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

Fit autoregressive models with skewed generalized error distribution (SGED) noise whose parameters vary with level

Dynamic mean-variance allocation under regime-switching jump-diffusions with wealth floors - fully analytical (SSRN 6534579)

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

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.

End-to-End Python implementation of Ang et al's (2026) Agentic 'Self-Driving Portfolio'. Implements: Black-Litterman equilibrium priors, Grinold-Kroner building blocks, Campbell-Shiller CAPE analysis, Ledoit-Wolf covariance shrinkage, Risk Parity, Hierarchical Risk Parity, and Robust Mean-Variance optimization across 18 asset classes.

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.

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

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

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.

Automatized-analysis-via-yfinance-API

Regime-Invariant Specialist Pools (RISP): reward-independent specialist retention + episode-invariant decision-focused training for non-stationary markets. Theory, 11-arm experiments, three papers — honest nulls included. Companion to GAUSE.

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).

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.