v0.1.0 — Institutional Point-in-Time Regime Engine

🚀 What's New in v0.1.0

Initial institutional-grade release of RegimeLab: a deterministic market regime detection engine, systemic risk telemetry pipeline, and walk-forward adaptive asset allocation framework.

Key Features & Architecture

• Strict Point-in-Time Alignment: Native asof temporal cursors and lagging logic ensuring zero look-ahead bias across all multi-asset inputs.
• Three Core Classifiers:
  • Hamilton Markov-Switching Regression (statsmodels).
  • Multivariate Gaussian Hidden Markov Models (hmmlearn).
  • Gaussian Mixture Models (scikit-learn) with ridge covariance floor.
• Canonical State Ordering: Deterministic state labeling to mathematically prevent label switching across rolling folds.
• Systemic Risk Telemetry: Kritzman Absorption Ratio (PCA), VIX Term Structure (Contango/Backwardation), and Breadth momentum over Sector SPDR baskets.
• Walk-Forward Engine: Expanding-window backtesting with out-of-sample parameter freezing and realistic transaction cost modeling.
• Dual Presentation Layer: Static Rich CLI dashboards, interactive Plotly HTML reports, and Textual TUI.
• Formal Verification: Causality property-based tests via hypothesis and strict static typing with mypy.