v-star: The Actuarial Engine That Made Me Stop Using Excel
A zero-dependency Go engine for concurrent financial simulations. 1M policies in under 300ms, Monte Carlo at 100k paths in ~100ms, CLI-first with composable Unix-style pipes. The spreadsheet killer nobody asked for but everyone needs.
Most actuarial software is either an enterprise tool that costs six figures or an Excel model that breaks when you breathe on it. I've spent enough time in both to know there's a gap you could drive a truck through.
So I built v-star.
It's a high-performance actuarial engine in Go. Zero dependencies. Just the standard library. It handles concurrent financial simulations, mass policy valuations, and Monte Carlo interest rate modeling at speeds that'll make you wonder why you ever tolerated spreadsheets.
The Name
v-star (v*) is actuarial notation. When an annuity's payments compound at rate j but are discounted at rate i, the adjusted discount factor is:
v* = (1 + j) · v
It's an inside joke from university — one my lecturer and coursemates will recognize. If the math is niche enough, the project name should be too.
What It Actually Does
Policy valuation — Stream-processes CSV files of insurance policies and calculates present values. Handles over a million records in under 300ms. It streams data instead of loading everything into RAM, so your laptop won't start sounding like a jet engine.
Monte Carlo simulation — Generates interest rate paths for stochastic modeling. 100,000 paths with 10 time steps in about 100ms. Uses geometric Brownian motion with configurable drift and volatility. You can sit there tweaking parameters and watching it churn through simulations like they're nothing.
CLI-first — No web dashboard, no config files, no GUI. Just flags and pipes:
./v-star -i 0.05 -j 0.02
./v-star read policies.csv --benchmark
./v-star montecarlo --paths=100000 --steps=10
Everything is composable. Pipe stuff in, get stuff out. Unix philosophy applied to actuarial science.
Why This Exists
I got tired of tools that treat actuarial work like it needs to be hidden behind layers of abstraction. The math isn't that complicated. The data isn't that special. What's hard is doing it fast, at scale, and in a way you can actually audit.
v-star is the opposite of a black box. Every calculation is readable. Every assumption is visible. No hidden columns, no macros that run in the background, no "we've always done it this way."
The code is on GitHub. Go read it. Or don't. But if you're still running actuarial models in Excel, ask yourself why.