Rug Pull Detector
A machine-learning system for flagging meme-coin rug pulls from raw chart data. The important choice was a fair tournament: every model trains on the same feature window, so deep-learning and boosted baselines can be compared honestly.
- Role
- ML Engineer
- Period
- 2025 to 2026
- Status
- Production
How the system fits together.
The calls that shaped it.
- 01
Built a multi-model tournament: TSMixer (winning architecture), LSTM, Transformer, XGBoost, and LightGBM all train on the same prepared windows so each model is evaluated against an identical benchmark. Stratified 5-fold CV with class weighting handles the heavy class imbalance.
- 02
Designed the 50+ feature pipeline that downsamples raw 1-second ticks to 5-second buckets, then layers price velocity, RSI, ROC, buy/sell pressure, three-level wavelet decomposition, rolling-window stats (mean, std, Bollinger bands), and holder-concentration metrics from on-chain data.
- 03
Production training loop: GPU with mixed precision, early stopping with patience, learning-rate scheduling, model checkpointing, and per-fold artefacts so any fold can be inspected after the run. Outputs full classification report with PR curve and confusion matrix.
The interesting work isn't the stack. It's the boundaries.
What it runs on.
- 01 PyTorch (TSMixer, LSTM, Transformer) plus XGBoost and LightGBM baselines
- 02 Stratified 5-fold cross-validation with class weighting
- 03 PyWavelets for multi-level wavelet decomposition features
- 04 Optuna hyperparameter search across all model families
- 05 CSV + JSON ingest with downsampling, outlier handling, and RobustScaler