regime

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).

Bridges the power of Hidden Markov Models (HMM) and Natural Language Processing (NLP) to detect market regimes and predict optimal trading strategies.

Fixed income analytics for bank treasury and ALM — DV01, convexity, OCI/CET1 scenario analysis, HMM regime detection, CVXPY portfolio optimisation

Using multiple time series observations, split time into regimes using an array of representation learning techniques with explainability and robustness techniques for the transitions

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.

Jurisdictional Regimes

Final Project for DATA271, Dr. Johnson, Spring 2025, Cal Poly Humboldt

India Macro-Regime Platform - 5-state Gaussian HMM

Modelling boreal carbon dynamics under different fire regimes

RAMME: Market Microstructure Engine Real-time market microstructure simulator with regime-based ticks, live plotting, and PnL tracking.

MCP server for real-time market regime classification — give AI agents market awareness. SPY regime detection, VIX analysis, trading implications.