Artificial Intelligence

 
Do you have any experience in Neural Networking or Machine Learning Trading?
 

Neural Network

Neural Network: discussion/development threads

  1. Better NN EA development thread with indicators, pdf files and so on.
  2. Better NN EA final thread 
  3. taking NEURAL NETWORKS to the NEXT LEVEL - very interesting thread
  4. Neural Networks thread (good public discussion)
  5. How to build a NN-EA in MT4: usefull thread for developers.
  6. Radial Basis Network (RBN) - As Fit Filter For Price: the thread 

Neural Network: Indicators and systems development

  1. Self-trained MA cross!: development thread for new generation of the indicators
  2. Levenberg-Marquardt algorithm: development thread

Neural Network: EAs

  1. CyberiaTrader EA: discussion thread and EAs' thread.
  2. Self learning expert thread with EAs' files here.
  3. Artificial Intelligence EAs threads: How to "teach" and to use the AI ("neuron") EA thread and Artificial Intelligence  thread
  4. Forex_NN_Expert EA and indicator thread.
  5. SpiNNaker - A Neural Network EA thread

Neural Network: The Books

  1. What to read and where to learn about Machine Learning (10 free books) - the post.

The article

CodeBase

Neural networks made easy (Part 19): Association rules using MQL5
Neural networks made easy (Part 19): Association rules using MQL5
  • www.mql5.com
We continue considering association rules. In the previous article, we have discussed theoretical aspect of this type of problem. In this article, I will show the implementation of the FP Growth method using MQL5. We will also test the implemented solution using real data.
 

hi friend!

thanks so much! but I mean your personal experience with AI. do you get real profit by using this method? do you think it will be a good method instead of using Technical methods?

 
Shahab Shamsi #:

hi friend!

thanks so much! but I mean your personal experience with AI. do you get real profit by using this method? do you think it will be a good method instead of using Technical methods?

No, I do not have practical experience sorry.
But there is one thread in Russian forum where the users are talking about it, and it is 2,739 pages on the thread for now (yes, the thread is very popular one).

I am not fully understand about what they are talking about ... something related to this page:
https://cs.stanford.edu/people/karpathy/convnetjs/demo/rldemo.html

..and something about the following ("machine translation" from Russian):

Forum on trading, automated trading systems and testing trading strategies

Machine learning in trading: theory, models, practice and algorithmic trading

mytarmailS , 2022.09.07 07:24

It makes no sense to look at the old model, it does not capture market changes.

I propose to implement as suggested)))
In the sliding window, retrain the model and look at the importance of the features, or simply take some kind of good feature determinant and look at it in the sc. window


Here is an example on a randomly generated sample of 5 features and 1 binary target

for forest and feature selector

X <- matrix (rnorm( 1000 ),ncol = 5 )
Y <- as .factor(sample( 0 : 1 ,nrow(X),replace = T))

head(X)
head(Y)

#install.packages( "randomForest" )
library (randomForest)
rf_imp_mat <- matrix (ncol = ncol(X),nrow = nrow(X))
for (i in 30 :nrow(X)){
  ii <- (i- 29 ):i
  rf <- randomForest(Y[ii]~.,X[ii,],ntree= 100 )
  rf_imp_mat[i,] <- importance(rf)[, 1 ]
}

#install.packages( "FSelectorRcpp" )
library (FSelectorRcpp)
fs_imp_mat <- matrix (ncol = ncol(X),nrow = nrow(X))
for (i in 30 :nrow(X)){
  ii <- (i- 29 ):i
  infg <- information_gain(y = Y[ii],x = as .data.frame(X[ii,]))
  fs_imp_mat[i,] <-  infg$importance
}

par(mfrow=c( 1 , 2 ))
matplot(rf_imp_mat , t= "l" ,lty= 1 ,main= "rf_var_imp" )
matplot(fs_imp_mat , t= "l" ,lty= 1 ,main= "fs_var_imp" )


In R, of course, it is not customary to write in cycles if it is not necessary, but this style confuses beginners, and middle peasants, like me, also confuses ..

but you can write short and so, the code is 3 times less, the result is the same

X <- matrix (rnorm( 1000 ),ncol = 5 )
Y <- as .factor(sample( 0 : 1 ,nrow(X),replace = T))
idx <- embed( 1 :nrow(X),dimension = 30 )[, 30 : 1 ]

library (randomForest)
rf_imp_mat <- t(apply(idx, 1 ,function(i) importance(randomForest(Y[i]~.,X[i,]))[, 1 ]))
library (FSelectorRcpp)
fs_imp_mat <- t(apply(idx,1,function(i) information_gain(y=Y[i],x=as.data.frame(X[i,]))$importance))

par(mfrow=c( 1 , 2 ))
matplot(rf_imp_mat , t= "l" ,lty= 1 ,main= "rf_var_imp" )
matplot(fs_imp_mat , t= "l" ,lty= 1 ,main= "fs_var_imp" )


Also , different feature selectors for every taste, probably 5% of what is in R-ke

I am not participating on this talking/subject (because I am not a specialist with it sorry as it is very specific subject for me).
 
There is a programming language which is a leader in AI, it is python
 
Mohammed Abdulwadud Soubra #:
There is a programming language which is a leader in AI, it is python

I think you can work with other professional tools like MATLAB too!

 
Shahab Shamsi #:

hi friend!

thanks so much! but I mean your personal experience with AI. do you get real profit by using this method? do you think it will be a good method instead of using Technical methods?

The AI of any trading robot uses technical methods as a starting point to make trades since it is easiest to work with. It bases its trades on the information that the chart provides and some really sophisticated ones also integrate fundamental aspects. Implementing genetic algorithms into an EA further enhances its intelligence, as it uses the selection, crossover and mutation that we see in nature to optimize whatever strategy it uses. It's actually one of the fields that I'm currently studying in my free time and the results look really promising. So the question shouldn't be about AI vs. technical trading, and instead should revolve around "does a trading AI enhance technical analysis?" and the answer to that is a very clear YES!

 
Suren Khosravi #:

The AI of any trading robot uses technical methods as a starting point to make trades since it is easiest to work with. It bases its trades on the information that the chart provides and some really sophisticated ones also integrate fundamental aspects. Implementing genetic algorithms into an EA further enhances its intelligence, as it uses the selection, crossover and mutation that we see in nature to optimize whatever strategy it uses. It's actually one of the fields that I'm currently studying in my free time and the results look really promising. So the question shouldn't be about AI vs. technical trading, and instead should revolve around "does a trading AI enhance technical analysis?" and the answer to that is a very clear YES!

hi friend! 

really answer this question needs to have a deep acknowledge of the market. as you know the market is semi random and the news also affects it's trends. so I think there is many obstacles in this field. you should determine how much percent of the market is predictable and how you can extract this part from the data!

 
Shahab Shamsi #:

hi friend! 

really answer this question needs to have a deep acknowledge of the market. as you know the market is semi random and the news also affects it's trends. so I think there is many obstacles in this field. you should determine how much percent of the market is predictable and how you can extract this part from the data!

Future price uncertainty is part of the market, but that doesn't mean it's random. What many call "random" is simply a pattern they don't yet understand and thus erroneously classify it as such. The markets always rely on a set of repeating patterns that express themselves in different ways at different times. If that wasn't true, hedge funds and investment banks wouldn't bother trading the markets at all. Now, without AI you'd have to figure out the changes of those patterns yourself, which is time consuming. Not to mention that by the time you have figured out what that pattern is, the pattern itself might have already morphed into a new one. The use of AI gets rid of all this human fallibility and recognizes, adapts, memorizes and calculates those changing patterns and integrates all that into its trading strategy. It's a much more efficient use of technical, as well as fundamental analysis. 

 
Suren Khosravi #:

Future price uncertainty is part of the market, but that doesn't mean it's random. What many call "random" is simply a pattern they don't yet understand and thus erroneously classify it as such. The markets always rely on a set of repeating patterns that express themselves in different ways at different times. If that wasn't true, hedge funds and investment banks wouldn't bother trading the markets at all. Now, without AI you'd have to figure out the changes of those patterns yourself, which is time consuming. Not to mention that by the time you have figured out what that pattern is, the pattern itself might have already morphed into a new one. The use of AI gets rid of all this human fallibility and recognizes, adapts, memorizes and calculates those changing patterns and integrates all that into its trading strategy. It's a much more efficient use of technical, as well as fundamental analysis. 

hi friend!

I'm not agree with you! we have a normal distribution in mathematics which is made by random data. however it's not completely Gaussian but you can claim it's a semi random data. what you say is the patterns which made by predictable data section.

 
Shahab Shamsi #:

hi friend!

I'm not agree with you! we have a normal distribution in mathematics which is made by random data. however it's not completely Gaussian but you can claim it's a semi random data. what you say is the patterns which made by predictable data section.

Semi-random is kind of an oxymoron. Oxford defines the word random as follows: "made, done, or happening without method or conscious decision". So randomness is a term people use to describe processes they perceive to be chaotic, but that doesn't make it so. Furthermore, when you attach the word "semi" to it, you are actually disproving that notion. If there is no method to the markets, then trading the markets would be absolutely pointless and big money wouldn't waste their precious time and resources on it. This isn't just something I'm saying off the bat, as I work in this industry and know what I'm talking about. Investment banks and hedge funds spend hundreds of millions of dollars on R&D to get the brightest mathematicians from elite universities such as Yale, Harvard and Oxford to work for them and find market patterns which they can exploit to their advantage. If the markets were random, they wouldn't spend a dime on this research. The foreign exchange department of my former employer was literally hoarding all the quant algorithms that have proven to be profitable over the years, made high-frequency trading bots out of them and recorded those profits in the "other assets" part of the balance sheet (which is quite a significant sum if you look at the balance sheets of investment banks).

I suggest you look a bit more into quantum physics. Science is finding more and more evidence that such a thing as "randomness" doesn't really exist. Everything is interconnected, it's just a matter of filtering out the noise to see the clear picture.