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Hi bingk6 --

Perhaps we are thinking the same things, but I prefer to say it a little differently.

1. Optimization simple means an organized search through a large number of alternatives, assigning each alternative a score so that the alternatives can be ranked. In my opinion, the reason we are optimizing is not specifically to find something that worked in the past (although we do find that in the process), but to find some general characteristics that precede profitable trading opportunities that will hopefully continue to work in the future.

2. Your comments imply that the Monte Carlo analysis is applied to the out-of-sample results. Am I misunderstanding? Monte Carlo analysis is usually applied to in-sample data to determine the robustness of the parameters -- the sensitivity to small changes in parameter values. The results from Monte Carlo runs are incorporated into the objective function used to assign the score to each alternative. Applying Monte Carlo analysis to the previously out-of-sample results is the start of another stage of model building using that previously out-of-sample data now as an in-sample data set. A new out-of-sample data set will be required to test for model validity.

Thanks,
Howard

<blockquote data-quote="howardbandy" data-source="post: 196640" data-attributes="member: 9179">Hi bingk6 --Perhaps we are thinking the same things, but I prefer to say it a little differently.1.&nbsp; Optimization simple means an organized search through a large number of alternatives, assigning each alternative a score so that the alternatives can be ranked.&nbsp; In my opinion, the reason we are optimizing is not specifically to find something that worked in the past (although we do find that in the process), but to find some general characteristics that precede profitable trading opportunities that will hopefully continue to work in the future.2.&nbsp; Your comments imply that the Monte Carlo analysis is applied to the out-of-sample results.&nbsp; Am I misunderstanding?&nbsp; Monte Carlo analysis is usually applied to in-sample data to determine the robustness of the parameters -- the sensitivity to small changes in parameter values.&nbsp; The results from Monte Carlo runs are incorporated into the objective function used to assign the score to each alternative.&nbsp; Applying Monte Carlo analysis to the previously out-of-sample results is the start of another stage of model building using that previously out-of-sample data now as an in-sample data set.&nbsp; A new out-of-sample data set will be required to test for model validity.Thanks,Howard</blockquote>

[QUOTE="howardbandy, post: 196640, member: 9179"] Hi bingk6 -- Perhaps we are thinking the same things, but I prefer to say it a little differently. 1. Optimization simple means an organized search through a large number of alternatives, assigning each alternative a score so that the alternatives can be ranked. In my opinion, the reason we are optimizing is not specifically to find something that worked in the past (although we do find that in the process), but to find some general characteristics that precede profitable trading opportunities that will hopefully continue to work in the future. 2. Your comments imply that the Monte Carlo analysis is applied to the out-of-sample results. Am I misunderstanding? Monte Carlo analysis is usually applied to in-sample data to determine the robustness of the parameters -- the sensitivity to small changes in parameter values. The results from Monte Carlo runs are incorporated into the objective function used to assign the score to each alternative. Applying Monte Carlo analysis to the previously out-of-sample results is the start of another stage of model building using that previously out-of-sample data now as an in-sample data set. A new out-of-sample data set will be required to test for model validity. Thanks, Howard [/QUOTE]

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