Start by framing the problem as a detection system with clear objectives: high recall for compliance, controlled false positives to protect market makers. Walk through the full pipeline: data ingestion, feature engineering, graph-based clustering, model training with synthetic and real labels, threshold tuning, and daily scoring with confidence intervals. Emphasize collaboration with Compliance and the importance of explainability for flagged cases.
Pro tip: Involve Compliance early to define what constitutes actionable evidence and to secure ground truth; their feedback will shape your precision/recall trade-off and ensure your system aligns with regulatory expectations.
Collect order and trade logs for BTC-USD and low-liquidity altcoin pairs. Engineer features such as trade size distribution, order cancellation rates, inter-trade time intervals, price impact, and self-trade patterns.
Build a graph where nodes are accounts and edges represent shared attributes (e.g., IP, device, funding source, timing). Use community detection or connected components to infer shared control and identify clusters likely to be wash trading rings.
Train a supervised model (e.g., gradient boosting) using labeled data from synthetic wash trades and any enforcement ground truth. Tune the decision threshold to balance precision and recall, prioritizing high recall for compliance while minimizing false positives for market makers.
Backtest the model on historical data with injected synthetic wash trades to evaluate detection performance. Use time-based cross-validation and simulate different market conditions to ensure robustness.
Generate daily risk scores per account or pair with confidence intervals via model calibration (e.g., Platt scaling). Surface flagged cases to Compliance with explainable features and a prioritized queue, ensuring legitimate market makers are not penalized.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.