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

TL;DR

This paper introduces a pseudo-PCA method for dimension reduction of OHLC financial data, enabling better visualization and interpretation by extracting key features while respecting data constraints.

Contribution

It proposes a novel feature representation for OHLC data and develops a pseudo-PCA technique that preserves data reversibility and enhances analysis effectiveness.

Findings

Effective dimension reduction demonstrated on simulated data

Improved interpretability of OHLC data features

Successful application to China's agricultural market data

Abstract

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.