Unsupervised Anomaly Detection in Digital Currency Trading: A Clustering and Density-Based Approach Using Bitcoin Data

This study investigates the application of the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for detecting anomalies in Bitcoin trading data. With the growing significance of Bitcoin in the financial market, identifying irregular trading patterns is crucial for maintaining market integrity and preventing market manipulation. Utilizing a dataset from Kaggle, which includes features such as date, timestamp, open, high, low, close, volume, and number of trades, the data was aggregated from minute-by-minute to hourly intervals for more manageable analysis. The DBSCAN algorithm effectively identified a primary cluster comprising 29,612 data points and flagged 2 points as anomalies, achieving a precision of 1.0, recall of 0.0068, F1-score of 0.0135, and an AUC-ROC of 0.5034. The optimal parameters, determined through sensitivity analysis, were epsilon (ε) = 0.1 and min_samples = 3, yielding the highest silhouette score of 0.21499. These results underscore the algorithm's ability to accurately label anomalies while highlighting the challenge of comprehensive anomaly detection. The study contributes to the field of financial anomaly detection by demonstrating the effectiveness of DBSCAN in analyzing high-dimensional, noisy datasets. It also addresses gaps in the literature regarding the application of density-based clustering methods to Bitcoin trading data. Despite its contributions, the study acknowledges limitations, such as potential data aggregation impact and the need for further validation with different datasets. Future research directions include integrating additional features like social media sentiment and exploring hybrid approaches that combine supervised and unsupervised methods.