I post stuff for a week or less about system development. I want to start and end this in a quiet manner. So you've got two systems, both trading under a different tendency. You've done all the tests and analyzed all the key figures required to run and manage the system solo. The obvious next step is to find out how you manage them within a portfolio of other models. Obviously, you run the multiple systems under the dataset you developed both models under. Literally, you repeat the tests you've done for a single system in a portfolio. You get the correlation factors, equity curve volatility (Std. Dev.) figures and the test out a bunch of allocation models with it. So now, you take each of the new models and create a new market dataset excluding the tendencies each of the models use. In another words, you tweak the original market data so that the potential system fails. So you take the slip-forward dataset for each of the systems (including the live models) and iterate through all combinations. At this point you get a good picture of "what happens" when a specific and combinations of models fail and you get a post-slipped market correlation factors of each of the models. Obviously, you re-factor the "fitted" results and the "slipped" results together to get the optimal allocation scheme for your portfolio. And again, you get a better picture of whether the new systems are significant enough to add to your live server.