Historical financial data are frequently used in technical analysis to identify patterns that can be exploited to achieve trading profits. Although technical analysis using a variety of technical indicators has proven to be useful for the prediction of price trends, it is difficult to use them to formulate trading rules that could be used in an automatic trading system due to the vague nature of the rules. Moreover, it is challenging to determine a specified combination of technical indicators that can be used to detect good trading points and trading rules since different stock may be affected by different set of factors. In this paper, we propose a novel trading point forecasting framework that incorporates a bicluster mining technique to discover significant trading patterns, a method to establish the fuzzy rule base, and a fuzzy inference system optimized for trading point prediction. The proposed method (called BM-FM) was tested on several historical stock datasets and the average performance was compared with the conventional buy-and-hold strategy and five previously reported intelligent trading systems. Experimental results demonstrated the superior performance of the proposed trading system.

中文翻译：

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基于双簇挖掘和模糊推理的自动交易点预测


历史财务数据经常用于技术分析，以识别可用于实现交易利润的模式。尽管使用各种技术指标的技术分析已被证明对于预测价格趋势很有用，但由于规则的模糊性，很难使用它们来制定可用于自动交易系统的交易规则。此外，由于不同的股票可能受到不同因素的影响，因此确定可用于检测良好交易点和交易规则的特定技术指标组合具有挑战性。在本文中，我们提出了一种新颖的交易点预测框架，该框架结合了双聚类挖掘技术来发现重要的交易模式、建立模糊规则库的方法以及针对交易点预测优化的模糊推理系统。所提出的方法（称为 BM-FM）在多个历史股票数据集上进行了测试，并将平均性能与传统的买入并持有策略和五个先前报告的智能交易系统进行了比较。实验结果证明了所提出的交易系统的优越性能。