RiskPY: Enterprise Actuarial Engine 🚀
A hyper-fast, C++ powered pricing framework for actuaries and quants (P&C, Life, Health): declarative rating, Monte Carlo, chain-ladder reserving, Fourier aggregate loss, option pricing, batch Excel export, and an optional desktop GUI.
📖 Documentation & user manual → slimboi34.github.io/RiskPY
Worked examples, every chart the library produces, and the full API — start there.
pip install open-riskpy # core, zero dependencies
pip install open-riskpy[sim] # + the Monte Carlo engine
pip install open-riskpy[viz] # + 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.formula
def annual_loss(claim_count, severity, inflation):
return claim_count * severity * (1 + inflation)
result = model.run(200_000, seed=42)
print(result.summary()) # mean, VaR, TVaR at every level
result.plot() # distribution with the tail marked
v0.2.8 — a generic Monte Carlo engine (any formula, any distributions), a six-chart visualisation layer, and a quantitative finance module: Black–Scholes with Greeks and implied vol, GBM and jump-diffusion paths, portfolio VaR/ES, and Heston pricing by Fourier inversion. Plus a documentation site. See RELEASE_NOTES.md.
Install (end users)
pip install open-riskpy
# upgrade
pip install -U open-riskpy
# The generic Monte Carlo engine (riskpy.mc) — NumPy only
pip install "open-riskpy[sim]"
# Charts (riskpy.viz) — NumPy + Matplotlib
pip install "open-riskpy[viz]"
# Optional desktop GUI
pip install "open-riskpy[gui]"
Pre-built wheels are published for common platforms. From source you need a C++17 compiler and CMake (pulled automatically by the build backend when missing).
Core install has no heavy Python deps (matplotlib is optional via [gui]).
import riskpy is headless-safe (no Tkinter required). The GUI (UnderwritingApp) loads only when you use it.
Install (developers)
git clone https://github.com/slimboi34/RiskPY.git
cd RiskPY
make install # pip install -e ".[dev]"
make test # pytest
make smoke # quick import check
# or: make check
Without Make:
pip install -e ".[dev]"
pytest tests/
Core features
| Module | Role |
|---|---|
| FactorModel | Multiplicative rating rules (exact + numeric bands) |
| MonteCarloSimulator | P&C, life, health, GBM, catastrophe simulations |
| ActuarialMath | PV, FV, loss ratio, mortality |
| LossTriangle | Chain ladder, IBNR |
| ExperienceRating / ExposureRating | Credibility, ILFs, layers |
| RateAnalyzer | On-level, trend, combined ratio, indicated rate change |
| FourierTransform | FFT / iFFT, convolution, compound-Poisson PMF |
| UnderwritingApp | Tkinter GUI + CSV batch → Excel |
| RiskEngine / ExcelExporter | Low-level orchestration & xlsx |
Docs: docs/API_REFERENCE.md · docs/TUTORIALS.md · docs/PUBLISHING.md
Quick examples
Factor pricing
from riskpy import FactorModel
model = FactorModel(initial_base_rate=1000.0)
model.add_multiplier("state", "FL", 3.0)
model.add_numeric_band_multiplier("age", 16, 25, 2.0)
premium = model.calculate({"state": "FL", "age": 19.0}) # 6000.0
Fourier compound Poisson (no sampling error)
from riskpy import FourierTransform
# Severity always 1 → aggregate is Poisson(λ)
pmf = FourierTransform.compound_poisson_pmf(
severity_pmf=[0.0, 1.0],
expected_frequency=2.0,
grid_size=64,
)
Monte Carlo
from riskpy import MonteCarloSimulator
import numpy as np
sim = MonteCarloSimulator(trials=100_000)
losses = sim.simulate_aggregate_loss(5.0, 10.0, 1.5)
print(np.percentile(losses, 99))
Batch CSV → Excel (needs Tk/desktop for the app wrapper)
from riskpy import UnderwritingApp, FactorModel
app = UnderwritingApp(title="Auto Rater", excel_template="template.xlsx")
app.add_field("state", "State", "A", choices=["NY", "CA", "FL"])
app.add_field("age", "Age", "B")
app.set_premium_column("C", "Premium")
app.set_factor_model(FactorModel(100.0))
total, n = app.calculate_batch("book.csv", "quotes.xlsx")
Releasing to GitHub + PyPI (maintainers)
One-time: configure PyPI Trusted Publishing for this repo (workflow publish.yml, no environment name). Details in docs/PUBLISHING.md.
# 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 builds the sdist and wheels, verifies them, uploads to PyPI, and cuts the GitHub Release with artifacts attached.
| Workflow | When | What |
|---|---|---|
| CI | every push/PR to main |
build + pytest on Linux/macOS/Windows, Python 3.10–3.13 |
| Publish | push of a v* tag (or manual) |
verify → sdist + cp310–cp314 wheels → PyPI → GitHub Release |
Building from source (details)
| Tool | Notes |
|---|---|
| C++17 compiler | Apple Clang, GCC ≥ 9, MSVC 2019+ |
| CMake ≥ 3.15 | Injected by the build backend if needed |
| Python ≥ 3.10 | Prebuilt wheels ship for 3.10–3.14 |
| Ninja / ccache | Optional; speeds CI and local rebuilds |
export CMAKE_BUILD_PARALLEL_LEVEL=$(sysctl -n hw.ncpu 2>/dev/null || nproc)
pip install .
pytest tests/
License
MIT — free for personal and commercial use.
- GitHub: github.com/slimboi34/RiskPY
- PyPI: pypi.org/project/open-riskpy
1 maintainer
Metadata
1 maintainer
Release files for open-riskpy 0.2.9
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