should use something like XGBoost to identify signals for trading, and use my RL model to assess whether trading those signals is a good idea and if so, how much to put on them. That’s pretty much the reverse of what I’ve been contemplating for the past few days. However with some theoretical basis (and actual practice) supporting this approach, I think I’ll give it a go. As for features, I’m thinking that endogenous features (related to the asset I’m trading) should be used for the base model, XGBoost in this case, and exogenous data for the ‘supervisory’ model. That’s my intuition anyway, so I’ll go with that, at least for starters.