Machine learning in trading: theory, models, practice and algo-trading - page 1257

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Philipp Negreshniy:
success depends on luck accompanying fools, only they can do ME and make it look promising;)

You're right, the smart usually have no luck in life, because they are afraid of risk, which fools simply do not see, smart sometimes need to act as a drunk, energetically on the sly, as an option to make decisions and act under the degree.

 
Kesha Rutov:

Do you know this "something", this "basic strategy (BS)"?

To begin with, take any BS. MO will show its performance and development potential, if any). Then, either change the BS, or develop.

Everything remains the same as it is without ME. MO does not replace the head.))

 

Feature Selection using Genetic Algorithms in R


This is a post about feature selection using genetic algorithms in R, in which we will do a quick review about:

  • What are genetic algorithms?
  • GA in ML?
  • What does a solution look like?
  • GA process and its operators
  • The fitness function
  • Genetics Algorithms in R!
  • Try it yourself
  • Relating concepts
Feature Selection using Genetic Algorithms in R
Feature Selection using Genetic Algorithms in R
  • Pablo Casas
  • www.r-bloggers.com
This is a post about feature selection using genetic algorithms in R, in which we will do a quick review about: What are genetic algorithms? GA in ML? What does a solution look like? GA process and its operators The fitness function Genetics Algorithms in R! Try it yourself Relating concepts Animation source: "Flexible Muscle-Based Locomotion...
 
Yuriy Asaulenko:

To begin with, take any BS. MO will show its performance and development potential, if there are any). Next, either change the BS, or develop it.

Everything remains the same as without MO. MO does not replace the head.))

I've already shown how it works for me and suggested my target that determines trends and fluxes, in which any impulse and channel strategies can be traded, respectively.

 
The posts have been deleted again...(.
The bad example is infectious.
 
Elibrarius:
The posts were deleted again...(
The bad example is infectious.

it is "to eat" and no one will discuss it here anyway, so I deleted so as not to break the idyll)

 
Maxim, what is the Bayesian tree? What is the difference between it and a regular one?
 
Maxim Dmitrievsky:

This is "eat" and no one will discuss it here anyway, so I deleted so as not to break the idyll)

Well, at least keep interesting articles in your blog. Just here no one deletes trash, which makes it difficult to find something interesting.
 
elibrarius:
Well, at least keep interesting articles in your blog. No one deletes garbage here, which makes it difficult to find anything interesting.

To understand Bayesian trees you should first read about Metropolis-Gastnigs algorithm, Monte Carlo algorithm over Markov chains, by analogy with trees will

the BART document itself

http://www-stat.wharton.upenn.edu/~edgeorge/Research_papers/BART%20June%2008.pdf

the point is that they don't retrain and give a probabilistic output estimate (posterior)

 
Maxim Dmitrievsky:

To understand Bayesian trees you should first read about the Metropolis-Hastnigs algorithm, the Monte Carlo algorithm over Markov chains, by analogy with trees will

the BART document itself

http://www-stat.wharton.upenn.edu/~edgeorge/Research_papers/BART%20June%2008.pdf

the point is they don't retrain and give a probabilistic output (posterior)

A bunch of formulas ((

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