Welcome to Hidden Regime
Markets don’t just move randomly. Beneath the surface noise, financial markets operate in distinct behavioral regimes - periods of sustained bull runs, defensive bear markets, sideways consolidation, and crisis conditions. Each regime has different statistical properties, different risk characteristics, and requires different trading approaches.
The problem? These regimes are hidden. Traditional technical analysis and simple moving averages miss the sophisticated state-switching behavior that drives market dynamics. You can’t see them on a price chart, but they’re there, governing how markets behave.
What is Hidden Regime?
Hidden Regime is where we uncover these invisible market states using advanced statistical models. Specifically, we use:
- Hidden Markov Models (HMMs) for regime detection
- Bayesian uncertainty quantification for parameter confidence
- Online learning for real-time adaptation
- Model Context Protocol integration for AI systems
This isn’t your typical trading blog with subjective chart analysis. We build mathematical models that can detect regime changes, quantify uncertainty, and adapt to changing market conditions in real-time.
Our Approach
From Manual to Mathematical
Instead of manually drawing support and resistance lines, we let the data tell us when market structure has fundamentally changed. Our models detect regime transitions automatically, often days before they become obvious on charts.
From Static to Adaptive
Rather than using fixed parameters that worked last year, our online learning systems continuously adapt to new market conditions while maintaining stability and avoiding overfitting.
From Point Estimates to Uncertainty
Instead of claiming “the market is in a bull regime,” we quantify: “there’s a 73% probability of bull regime with 90% confidence interval of 65%-81%.” This uncertainty matters for risk management.
What You’ll Find Here
Technical Deep Dives: Implementation details, mathematical foundations, and code walkthroughs for building your own regime detection systems.
Market Analysis: Real-world application of these models to stocks, ETFs, and market indices with actual results and performance metrics.
Research Progress: Our journey from basic HMMs to sophisticated Bayesian systems with MCMC sampling and Model Context Protocol integration.
Open Source Tools: The hidden-regime Python package and related tools for regime detection and analysis.
Current Focus
We’re actively developing:
- Online Hidden Markov Models - Real-time regime detection with streaming market data
- Bayesian Parameter Uncertainty - MCMC sampling for robust risk management
- Model Context Protocol Integration - Making regime intelligence available to AI systems
- Multi-asset Regime Analysis - Understanding correlations and regime spillovers
Who This Is For
This blog targets quantitative traders, financial engineers, and researchers interested in the intersection of machine learning and finance. We assume familiarity with:
- Basic statistics and probability
- Python programming
- Financial markets concepts
- Some exposure to machine learning
If you’re new to any of these areas, the content will still be accessible, but you might need to reference additional resources for background concepts.
Coming Soon
Our next posts will cover:
- “NVIDIA’s Hidden Regimes: A Case Study” - Applying our models to real market data
- “Building Your First Regime Detector” - Step-by-step implementation guide
- “Online Learning for Market Regimes” - Adapting models in real-time
Connect
- Python Package:
pip install hidden-regime - GitHub: github.com/hidden-regime/hidden-regime
- Email: contact@hiddenregime.com
The markets have been hiding their true structure from us. Time to change that.
This is the beginning of a systematic exploration into quantitative regime detection. The models are mathematical, the analysis is rigorous, and the applications are practical. Welcome to Hidden Regime.