Neural networks are state-of-the-art, trainable algorithms that emulate certain major aspects in the functioning of the human brain. This gives them a unique, self-training ability, the ability to formalize unclassified information and, most importantly, the ability to make forecasts based on the historical information they have at their disposal.
Neural networks have been used increasingly in a variety of business applications, including forecasting and marketing research solutions. In some areas, such as fraud detection or risk assessment, they are the indisputable leaders. The major fields in which neural networks have found application are financial operations, enterprise planning, trading, business analytics and product maintenance. Neural networks can be applied gainfully by all kinds of traders, so if you're a trader and you haven't yet been introduced to neural networks, we'll take you through this method of technical analysis and show you how to apply it to your trading style.
Most people have never heard of neural networks and, if they aren't traders, they probably don't need to know what they are. What's really surprising, however, is the fact that a huge number of those who could benefit richly from neural network technology have never even heard of it, take it for a lofty scientific idea or think of it as of a slick marketing gimmick. There are also those who pin all their hopes on neural networks, lionizing the nets after some positive experience with them and regarding them as a silver-bullet solution to any kind of problem. However, like any trading strategy, neural networks are no quick-fix that will allow you to strike it rich by clicking a button or two. In fact, the correct understanding of neural networks and their purpose is vital for their successful application. As far as trading is concerned, neural networks are a new, unique method of technical analysis, intended for those who take a thinking approach to their business and are willing to contribute some time and effort to make this method work for them. Best of all, when applied correctly, neural networks can bring a profit on a regular basis.
Use Neural Networks To Uncover Opportunities
A major misconception is that many traders mistake neural networks for a forecasting tool that can offer advice on how to act in a particular market situation. Neural networks do not make any forecasts. Instead, they analyze price data and uncover opportunities. Using a neural network, you can make a trade decision based on thoroughly analyzed data, which is not necessarily the case when using traditional technical analysis methods. For a serious, thinking trader, neural networks are a next-generation tool with great potential that can detect subtle non-linear interdependencies and patterns that other methods of technical analysis are unable to uncover.
The Best Nets
Just like any kind of great product or technology, neural networks have started attracting all those who are looking for a budding market. Torrents of ads about next-generation software have flooded the market - ads celebrating the most powerful of all the neural network algorithms ever created. Even in those rare cases when advertising claims resemble the truth, keep in mind that a 10% increase in efficiency is probably the most you will ever get from a neural network. In other words, it doesn't produce miraculous returns and regardless of how well it works in a particular situation, there will be some data sets and task classes for which the previously used algorithms remain superior. Remember this: it's not the algorithm that does the trick. Well-prepared input information on the targeted indicator is the most important component of your success with neural networks.
Is Faster Convergence Better?
Many of those who already use neural networks mistakenly believe that the faster their net provides results, the better it is. This, however, is a delusion. A good network is not determined by the rate at which it produces results and users must learn to find the best balance between the velocity at which the network trains and the quality of the results it produces.
Correct Application of Neural Nets
Many traders apply neural nets incorrectly because they place too much trust in the software they use all without having been provided with proper instructions on how to use it properly. To use a neural network the right way and, thus, gainfully, a trader ought to pay attention to all the stages of the network preparation cycle. It is the trader and not his or her net that is responsible for inventing an idea, formalizing this idea, testing and improving it, and, finally, choosing the right moment to dispose of it when it's no longer useful. Let us consider the stages of this crucial process in more detail:
Finding and Formalizing a Trading Idea
A trader should fully understand that his or her neural network is not intended for inventing winning trading ideas and concepts. It is intended for providing the most trustworthy and precise information possible on how effective your trading idea or concept is. Therefore, you should come up with an original trading idea and clearly define the purpose of this idea and what you expect to achieve by employing it. This is the most important stage in the network preparation cycle.
Improving the Parameters of Your Model
Next, you should try to improve the overall model quality by modifying the data set used and adjusting the different the parameters.
Disposing of the Model When it Becomes Obsolete
Every neural-network based model has a life span and cannot be used indefinitely. The longevity of a model's life span depends on the market situation and on how long the market interdependencies reflected in it remain topical. However, sooner or later any model becomes obsolete. When this happens, you can either retrain the model using completely new data (i.e. replace all the data that has been used), add some new data to the existing data set and train the model again, or simply retire the model altogether.
Many traders make the mistake of following the simplest path - they rely heavily on and use the approach for which their software provides the most user-friendly and automated functionality. This simplest approach is forecasting a price a few bars ahead and basing your trading system on this forecast. Other traders forecast price change or percentage of the price change. This approach seldom yields better results than forecasting the price directly. Both the simplistic approaches fail to uncover and gainfully exploit most of the important longer-term interdependencies and, as a result, the model quickly becomes obsolete as the global driving forces change.
The Most Optimal Overall Approach to Using Neural Networks
A successful trader will focus and spend quite a bit of time selecting the governing input items for his or her neural network and adjusting their parameters. He or she will spend from (at least) several weeks - and sometimes up to several months - deploying the network. A successful trader will also adjust his or her net to the changing conditions throughout its life span. Because each neural network can only cover a relatively small aspect of the market, neural networks should also be used in a committee. Use as many neural networks as appropriate - the ability to employ several at once is another benefit of this strategy. In this way, each of these multiple nets can be responsible for some specific aspect of the market, giving you a major advantage across the board. However, it is recommended that you keep the number of the nets you use within the range of five to 10. Finally, neural networks should be combined with one of the classical approaches. This will allow you to better leverage the results achieved in accordance with your trading preferences.
You will experience real success with neural nets only when you stop looking for the best net. After all, the key to your success with neural networks lies not in the network itself, but in your trading strategy. Therefore, to find a profitable strategy that works for you, you must develop a strong idea about how to create a committee of neural networks and use them in combination with classical filters and money management rules.
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