⚡ Why RegimeLab?

Most open-source regime detection scripts fit a Hidden Markov Model (HMM) on the entire in-sample dataset and claim predictive power. In production quantitative finance, this fails due to three fatal flaws:

1. Look-Ahead Bias: Training filters without strict point-in-time (asof) truncation leaks future distribution moments into past states.
2. Label Switching: HMM/GMM state indices are mathematically interchangeable between refits. Without canonical sorting, "State 0" randomly alternates between Bull and Bear across rolling windows.
3. Calendar Desynchronization: Multi-asset cross-sections suffer from holiday mismatches, halted assets, and survivorship bias.

RegimeLab solves these operational hurdles, providing a turnkey, causal quantitative engine for systematic asset allocation and macro risk monitoring.

📊 Feature Matrix: Raw Tooling vs. RegimeLab

🚀 Quickstart

1. Installation

# Install from PyPI
pip install regimelab

# Or install with interactive TUI support
pip install "regimelab[tui]"

2. Python API Usage

Current Market Regime Detection (3 lines)

from regimelab import Settings
from regimelab.pipeline import run_single_asof

# Run point-in-time regime inference for any historical or current date
settings = Settings(data={"provider": "yfinance"})
payload = run_single_asof(settings, asof="2024-12-31")

print(f"Detected Regime: {payload.regime.value}")
print(f"Confidence: {payload.probabilities.confidence:.2%}")
print(f"Target Allocation: {payload.target_weights.weights if payload.target_weights else {}}")

Extract Causal Systemic Risk Features

from regimelab import Settings
from regimelab.data.fetcher import load_aligned_panel
from regimelab.features import build_feature_matrix

settings = Settings(data={"provider": "yfinance"})
panel = load_aligned_panel(settings)
features = build_feature_matrix(panel, settings)

# Inspect causal feature matrix
print(features[["absorption_ratio", "absorption_delta", "vix_term_spread", "breadth"]].tail())

Walk-Forward Backtesting Engine

from regimelab import Settings
from regimelab.pipeline import run_pipeline

settings = Settings(
    data={"provider": "synthetic"}, # Fully offline, reproducible dataset
    model={"classifier": "hmm", "n_states": 4},
    backtest={"transaction_cost_bps": 5.0, "confidence_floor": 0.5},
)

result = run_pipeline(settings, command="backtest_run")
metrics = result.backtest.metrics

print(f"Strategy CAGR: {metrics.cagr:.2%}")
print(f"Sharpe Ratio:  {metrics.sharpe:.2f}")
print(f"Max Drawdown:  {metrics.max_drawdown:.2%}")

3. CLI & Terminal Dashboard

RegimeLab ships with a powerful Typer CLI:

# 1. Run full walk-forward pipeline and generate interactive HTML report
regimelab run --report market_report.html

# 2. Inspect point-in-time telemetry for a specific date (JSON output)
regimelab asof 2023-10-15

# 3. Launch the full interactive Textual Terminal Dashboard
regimelab tui

Additional CLI commands include regimelab report OUTPUT for direct HTML generation and regimelab asof YYYY-MM-DD --output telemetry.json for persisted JSON payloads.

🧠 Core Methodology & Architecture

flowchart TD
    A["📦 Data Layer (PIT Alignment & Caching)"] --> B["⚡ Causal Features (PCA Absorption, VIX Spread, Breadth)"]
    B --> C["🧠 Models (Hamilton / HMM / GMM + Anti-Switch Ordering)"]
    C --> D["📈 Walk-Forward Engine (Dynamic Allocations)"]
    C --> E["📊 Telemetry, HTML Report & Textual TUI"]

1. Canonical State Labeling (regimelab.models.labeling)

To eliminate label switching, RegimeLab fits the underlying statistical model (Gaussian HMM, Hamilton Markov Switching, or GMM) and evaluates the conditional distribution parameters of each state. States are sorted by risk-adjusted return ($\frac{\mu}{\sigma}$) and mapped deterministically to:

• BULL_TREND (High return, low volatility)
• NEUTRAL_TRANSITION (Moderate return, mean-reverting)
• HIGH_VOL_BEAR (Negative drift, elevated variance)
• RISK_OFF (Severe drawdown regime)

2. Kritzman Absorption Ratio (regimelab.features.absorption)

Quantifies market fragility via Principal Component Analysis (PCA) over rolling multi-asset return covariance matrices:

$$ \text{Absorption Ratio} = \frac{\sum_{i=1}^{k} \sigma^2_{PC_i}}{\sum_{j=1}^{N} \sigma^2_j} $$

A rapid spike in the absorption ratio ($\Delta \text{AR} > 1.5$) indicates tightening cross-asset coupling, signaling systemic vulnerability prior to market crashes.

⚙️ Configuration (regimelab.toml)

Customize execution parameters via regimelab.toml, environment variables (REGIMELAB_DATA__PROVIDER=yfinance), or Python kwargs:

[data]
provider = "yfinance"          # "yfinance", "synthetic", or "parquet"
start = "2005-01-01"
benchmark = "SPY"
calendar_anchor = "SPY"

[model]
classifier = "hmm"             # "hmm", "gmm", or "hamilton"
n_states = 4
covariance_type = "diag"
min_train_observations = 756

[backtest]
transaction_cost_bps = 5.0
confidence_floor = 0.5
refit_frequency_days = 63

🧪 Testing & Formal Verification

RegimeLab is built with property-based testing (hypothesis) to mathematically guarantee absence of look-ahead leakage:

# Run test suite with causality property tests
uv run pytest -q

# Run strict mypy type checking
uv run mypy src/regimelab

# Lint with ruff
uv run ruff check .

📄 License

MIT License. Developed for quantitative researchers, portfolio managers, and systematic trading engineers.