Ensembles only thrive with diverse components. TC will certainly incentivize more diversity. It is just a question of how much accuracy is given up (component-wise) to get it. First, letâs assume that a large portion of modellers switch to striving primarily for TC (which may not even happen). Even if they do, it is quite possible an ensemble based (largely) on TC feedback will turn out to be about the same as the one that was based on CORR/MMC feedback (even though the components will be quite different than before). Or it could be a lot better, or modestly better, or worse. Weâll just have to see. Given the way TC is made, worse seems unlikely so this is probably a good bet on Numeraiâs behalf. But nothing is guaranteed, and if the users hate it then maybe it wonât work out even though it technically should. I imagine with the huge magnitudes TC is capable of paying compared to CORR that it would be a good incentive, but then again the burns can be just as big. In a bad round, youâll be thankful for a low payoff factor if you are betting on 2x TC. (Your earn/burn is capped by the 0.25 round payoff/burn limit * payoff factor, so if payoff factor is 0.45, what could have been a 25% burn will only be 0.25*0.45 = .1125 which is bad enough.)
On the question of âcan you optimize for TC?â In the sense of can you just put TC into a loss function, then no you canât do that, but that doesnât mean you are 100% in the dark. You can certainly make educated guesses about the types of methods and niches you could explore that you could reasonably expect not many others to be exploring, i.e. maybe donât make a vanilla xgboost model if you are shooting for high TC. Although even if you do, youâll probably be at least positive on TC over time (the integration_test models both have positive TC) â a ânormalâ straightforward model with fairly high metamodel correlation getting decent CORR scores probably wonât lose (on average) betting 0.5x or 1x TC along with CORR. But to really excel on TC youâre gonna have to do something weirder (and be ok with more volatility in results). If you must absolutely have a definite function to optimize on, FNC3 looks like the one (or make a custom one that is similar). Some high TC models are doing very bad on FNC3 (and CORR), but very few high FNC3 models are getting negative TC so that seems fairly safe. Could be a moving target thoughâŚ