open-riskpy 0.1.0

A high-performance C++ powered actuarial pricing and underwriting framework.

RiskPY

A hyper-fast, C++ powered pricing framework built explicitly for actuaries across all lines of insurance (P&C, Life, Health).

The Painkiller

Currently, actuaries are forced to write complex, slow if/else ladders in Python or rely on clunky Excel spreadsheets to process rating models and massive stochastic simulations. RiskPY solves this by providing a declarative, high-performance C++ backend combined with a gorgeous built-in Desktop UI.

You write clean, readable pricing rules in Python. We execute them instantly in C++.

Features

• Native FactorModel Engine: A declarative C++ rating engine matching top P&C insurance pricing stategies.
• Built-in ActuarialMath: Core life/annuity computations exposed directly from C++ to python.
• Bulk CSV Batching: Crunch 10,000+ policy quotes simultaneously without Python GIL bottlenecks.
• Monte Carlo Predictive Modeling: Simulate millions of Stochastic Tail Risk scenarios natively in C++ (Lognormal Severities for P&C, Systemic Mortality Shocks for Life) instantly generating predictive Histograms in Tkinter.

Documentation

• How It Works: Explore the dual C++ to Python architecture allowing zero-cost rule evaluation.
• Actuarial Use Cases: Explore example snippets building P&C Multiplicative Raters, Lognormal Monte Carlo Tail Sims, and Core Life Annuity Present Value tables.
• Publishing & Accessibility: Discover how to upload this module to GitHub natively and distribute it across the world via PyPI twine.

Installation

pip install open-riskpy

Quick Start

from riskpy import UnderwritingApp, FactorModel

app = UnderwritingApp(title="Actuarial Pricing Tool")

# 1. Declare User Form Fields
app.add_field("state", "Location State", "A", choices=["NY", "CA", "FL"])
app.add_field("age", "Client Age", "B")

# 2. Build the Multiplicative Rating Engine
model = FactorModel(initial_base_rate=500.0)
model.add_multiplier("state", "CA", 2.0)  
model.add_multiplier("state", "FL", 3.0)  
model.add_numeric_band_multiplier("age", 18, 25, 1.4) 

app.set_factor_model(model)

# 3. Launch the Dual-Pane GUI (Pricing forms & Interactive Monte Carlo Simulations)
app.run()