The Basics Of Developing A Neural Trading
by Lou Mendelsohn
As traders begin to experiment with and apply artificial neural systems to financial forecasting, there
are pitfalls to avoid in the design and training of these systems so this new technology can be used
effectively and profitably. If you want to develop a neural system to predict the Standard & Poor's 500
stock index (S&P 500) or the Dow Jones Industrial Average (DJIA) for the next day, for instance, you
would need to specify five factors: the output that you want to forecast, the input data requirements, the
type of neural system to apply to the problem, its size and structure.
First, a neural system can predict four outputs: classification, pattern, real numbers (such as tomorrow's
S&P 500 or DJIA close) and optimization.
Second, you need to select the input data that will be used by the neural system. The data should be
related to the output that you want to forecast. Unlike conventional technical trading systems, neural
systems work well when you combine technical and fundamental data. Remember that apparently
irrelevant data could conceivably allow the network to make distinctions that are not readily apparent, so
don't be afraid to include such data as an input. During training, the neural system will sift through all the
input data to determine relevance and may turn up with something that could surprise you.