Finally, the system has to be trained and tested before it can be used as a predictive tool in real-time trading. Input data should be selected based on its relevance to the output that you want to forecast. Unlike conventional technical trading systems, neural systems work best when both technical and fundamental input data are used. The more input data, the better the system can discriminate the hidden underlying patterns that affect its predictiveness. For instance, it is preferable to use changes in price levels rather than actual daily prices as your inputs and output.
Neural systems consist of one or more interconnected layers of neurons.
One choice of system architecture successfully applied to financial forecasting is known as a feed-forward network with back propagation supervised learning. This design has two or more layers. Neurons within a layer are not interconnected, while neurons in one layer receive inputs from each neuron in the previous layer and send outputs only to each neuron in the following layer. This is accomplished by assigning connection weights or strengths to the connections. The input layer receives input data.