以成交量为中心的范围条：面向机器学习应用的新型金融市场可解释表示
Volume-Centred Range Bars: Novel Interpretable Representation of Financial Markets Designed for Machine Learning Applications
摘要：
金融市场是非平稳多维时间序列的来源，几十年来一直受到人们的关注。每种金融工具都有其特定的随时间变化的特性，这使得分析成为一项复杂的任务。因此，提高理解和发展更多的信息，概括性的市场表现是在金融市场，包括风险评估，多样化，交易和订单执行的成功运作的关键。在这项研究中，我们提出了一个基于量价的市场表示，使金融时间序列更适合机器学习管道。我们使用统计方法来评估代表性。通过研究问题，我们调查，i）所提出的表示是否允许更有效地设计机器学习模型; ii）所提出的表示是否导致价格水平市场模式的性能提高; iii）所提出的表示是否在流动性市场上表现更好，以及iv）SHAP特征交互是否可靠，可以在所考虑的设置中使用。我们的分析表明，所提出的基于量的方法可以成功的金融时间序列模式的分类，也导致更好的分类性能比基于价格水平的方法，特别是在流动性更强的金融工具。最后，我们提出了一种直接从基于树的模型中获得特征交互的方法，并将结果与SHAP方法进行了比较。这导致这两种方法之间存在显着的相似性，因此我们声称SHAP特征相互作用对于在金融市场环境中使用是可靠的。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
