Discussion of article "Third Generation Neural Networks: Deep Networks" - page 4

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krzysiaczek99:

to do such comparative test  you have to be sure that any of this methods has really predictive power otherwise it is comparision of random results with unknown variance depending of the setup and chosen data so outcome is random also. Are you sure if any of those methods has really predictive power ?? Than it would require a lot of testing against a lot of symbols and even after this you would not be sure if one method is better than other as the variance distribution of the results is unknown and results it will just depend of test setup and chosen data.

Krzysztof

Thanks for your patience with me!

" ...Are you sure if any of those methods has really predictive power ??"

I don't want you to develop a perfect system with the absolute perfect indicators predicting the future!

We all know indicators only compress the existing chart of elapsed quotes into numbers and we attribute these numbers to the future. Right or wrong will tell us the market.


"... so outcome is random also."

This I don't understand. :(

In order to get comparable results I suggested an existing EA (MARSI) which is based on a known indicator EMA_RSI and asked you to use the same strategy.

If you backtest your EA and the existing MASI on the same historic data the final results of both EA (yours and the MARSI-EA) are comparable with each other - but still valid is: good or bad will tell us the market.

calli

 
krzysiaczek99:

So when you plan to release your new article ??


Krzysztof

Hi,

Located on checking the moderators. I will soon be published.

 
calli:

Thanks for your patience with me!

" ...Are you sure if any of those methods has really predictive power ??"

I don't want you to develop a perfect system with the absolute perfect indicators predicting the future!

We all know indicators only compress the existing chart of elapsed quotes into numbers and we attribute these numbers to the future. Right or wrong will tell us the market.


"... so outcome is random also."

This I don't understand. :(

In order to get comparable results I suggested an existing EA (MARSI) which is based on a known indicator EMA_RSI and asked you to use the same strategy.

If you backtest your EA and the existing MASI on the same historic data the final results of both EA (yours and the MARSI-EA) are comparable with each other - but still valid is: good or bad will tell us the market.

calli

In simple words. There is no way to determine that any strategy is working or not for sure so result of such comparative test will depend of chosen data and luck. Only what you would be able to say after such test is that for this data and this setting this strategy performed better/worse

 

Krzysztof

 

Wow. This is listed in the mt5 directory, which it most certainly does not appear to be...

 

HI Vladimir, 

 

Here is from Brazil!!!

 

I read your instructions about the Neural Network using R but I have a dumb question (sorry, I am newbie in this!)

 

In the tutorial that you wrote ( https://www.mql5.com/en/articles/1103#ch_3), in the "Section 3.3.1 - Source Data" you described a function, called pr.OHLC that I understood very well. 

 

Bu  you show some results that it was not clear to me which are the parameters necessary to the results below

 

> head(price)
        Open    High     Low   Close      Med     CO
[1,] 1.33848 1.33851 1.33824 1.33844 1.338375 -4e-05
[2,] 1.33843 1.33868 1.33842 1.33851 1.338550  8e-05
[3,] 1.33849 1.33862 1.33846 1.33859 1.338540  1e-04
[4,] 1.33858 1.33861 1.33856 1.33859 1.338585  1e-05
[5,] 1.33862 1.33868 1.33855 1.33855 1.338615 -7e-05 

Could you, please, help me with this?

 

Best,

 

Fábio 

Third Generation Neural Networks: Deep Networks
Third Generation Neural Networks: Deep Networks
  • 2015.02.05
  • Vladimir Perervenko
  • www.mql5.com
This article is dedicated to a new and perspective direction in machine learning - deep learning or, to be precise, deep neural networks. This is a brief review of second generation neural networks, the architecture of their connections and main types, methods and rules of learning and their main disadvantages followed by the history of the third generation neural network development, their main types, peculiarities and training methods. Conducted are practical experiments on building and training a deep neural network initiated by the weights of a stacked autoencoder with real data. All the stages from selecting input data to metric derivation are discussed in detail. The last part of the article contains a software implementation of a deep neural network in an Expert Advisor with a built-in indicator based on MQL4/R.
 
fabiocarvalho:

HI Vladimir, 

 

Here is from Brazil!!!

 

I read your instructions about the Neural Network using R but I have a dumb question (sorry, I am newbie in this!)

 

In the tutorial that you wrote ( https://www.mql5.com/en/articles/1103#ch_3), in the "Section 3.3.1 - Source Data" you described a function, called pr.OHLC that I understood very well. 

 

Bu  you show some results that it was not clear to me which are the parameters necessary to the results below

 

Could you, please, help me with this?

 

Best,

 

Fábio 

Hi Fabio,

What's not clear?

pr.OHLC <- function (o, h, l, c) 
{
  #Unite quote vectors into a matrix having previously expanded them
  #Indexing of time series of vectors in R starts with 1. 
  #Direction of indexing is from old to new ones.   
  price <- cbind(Open = rev(o), High = rev(h), Low = rev(l), Close = rev(c))
  Med <- (price[, 2] + price[, 3])/2 #We calculate average price (HIgh + Low)/2
  CO <- price[, 4] - price[, 1] # We calculate body candles (Close - Open)
  #add Med and CO to the matrix
  price <- cbind(price, Med, CO)#We are putting it all in a matrix
}
 

Hi Vladimir,

 

Is there a possibility to have the files for MT5 ?

Regards

 

Fabio lima 

 
fabioflimaster:

Hi Vladimir,

 

Is there a possibility to have the files for MT5 ?

Regards

 

Fabio lima 

Hi Fabio,

I'm sorry.

I do not write on MKL5.

Best regards

Vladimir


 

One question . I am not clear on the order of the price vector .

You do a reversal here : price <- cbind(Open = rev(o), High = rev(h), Low = rev(l), Close = rev(c))

What is the original order of o,h,l,c ?  

 
jake89:

One question . I am not clear on the order of the price vector .

You do a reversal here : price <- cbind(Open = rev(o), High = rev(h), Low = rev(l), Close = rev(c))

What is the original order of o,h,l,c ?  

Hi,

The MT4 numbering bars from the most recent to oldest. The R on the contrary, from the old to the new, new bar last.

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