# A Comparative Analysis of Statistical and Machine Learning Models for Outlier Detection in Bitcoin Limit Order Books

arXiv: https://arxiv.org/abs/2507.14960 · HTML: https://mlsanity.com/paper/2507.14960/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 30 references that are in the map.
- First posted on arXiv 20 Jul 2025.

## Builds on (most cited first, 5 of 5)

- [Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features](https://mlsanity.com/paper/1804.04888/) (2018)
- [A Logic-based Multi-agent System for Ethical Monitoring and Evaluation of Dialogues](https://mlsanity.com/paper/2109.08294/) (2021)
- [Dataset Optimization Strategies for MalwareTraffic Detection](https://mlsanity.com/paper/2009.11347/) (2020)
- [A Stock Trading System for a Medium Volatile Asset using Multi Layer Perceptron](https://mlsanity.com/paper/2201.12286/) (2022)
- [DNN-ForwardTesting: A New Trading Strategy Validation using Statistical Timeseries Analysis and Deep Neural Networks](https://mlsanity.com/paper/2210.11532/) (2022)

## Built on by (most cited first, 1 of 1)

- [AgenticAITA: A Proof-Of-Concept About Deliberative Multi-Agent Reasoning for Autonomous Trading Systems](https://mlsanity.com/paper/2605.12532/) (2026)

## Abstract (arXiv)

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.