Skip to content

How It Works

riskpy is a hybrid framework designed to give actuaries the ease of Python with the speed of C++.

The Architecture

The compiled core is a C++ extension module (riskpy.cpp_underwriter) built with pybind11 and scikit-build-core. The modelling layers — riskpy.mc, viz, quant, life, reserving, rates and credit — are readable Python on top of NumPy.

  1. The Python Frontend: UnderwritingApp is Python. It renders the Tkinter GUI, collects user inputs and reads CSV files.
  2. The C++ Backend Engine: FactorModel, RiskEngine, MonteCarloSimulator, LossTriangle and the other core classes are C++ exposed through pybind11. FactorModel stores its rules and evaluates them in C++, and inputs cross into C++ as std::variant values, so text (like "FL" for State) and numbers (like 25 for Age) share one input map.
  3. Excel Output: ExcelExporter writes .xlsx binaries with the OpenXLSX C++ library.

app.calculate_batch() reads the CSV and loops over its rows in Python, calling the C++ engine once per row, then hands the whole book to the C++ Excel writer in a single call. The C++ code does not release Python's Global Interpreter Lock (GIL): the speed comes from compiled arithmetic, not from parallel threads.

Why This Matters

If you try to loop through 1,000,000 rows of an Excel spreadsheet in pure Python using if/else ladders to calculate premiums, it takes minutes. If you try to run 100,000 Monte Carlo simulations using standard Python generators, it takes even longer.

By compiling the math into standard C++ <random> and <cmath> libraries, riskpy handles millions of iterations quickly. You construct the model in Python, but you execute it in C++.