Discussion of article "Practical Use of Kohonen Neural Networks in Algorithmic Trading. Part II. Optimizing and forecasting" - page 2
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GA will never converge to a global optimum, it's an evolutionary method aimed at diversity. It does not have to converge to anything at all.
https://www.monographies.ru/en/book/view?id=707
It's not that it converges differently. It's that it's limited to 10K runs. It takes me less than 2 minutes to do an Optimisation. Why you can't automatically do a hundred such optimisations and show the total result is a mystery.
It's not that it converges differently. It's that it's limited to 10K passes. It takes me less than 2 minutes to do an Optimisation. Why you can't automatically do a hundred such optimisations and show the total result is a mystery.
then you can put bruteforce and do a random search of parameters inside the bot, and save it in a table.
then you can put bruteforce and do a random search of parameters inside the bot, and save it to a table.
Randomisation and optimisation are different things.
Randomisation and optimisation are different things.
naturally ) any selection criteria are added and that's it, distributions are narrowed gradually
naturally ) any selection criteria are added and that's it, the distributions are narrowed gradually
Let's not reduce to the obvious - implement any optimisation algorithm yourself.
Let's not reduce it to the obvious - implement any optimisation algorithm yourself.
The question is not that, but whether naked genetics is needed at all. But if you come up with something interesting, good.
That's not the question, it's whether you need naked genetics at all. But if you come up with something interesting, good.
Well, I can't go through 30 million passes like that with full brute force. And to get a good result in 70 seconds is probably good. But there are no methods of improvement, except manual start.
While looking at variants on the topic of optimisation(this one, for example), I came to the conclusion that it probably won't be difficult to create auto-optimisation on real ticks. It became interesting, whether the results of TC improve?
it is not difficult to create auto-optimisation on real ticks.
I encountered the problem of repeatability: it does not exist from pass to pass. What are some ways to get the same autopotimisation passes?
Faced with the problem of repeatability: it does not exist from pass to pass. What are some ways to get the same autopotimisation passes?
MathSrand() if there is an HSC there.
MathSrand() if there is a DST.
Thanks, it helped.