I was thinking about two types of parameters one can use in a model. Open-ended parameters like SMAs or percentage-based filters, where the value used can be anything from zero to infinite and those like percent, where the value can only be within a certain range, like 0% to 100%. Are either of these two types more susceptible to curve-fitting? It would seem that you could say the first type is because the ability to calibrate the value of the parameter to historical data is nearly infinite, so someone could come to the conclusion that the 53-day SMA with a 13.2% volatility filter were the optimal values for an entry strategy, whereas with the second type, your ability to fit to a curve is limited by the range the variable can take on, meaning you'd be better off basing a model on the second type, to the extent that you can. I'm just thinking out loud a bit, so if this simple comparison and conclusion is flawed, I'm happy to hear why. I realize that you'd kind of have to ignore the potential for infinite subdividing of the range-constrained parameter, so that you don't end up with a value like 15.898798798% as your model input.