This report focuses on anomaly detection within Osmosis DEX swap transactions, the largest decentralized exchange in the Cosmos ecosystem. The core objectives of this study are two: first, to evaluate the feasibility of detecting outliers in Osmosis DEX transactions using the available data, and second, to assess the real-world impact of these identified anomalies on the proposed market.
To achieve these objectives a pipeline, from data indexing and preprocessing to model training and deployment has been designed and implemented. For that, some Big Query tables are created following different approaches depending on the use case and the data sources available, but always ensuring the quality and efficiency of the pipeline.
Based on the obtained data, a variety of anomaly detection techniques have been explored, including One-Class SVM, Isolation Forest, and KMeans among other models. After the evaluation, the dense autoencoder has emerged as the most effective approach for detecting anomalies in this specific context.
The dense autoencoder has a Silhouette Score of 0.909 when the maximum is 1. However, the true strength of this model arises when assessing the impact of identified outliers on market metrics such as volatility, price evolution and volume. The Mann-Whitney U Test and the Kolmogorov-Smirnov Test have been evaluated, and their results demonstrate the statistical influence of these outliers on the market.
Although the main objectives have been achieved, the report concludes by outlining future directions and opportunities for improvement. These include cost optimization in the data pipeline, refinement of evaluation metrics, and further research into factors influencing market behavior.
