The (open-high-low-close) OHLC data is the most common data form in the field of finance and the investigate object of various technical analysis. With increasing features of OHLC data being collected, the issue of extracting their useful information in a comprehensible way for visualization and easy interpretation must be resolved. The inherent constraints of OHLC data also pose a challenge for this issue. This paper proposes a novel approach to characterize the features of OHLC data in a dataset and then performs dimension reduction, which integrates the feature information extraction method and principal component analysis. We refer to it as the pseudo-PCA method. Specifically, we first propose a new way to represent the OHLC data, which will free the inherent constraints and provide convenience for further analysis. Moreover, there is a one-to-one match between the original OHLC data and its feature-based representations, which means that the analysis of the feature-based data can be reversed to the original OHLC data. Next, we develop the pseudo-PCA procedure for OHLC data, which can effectively identify important information and perform dimension reduction. Finally, the effectiveness and interpretability of the proposed method are investigated through finite simulations and the spot data of China's agricultural product market.

翻译：OHRLC数据是财务领域和各种技术分析调查对象中最常见的数据形式。随着OHRLC数据日益增强的特点,必须解决以可视化和简单解释的易懂方式提取其有用信息的问题。OHRLC数据固有的局限性也给这一问题带来挑战。本文件建议采用一种新颖的方法,在数据集中描述OHRLC数据的特点,然后进行尺寸缩小,将特征信息提取方法和主要组成部分分析结合起来。我们把它称为假PCA方法。具体地说,我们首先提出一种新的方法来代表OHRL数据,这将消除固有的限制,并为进一步分析提供方便。此外,OHLC原始数据及其基于特征的表述之间有一个一对一的匹配。这意味着对基于特征的数据的分析可以逆转到原OHLC数据。接下来,我们为OHLC数据制定伪PCA程序,可以有效识别重要信息并进行尺寸缩小。最后,通过固定模拟和中国农产品的市场点数据,对拟议方法的有效性和可解释性进行了调查。