quActuary Documentation

quActuary is a Python library for classical and Quantum-accelerated actuarial loss modeling. It provides frequency and severity distribution models, excess-loss and risk measure pricing, and integration with Qiskit for quantum-enhanced simulations.

Features

  • Extensive frequency distributions: Poisson, Binomial, Negative Binomial, Geometric, etc.

  • Support for the generalized Panjer (a, b, k) frequency distribution class.

  • Comprehensive severity distributions: Exponential, Gamma, Lognormal, Pareto, Continuous Uniform, etc.

  • Mixture models, empirical distributions, and QMC wrappers.

  • Classical and quantum pricing models with backend switching.

  • MCP Server Integration: Use quActuary tools directly in Claude via Model Context Protocol.

Installation

pip install quactuary

To use the MCP Server integration:

pip install quactuary[mcp]

Getting Started

import quactuary as qa
import quactuary.book as book

from datetime import date
from quactuary.book import (
   LOB, PolicyTerms, Inforce, Portfolio)
from quactuary.distributions.frequency import Poisson
from quactuary.distributions.severity import Exponential
from quactuary.pricing import PricingModel

# Frequency model example
freq = Poisson(mu=3.5)
print(freq.pmf(2))

# Severity model example
sev = Exponential(scale=1000.0)
print(sev.pdf(500.0))

### Classical pricing model example ###

# Define the policy terms
glpl_policy = PolicyTerms(
   effective_date=date(2026, 1, 1),
   expiration_date=date(2027, 1, 1),
   lob=LOB.GLPL,
   exposure_base=book.SALES,
   exposure_amount=10_000_000_000,
   retention_type="deductible",
   per_occ_retention=1_000_000,
   coverage="occ"
)

# Define the inforce block model
glpl_inforce = Inforce(
   n_policies=700,
   terms=glpl_policy,
   frequency=freq,
   severity=sev,
   name = "GLPL 2026 Bucket"
)

portfolio = Portfolio(glpl_inforce)

# Initialize the pricing model
pricing = PricingModel(portfolio)

# Run the classical simulation
result = pricing.simulate()
result.estimates

### Output: ###
# 0.18495897346170082
# 0.0006065306597126335
# {'mean': np.float64(2449951.874110688),
#  'variance': np.float64(4860457942.058481),
#  'VaR': 2565751.9589995327,
#  'TVaR': 2596372.4576630797}

Documentation

User Guide:

API Reference:

Performance:

Development:

Indices and Tables