Explainable Machine Learning for High-Frequency Trading Dynamics Discovery

High-frequency trading (HFT) plays an essential role in the financial market. How to discover & reveal trading dynamics remains a challenge in FinTech. In this study, we propose a novel explainable machine learning approach: Feature-Interpolation-based Dimension Reduction SCAN (FIDR-SCAN) to handle the challenge by creating a trading map. The trading map deciphers an HFT security’s trading dynamics by marking the status of each transaction, grouping transactions in clusters, and identifying the trading markers. The proposed method presents new feature interpolation techniques to build a more informative and explainable feature space to unveil hidden trading behaviors. It mines HFT data in their low-dimensional embedding to seek exceptional trading markers and classify the statuses of transactions. We validate the meaningfulness and effectiveness of the trading markers discovered from FIDR-SCAN in trading besides examining its special characteristics. Besides, we apply the proposed algorithm to cryptocurrency data and achieve reliable performance. To the best of our knowledge, this study is the first of its kind using interpretable machine learning to reveal HFT trading dynamics