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.

Implementations of various trading strategies

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.

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

The next-generation open-source platform for quantitative FX risk modeling, scenario analysis, and regulatory stress testing.

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

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

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.

Bayesian Estimation of Loss Aversion in Regime-Switching DSGE

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.

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.

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.

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.

Final project for Dynamic Asset Pricing Theory Course @ Tunis Business School |