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October 2006 www.traders-mag.com

Neural Networks

Neural networks are said to be one of the mostadvanced trading-systems. Most impressive are theirability to learn and to adapt to current marketbehaviour. But there are a lot of rumours about theirperformance, too. It is for sure that even neuralnetworks´ success depends on human actions sincethe results are determined by the quality of inputdata.

Optimal VisionIn the modern world the artificial intelligence technologies, and neuralnetworks that stem from them, can multiply your profits. The majorchallenge that remains here is developing a network or group ofnetworks that will be able to assist you with your trading decisionswithout you spending more time and money than you want or cancommit to this process.

First of all, to make money with neural networks on a regular basisand save valuable time and effort, a trader should know what to expectfrom them, how to use them and to what ends. However, few traderscan make neural networks bring profits. Many believe that neuralnetworks are just another marketing gimmick or sly attempt by somesoftware developers to make a fast cash with a slick name.

Obviously, neural networks alone are no get-rich-quick schemes.A billion-dollar Wall Street capital management firm makes millionsof dollars not simply because it uses product X, and employs neuralnetwork Y. Unlike individual traders, this company is in possession ofvast computer facilities and teams of PhDs, but what it also has at itsdisposal is the optimal vision of both neural networks and what canbe achieved using them.

What Are Neural NetworksNeural networks are genetic algorithms that successfully reproduce

some of the vitally important functions of one of nature’s mostamazing achievements – the human brain, enhancing its power withthe help of other advanced technological means. A neural networkpossesses the ability to learn, it is able to memorise a large amount ofvarious information and then to formalise it. Furthermore, the mostprecious quality of a neural network is its ability to provide forecastsbased on the data it has processed. This unique quality has made neuralnetworks indispensable in a wide variety of business and research-related applications in virtually every industry. Moreover, trading isno exception.

Many traders have long been profiting by using neural networks,whilst others are only beginning to realise the multiple advantagesthese valuable tools offer. Probably the biggest advantage of neuralnetwork is that there is hardly any other tool on the market that canreally give you an edge over other market players. The traditionalsystems and approaches give rather modest results, the variousadvanced software applications rival mostly in hype. Frankly, few canactually be used at all. Many of your competitors are high-classprofessionals who will never miss a chance to pocket your hard-earnedbuck. To make matters more complicated, the market is choke-full ofa large and diverse number of gurus, inventors, researchers and justgeniuses who tout for some ingenious method, system, software ortraining course and totally baffle anyone entering the world of trading.

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F1) Tradecision Model Builder

Using a custom target while developing a neural model might be a way ofgaining a trading edge. For example, advanced users can forecast aprobability that during the next 8 bars a new highest high will occur.

Source: www.tradedecision.com

In this respect, neural nets are a great solution. So how can theyactually help?

What Should be UnderstoodFirst About Neural NetworksThe key aspect that should be fully realised by any one who wants touse neural networks in trading is that neural networks are notnecessarily forecasting tools as such. They are a state-of-the-art,technology-based, and highly advanced method of technical analysisthat can uncover a host of otherwise unreachable opportunities. Neuralnetworks give you the kind of information you would not be able toreceive by using any other technical analysis method. No othermethod of technical analysis is able to detect the deeply hidden andsubtle non-linear interdependencies in your data.

To be able to use neural networks, you do not need to bone up onthe different architectures of neural networks and training algorithms,or even have any nerdy mathematical background. However, you doneed to have a clear vision of how neural networks operate.

The neural networks’ operation is based on trying to detect anyexisting interdependencies between the input data and output data.The structure of the network changes in accordance with the tracedpatterns. As a trained network is fed some new data, it searches forpatterns that are similar to the ones it already contains. The newlyfound and old patterns are then checked for any existing similarities.

Your role as a trader focuses on the preparation of the input datafor the network. Data pre-processing, selection of the networkarchitecture, control over the training process, and so on, are handledautomatically by any professional trading software with a neuralnetwork capability.

How Neural Networks Can be Used in TradingYou should bear in mind that no matter how sophisticated yoursoftware, your role in the successful application of neural networks isstill crucial. The most important stage of the network’s use – inputdata preparation – remains solely your responsibility. Even the mostadvanced trading software application with artistically created neuralnetwork functionality will not be able to assist you in this matter.

You must fully realise that a neural network cannot be used toinvent bright and novel ideas, nor can it determine the time when itslife span should end. A neural network is a tool that can only deliverprecise and reliable information using the leads that you provide.Therefore, to use your neural network gainfully, you should pay ampleattention to network preparation procedures: coming up with an originaltrading idea, formalising this idea, testing and polishing the idea, andfinally, getting rid of it when it is no longer of use.

The trading idea you decide to use must have a definite purpose– only when you know exactly what you want to achieve by usingyour idea, can you count on a neural net assisting you in achievingyour goal. Special attention should be paid to improving the neuralnetwork model you have created by the modification of your inputdata set and adjustment of the different parameters.

Despite their unique abilities, neural networks still remain rathersimple algorithms compared to the human brain. That is why it isrecommended that neural networks be used in committees, wherebyeach of the networks would be responsible for a certain aspect of the

market. Basically, your committee can contains as many networks asyou like, but the number that can be recommended based on ourexperience is 5-10.

As mentioned above, neural networks are not a forecasting tool.That is why most attempts to use neural nets to forecast price directlyare doomed to failure from the very outset. Neural networks make nopredictions. They anticipate the direction of future market moves, suchas future price action. For example, if a trend starts rising, your neuralnetwork can help you estimate the probability that the rise willcontinue.

Lastly, remember that neural networks are a technical analysismethod, and as any other technical analysis method they should beused in a combination with other methods.

How Can a Neural Net be Improved?The sad fact is that most of the time, most traders cannot possiblyimprove a neural network. But do not worry - actually you do not reallyneed to. It is typically not the neural network that needs to beimproved in order to achieve better results. Again, what really needsto be improved is the data sets that you use. From our extensiveexperience with neural networks, we can say that the followingmethods are the most efficient in improving neural network data sets:

1. Make the tasks you set for your neural network as simple aspossible.There is an enormous multitude and diversity of combined factorsthat influence future prices. Therefore, it is more prudent toevaluate the current situation and the impact it may have on the

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Dima Vonko is the CEO of Alyuda Research,the company that develops Tradecisiontrading software (www.tradecision.com) foradvanced technical analysis. This article isbased on his more than four years ofexperience in providing consulting servicesrelated to technical analysis, neuralnetwork applications, financial forecastingand modelling. He can be reached atdima.vonko@tradecision.com.

Dima Vonko

future price than trying to guess precisely what the price will bein two days, for example. The easier the issue a neural network isfaced with is, then the better the chances are for success. On theother hand, it is harder to build a trading system based on answersto questions that are simple for neural networks. The majorchallenge of a trader who employs any new methods of artificialintelligence is to achieve the right balance. This task of finding asolution gives the trader the benefits that can be made from theuse of the artificial intelligence methods. As an evaluation tool, aneural network can beat any of the other commonly usedmethods. If you make the targeted output simple enough, yourchances for success will be much better.

2. Select the inputs by using your own expertise.While selecting the input information for your network, you shouldremember that the network should be supplied with only the datafrom only the main market driving forces. The network should notbe overloaded with information. On the other hand, all of theimportant existing factors should be reflected in your data.Experience shows that using a blind search or genetic algorithmsfor finding input variables and their parameters does not help asmuch as expected.

3. Do not overlook the following important factors whileselecting the neural networks inputs.

The following three types of input data are very important for a neuralnetwork, but are often ignored during the selection of the input variables:

1) Different market phases react differently to the various externaldriving forces, for instance, bearish news is often ignored duringa bullish trend.

2) Various economic indicators, such as the price performance of thesector funds or momentum indicators of the related indices arevery important input data, containing a large number of thedriving forces that affect the price you are modelling.

3) Define correctly the time limits for the training and test datasets.

Just like with the classic trading systems, it is not reasonable tobuild a system that performs well inside markets and then use it intrend situations. The overall situation in the market and the behaviourof the major driving forces during the neural network’s training periodshould come as close as possible not only to the situation and drivingforces during the test period, but also to the actual period you aretrading with neural networks. If the situations during the training andtesting periods vary, you will not be able to evaluate the performanceof your neural network accurately. Furthermore, if the training andactual trading periods’ situations vary, your neural network willproduce errors more frequently.

Pitfalls to be AvoidedThere are several common mistakes that traders should be aware ofwhile working with neural networks:

1. Over-reliance on one’s neural network.On no account should neural networks be used alone: howeverpowerful, they are just another technical analysis method thatshould only be applied as part of your trading combination. Inaddition, many traders rely too heavily on their software, and tendto use that part of the functionality it provides that is automatedthe most. Due to this, their trading becomes “lop-sided” and, veryoften, a rank-and-file element of the trading combination carriestoo much weight to produce poor results.

2. Worshiping fast convergence.Often traders mistake the relatively high velocity at which theirnetworks deliver results for having created a well-articulated andhigh-quality neural network trading system. Naturally, softwaredevelopers follow suit: the faster, the better. Is high speed really agood sign here? We do not think so. In the case of neural networks,quality and speed are two parts of the same equation, and a goodneural network is tantamount to a good compromise betweenthe two.

3. Looking for the ideal network.Do not buy any of the opinionated statements by any new-fangledpundits who claim to have invented the most efficient neuralnetwork ever. A 10% increase in the performance efficiency wouldbe a rarity. Concentrate on composing good input data sets – theyare by far much more important.

Your Only Recipe for SuccessIt is not genetic algorithms, fuzzy logic or any other sophisticatedtechnology that can help you succeed with neural networks. Do notsearch for any omnipotent, brand-new types of neural networks ortraining algorithms. Do not waste your time on scouring the marketfor some special trading or neural network software.

To succeed with neural networks, you only need to know how tobuild a committee of neural networks so that each of the constitutingmodels would be a marginally simplified object, as well as be ableto combine the committee you created with the classic filters andmoney management rules, regulating your risk preferences andtrading style.