A Comparative Analysis of Statistical and Machine Learning Models for Outlier Detection in Bitcoin Limit Order Books
Ivan Letteri
在加密货币限价单簿(LOB)中检测异常值对于理解市场动态至关重要,特别是在高度波动和新兴的监管环境中。 这项研究对强大的统计方法和先进的机器学习技术进行了全面的比较分析,用于在加密货币LOB中实时异常识别。 在一个名为AITA Order Book Signal(AITA-OBS)的统一测试环境中,我们评估了13个不同模型的功效,以确定哪些方法最适合检测潜在的操纵交易行为。 通过在主要交易所的26,204条记录数据集上进行的经验评估表明,表现最佳的模型Emporical Covariance(EC)实现了6.70。
The detection of outliers within cryptocurrency limit order books (LOBs) is of paramount importance for comprehending market dynamics, particularly in highly volatile and nascent regulatory environments. This study conducts a comprehensive comparative analysis of robust statistical methods and advanced machine learning techniques for real-time anomaly identification in cryptocurrency LOBs. Within a unified testing environment, named AITA Order Book Signal (AITA-OBS), we evaluate the efficacy of ...