Statistical Jump Models in Python, with scikit-learn-style APIs

This package implements hypothesis testing procedures that can be used to identify the number of regimes in a Markov-Switching model.

A quantitative trading framework that leverages daily OHLCV stock data and a Hidden Markov Model (HMM) to dynamically identify market regimes and generate momentum-based trading signals.

Implementations of various trading strategies

Implementation of financial market regime identification models including traditional statistical approaches and deep learning methods (GRSTU), featuring a novel application of Temporal Fusion Transformers to regime classification.

Likelihood ratio based tests for regime switching

[FUSION 2024] A Gaussian Process-based Streaming Algorithm for Prediction of Time Series With Regimes and Outliers

Automatized-analysis-via-yfinance-API

Predicting economic recession in developed and developing countries using regime-switching model

QuantHedge-MM implements advanced computational methods for pricing and hedging options in markets with stochastic regime shifts. Built for quants and researchers, it extends Black-Scholes to Markov-modulated models.

Implementing markov switching models as described in the paper "Optimal Trend Following Rules in Two-State Regime-Switching Models" to generate investment signals for stocks in a portfolio consisting of the top 20 stocks in the Nifty Smallcap 250 as scored by EV/EBITDA & P/E ratios.

Bayesian Estimation of Loss Aversion in Regime-Switching DSGE

Modeling the COVID-19 Infection Rates by Regime Switching Unobserved Components Models

End-to-End Python implementation of Markov-Switching VAR framework for detecting endogenous financial fragility. Replicates Delli Gatti et al.'s (2025) methodology using EM algorithm, Hamilton filtering, and HP spectral decomposition to empirically test Minsky's Financial Instability Hypothesis in macroeconomic data.

STAT 230A Final Project: Exploring Regime-adaptive Linear Models for Time Series Forecasting

📊 Explore regime changes and real financial cycles through Minsky's hypothesis in a nonlinear framework, enhancing macroeconomic and financial analysis.

Building a balanced Vanguard ETF portfolio with data-driven optimization—exploring advanced methods, robust backtesting, and an interactive Dash app to pick your optimal mix.

This project reimagines the classical Merton portfolio optimization problem using Deep Reinforcement Learning (DRL). Instead of static, closed-form allocation rules, we design an intelligent agent that dynamically adjusts exposures to risky and risk-free assets under changing market regimes.

This repository contains the code for the submitted paper: Kento Okuyama, Tim Fabian Schaffland, Pascal Kilian, Holger Brandt, Augustin Kelava (2025). Frequentist forecasting in regime-switching models with extended Hamilton filter.