Fundamental package for quantitative finance with Python.

CUSUM (Cumulative Sum) filter for detecting structural shifts in financial time series, implemented in Python

25 structural break detection methods for univariate time series: XGBoost, Neural Networks, Ensembles, Reinforcement Learning, and Statistical approaches. Evaluated on cross-dataset generalization.

End-to-End Python implementation of Mukhia et al.'s (2025) methodology for detecting political risk transmission in stablecoin markets. Implements dynamic programming for endogenous breakpoint detection, Empirical Mode Decomposition, Cholesky-identified structural shocks, and AAFT surrogate validation to quantify political uncertainty spillovers.

End-to-End Python implementation of Liu & Cheng's (2026) methodology for U.S. Treasury yield curve forecasting. Combines Factor-Augmented Dynamic Nelson-Siegel models, High-Dimensional Random Forests, and Distributionally Robust Optimization (DRO) for risk-aware ensemble forecasting under ambiguity.

Autoregressive (AR) models with advanced techniques: model selection, diagnostics, structural breaks, rolling forecasts, Fourier seasonality, exogenous variables, business cycle analysis, and benchmarking for economic time series.

📈 Forecast U.S. Treasury yield curves with a robust machine learning approach, enhancing accuracy and decision-making in finance.