Paper: arXiv 2503.08692

Abstract

We propose a simple yet robust unsupervised model to detect pump-and-dump events on tokens listed on the Poloniex Exchange platform. By combining threshold-based criteria with exponentially weighted moving averages (EWMA) and volatility measures, our approach effectively distinguishes genuine anomalies from minor trading fluctuations, even for tokens with low liquidity and prolonged inactivity. These characteristics present a unique challenge, as standard anomaly-detection methods often over-flag negligible volume spikes. Our framework overcomes this issue by tailoring both price and volume thresholds to the specific trading patterns observed, resulting in a model that balances high true-positive detection with minimal noise.

Complexity vs Empirical Score

  • Math Complexity: 2.0/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper uses basic statistical concepts like exponentially weighted moving averages and volatility, but lacks heavy derivations or advanced mathematics. It is heavily data-driven with a focus on real-world exchange data and practical implementation.

Research Flowchart

  flowchart TD
  A["Research Goal"] -->|Identify anomalous pump-and-dump events in low-liquidity crypto| B["Data & Inputs"]
  B --> C["Poloniex Exchange Data<br/>Price & Volume Time Series"]
  C --> D["Methodology: Preprocessing"]
  D --> E["EWMA & Volatility Calculation"]
  E --> F["Threshold-Based Detection<br/>Adaptive Price/Volume Filters"]
  F --> G["Outcome"]
  G --> H["High True-Positive Detection<br/>Minimized False Positives"]
  G --> I["Robust against Market Noise<br/>Effective for Low Liquidity Tokens"]