When doing a WF with optimization each data sample has its own IS and OOS bits of data so in reality it is just 5 separate overlapping IS and OOS WF tests and you are hoping that they all give similar results and all work best with exactly the same or similar settings. That then tells you how robust your strategy is. The ideal development is to develop a strategy on a section of IS data and once you are happy it works as you want it to then test it on the rest of the data available. Then if it works the same or similar on that data then break it down into 5 overlapping IS and OOS sections of data and then WF optimize on that. If all the results are similar and the setting similar then that is good. If happy then set it live and forward test on demo with the settings from the very latest data sample.
I think a common mistake is to WF optimize with massive amounts of variables and with massive ranges for those variables. You should have an idea of a small range that you think your strategy should work ok in and not fall apart just because some extreme value is not available to it for one section of data. Not only should this give more confidence that your strategy is pretty robust within a small range of values but it also considerably speeds up WF testing.
