mrv-lib 0.0.1

Market Regime Validity Library for Model Risk Governance

mrv-lib: Market Regime Validity Library

The Gold Standard for Model Risk Diagnostics in Non-Stationary Markets.

mrv-lib is an open-source Python library designed to quantify and diagnose the stability of market regime identification models. Built upon the theoretical framework of Inference Collapse and Ordinal Robustness, it provides financial institutions with a rigorous toolset to meet Basel IV and SR 11-7 model risk governance requirements.

Why mrv-lib?

Traditional market regime models often suffer from "Stability Illusions." A model may appear robust at daily resolutions but fail to capture structural shifts during high-frequency intraday stress events. mrv-lib exposes these vulnerabilities by measuring:

• Representation Sensitivity: How sensitive are your regime labels to feature engineering and preprocessing?
• Resolution Dissonance: Does your model's daily output contradict its high-frequency signals?
• Identifiability Boundaries: Is the market currently in a "Zone of Collapse" where absolute labels are mathematically unreliable?

Key Features

1. Sensitivity Diagnostic (RSS)

Automated stress-testing of regime labels across multiple feature sets (Representation) and temporal scales (Resolution). It calculates the RSS (Representation Stability Score) to quantify model robustness.

2. Identifiability Index

Calculates the Identifiability Index (\(\mathcal{I}\)) based on structural drift and regime separation. It identifies the "Phase Boundaries" where model inference begins to collapse.

3. Ordinal Robustness

When absolute labels (ARI) collapse, mrv-lib measures Ordinal Consistency (Spearman's Rho) to determine if the risk ranking remains valid for fail-safe hedging.

Installation

pip install mrv-lib

Quick Start

import mrv_lib as mrv
import pandas as pd

# Load your market data (OHLCV)
data = pd.read_csv("market_data.csv")

# Initialize the diagnostic scanner
scanner = mrv.Scanner(resolution=['5m', '1h', '1d'])

# Run representation stability test
results = scanner.run_representation_test(data, model="HMM")

# Get the RSS (Representation Stability Score)
print(f"Model RSS: {results.rss_score}")

# Detect Identifiability Boundaries
boundary = mrv.detect_boundary(data)
if boundary.is_collapsed:
    print(f"Warning: Entering Inference Collapse Zone. Identifiability Index: {boundary.index}")

Command-Line Interface

After installation, you can run diagnostics from the shell:

mrv-lib market_data.csv --resolution 5m 1h 1d --model HMM

Project Layout

mrv-lib/
├── src/
│   └── mrv_lib/
│       ├── __init__.py
│       └── core.py
├── tests/
├── README.md
├── LICENSE
└── pyproject.toml

Theoretical Foundation

The methodology of mrv-lib is documented in a series of peer-reviewed research papers:

• Regime Labels Are Not Representation-Invariant: Evidence of instability across feature sets.
• Regime Labels Are Not Resolution-Invariant: Documentation of the 14-hour lag in daily risk reporting.
• Inference Collapse and Ordinal Robustness: Defining the phase boundaries of market state identification.

For academic citations, please refer to the documentation.

Commercial Support & SaaS

For enterprise-grade features including real-time alerting, Basel IV Compliance Reporting, and the Fail-Safe Actuator engine, please visit ModelGuard.co.nz.

• ModelGuard Sentinel: Real-time monitoring for institutional trading desks.
• ModelGuard Advisory: Professional consulting for RBNZ/APRA regulatory alignment.

Maintainers

Maintained by ModelGuard Lab. Lead Architect: Kai Zheng.

License

mrv-lib is released under the MIT License. See LICENSE for details.