mrv-lib 0.2.0

Model Risk Validator for Model Risk Governance

mrv-lib: Model Risk Validator

Your regime model might be producing different risk labels depending on which features you feed it — and your current validation doesn't catch this. Most regime models (GMM, HMM) are validated on in-sample fit, but nobody checks whether the labels survive a change in input representation. If they don't, downstream risk decisions are sitting on an arbitrary modelling choice. mrv-lib tests for this.

mrv tests whether your market regime model produces stable, reliable labels — or whether they silently depend on undisclosed modelling choices like feature selection, temporal resolution, or rolling window parameters. Built for SR 11-7 / Basel IV model risk governance.

What it does

Test	Question	Status
Representation Invariance	Do regime labels change when you use different risk factors?	v0.1.0
Resolution Invariance	Do labels agree across 5m / 15m / 1h / 1d frequencies?	v0.2.0
RSS — Regime Stability Score	A single score combining rep + res into actionable governance signal	v0.3.0

v0.2.0 also includes: business impact function (impact_fn), continuous monitoring with alerts, disagreement attribution (LOO / frequency-pair / temporal), SR 11-7 compliant report with auto-generated findings, and a findings engine with severity classification.

Install

pip install mrv-lib

# Optional: IB data download
pip install mrv-lib[ib]

# Optional: regime models (GMM/HMM)
pip install mrv-lib[validator]

# Everything
pip install mrv-lib[all]

Quick start

Notebooks: See examples/paper1_representation_invariance.ipynb and examples/paper2_resolution_invariance.ipynb for step-by-step walkthroughs.

# 1. Download data (requires IB Gateway running)
python run.py download config.yaml

# 2. Run representation invariance test + generate PDF
python run.py run config.yaml rep

# 3. Run resolution invariance test + generate PDF
python run.py run config.yaml res

# 4. Regenerate PDF from existing results
python run.py report

Or from Python:

from mrv.pipeline import run, download, validate, report

download("config.yaml")             # fetch data from IB
run("config.yaml", "rep")           # validate + PDF
run("config.yaml", "res")           # validate + PDF

# Step by step (full control)
from mrv.pipeline import load_data, compute_factors, fit_labels, validate, report
from mrv.utils.config import load

cfg = load("config.yaml")
prices = load_data(cfg, "rep")
# ... user can replace any step

Configuration

All settings in one config.yaml:

download:
  data_dir: data
  symbols: [SPY, USDJPY, CL=F, IEF, GLD]
  freq: [5m, 15m, 1h, 1d]
  start: "2026-01-01"
  ib:
    host: 127.0.0.1
    port: 4002

factors:
  vol_window: 20
  drawdown_window: 60
  tail_window: 60
  tail_alpha: 0.05

validator:
  rep:
    assets:
      SPY: data/SPY_1d.csv
    model: gmm
    n_states: 3
    factors:
      - [vol, drawdown, maxdd, var, cvar]
      - [vol, drawdown, var, cvar]
      - [real_skew, vol_stab, var, cvar]

  res:
    assets:
      SPY: [data/SPY_5m.csv]
    model: gmm
    n_states: 2
    episode: 2026_iran

Custom factors and models

from mrv.data.factors import register_factor

def momentum(returns, price, windows):
    return price.pct_change(windows.get("mom_window", 20)).rename("momentum")

register_factor("momentum", momentum)

from mrv.models import register_model

def my_kmeans(features, n_states, **kwargs):
    from sklearn.cluster import KMeans
    return KMeans(n_clusters=n_states).fit_predict(features.values)

register_model("kmeans", my_kmeans)

Project layout

mrv-lib/
├── config.yaml              # Configuration
├── run.py                   # CLI entry point
├── templates/
│   ├── template.tex         # Academic report template
│   └── sr11_7_template.tex  # SR 11-7 regulatory report template
├── examples/
│   ├── paper1_representation_invariance.ipynb
│   └── paper2_resolution_invariance.ipynb
├── src/mrv/
│   ├── pipeline.py          # data -> factors -> model -> validate -> report
│   ├── data/
│   │   ├── reader.py        # Load, validate, resample OHLCV
│   │   ├── factors.py       # Factor registry + 7 built-in risk factors
│   │   └── normalize.py     # Rolling z-score, min-max
│   ├── models/
│   │   ├── __init__.py      # Model registry + fit()
│   │   ├── gmm.py           # Gaussian Mixture Model
│   │   └── hmm.py           # Hidden Markov Model
│   ├── validator/
│   │   ├── base.py          # BaseValidator (subclass for custom tests)
│   │   ├── rep.py           # Representation Invariance test (Paper 1)
│   │   ├── res.py           # Resolution Invariance test (Paper 2)
│   │   ├── metrics.py       # ARI, AMI, NMI, Spearman, VI
│   │   ├── attribution.py   # LOO, frequency-pair, temporal hotspots
│   │   ├── findings.py      # SR 11-7 findings engine
│   │   ├── monitor.py       # Continuous monitoring + alerts
│   │   └── report.py        # JSON -> LaTeX -> PDF
│   └── utils/
│       ├── config.py        # YAML config loading
│       ├── download.py      # IB data download
│       └── log.py           # Logging setup
├── reports/                  # Output (gitignored)
└── tests/                    # 123 tests

Output

Each run creates a timestamped directory under reports/:

• result.json -- Complete data (reusable for report regeneration)
• report.pdf -- Professional report with cover page, dashboard, heatmaps, and remediation plan
• summary.txt -- Plain text quick view
• {asset}_ari_heatmap.png -- ARI heatmap per asset
• {asset}_timeline.png -- Regime timeline (res validator)
• pipeline_summary.csv -- Summary metrics per asset

Research

Based on the following PhD research:

• Zheng, Low & Wang (2026). Regime Labels Are Not Representation-Invariant. Submitted.
• Zheng, Low & Wang (2026). Regime Labels Are Not Resolution-Invariant. Submitted to Finance Research Letters.

License

MIT. See LICENSE.

Maintainers

ModelGuard Lab -- Author: Kai Zheng.