  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.
