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

arXiv: https://arxiv.org/abs/2103.12419 · HTML: https://mlsanity.com/paper/2103.12419/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 38 references that are in the map.
- First posted on arXiv 23 Mar 2021.

## Builds on (most cited first, 7 of 7)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing](https://mlsanity.com/paper/2001.00973/) (2020)
- [Metrics for Explainable AI: Challenges and Prospects](https://mlsanity.com/paper/1812.04608/) (2018)
- [What do we need to build explainable AI systems for the medical domain?](https://mlsanity.com/paper/1712.09923/) (2017)
- [What Does Explainable AI Really Mean? A New Conceptualization of Perspectives](https://mlsanity.com/paper/1710.00794/) (2017)
- [Practical Deep Reinforcement Learning Approach for Stock Trading](https://mlsanity.com/paper/1811.07522/) (2018)
- [Classification-based Financial Markets Prediction using Deep Neural Networks](https://mlsanity.com/paper/1603.08604/) (2016)

## Abstract (arXiv)

Financial markets are a source of non-stationary multidimensional time series which has been drawing attention for decades. Each financial instrument has its specific 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 operation in financial markets, including risk assessment, diversification, trading, and order execution. In this study, we propose a volume-price-based market representation for making financial time series more suitable for machine learning pipelines. We use a statistical approach for evaluating the representation. Through the research questions, we investigate, i) whether the proposed representation allows the more efficient design of machine learning models; ii) whether the proposed representation leads to increased performance over the price levels market pattern; iii) whether the proposed representation performs better on the liquid markets, and iv) whether SHAP feature interactions are reliable to be used in the considered setting. Our analysis shows that the proposed volume-based method allows successful classification of the financial time series patterns, and also leads to better classification performance than the price levels-based method, excelling specifically on more liquid financial instruments. Finally, we propose an approach for obtaining feature interactions directly from tree-based models and compare the outcomes to those of the SHAP method. This results in the significant similarity between the two methods, hence we claim that SHAP feature interactions are reliable to be used in the setting of financial markets.