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scripts/

generate_synthetic_dataset.py

Generates a synthetic labelled feature matrix for local training, demos, and tests, without needing live Stellar Horizon data.

The output schema matches detection/feature_engineering.py::build_feature_matrix (wallet + all Benford / trade-pattern / volume-timing / wallet-graph feature columns), plus a label column (1 = wash-trading-like, 0 = legitimate). Roughly half the rows are generated with "legitimate" distributions and half with "wash-trading-like" distributions, then shuffled.

Usage

python -m scripts.generate_synthetic_dataset \
    --n-wallets 500 \
    --seed 42 \
    --output data/synthetic_dataset.parquet
Flag Default Description
--n-wallets 500 Number of synthetic wallet rows to generate
--seed 42 Random seed (controls both data generation and the final shuffle)
--output data/synthetic_dataset.parquet Output parquet path

Training on the generated dataset

python -m detection.model_training --data-path data/synthetic_dataset.parquet

This trains every model in MODEL_REGISTRY (Random Forest, XGBoost, LightGBM) with SMOTE-balanced training data, writes the fitted models to config.MODEL_DIR, and writes metrics.json (AUC-ROC / PR-AUC / F1 per model) alongside them.