I’m a bit confused by what you have discussed. WF breaks the data down into segments and then optimises part of that segment to find the best working variable values. It then uses those values on an out of sample part of the data so that we can see how it would have worked walking forward. Overall results are those OOS tests added together compared to the IS.
To simulate this you have to choose some recent history (a day, a month, a year, a decade? We are already curve fitting!) and optimise it to see what values worked best and then trade those values until you next feel like optimising. So to simulate this in a back test you would need to for example optimise based on December 1995’s data and then see how those values performed in January 1996 and record those results. Then re-optimise on just January 1996 data and use those values in February and record those results. Then re-optimise on February’s data and use that in March and so on and so on. An alternative is to optimise on all of history up till end of December 1995 and then use those values in January 1996 and then at the end of January re-optimise using all data up to the end of January 1996 and use those values in February.
I once coded a strategies that did both of these things and in back testing they worked great but in forward testing they failed terribly as it seems that the most important thing is the robustness of the strategy itself rather than having a strategy that you throw a broad range of variable values at time after time and keep your fingers crossed that they work as well this month as they did last month. I called my strategies ‘self optimising’ and Leo wrote something similar with his Neural Network concept. I gave up on the concept after spending a lot of time and effort on it. It was not a total waste of time as it confirmed to me that re-optimising is not the holy grail – it is just a way of proving whether last months curve fit still works this month!
