对比13种模型，找出比特币订单簿中异常交易的最佳检测方法
A Comparative Analysis of Statistical and Machine Learning Models for Outlier Detection in Bitcoin Limit Order Books
- 在统一环境AITA-OBS中测试13种统计与机器学习模型
- 最优模型Empirical Covariance实现6.70%收益提升，显著优于基准
- 适合关注加密货币量化交易与风控的研究者和从业者
在高波动且监管尚不完善的加密货币市场中，识别订单簿中的异常行为对理解市场动态至关重要。本研究在统一测试平台AITA Order Book Signal（AITA-OBS）上，对13种鲁棒的统计方法与先进机器学习技术进行综合对比，评估其在实时异常检测中的表现。基于某主要交易所26,204条记录的回测分析显示，表现最优的模型——经验协方差（Empirical Covariance, EC）——实现了6.70%的超额收益，显著优于标准买入并持有基准。研究揭示了模型复杂度、交易频率与性能之间的权衡关系，为加密货币市场微观结构研究提供了严谨的异常检测基准，并展示了其在算法交易与风险管理中的潜力。
原文摘要 · Abstract (English)
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 thirteen diverse models to identify which approaches are most suitable for detecting potentially manipulative trading behaviours. An empirical evaluation, conducted via backtesting on a dataset of 26,204 records from a major exchange, demonstrates that the top-performing model, Empirical Covariance (EC), achieves a 6.70% gain, significantly outperforming a standard Buy-and-Hold benchmark. These findings underscore the effectiveness of outlier-driven strategies and provide insights into the trade-offs between model complexity, trade frequency, and performance. This study contributes to the growing corpus of research on cryptocurrency market microstructure by furnishing a rigorous benchmark of anomaly detection models and highlighting their potential for augmenting algorithmic trading and risk management.
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