OmniTrader Professional Forum
Feature Requests
Improving the GA
8 replies, 1189 viewings
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OmniTrader Professional Forum
Feature Requests
Improving the GA
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Last Activity 7/19/2022 6:39 AM 8 replies, 1189 viewings |
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John W Regular  Posts: 96 Joined: 6/18/2011 Location: Sydney, NSW, Australia  User Profile |
Here's a simple example when training for 300 iterations and re-initializing every 100 iterations. In the first 100 iterations the rules formed didn't do much to improve the equity curve. The rules generated in the 101-200 iterations caused a dramatic lift in performance, and the 201-300 iterations caused a performance decline. Wouldn't it be great if the settings used by the GA in the period 101-200 could be extended for more iterations because it appears those settings are finding valuable rules and getting good training results? Question - would adding an 'n' bar extension period (e.g. 2X the interval size) using the settings for the 'best' re-initialization interval result in better rules? In this simple example after 300 bars had been run, the interval 101-200 settings would be applied to see if further valuable rules could be gleaned by running a further 200 iterations. Or could the GA automatically use the best interval settings and then let it decide when to terminate - when those settings no longer generate rules that improve results at a rapid rate? [Edited by John W on 2/1/2020 10:39 PM]  Attached file : BestGAMove.png (17KB - 675 downloads) | ||
| Jim Dean Sage    Posts: 3433 Joined: 3/13/2006 Location: L'ville, GA User Profile |
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| John W Regular Posts: 96 Joined: 6/18/2011 Location: Sydney, NSW, Australia User Profile |
I ran an experiment where I kept the rules as generated, and then deleted and renewed the data samples as the GA progressed its iterations. Each red triangle in the graph indicates a data sample change. The starting data sample ceased generating meaningful additional rules at n=5000 I changed the data sample at n=10,000 and that data set stopped generating much after n=18,000. I changed again at n=75,000 and had some very quick rule formation for about 1000 iterations. I changed the data set again at 80,000 and that has just kept on generating rules. Based on the graph above there are data sets that are way better (or way worse) for rule generation. If the GA can find and use the better data sets then that should enhance GA rule generation and GA fitness. Suggestion - instead of importing one data set could the GA import 'n' data sets at its formation. Allow the GA to gravitate over time to samples from those data sets that generate more rules. This could be as simple or as complex as Nirvana wish, but a simple method would be if the data set ceases to train meaningful rules after 'x' iterations then switch to the next data set. [Edited by John W on 2/28/2020 4:16 PM] Attached file : TrainNewData.png (26KB - 638 downloads) | ||
| mgerber Regular Posts: 61 Joined: 3/30/2006 Location: Issaquah, WA User Profile |
The mutation idea has been frustrating; nothing to do but watch and hope the good mutation runs keep going . . . I have been dong a version of your data recharging idea; doing multiple new data runs and culling effective genes. But your proposal sounds much better; keeping the knowledge base and improving it over the different data runs. Looking forward to trying that out over the weekend. --Mark G. | ||
| Mel Veteran Posts: 235 Joined: 3/18/2006 User Profile |
[Edited by Mel on 2/29/2020 8:17 AM] | ||
| John W Regular Posts: 96 Joined: 6/18/2011 Location: Sydney, NSW, Australia User Profile |
There is a GA setting to 'enable internal validation' - I switch it on all the time now to reduce the risk of curve fit, that's a must if employing the concept proposed. But with your thoughts in mind, perhaps there is an additional stronger safeguard to avoid curve fit with the concept proposed: That is for the GA to keep 2 sets of results - one set using the concept as suggested over 'n' iterations, and a second set over 'n' iterations using ALL the sample data. Then compare and see which of the 2 sets, or both, holds up in the forward test. | ||
| Buffalo Elite  Posts: 603 Joined: 7/11/2007 Location: Braintree, MA User Profile |
First hold back some symbols from all testing for validation Say you use a 10 yr BT - first 5 yrs it trains then starts walk-forward testing of old rules as it finds new rules for the next 5 years, maybe on a yr-to-yr basis Then it compares the end result - surviving WF rules to the fresh data from the symbols it didn't look at to validate the end result. HR +- 5%, ppt +- .25% IDK - have them meet some validation criteria Just spitballin but I love the idea of validating the result I currently don't train on approx 30% of my symbol list. Then I compare the results from the trained data to results on just the untrained. If they're not close I start over - it's curve fit | ||
| John W Regular Posts: 96 Joined: 6/18/2011 Location: Sydney, NSW, Australia User Profile |
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| Mel Veteran Posts: 235 Joined: 3/18/2006 User Profile |
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