Hi Dan -- it's me again...

So -- this is an easy one. I've posted about this before and provided charts and examples. but no one really gets it because everyone believes more historical data is better, right? Wrong...

A strategy combines one or more indicators. An indicator is a simple algebraic formula -- e.g. y = 2x + 3. The "2" is analogous to an indicator setting. And how did the "2" get into this formula? Because it fit the data better than any other constant.

When you optimize a strategy, it simply computes the best constants for the algebraic formulas. If you use different data then you get different constants.

Look at your result. The algebraic formula gives a nice result for the portion of data used to compute its settings, and a crappy result for unrelated data.

Since you trade in the present, then it doesn't make sense to use old data to compute these constants. All it does is skew the computation in favor of old data.

When you optimize a strategy you'll find you get better results (when trading) if you train strategies using recent data -- i.e. just enough data that results in about 100 - 200 trades. If you use more than that, then you are simply shooting yourself in the foot.

Sorry -- this is not a case of "more is better". This is not artificial intelligence -- i.e. the strategy's algebraic formula is not a database that "learns" by providing more input data. But good luck trying to convince anyone -- even when the evidence is staring them in the face.

Related to this -- most people believe that after a while their strategies "go bad". I'm serious -- they actually believe an algebraic formula stops working. Of course, that doesn't make sense. What happens is the market data changes and the settings that were previously computed no longer match well with the current data. When this happens you do not need to generate new strategies -- rather, "refresh" your current strategies by re-optimizing using recent data.