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Financial markets are a source of non-stationary multidimensional time serieswhich has been drawing attention for decades. Each financial instrument has itsspecific changing-over-time properties, making its analysis a complex task.Hence, improvement of understanding and development of more informative,generalisable market representations are essential for the successful operationin financial markets, including risk assessment, diversification, trading, andorder execution. In this study, we propose a volume-price-based marketrepresentation for making financial time series more suitable for machinelearning pipelines. We use a statistical approach for evaluating therepresentation. Through the research questions, we investigate, i) whether theproposed representation allows the more efficient design of machine learningmodels; ii) whether the proposed representation leads to increased performanceover the price levels market pattern; iii) whether the proposed representationperforms better on the liquid markets, and iv) whether SHAP featureinteractions are reliable to be used in the considered setting. Our analysisshows that the proposed volume-based method allows successful classification ofthe financial time series patterns, and also leads to better classificationperformance than the price levels-based method, excelling specifically on moreliquid financial instruments. Finally, we propose an approach for obtainingfeature interactions directly from tree-based models and compare the outcomesto those of the SHAP method. This results in the significant similarity betweenthe two methods, hence we claim that SHAP feature interactions are reliable tobe used in the setting of financial markets.

Volume-Centred Range Bars: Novel Interpretable Representation of Financial Markets Designed for Machine Learning Applications