riskhub-engine

The pure-Python risk engine behind Risk Hub (source), packaged as a library. It covers market, credit, counterparty, FRTB, liquidity, rates, operational and model risk. It uses only the standard library: no NumPy, no SciPy, nothing to compile.

Every function is a line-for-line port of the site's JavaScript engine. The two are checked against each other on shared fixtures, with a relative tolerance of 1e-9 or tighter. That parity suite ships in tests/parity/.

pip install riskhub-engine                                   # once on PyPI
pip install git+https://github.com/louisdelacour21/risk-python-library.git      # from GitHub

Python 3.9 or later.

Quick start

from riskhub_engine import market, credit, options, model_risk

r = market.simulate(1000, seed=7)                       # GARCH-t daily returns
print(market.historical(r[-500:], 0.99))                 # {'var': 0.0243..., 'es': 0.0298...}
print(market.garch(r[-500:], 0.99))                      # GARCH(1,1) VaR / ES with the fitted a, b

bt = market.rolling_backtest(r, "historical", 0.99, 250)
n, x = len(bt["hit"]), sum(bt["hit"])
print(market.kupiec(n, x, 0.01), market.basel_zone(n, x, 0.01))   # {'lr': 1.44, 'p': 0.23} green

m = credit.merton(E=40.0, sigE=0.6, D=80.0, r=0.03, T=1.0)
print(m["pdRN"], m["spread"])                            # risk-neutral PD and credit spread
print(credit.cds_bootstrap([1, 3, 5], [0.010, 0.014, 0.017], 0.03, 0.4))   # piecewise hazard rates

print(options.bs(100, 100, 1.0, 0.2, 0.03, 0.0, "C"))    # price 9.41 and greeks; "P" for a put
print(model_risk.psi(r[:500], r[500:]), model_risk.ks2(r[:500], r[500:]))  # drift between two samples

Modules

Each module defines __all__, so help(riskhub_engine.credit) lists what it contains.

Conventions

• Inputs are plain Python lists and floats. Results are floats, lists or dicts with named fields.
• VaR and ES are returned as positive losses, in the units of the returns.
• Random draws use seeded generators (Mulberry32, as in the JavaScript engine), so a given seed gives the same numbers in Python and in the browser.

Tests

pip install -e .[test]
pytest

tests/test_parity.py runs the parity scripts tests/parity/check_*.py against the JavaScript fixtures. tests/test_api.py checks the public modules and runs the quick start above.

Development

This repository is risk-python-library. The source of truth is site/engine/ in the Risk Hub repository, which is also what runs in the browser through Pyodide. tools/sync_engine.py regenerates src/riskhub_engine/ and tests/parity/ from it. Run it after every engine change, then run pytest. The themed modules are declared in THEMES in that script.

Licence

MIT. Not investment advice. The models are teaching and research implementations; validate them before any production use.