Build Neural Network Indicator in MT4 using Neuroshell

Hi Tovim,

Thanks for the link, nice to read.

Genehunter is one of genetic optimization product from ward.
This genetic optimization technique has been used also in neuroshell trader, predictor, classifier and chaos hunter.
As far as I know, the significant point in CH is it used genetic optimization to find formula relation between inputs and output. The other products only find optimum parameters either indicator parameters, and the treshold values that can generate optimum trading profit.
I use CH also to find formula in other application.
If you use Predictor/Classifier, you can search optimum trading by selecting either using genetic or neural network. If you have NST 6, you have both and brute force method in addition.
 
Hi Arryex,
You really seem to know a lot about NNs.
Would you consider EOD NST 6 worth the money at $1,500 ?
 
Hi Pat494,

You can find the comparison between NST on the following link: Advanced Neural Network Software for Financial Forecasting and Stock Prediction

Several benefits that important for me are: good technical support (through email with a fast response), most of trading tips (T&C magazine), examples, user tips, documentation, additional samples can be found for a registered user at Ward Systems Group Tech Support Site, integrated trade with interactivebroker or pfgbest, free upgrade, programming interface for data feed and indicators, etc.

The weakness I found, NST is a black box, you will not know how NST produced a neural net (neural network configuration, weight and bias values are unknown). If you do not need them and only concern the profitable trade, then you may have it. This black box also found in NS-Predictor and Classifier. If you want to know more about neural net itself NS2 is the right one (even old classic but still powerful) or Matlab the most sophisticated (need additional programming), and Chaos Hunter may suit for you if you want to know more the formula relation between input and output.

You can try for 30 days and you may return if you do not want. It was one time fee, means you will get a free live time update and technical support. For me, worth or not a product is depend on your objective.
 
Whilst surfing around I came across this article by Dima Vonko

In this article the author explains the basics about Neural Networks and looks at the myths that have formed around them.
In this age of previously unheard of technological progress many technology-related things either come unnoticed as they appear or, vice versa, are vastly extolled and turned into totems that inevitably attract a following of ardent worshippers. If such a popular technology-related phenomenon can make a difference to your business, it is, sometimes, vital to learn about this phenomenon as much as possible before you start with it so that you know what to expect from the selected technology and what to beware of while using it. For a modern trader, one of such potentially important phenomena is neural nets.
So what is the neural network technology, what should and what shouldn’t a trader expect from it if he selects to use it to achieve his trading goals?
Dispelling the Myths
Myth 1: Supernatural Intelligence
One of the commonly held misconceptions about neural networks is that they represent the kind of Artificial Intelligence which is it not only capable of fully replacing the human brain, but which also possesses some nearly supernatural power, thus enhancing the capacity and functions of this brain to the point when any kind of task can be solved almost miraculously without any effort on the part of the proud owner of this Holy Grail. This vulgar and frequently occurring notion of an undoubtedly valuable trading tool is dangerous in several ways. Let us see why.

First of all, neural networks are not all things to all traders. To understand what neural networks can and cannot do one should look into what they are. Neural networks are algorithms, loosely based on the nervous systems of humans and animals. Neural networks can detect and use to advantage the numerous interdependencies in data that are hidden from the human eye due to the data’s complexity and non-linearity. This has been proven by the broad experience of neural networks’ application in a wide array of industries, and trading is no exception. However, on no account should you consider neural networks to be something that will think or decide for you.
Myth 2: Magic Software
The second as, if not more, dangerous, misconception about neural networks emanates directly from the first one: somewhere out there, there is a heaven-sent trading software that basically works as a minting machine and all you need to do is find it. This misconception is not dangerous only because you will lose time and money while looking for what doesn’t simply exist, but also because your delusions are well-known to those who crank out one-magic-button, slipshod software programs and fob them off on the seekers of the neural Holy Grail. Normally, those who try to exploit others’ delusions make poor professionals and, thus, poor software too. Remember – neural network software can only do what neural networks themselves can do, and they can do a lot if you know how to apply them and what software to purchase. However, no neural network software can tell you the exact time and the type of action you should take at this particular time to profit.
Myth 3: Neural Networks Can Predict Precise Figures
The third frequent misconception is that by using a neural network you will be able to predict the future prices. Many traders believe that their networks are capable of telling them when to buy and when to sell. If you understand that those people are wasting their time and money you will probably be a success with neural networks. No neural network, no matter how sophisticated or well-built, will be able to precisely inform you about the future price or, at the push of a button, tell you, and you alone, when it’s best to buy or sell (for, otherwise, there would no longer be a market). However, you can, undoubtedly, predict the likelihood of other important things happening, which will help you make better trading decisions. Therefore, even with what neural networks really can do, they remain the most powerful market analysis tool ever in situations, involving noisy data or non-linear dependencies. In other situations, using neural networks may be inexpedient. We will dwell on the predicting ability of neural networks and on what and how they can actually forecast later in this article.
Myth 4: Some Nets Are Significantly Better than Others
Many traders who want to employ AI for making their trading solutions mistakenly believe that the quality of the neural network capabilities of the different trading applications on the market varies significantly, and there is some special neural network somewhere that will eclipse all the rest in terms of the quality of the forecasting results. However, practice and experience show that the quality of different neural networks, no matter how much touted for, differs within the range of 10%, and even so it varies for different tasks and data sets. Of course, while selecting a trading software program one should look at the AI background of its developers (building a good neural network takes a great deal of skill and experience), but, at the same time, the application must provide the rest of the required functionality (such as, for example, the charting functionality) with excellent quality. In other words, one should look for a successful combination of neural network functionality and other vital functionality.
Looking for the only magic net is much like looking for one magic technical indicator. Aside from that, this quest often feeds those who are after a quick buck.
Myth 5: The Quality of the Forecasting Result Depends Solely on the Quality of the Network Used
The quality of the forecasting results does depend on the quality of the network you apply, but for not more than 10-15 %. The rest depends on how well the trader has prepared the data sets the network works with. The data sets must be sufficiently representative. They must include all the important influencing factors. Besides, the application of a neural network must be combined with Money Management and classical filters

What Neural Networks Can Do for You and What You Need to Know to Make Them Work
Neural networks are definitely not a solution to all problems and they shouldn’t be regarded so. What they are is a most powerful, technology-based method of technical analysis that can become an inestimable addition to your trading arsenal. Just like any other method, neural networks have their advantages and limitations, but their unique ability to track even the most subtle interdependencies in the available data no other method can establish, as well as build patterns based on this analysis, definitely make neural nets stand out from the rest of the existing methods and tools.


You can effectively use neural nets to:•estimate the likelihood of a trend continuing;
•classify market phases;
•produce time estimates of highs and lows for various timeframes and combine results into a committee;
•predict the probability of a new, strong upswing after an uptrend, followed by a classic correction;
•track inter-market dependencies.
In other words, you receive a TA tool which will be a lot more efficient than classic TA methods anywhere where there is too much noise or where the interdependencies in the data are floating and significantly non-linear. For example, if after analyzing a number of charts you have discovered that the closer an uptrend is to a pivot point, the closer the bar’s Close is to the bar’s High, and you are planning to create an oscillator to anticipate reversal, you should use classic math as was done by the inventor of the Stochastic oscillator George Lane. But if you are trying to find a formula for the inter-relationship between S&P, InterestRates, $/Euro, Oil prices, and so on, you will make sure that the classical correlation or ratios won’t be any use, since although interdependencies do exist, they are not stationary or linear. These interdependencies oscillate, ”float” through time and are influenced by noise. In this case, neural networks can solve the task better than the classical statistics.

When used in a combination with other technical analysis methods, and when sufficient attention is paid to the preparation of data sets (this procedure is, actually, central to success with neural networks), neural networks will undoubtedly provide the punch you need to success on the market. After all, this has been proven by both time and experience.


Interesting :smart:
 
I have read this articles long time ago after purchasing tradedecision (join this t2w). Just for your information, Dima Vonko is founder of Alyuda Research who created tradecision.

So long not to use this software, good chance to test the latest version 4.8.874 as well as forex version, expecting more proof others myth....:smart:
 
arryex,
For CH, what do you mean by changing the input formula?

For deciding what to predict did you do any sort of testing what would work better?


pat494
For NSDT value, I wouldnt pay up for it myself. Black box and no dll export are the reasons. Only if you are interested in some
indicators that are only for neuroshell should you buy it.

I am using CH to predict indexes. I have found them to be a lot easier to model than currencies. Does anyone here do the same?
 
...a nice reading on the subject. :confused:

yeah I read this 'The Million Model Test ' pdf. 1st look at this

1. fixed number of training and testing data sets
a) 2683 training and 30 test observations
b) 2533 training and 180 test observations

Why they think its enough just 2683 training samples. By setting the training size to this value they assume that the future pattern is included in this 2683 samples and SVM will learn it from it. This is childish assumption...I would train on 100K samples instead:D

2. rolling window
a) training sample of 1000 entries and 30 testing days
b) training sample of 1000 entries and 30 testing days and add the previous testing data to the learning pool

the same as above...........................

The problem of this investigation is that they used RBF kernel with very small training size windows. This is most likely because of usage of daily data which does not give enough data samples. In such setup with RBF kernel its also not possible to increase sliding window size as computational time rises expotentially in case of such kernel and increase of training size. Other type of kernel e.g. linear should be used instead.

They also don't split results to full confusion matrix but giving accuracy what is not so applicable for trading as it don't give you an information about %profitable trades. The financial data is very unbalanced and they don't consider it at all !!!

Anyway, good that somebody did such analysis but from guys with PhD ans MsC titles I would expect much more, for me it is beginners job :D

Krzysztof
 
arryex,
For CH, what do you mean by changing the input formula?

For deciding what to predict did you do any sort of testing what would work better?

I change gradually the input formula, from arithmetic, algebra, etc including the technical indicator. Then select the good one during the optimization and OOS test.
Even CH will search from available population, I prefer start from few inputs first rather than putting a lot of inputs initially.

Selecting output prediction, I prefer to test first using NST, more easier, I prefer to use neural output indicator, or lead value of indicators. I am sure you can find the good one, NST have more than 800 indicators...
 
Hi Mate,

I read through the forum, interesting one.
Are you still kind enough to give your macd indicator?

Ta
ams
 
I have found that using more than 3-4 indicators as inputs pretty much kills any possible robustness of systems generated by ch. Same thing with chaos input, it is a completely useless feature.

For me, ch does not use more than 30-40% of the cpu. Does anyone else have the same problem? I emailed ward systems about it but they just gave me some bs reply.

I tried to make a system for gold and this was the best I came up with:
imgur: the simple image sharer

Most other ones started losing money when gold started moving sideways.

Does neuroshell have good indicators that are exclusive to it? Now I am using multicharts for exporting and I would hate to be locked into nsdt just because of some indicator.

What is a neural output indicator? When I searched with google, only this thread came up.
 
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I tried to make a system for gold and this was the best I came up with:
imgur: the simple image sharer

Most other ones started losing money when gold started moving sideways.


.

The equity curve I notice flattens out and if commissions were deducted would be soon in negative territory.

NNs just haven't arrived yet imho, at least not for the average joe
 
The equity curve I notice flattens out and if commissions were deducted would be soon in negative territory.

NNs just haven't arrived yet imho, at least not for the average joe

Well , That's not quite True . I believe they are here , they've been here . It's left to the trader/individual to come up with a Good Recipe(inputs) for your neural network ..Here's a model , and it's Predicted close on the test data ...I know If some Hedge fund sees my graph , they'll probably lose their minds(it's considered an impossibility..lol)
 

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Well , That's not quite True . I believe they are here , they've been here . It's left to the trader/individual to come up with a Good Recipe(inputs) for your neural network ..Here's a model , and it's Predicted close on the test data ...I know If some Hedge fund sees my graph , they'll probably lose their minds(it's considered an impossibility..lol)

It looks an almost exact fit. Almost too good to be possible but seeing that you are a vendor perhaps you would be willing to share some more info on these results ??
 
It looks an almost exact fit. Almost too good to be possible but seeing that you are a vendor perhaps you would be willing to share some more info on these results ??

Am not a vendor , and I promise you am not selling anything( I mean , why ? lol) . I'm using Tradingsolutions software (from Neurosolutions) to Train and test my data. Believe or not , but I just discovered these inputs and still testing and running them as we speak ...

Just got my hands on Neuroshell daytrader Pro , and MLP network software but I don't have chaoshunter or Genehunter , so I'll run some test to see if I have similar result and post them ..
 
Every time I manage to make some system looks too good to be true, it is just because there is some forward looking leak somewhere.

Tradingsolutions is not programmed that well so I will not be surprised if that turns out to be the case here.

But, by all means, do share the inputs with us if you really have managed to perfectly predict the next days close :)
 
Those were my initial thoughts , but there's an easy way to solve that problem - Just feed the Network with another data with the CLOSE values , and if Your Network configurations are somehow spying on the CLOSE inputs on the test data , then you should get the same " Perfect " result . Here's my Open-High-low- close(OHLC) data , and as you can see the Directional Accuracy was the norm " around 50 % - random walk " .

Compare that with my inputs added to the data , and you can see the big difference. Am still working on lowering my error but the Directional Accuracy was on point/good..

The inputs I created are a result of 4 years of Trial and error , and it's going to be very difficult for me to give them away . - You know , it's like giving away the secret ingredient to your sauce in your restaurant ..:cheesy: .

Just wanted folks to understand there are things out there to Research ..It's all about the INPUTS , and am still working on them(Trying to reduce the error on the real values )..

I hope you know if input has a future leak result will be perfect....
 
Reinforced Learning

Interesting paper about usage of Reinforced Learning for stocks.

Concept of segmentation of data and 'Profit Made Good' indicator is maybe worth a deeper look
 

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taomql4,

so, if I understand you correctly, if you use another instrument then the system falls apart?

It would be amazing if it did work, especially if the indicators are really just the same standard ones that everyone uses. For what instrument is your system? Does it use other instruments as inputs?
 
taomql4,

so, if I understand you correctly, if you use another instrument then the system falls apart?

It would be amazing if it did work, especially if the indicators are really just the same standard ones that everyone uses. For what instrument is your system? Does it use other instruments as inputs?

The only Neuroshell 2 I have is in Russian , so I can't use it ..Unable to Configure the settings
The Model was built using GBPUSD historical data , and I don't use other instruments. . Only the indicators on GU , they'll describe the CLOSE values more than any other instrument(I feel.. )
 
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I did the similar one, seems good also when implementing TS on stock, but NST also can get the similar one on XAUUSD
 

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