Why I Use Go (Instead of Python, R, or VBA) 🚀

From spreadsheets to simulations — a personal take on modern actuarial programming that'll make you question everything you thought you knew about actuarial education

Backstory: From Formulas to Functions (And My Sanity) 📊

In actuarial science, we're often trained to live in spreadsheets, scripting languages, and statistical software that makes you want to throw your laptop out the window. I've written thousands of formulas in Excel, built models in R that crashed mysteriously, and debugged NumPy code in Jupyter notebooks until 3 AM. But when I started building actual systems — not just models that work "most of the time" — those tools fell @#$% short.

That's where I started exploring Go (aka Golang). A language that's blazing fast, beautifully structured, compiled to perfection, and surprisingly simple (unlike my relationship with R's error messages). Over time, it became my main tool for building actuarial applications — from Actuworry (an open-source reserving simulator that doesn't make you cry) to tools for life insurance pricing and strategy modeling.

Why Go? (A Practical Case for Students, Actuaries, Quants & Data Folks Who Value Their Time and Want to make money)

1. Go Is Fast — Like, Really @#$% Fast 🔥

Go compiles to native machine code. That meanssimulations that take minutes in Python or hours in R run in seconds with Go. There's no interpreter overhead, no GIL bottleneck, or hidden type coercion surprises that make you question your life choices.

// Go: Monte Carlo simulation snippet
func runSimulation(policies []Policy, scenarios int) []Result {
    results := make([]Result, len(policies))
    var wg sync.WaitGroup
    
    for i, policy := range policies {
        wg.Add(1)
        go func(p Policy, idx int) {
            defer wg.Done()
            results[idx] = simulatePolicy(p, scenarios)
        }(policy, i)
    }
    
    wg.Wait()
    return results
}
// This runs in parallel. Effortlessly. No drama.

In one reserving prototype, my Go version ran as fast than the Python equivalent using pandas and NumPy. , NumPy is written in C and C++ so you know its fast,And it used less memory. And I didn't have to install 47 dependencies. And it didn't break when I looked at it wrong.

2. Concurrency Made Simple (Finally!) 🎯

Need to simulate 10,000 policy paths for some wierd resaon?(i have no idea why) Or run stress testing across assumption sets that would make a CPU weep? Go's goroutines and channels make concurrent code readable and powerful without requiring a degree in computer science.

// Want to run something concurrently in Go?
go calculateReserves(policyBatch)

// That's it. Seriously. That's the whole thing.
// Compare this to Python's multiprocessing hell:

# Python concurrent code (prepare for pain)
from multiprocessing import Pool, Queue, Process
import threading
# ... 47 more lines of setup code
# ... error handling for when it inevitably breaks
# ... crying

In Python, you'd need multiprocessing, asyncio, or a framework. In R, you'd probably give up and go get a coffee ☕. In Go, it's just go someFunc(). That's it. That's the end.

3. One Binary, No Drama (Deployment in Heaven)

Remember fighting with virtual environments? Or pip install errors that made you question reality? Or R package version mismatches that sent you into therapy? Or explaining to your boss why the model works on your machine but not theirs?

# The Python deployment experience:
pip install -r requirements.txt
# Error: package X conflicts with Y
# Error: Python version mismatch
# Error: your computer is cursed
# Error: have you tried turning it off and on again?

# The Go deployment experience:
go build
# Done. Ship it. 🚀

With Go, I compile my app into a single static binary. I can send it to anyone — no runtime needed. No dependencies. No excuses. No "works on my machine" syndrome. This makes it perfect for production-grade simulations, internal tools, and even actuarial APIs that don't make your Dev team cry.

4. Clean Code, Strong Types (Type Safety = Mental Safety)

Go is statically typed, which catches many bugs at compile time instead of during your 3-hour Monte Carlo simulation. It encourages you to write clear, testable, and maintainable code that doesn't make future-you want to time-travel back and slap present-you.

// Go catches this at compile time:
func calculatePremium(age string, amount float64) float64 {
    return age * amount // ERROR: can't multiply string by float64
}

// Python lets this explode at runtime:
def calculate_premium(age, amount):
    return age * amount  # Hope and pray age is a number!

No duck typing surprises. No undefined errors halfway through a 3-hour simulation. It'll tell you you're wrong early, loudly, and helpfully.

And tools like go test, go fmt, and go mod tidy are built in. You don't need to install linters, formatters, or dependency managers. Go handles that @#$% out of the box like a responsible adult.

5. Strong Standard Library (Batteries Actually Included) 🔋

Go's standard library covers almost everything I need: math, file I/O, HTTP servers, JSON encoding, cryptography, time handling — all baked in and ready to rock.

// Need an HTTP server for your actuarial API?
package main

import (
    "encoding/json"
    "net/http"
)

type Premium struct {
    Age    int     `json:"age"`
    Amount float64 `json:"amount"`
    Rate   float64 `json:"rate"`
}

func calculateHandler(w http.ResponseWriter, r *http.Request) {
    // Your actuarial logic here
    premium := Premium{Age: 35, Amount: 100000, Rate: 0.05}
    json.NewEncoder(w).Encode(premium)
}

func main() {
    http.HandleFunc("/calculate", calculateHandler)
    http.ListenAndServe(":8080", nil)
}
// That's a working web server. No Flask. No Django. No tears.

I rarely need third-party libraries. Compare that to Python or R, where your script starts with 10 lines of imports just to plot a graph or load a CSV without everything catching fire.

Technical Comparison (Go vs Python vs R vs Your Sanity)

Language Roast ☕ (No Programming Language is Safe)

• Python: "It's fast and simple… if you don't use loops, need concurrency, or care about deployment." The GIL and its speed on large-scale projects is a meme for a reason. Also, pip install errors are a form of psychological warfare.
• R: "It works until you hit '[1] NA' and spend 3 hours debugging what should be a simple calculation." R is like that brilliant professor who explains calculus using interpretive dance — technically correct, practically useless. Also, whoever thought <- was a good assignment operator was clearly having a bad day.
• Rust: "You will write the most memory-safe actuarial tool ever — eventually, after sacrificing your firstborn to the borrow checker." Rust is like dating someone who's perfect but makes you explain every life decision. expected `&str`, found `String` — thanks, I hate it.
• Zig: "The language that makes Go feel like a luxury sedan." Great potential, but too early for production actuarial use. Plus I still don't know how to use it properly. std.debug.print("Hello World!", .{}) killed me. What are those curly braces even for?? Who hurt you, Zig developers?
• VBA: "The language equivalent of using a spoon to dig a tunnel." If you're still writing VBA in 2025 and later, I'm calling an abuse hotline. Excel macros are not in any career path worth pursuing, they're a cry for help.
• Java: "Enterprise-grade verbosity for when you want to write 50 lines of code to print 'Hello World'." Java is like that coworker who takes 20 minutes to explain what could be said in 2 sentences. AbstractSingletonProxyFactoryBean is a real class name, not a joke.

Modern Language Suggestions for Actuarial Workflows 🛠

If you're still stuck in Excel cause people wanna use a spreadsheet as a database, R purgatory, Python dependency limbo, or VBA torture chamber, here's a nudge toward exploring other options that won't make you question your career choices:

• Go: For building pricing/reserving engines, CLI tools, APIs, internal microservices that actually work
• Rust: For safe, high-performance actuarial engines when you need maximum control and don't mind fighting the compiler for 6 months
• Julia: For numerical analysis, statistical models, and actuarial forecasting (it's like R but actually fast)
• Elixir: For real-time policy monitoring, data pipelines, and concurrent dashboards that don't crash when someone looks at them wrong
• TypeScript + Vue: For building modern interfaces for internal actuarial tools. @#$% React and its over-engineered ecosystem
• Scala: If you're in a bank and forced to use Spark — at least do it with class (pun intended)
• Haskell: For when you want your actuarial models to be mathematically beautiful and completely incomprehensible to your coworkers

My Current Stack (The Tools That Don't Make Me Cry)

• Go: Core engine (pricing, simulation, validation, everything important)
• Python: Exploratory data analysis, plotting, quick prototyping that I'll rewrite in Go later
• R: Stats-heavy coursework, historical actuarial studies, making lecturers happy
• Vue + Tailwind: Frontend blog and internal dashboard UI that doesn't look like it's from 2003
• PostgreSQL: When I need a database that works and doesn't randomly corrupt my data because of "macro security issues"

What I'm Building in Go (And You Should Too)

• Actuworry: A simulation engine for actuarial pricing/reserving that doesn't make you want to switch careers
• Life Pricing Tool: Command-line tool for life insurance pricing/reserves (built with Cobra CLI)
• Quant Backtesting System: Simulations and signal testing for when Excel just isn't cutting it anymore (in progress, will be open source)
• Whatever I Can Get Away With: Give me a chance and I'll write it in Go. Calculator? Go. Web scraper? Go. Microwave controller? Probably Go.
• Internal Actuarial API: RESTful service using the standard library for policy calculations that other teams can actually use, I'll need a job before I can get anywhere with this
Here's a CV and Resume Link

Getting Started: Your First Go Actuarial Program

Want to dip your toes in? Here's a simple actuarial calculation in Go that's already more robust than most Excel models:

package main

import (
    "fmt"
    "math"
)

type Policy struct {
    Age         int
    CoverageAmount float64
    Term        int
}

func calculatePremium(p Policy, mortalityRate float64, interestRate float64) float64 {
    // Simple net single premium calculation
    discountFactor := math.Pow(1+interestRate, float64(-p.Term))
    survivalProb := math.Pow(1-mortalityRate, float64(p.Term))
    
    return p.CoverageAmount * discountFactor * survivalProb
}

func main() {
    policy := Policy{
        Age:            35,
        CoverageAmount: 100000,
        Term:           20,
    }
    
    premium := calculatePremium(policy, 0.001, 0.05)
    fmt.Printf("Premium for age %d: $%.2f\n", policy.Age, premium)
}

// Run this with: go run main.go
// No dependencies, no virtual environments, no tears

Resources to Get You Started (No Excuses Now) 📚

• A Tour of Go: Interactive tutorial that won't bore you to death
• Effective Go: Best practices straight from the source
• Awesome Go: Curated list of Go packages and resources
• pkg.go.dev: Go package documentation that's actually helpful
• Go by Example: Practical examples for common programming tasks

Final Thoughts (The TL;DR You Actually Want) 💭

Go may not be as statistically rich as R or as instantly approachable as Python — and it certainly won’t replace Excel’s role as the world’s unofficial actuarial dashboard — but when your work moves beyond ad-hoc analysis into systems, scalable pipelines, and production-grade tools, Go starts to look like the missing piece.

Python shines when you need quick prototyping, data wrangling, or leveraging massive libraries like pandas, NumPy, or scikit-learn. R dominates in statistical modelling, rapid exploratory analysis, and academic-style reporting. Excel is unbeatable for instant visualization, business-friendly presentation, and light-weight scenario work. But, let’s be honest: if you’re using Excel or CSVs as a database, it’s time for an intervention — that’s not a database, it’s more like a ledger.

And if you find yourself trapped in the dark abyss of VBA debugging, asking, “Is there more to life than Subs and Dim statements?” — you’re not alone. VBA is that friend you call only when all else fails or when you’re employed enough to suffer through 2 AM macro chaos.

But here’s where Python, R, and Excel all start showing cracks: Excel crashes under big data, Python slows down in CPU-heavy loops without C glue, and R can turn fragile in deployment. Go swoops in with speed, memory safety, and easy concurrency, letting you run hefty actuarial simulations, stream real-time data, and build services that won’t throw a tantrum under pressure.

Worst case? You build a CLI or backend tool running 10x faster than your current Python/R script and earn some quiet office street cred. Best case? You become the hero who rescues coworkers from 2 AM Excel macro meltdowns and Monte Carlo waiting games.

"Give a person a Python script, and they'll struggle with dependencies for an hr, give a person R and they'll need glasses, give a person VBA after the 4th hr they will become philosophers" - Anonymous Actuarial Programmer (probably crying)"

Here are some good quick reads:

Empowering actuaries with data science: For actuaries venturing into the realm of programming

Skills for graduating actuaries: The actuary from 50 years ago, writing out calculations by hand, would have found it difficult to imagine today’s actuary having such immense data and computing power at their disposal.