proteusllp-actuarial-library 0.1.0

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 development!

📚 Development Guide - Get started with development setup and testing

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, beta=2).generate()
expenses = distributions.LogNormal(mu=1, sigma=0.5).generate()

# Apply statistical dependencies
copulas.GumbelCopula(alpha=1.2, n=2).apply([losses, expenses])

# Variables are now correlated
total = losses + expenses

Installation

# Basic installation
pip install proteus-actuarial-library

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

Documentation

• 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 a proof of concept. 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 in github

Contributing

You are welcome to contribute pull requests