However, it's not as easy as it sounds. Developing a profitable neural trading system is very much an art and not a science that can be followed cookbook-style. There are many serious design issues that must be addressed when developing and training a neural trading system, if it is to be predictive, and most importantly, profitable. Before long, today's emphasis on single-market technical analysis will loose its appeal, as traders apply neural trading systems to the task of forecasting prices and trends in the financial markets. These are just a few of the questions that need to be answered as traders become more acquainted with this new trading technology.
Neural systems consist of layers of neurons which are connected to each other.
There are typically three types of layers, an input layer which receives information such as technical price data that is supplied to the system for analysis, a hidden layer which is used by the system for internal processing, and an output layer which provides forecasted output generated by the system such as tomorrow's price range and trading signal for a particular market. Typically, neurons within a layer do not connect to each other. For example, you may want to develop a trading system to predict the next day's Treasury Bond prices. Designing the appropriate architecture for your neural system is quite exacting, with more than a dozen different neural designs available. It is critical that the system's architecture, learning method, input data, outputs, and massaging techniques are judiciously selected in order for the system to train properly.