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The (open-high-low-close) OHLC data is the most common data form in the fieldof finance and the investigate object of various technical analysis. Withincreasing features of OHLC data being collected, the issue of extracting theiruseful information in a comprehensible way for visualization and easyinterpretation must be resolved. The inherent constraints of OHLC data alsopose a challenge for this issue. This paper proposes a novel approach tocharacterize the features of OHLC data in a dataset and then performs dimensionreduction, which integrates the feature information extraction method andprincipal component analysis. We refer to it as the pseudo-PCA method.Specifically, we first propose a new way to represent the OHLC data, which willfree the inherent constraints and provide convenience for further analysis.Moreover, there is a one-to-one match between the original OHLC data and itsfeature-based representations, which means that the analysis of thefeature-based data can be reversed to the original OHLC data. Next, we developthe pseudo-PCA procedure for OHLC data, which can effectively identifyimportant information and perform dimension reduction. Finally, theeffectiveness and interpretability of the proposed method are investigatedthrough finite simulations and the spot data of China's agricultural productmarket.

Dimension reduction of open-high-low-close data in candlestick chart based on pseudo-PCA