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

Systematic multi-asset allocation strategy using Hidden Markov Models to identify VIX volatility regimes and dynamically rotate between TLT, GLD, and SPY

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.

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

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.

Automated volatility arbitrage engine exploiting rough volatility mispricing in short-dated equity options. Combines Monte Carlo pricing with Gaussian HMM regime detection to trade only during calm markets. Connects to Interactive Brokers for live/paper trading with full validation suite.

Unsupervised latent regime discovery for crypto markets. HMM, VAE, and temporal contrastive models identify hidden market states from multi-exchange data. FastAPI + React dashboard. Docker Compose.

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

Likelihood ratio based tests for regime switching

Online HMM-based statistical arbitrage for Brent, WTI & Dubai crude oil futures. Filter-based EM algorithm detects market regimes in real-time to time spread trades. Achieves Sharpe 1.58 & 21.7% annualized return out-of-sample (2023–24). Based on Fanelli et al. (2024).

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, available at https://arxiv.org/abs/2512.18149.

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.

Quantitative regime-switching trading framework using Hidden Markov Models (HMM) to adapt market exposure based on changing volatility and return environments.

Automatized-analysis-via-yfinance-API

MATLAB replication and extension of Chang, Choi & Park (2017): endogenous regime switching with latent AR(1) dynamics. Applied to US GDP, VIX, WTI, and equity returns.

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.

Modelado de Series Temporales Económicas: De la Tasa de Cambio Relativa a los Modelos de Transición de Régimen Estocásticamente Estructurados