Proper system design is critical to performance. First, appropriate data inputs must be selected. These inputs are preprocessed via various statistical and analytic techniques. Training with market data consists of an iterative process through which the neural network discovers hidden patterns in the data. It records errors in judgment by comparing the forecast with the known results.
Word goes back through the layers, modifying connection weights between neurons.
A system is fully trained when its overall error level is minimal. The impact on trading performance when intermarket and fundamental data inputs are incorporated into a neural-based system can be shown in two systems developed for the Deutsche mark futures market at the Chicago Mercantile Exchange. These predicted outputs then go into a final system that generates buy and sell trading signals. The left-hand table above summarizes the data inputs for both systems. It uses the same technical data inputs as system 1 plus intermarket and fundamental data inputs thought to be related to the D-mark market.