neural technology


definition of ‚neural network‘

a series of algorithms that attempt to identify underlying relationships in a set of data by using a process that mimics the way the human brain operates. neural networks have the ability to adapt to changing input so that the network produces the best possible result without the need to redesign the output criteria.

breaking down ‚neural network‘

the concept of neural networks is rapidly increasing in popularity in the area of developing trading systems. at one point in time, it would have seemed impossible to make a system that would be able to adapt to changing markets, but recent developments in technology have now made having these types of systems a reality.
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.neural networks can be applied gainfully by all kinds of traders. 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.

well-prepared input information on the targeted indicator is the most important component of your success with neural networks.

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.
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.

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.


conclusion

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

(by dima vonko)