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

机译：将扩展分类器系统应用于基于技术分析的贸易利率期货

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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.
机译：在实践中，难以在交易利率衍生商品的过程中获得利润。这可能归因于现有定价模型的复杂性，这些模型来自术语结构和产量曲线，这两者都不能适应短期市场动态。在这项研究中，我们使用扩展分类器系统（XCS）来模拟利率期货的市场行为，其目的是提供有效的交易决策支持。选择了几种技术指标及其第一和二阶衍生品作为市场描述性变量，然后用于XCS培训。最后，从XCS知识发现过程中生成了由考虑构建自动交易系统的相关动作的分类器的自适应规则，这些规则包括有助于构建自动交易系统。为台湾交易的10年政府债券期货市场数据被选为实证研究，以验证XCS模型的准确性和盈利能力。这些也用于在模型和XCS模型之后的随机行走和趋势之间进行比较评估。

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