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Developing a Neural Network Based on Standard Rule-Based Systems

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One of the most powerful but easiest approaches for building a neural-network-based model begins with an existing tradable rule-based system. In this approach, we break a rule-based model into its components, which can be used in developing a neural network. This process allows us to develop a tradable neural network model because our goal is to improve the existing system, not to develop a new one from scratch.

A NEURAL NETWORK BASED ON AN EXISTING TRADING SYSTEM

Let's investigate the process of developing a neural network based on an existing trading system. The steps involved are overviewed in Table 18.1.

Table 18.2 lists some of the applications of neural networks that are developed using existing trading systems. As you can see, the range of applications is broad.

TABLE 18.1 STEPS IN BUILDING A NEURAL NETWORK MODEL.

  1. Develop a good rule-based system first, using as few parameters as possible. Ideally, use fewer than four parameters in the system. Keep the system simple, without any fancy filters or exit rules. It will be the neural network's job to use the extra information you are supplying to improve your results. These extra inputs can include any information that you would have used to filter the original system.
  2. Analyze the results of your rule-based system. Examine where your entries and exits occur, ...

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