Uses of Package
com.quantfinlib.ml
Packages that use com.quantfinlib.ml
Package
Description
Statistical learning for markets, all pure Java:
GradientBoostedRegressor (stump boosting),
VolatilityForecaster (forward realized vol +
0-100 risk score), RegimeDetector (2-state
Gaussian Markov-switching model via Baum-Welch EM),
MarketImpactPredictor (learned impact + sweep
probability), IntradayLiquidityForecaster
(session volume profiles) and AnomalyDetector
(quote stuffing, price spikes).-
Classes in com.quantfinlib.ml used by com.quantfinlib.mlClassDescriptionGradient-boosted regression over decision stumps (XGBoost-style additive boosting with squared-error loss), implemented in pure Java with no dependencies.Intraday liquidity forecasting: accumulates per-bucket volumes across days into a seasonal profile (e.g. 24 hourly buckets) to predict when liquidity peaks — London open, the London/New York overlap, etc.ML market impact prediction: learns realized impact (bps) from order and book features using gradient-boosted trees, and estimates the probability a marketable order sweeps through the visible top of book.Machine Learning Risk Forecasting: predicts forward realized volatility from a return series using gradient-boosted trees over engineered features (multi-horizon realized vol, momentum, and shock magnitude), and maps the forecast to an intuitive 0-100 risk score.