proteusllp-actuarial-library 0.2.11

Proteus Actuarial Library: A package for building and running stochastic actuarial models in Python.

Proteus Actuarial Library

An actuarial stochastic modeling library in python.

Note This library is still in beta!

Introduction

The Proteus Actuarial Library (PAL) is a fast, lightweight framework for building simulation-based actuarial and financial models. It handles complex statistical dependencies using copulas while providing simple, intuitive syntax.

Key Features:

• Built on NumPy/SciPy for performance
• Optional GPU acceleration with CuPy
• Automatic dependency tracking between variables
• Comprehensive statistical distributions
• Clean, Pythonic API

Quick Start

from pal import distributions, copulas

# Create stochastic variables
losses = distributions.Gamma(alpha=2.5, theta=2).generate()
expenses = distributions.LogNormal(mu=1, sigma=0.5).generate()

# Apply statistical dependencies
copulas.GumbelCopula(theta=1.2).apply([losses, expenses])

# Variables are now correlated
total = losses + expenses

Installation

# Basic installation
pip install proteusllp-actuarial-library

# With GPU support
pip install proteusllp-actuarial-library[gpu]

Documentation

Read the full documentation on Read the Docs

• Usage Guide - Comprehensive examples and API documentation
• Development Guide - Setting up the development environment and running tests
• Examples - Example scripts showing how to use the library

Project Status

PAL is currently in early release preview (beta). There are a limited number of supported distributions and reinsurance contracts. We are working on:

• Adding more distributions and loss generation types
• Making it easier to work with multi-dimensional variables
• Adding support for Catastrophe loss generation
• Adding support for more reinsurance contract types (Surplus, Stop Loss etc)
• Stratified sampling and Quasi-Monte Carlo methods
• Reporting dashboards

Issues

Please log issues on our github page.

Contributing

You are welcome to contribute pull requests. Please see the Contributer License Agreement

📚 Development Guide - Get started with development setup and testing