RiskPY
A risk and actuarial engine: a C++ core with no Python dependencies, and a modelling layer in readable Python that covers Monte Carlo with dependence, life contingencies, claims reserving, yield curves and short-rate models, credit risk, option pricing, and twenty-two charts — with a verification suite that checks every identity the library claims on every push.
📖 Documentation → slimboi34.github.io/RiskPY
pip install open-riskpy # core, zero dependencies
pip install "open-riskpy[sim]" # + NumPy: simulation and the numeric layers
pip install "open-riskpy[viz]" # + Matplotlib: the charts
from riskpy.mc import Model, Poisson, LogNormal, Normal
model = Model(
claim_count = Poisson(mean=140),
severity = LogNormal.from_moments(mean=18_000, sd=42_000),
inflation = Normal(mean=0.043, sd=0.012),
)
model.correlate("claim_count", "inflation", 0.3)
@model.formula
def annual_loss(claim_count, severity, inflation):
return claim_count * severity * (1 + inflation)
result = model.run(200_000, seed=42, sampling="lhs")
print(result.summary()) # mean, VaR, TVaR at every level
result.plot("dashboard") # distribution, exceedance, convergence, tornado
v0.3.1 —
ExposureRating.layer_premiumprices ground-up layers (attachment 0 used to raise), the sdist ships build inputs only, and the internals and docs are tidied. It builds on v0.3.0: four new fields of risk (life, reserving, rates, credit), a rewritten Monte Carlo engine, twenty-two charts and the verification suite. See RELEASE_NOTES.md.
What is in the box
| Module | What it does | Needs |
|---|---|---|
riskpy.mc |
Generic Monte Carlo — any formula, twenty distributions with pdf/cdf/ppf/moments, mixtures and truncation, Latin hypercube, Iman–Conover rank correlation, a copula hook, and a Result that answers VaR, TVaR, sensitivity and convergence |
NumPy |
riskpy.viz |
Twenty-two charts, one theme, light and dark, palettes validated for contrast and colour-vision deficiency | Matplotlib |
riskpy.quant |
Black–Scholes, Greeks, implied vol (pure Python); GBM and jump-diffusion paths; historical, parametric and expected-shortfall VaR; Heston by Fourier inversion | — / NumPy |
riskpy.life |
Life tables (Gompertz, Makeham, the AMLCR standard table), insurances, annuities, premiums, reserves, joint lives, commutation functions | — |
riskpy.reserving |
Chain ladder, Mack standard errors, Bornhuetter–Ferguson, Cape Cod, ODP bootstrap, tail fitting — reproduces R's ChainLadder on GenIns to the unit |
NumPy |
riskpy.rates |
Yield curves (bootstrap, Nelson–Siegel, Svensson), bonds with duration, convexity, DV01, z-spread; Vasicek and CIR with closed-form bonds and simulated paths | — / NumPy |
riskpy.credit |
Merton, hazard rates and CDS, Vasicek / Basel ASRF with IRB capital, one-factor copula portfolio simulation, rating transition matrices | — / NumPy |
riskpy.verify |
The verification suite: 111 checks against closed forms, published tables and identities — 125 with SciPy installed as an oracle — plus benchmarks. riskpy-verify on the command line |
NumPy |
| Compiled core | FactorModel, MonteCarloSimulator, LossTriangle, FourierTransform, ExperienceRating, ExposureRating, RateAnalyzer, ActuarialMath, RiskEngine, ExcelExporter, the optional Tkinter UnderwritingApp |
— |
Every layer is imported lazily, so import riskpy on a machine with nothing
but the wheel installed works and gives you the compiled core.
A taste of each
from riskpy import life, reserving, rates, credit, quant, viz
# Life: the textbook's own numbers
sult = life.LifeTable.sult()
life.whole_life_annuity_due(sult, 40, i=0.05) # ä_40 = 18.457757 (AMLCR Table D.3)
life.net_premium_reserve(sult, 40, t=10, i=0.05, kind="endowment", n=20)
# Reserving: the GenIns triangle, with uncertainty
tri = reserving.genins()
mack = reserving.mack_chain_ladder(tri)
mack.total_reserve, mack.total_se # 18,680,856 ± 2,447,095
boot = reserving.bootstrap_chain_ladder(tri, n=5000, seed=1)
viz.exceedance(boot.result()) # a reserve distribution, charted
# Rates: a bond on a bootstrapped curve
curve = rates.YieldCurve.bootstrap([0.020, 0.025, 0.028, 0.030], [1, 2, 3, 4])
bond = rates.Bond(face=100, coupon=0.05, maturity=4, frequency=2)
bond.z_spread(bond.price(0.045), curve)
# Credit: Basel capital and a correlated portfolio
credit.basel_irb_capital(pd=0.01, lgd=0.45, ead=1e6).risk_weight # 92.32%
pds, lgds, eads = [0.01] * 500, [0.45] * 500, [1e6] * 500 # 500 identical loans
credit.credit_portfolio_loss(pds, lgds, eads, rho=0.2, trials=100_000, seed=1).var(0.999)
# Quant: still pure Python for the closed forms
quant.black_scholes(S=100, K=100, T=1, r=0.05, sigma=0.2) # 10.4506
quant.heston_price(S=100, K=100, T=1, r=0.03, v0=0.04, kappa=2, theta=0.04, xi=0.5, rho=-0.7)
Verification
riskpy-verify # 111 checks (125 with SciPy), ~1 second
riskpy-verify --bench # plus timings
A test suite says the code does what it did yesterday. The verification suite
says it is right: put–call parity, A_x = 1 − d·ä_x, Mack's standard error
on the reference triangle, the Basel risk-weight table, Φ⁻¹(Φ(x)) = x, the
contrast ratio of every palette colour — each computed and compared to a
source the library had no hand in. It runs in CI on every push, nightly with
the SciPy oracle, and the docs site publishes the latest report.
Install (developers)
git clone https://github.com/slimboi34/RiskPY.git
cd RiskPY
make install # pip install -e ".[dev]"
make test # pytest — 407 test cases
make verify # the verification suite
Without Make:
pip install -e ".[dev]"
pytest tests/
python -m riskpy.verify
From source you need a C++17 compiler and CMake ≥ 3.15 (pulled automatically by the build backend when missing). Pre-built wheels ship for Python 3.10–3.14 on Linux, macOS and Windows.
The compiled core
from riskpy import FactorModel, FourierTransform, MonteCarloSimulator, LossTriangle
model = FactorModel(initial_base_rate=1000.0)
model.add_multiplier("state", "FL", 3.0)
model.add_numeric_band_multiplier("age", 16, 25, 2.0)
model.calculate({"state": "FL", "age": 19.0}) # 6000.0
# Severity always 1 → aggregate is Poisson(λ), with no sampling error at all
pmf = FourierTransform.compound_poisson_pmf([0.0, 1.0], expected_frequency=2.0, grid_size=64)
sim = MonteCarloSimulator(trials=200_000, seed=7)
losses = sim.simulate_aggregate_loss(5.0, 8.0, 0.5)
import riskpy is headless-safe; the Tkinter GUI (UnderwritingApp) loads
only when you use it.
Releasing (maintainers)
# After merging to main and bumping version in pyproject.toml:
make release # tags vX.Y.Z and pushes — that is the whole release
The tag push checks the tag against the version, builds the sdist and wheels and smoke-tests their imports, uploads to PyPI via Trusted Publishing, and cuts the GitHub Release with artifacts attached. Details in docs/PUBLISHING.md.
| Workflow | When | What |
|---|---|---|
| CI | every push/PR to main |
build + pytest + verification on Linux/macOS/Windows, Python 3.10–3.13 |
| Verify | nightly | verification with the SciPy oracle, benchmarks, JSON report as an artifact |
| Docs | push to main |
regenerate the gallery and the verification report, build and publish the site |
| Publish | push of a v* tag |
version guard → sdist + cp310–cp314 wheels (import-tested) → PyPI → GitHub Release |
License
MIT — free for personal and commercial use.
- GitHub: github.com/slimboi34/RiskPY
- PyPI: pypi.org/project/open-riskpy
1 maintainer
Metadata
1 maintainer
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