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

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 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.

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).

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.

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.

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