Greetings --
Regarding "What I want it for is to create Perry Kaufman's Information Ratio which is CAR/Annualized SD. The resulting values show how likely the system is to be robust. Anything above 3.0 indicates an overfit system."
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CAR/SD has dimensions similar to those of Sharpe ratio. I am skeptical that it has a strong relationship to being overfit. A ratio of mean (such as CAR is) to standard deviation is a z-score. High values suggest data points that are relatively rare. That could result from a model that accurately identifies the profitable pattern -- just as we hoped it would -- which would be precisely the wrong reason for rejecting a system.
Poor OOS results could come from either a model that was overfit or a change in the data between the IS and OOS periods.
The best method of determining whether a model has been overfit to the data is strict (one time use) out-of-sample test. Which is what live trading will be, which is a good reason for following sound modeling, simulation, and data science procedures.
The best method of determining whether a model has been overfit to the data is strict (one time use) out-of-sample test. Which is what live trading will be, which is a good reason for following sound modeling, simulation, and data science procedures.
Best regards,
Howard
On Sun, Feb 14, 2016 at 5:52 AM, sladbrook@hotmail.com [amibroker] <amibroker@yahoogroups.com> wrote:
Thanks Gregg
That got it!
I've made the same bloody mistake in the past. I'm going to have to stick a note up on the wall to remind myself!
Thanks again
CK
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Posted by: Howard B <howardbandy@gmail.com>
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