This package implements the efficient dynamic programming approach to conduct estimation and testing for linear models in presence of structural breaks as described in Bai & Perron (1998) and Perron, Yamamoto, & Zhou (2020).
Fixed income analytics for bank treasury and ALM — DV01, convexity, OCI/CET1 scenario analysis, HMM regime detection, CVXPY portfolio optimisation
Bridges the power of Hidden Markov Models (HMM) and Natural Language Processing (NLP) to detect market regimes and predict optimal trading strategies.
From-scratch 2-state Gaussian HMM on 16y SPX realized vol — regime-tilted overlay lifts Sharpe 0.75->1.08 (+43.9%) and cuts max DD -33.9% -> -19.4%. No hmmlearn/sklearn; pure numpy forward-backward + Baum-Welch.
Modelling boreal carbon dynamics under different fire regimes
Using multiple time series observations, split time into regimes using an array of representation learning techniques with explainability and robustness techniques for the transitions
Jurisdictional Regimes
Final Project for DATA271, Dr. Johnson, Spring 2025, Cal Poly Humboldt
MCP server for real-time market regime classification — give AI agents market awareness. SPY regime detection, VIX analysis, trading implications.
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