The essence of optimisation - page 10

 
joo:
The main thing is that the cumulative trading results of a still living elite (and it is constantly renewed, some die sooner, some later) are always positive...
That's the thing :) you can't get a positive elite no matter what.
 
any offspring with only rudiments in their genes (although this is not the right term, as rudiments were at least useful in the past) are doomed to produce only random results.
 
TheXpert:
That's the funny thing :) you can't get a positive elite with a mashka.
Nah... you can't go far with just a mashka, though.)
 
 
I just don't understand one thing. this thread is about optimization and not a single word was said about such a method as genetic programming. i myself am extremely skeptical about any kind of numerical analysis in the market, but it seems to me that you as an apologist for this idea this method should be in the front row for consideration, because it solves the problem of using only standard inputs and adjusting them to history and creates "ideal" inputs from pieces of existing ones, thus being an ideal fitter/optimizer unconstrained by permanence of inputs
 
Alex_Bondar:

It happens.

At least show what's fake and what doesn't add up.

+1

It's the fuckin shit!

Look it up on spider if you like nonsensical reads about "can SBs be traded" and such.

It's about the fact that at any given time you can get the most out of the price if you know the right parameters, which you can only find out in hindsight.

found on mql4-m

liked this scientific explanation:

Neutron13.01.2009 08:04#

What you are trying to formalise here is called the stationarity of a parameter in some process. In this case we are talking about stationarity for one of the harmonics, if kotir is considered as a set of harmonic signals.

Indeed, the difference of the two sweeps (see your first post), is almost the first derivative of the higher muve. The bandwidth of an ideal digital differential operator is a straight line drawn from the origin (y=f) and ending at the Nyquist frequency (or 1/2 of it, can't remember) on the abscissa axis, which corresponds to a double TF and 1 on the ordinate axis. Given that in first approximation the cotier spectrum is proportional to 1/f, we obtain a window in the entire frequency range where all harmonics of the original BP are represented by a weight of 1. Thus, optimizing such RT on historical data using your proposed algorithm will only identify the harmonic with the maximum amplitude. And everything would be fine, but for one BUT - the position of such harmonic is not stationary in principle. Therefore, to build a profitable TS using two muves conversion is impossible - the optimization parameter is not stationary.

If we use several muves with different smoothing periods in TS and define the signal to buy as a weighted sum of signals from each crossover, we will get a trivial Fourier analysis. The world is one again in all its manifestations!

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Optimisation theory is a well developed area of mathematics, why reinvent the wheel.

There are also a large number of heuristic methods for finding extremes of functionals.

The "essence" lies in a high probability that the extremum is global, with a minimum of computation.

A very good contender for the "essence" is the "annealing simulation method".



 
pantural:

Optimisation theory is a well developed area of mathematics, why reinvent the wheel.

There are also a large number of heuristic methods for finding extremes of functionals.

The "essence" lies in a high probability that the extremum is global, with a minimum of computation.

A very good contender for the "essence" is the "annealing simulation method".

Yeah... the extremum of the robustness function is just a matter of finding it, that's all...
 
pantural:

Optimisation theory is a well developed area of mathematics, why reinvent the wheel.

There are also a large number of heuristic methods for finding extremes of functionals.

The "essence" lies in a high probability that the extremum is global, with a minimum of computation.

A very good contender for the role of "essence" is the "annealing simulation method".

In this respect it is of course well developed, if we are just talking about approximating random (in parameter space) test sample by hyperplane.

Well everything is of little importance, this is not the "OPTIMIZATION PROPERTY", what you are talking about is just a methodology to reduce machine calculations, it is of course important, but not so essential.


In fact the question is not an easy one and it generally goes into the vacuum of thinking about the essence of market laws, which everyone is over-saturated with because of the extreme noisiness of such considerations. So I won't even try.

I'll say in the style of Nostradamus:):)

A MODEL MUST CONTAIN RELEVANT A PRIORI DATA ABOUT THE PROCESS.

 
J.B:

In this respect it is of course elaborated, if we are talking about simply approximating with a hyperplane, a random (and parameter space) test sample.

Well everything matters little, this is not the "OPTIMIZATION PROPERTY", what you are talking about is just a methodology to reduce machine calculations, it is of course important, but not so essential.


In fact the question is not an easy one and it generally goes into the vacuum of thinking about the essence of market laws, which everyone is saturated with because of the extreme noise of such considerations. So I am not even going to try.

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