Can MQL programmers be considered as programmers? - page 6

 

Whether you are a programmer or not depends on the person.
MQL4-5 is one of the branches of programming.
There are different degrees of skill-compilation of algorithms-programs.
For example, if you can only use MQL4-5, you will be a programming god among novices and non-programmers.
If you're good at MQL4-5, among experienced programmers you will be a rookie loser.
It all depends on the environment you're in.
Everything in the world is relative.
A glass of water is bigger than a drop, a barrel of water is always bigger than a glass of water, and so on.


And if you prove something in front of professionals who only know how to use MQL4-5.

they'll trample you in a ditch with ***, with wild laughter and roaring.


P.s. Everyone has to be in his niche and argue only within his level.

 
Alexander Ivanov:

And if you prove something in front of professionals who only know how to use MQL4-5

they'll trample you in a ditch with ***, and with wild laughter and roaring.

They will not even be trampled. You will not even be trampled. There is no point.

 
Yuriy Asaulenko:

There won't even be trampling. There won't be any laughter or neighing either. There's no point.

There will always be those who like to do it...

like that )

 
Yuriy Asaulenko:

If you do this, you can consider yourself not a super-programmer, but a super-idiot. Instead of applying what has already been created many times, doing it yourself and wasting time on it. This "all by yourself" approach does not fit the concept of modern programming).

Please tell me where I can find, well, at least C++ working codes for GARCH.
 
Aleksey Ivanov:
Please tell me where I can find, well, at least C++ working codes for GARGH.

In R-Project, with source code. There seems to be some in Python modules, too. And all in C++. And if not in C++, who-what prevents to connect to these modules from any other application? You have only the interface behind you. Why do you need C++ code? - You don't need any code for the application.

PS Here's the first thing that came up on a search - garch for Python - Time Series Analysis (TSA) in Python - Linear Models to GARCH Judging by the search, garch C++ is also sufficient.

Time Series Analysis (TSA) in Python - Linear Models to GARCH
Time Series Analysis (TSA) in Python - Linear Models to GARCH
  • 2016.11.08
  • Brian Christopher
  • www.blackarbs.com
So what?  Why do we care about stationarity?  A stationary time series (TS) is simple to predict as we can assume that future statistical properties are the same or proportional to current statistical properties.Most of the models we use in TSA assume covariance-stationarity (#3 above). This means the descriptive statistics these models predict...
 
Yuriy Asaulenko:

If you do this, you can consider yourself not a super-programmer, but a super-idiot. Instead of applying what has already been created many times, doing it yourself and wasting time on it. The concept of modern programming does not fit this "all by yourself" approach.)

That's why I mentioned low productivity and the need to learn the technology "on the fly". If I have these problems, then what kind of a tank player am I? Programmer, I mean.

 
Aleksey Ivanov:
Please, tell me where I can find, at least, C++ working codes for GARCH.

The problem is that pure GARCH(1,1) is a virtually unworkable model.

You have to take the appropriate package, the most interesting one is rugarch. You have to simulate the mean, ARCH proper, and there are quite a lot of these models, you can get good results with EGARCH, besides that you need to simulate the distribution. There are many publications highlighting the results of using this package in the financial markets including Forex. You can find here ready-made codes and examples, it's very instructive.

If you look at Rugarch and get a good result, it is available on Srp, the codes are open source.

But you're way off from Srp because it's not sure that you'll get a decent result with GARCH. Anyway, it's incomparably more convenient to conduct experiments in R rather than in µl, because R is an interpreter.

 
СанСаныч Фоменко:

In any case, it is incomparably more convenient to carry out experiments in R rather than in µl, because R is an interpreter.

It is more convenient not because the interpreter is secondary, but because R is a modeling environment, including (or first of all) statistical.

By the way, despite the fact that R is interpreted, the language itself is a scripting language and serves mainly for linking words in a sentence, i.e. functionality and various packages among themselves. And the language itself takes up a negligible part of the program execution time.

Thus, all complaints about the speed of R are completely unfounded. This is about using R directly in TC, and pointlessness of rewriting codes in MQL).

 
СанСаныч Фоменко:

The problem is...

Useful information :

http://keldysh.ru/papers/2013/prep2013_19.pdf

M.A. Ananiev, N.A. Mitin

Comparison of Linear and Nonlinear Autoregressive Models of Conditional Heteroscedasticity Using RTS Index Return as an Example

ANNOTATION

In this paper we compare the forecasting capabilities of linear and non-linear conditional volatility models by the example of GARCH models for the RTS index return. Based on the daily closing prices of the RTS index for 10 years a set of parametric models is estimated and a set of volatility forecasts for different length horizons is built. The forecasting capabilities of the models are compared according to the selected criteria. Non-linear models have been developed to account for detected features of the time series, but the quality of the forecasts obtained with their help is sometimes questioned. The results of this study complement the results of other works: non-linear conditional volatility models show better results. A possible explanation for this success may be the fact that non-linear models give better forecasts at relatively short horizons, while at longer horizons they may give larger errors.

 
СанСаныч Фоменко:

The problem is that pure GARCH(1,1) is a virtually unworkable model.

You have to take the appropriate package, the most interesting one is rugarch. You have to simulate the mean, ARCH proper, and there are plenty of these models, you can get good results with EGARCH, besides that you have to simulate the distribution. There are many publications highlighting the results of using this package in the financial markets including Forex. You can find here ready-made codes and examples, it's very instructive.

If you look at Rugarch and get a good result, it is available on Srp, the codes are open source.

But you're way off from Srp because it's not sure that you'll get a decent result with GARCH. Anyway, it's incomparably more convenient to conduct experiments in R rather than in µl, because R is an interpreter.

San Sanych, let me tell you a terrible secret: so is MQL. It's also an interpreter.