Applying the Extended Classifier System to Trade Interest Rate Futures Based on Technical Analysis

Abstract

In practice, it is difficult to gain profit in the process of trading interest rate derivative commodities. This could be attributed to the complexity of existing pricing models, which are derived from the term structure and yield curve, both of which cannot adapt well to short-term market dynamics. In this study, we use the Extended Classifier System (XCS) to model the market behavior of interest rate futures, the purpose of which is to provide effective trading decision support. Several technical indicators and their first- and second-order derivatives are selected as the market descriptive variables, which are then used for XCS training. Finally, the adaptive rules of the classifiers, which consist of conditions with relative actions considered helpful for constructing the automatic trading system, are generated from the XCS knowledge discovery process. The market data of the 10-year government bond futures traded in Taiwan are chosen for empirical study to verify the accuracy and profitability of the XCS model. These were also used to conduct a comparative evaluation between the random walk and tendency following models and the XCS model.