A Rule-Based Approach to Trading by Jeffrey Owen Katz, Ph.D.
Last month, Katz presented his experience developing a trading system using a neural network–based approach. This month, he delves into a rule-based approach to trading.
Previously, I showed that attempts to model the markets by applying sophisticated technology such as neural networks in a simplistic manner does not work well. Present-day attempts to apply the neural-net novice approach fail because the market inefficiencies discovered by the simplistic application of sophisticated technologies have already been "traded away" by speculators who used the technology to discover and trade the inefficiencies.
Once observations have been made, these ideas must be formalized into computer programming instructions.
While inefficiencies may still be discovered by crude applications of such technology, more often than not the inefficiencies that are found will not be practicably tradable. In order to develop successful systems, isolated market inefficiencies that cannot be captured by the simplistic application of sophisticated technology must be identified. I decided my initial search for such inefficiencies might best be tackled not through the use of advanced technologies but through traditional rule-based systems.