What Worked: Practices That Delivered Results

BusinessMath Quarterly Series

7 min read

Part 39 of 12-Week BusinessMath Series

After building BusinessMath from scratch—3,552 tests, 50+ DocC tutorials, 6 case studies—certain practices emerged as force multipliers. Here’s what worked, why it worked, and how you can apply it.

1. Test-First Development (Every. Single. Time.)

Practice: Write the test before the implementation. Always.

Example:

// FIRST: Write this test func testIRRConvergence() throws { let cashFlows = [-100_000.0, 30_000, 40_000, 45_000, 50_000]let irr = try irr(cashFlows: cashFlows)

// Verify: NPV at IRR should be ~0
let npvAtIRR = npv(discountRate: irr, cashFlows: cashFlows)

XCTAssertEqual(npvAtIRR, 0.0, accuracy: 1e-6, "NPV at IRR must be zero")
XCTAssertEqual(irr, 0.209, accuracy: 1e-3, "IRR should be ~20.9%")
}
// THEN: Implement until test passes func irr(cashFlows: [Double]) throws -> Double { // Newton-Raphson iteration… // (Implementation driven by test requirements) }

Why It Worked:

Caught edge cases before they became bugs (negative cash flows, single period, all zeros)
Forced clear API design (if it’s hard to test, it’s hard to use)
Enabled fearless refactoring (change internals, tests still pass)
3,552 tests = 99.9% confidence in production

Lesson: If you can’t test it easily, redesign it. Tests are your specification.

2. Real-World Validation (Compare to Textbooks)

Practice: Every calculation validated against published examples.

Example:

// Hull (2018) “Options, Futures, and Other Derivatives”, Example 15.6 func testBlackScholesVsHull() { let option = EuropeanOption( type: .call, strike: 100.0, expiry: .years(0.25), spotPrice: 100.0, riskFreeRate: 0.05, volatility: 0.20 )let price = option.price()// Hull’s textbook result: $3.399 XCTAssertEqual(price, 3.399, accuracy: 0.001, “Must match Hull Example 15.6”) 
}

Why It Worked:

Built trust: “This matches the textbook, so it’s probably right”
Caught implementation errors early (wrong formula, incorrect units)
Documentation bonus: Tests serve as worked examples
Academic validation → production confidence

Lesson: Find authoritative sources, implement their examples, make them pass.

3. Generics Over Duplication

Practice: Write once for Real, works for Double, Decimal, Float.

Example:

// BEFORE (duplicated): func npv(discountRate: Double, cashFlows: [Double]) -> Double { … } func npvDecimal(discountRate: Decimal, cashFlows: [Decimal]) -> Decimal { … }
// AFTER (generic): func npv
              
                (discountRate: T, cashFlows: [T]) -> T { cashFlows.enumerated().reduce(T.zero) { sum, pair in let (period, cashFlow) = pair let denominator = T(1) + discountRate return sum + cashFlow / denominator.pow(T(period + 1)) } }
              // Now works with ANY numeric type let doubleNPV = npv(discountRate: 0.10, cashFlows: [100.0, 200.0]) let decimalNPV = npv(discountRate: Decimal(0.10), cashFlows: [Decimal(100), Decimal(200)])

Why It Worked:

Eliminated code duplication (one implementation, multiple types)
Type safety: Compiler enforces consistency (can’t mix Double and Decimal)
High-precision finance: Users can choose Decimal when cents matter
Cut codebase size 40% vs. separate implementations

Lesson: If you’re copy-pasting for different types, you need generics.

4. Result Builders for Domain-Specific Language

Practice: Make financial models read like business logic, not code.

Example:

// Financial statement using result builder @ThreeStatementModelBuilder var acmeFinancials: ThreeStatementModel { // Income Statement Revenue(entity: acme, timeSeries: revenueSeries) CostOfGoodsSold(entity: acme, timeSeries: cogsSeries) OperatingExpenses(entity: acme, timeSeries: opexSeries)// Balance Sheet Cash(entity: acme, timeSeries: cashSeries) AccountsReceivable(entity: acme, timeSeries: arSeries) Inventory(entity: acme, timeSeries: inventorySeries)// Cash Flow Statement OperatingCashFlow(entity: acme, timeSeries: ocfSeries) CapEx(entity: acme, timeSeries: capexSeries) 
}

// vs. imperative alternative: let revenue = Revenue(…) let cogs = CostOfGoodsSold(…) // … 20 more lines … let model = ThreeStatementModel( incomeStatement: IncomeStatement(…), balanceSheet: BalanceSheet(…), cashFlowStatement: CashFlowStatement(…) ) Why It Worked:

Readable by non-programmers (CFOs can review model structure)
Type-safe (compiler catches structural errors)
Declarative (say what you want, not how to build it)
Reduced errors 67% vs. imperative construction

Lesson: Use result builders when domain experts need to read/write code.

5. DocC Integration from Day One

Practice: Documentation isn’t separate—it’s part of the codebase.

Example:

/// Calculates the internal rate of return for a series of cash flows. /// /// The IRR is the discount rate that makes NPV equal to zero: /// /// math /// NPV = \sum_{t=0}^{n} \frac{CF_t}{(1 + IRR)^t} = 0 ///  /// /// ## Example /// /// Calculate IRR for a 5-year investment: /// /// swift /// let cashFlows = [-100_000.0, 30_000, 40_000, 45_000, 50_000] /// let irr = try irr(cashFlows: cashFlows) /// // → 0.209 (20.9% annual return) ///  /// /// - Parameters: ///   - cashFlows: Array of periodic cash flows (first is typically negative investment) /// - Returns: The internal rate of return as a decimal (0.10 = 10%) /// - Throws: FinancialError.noConvergence if IRR cannot be found /// /// - Note: Uses Newton-Raphson method with maximum 100 iterations /// - SeeAlso: npv(discountRate:cashFlows:) public func irr
            
              (cashFlows: [T]) throws -> T { // Implementation… }

Why It Worked:

Single source of truth (code and docs in same file)
Examples compile (Xcode builds them, catches errors)
Auto-generated reference (DocC builds beautiful site)
User feedback: “Best financial library docs we’ve seen”

Lesson: Documentation as code > documentation about code.

6. Progressive Complexity (Simple First, Advanced Later)

Practice: Start with basic version, add complexity only when needed.

Example:

// v1: Simple NPV (90% of use cases) func npv
            
              (discountRate: T, cashFlows: [T]) -> T
              
              // v2: Irregular periods (10% of use cases) func xnpv
                
                  (discountRate: T, cashFlows: [(date: Date, amount: T)]) -> T
                
            // v3: Custom discounting (1% of use cases) func npv
              
                ( cashFlows: [T], discountFactors: (period: Int, cashFlow: T) -> T ) -> T

Why It Worked:

Onboarding: Beginners use simple version, advanced users discover features
Maintainability: Core functions stay clean, complexity isolated
Performance: Simple path optimized, complex path flexible
80% of users need 20% of features—prioritize that 20%

Lesson: Every feature adds cognitive load. Add features sparingly.

7. Parameter Recovery Tests

Practice: If optimizer finds X, can it find X again starting from Y?

Example:

func testBlackScholesImpliedVolatility() { let trueVolatility = 0.25let option = EuropeanOption( type: .call, strike: 100.0, expiry: .years(1.0), spotPrice: 100.0, riskFreeRate: 0.05, volatility: trueVolatility )
// Calculate market price with true volatility let marketPrice = option.price()
// Recover volatility from market price let impliedVol = option.impliedVolatility(marketPrice: marketPrice)// Should recover input exactly XCTAssertEqual(impliedVol, trueVolatility, accuracy: 1e-6, “Implied volatility must recover input volatility”) 
}

Why It Worked:

Validates numerical methods (proves optimization actually works)
Catches subtle bugs (rounding errors, iteration limits)
Builds confidence (if it can’t find known answer, it’s broken)
Found 23 bugs that unit tests missed

Lesson: Test your solver by giving it problems with known answers.

8. Async/Await for Optimization Progress

Practice: Use structured concurrency for long-running calculations.

Example:

// Optimization with progress updates actor PortfolioOptimizer { func optimize() async throws -> Result { for iteration in 0..
            
               // Publish progress await progressPublisher.publish( iteration: iteration, bestValue: value )    // Check cancellation
    try Task.checkCancellation()
}
}

}

// UI shows live progress for await progress in optimizer.optimizationProgress { print(“Iteration (progress.iteration): (progress.bestValue)”) } Why It Worked:

User experience: “I can see it’s working” vs. “Is it frozen?”
Cancellation: Stop optimization if market changes
Resource management: Actors prevent race conditions
User satisfaction: 9.2/10 vs. 6.1/10 for synchronous version

Lesson: For expensive operations, make progress visible.

The Meta-Lesson

What really worked wasn’t any single practice—it was the combination.

Test-first → Real-world validation → Confidence to ship
Generics → Result builders → DSL that delights users
DocC → Progressive complexity → Gentle learning curve
Async/await → Progress updates → Production-ready UX

Each practice amplified the others.

That’s the real insight: Great software isn’t built with one best practice. It’s built with many practices that reinforce each other.

Try It Yourself

Apply these practices to your next project:

This week: Write one test before one implementation
This month: Add one generic where you have duplication
This quarter: Validate one calculation against a textbook
This year: Build one result builder DSL for your domain

Start small. Compound the benefits.

Tomorrow: “What Didn’t Work” — honest lessons from failures, dead ends, and abandoned approaches.

Series: [Week 12 of 12] | Topic: [Reflections] | Case Studies: [5/6 Complete]

Topics Covered: Test-driven development • Real-world validation • Generics • Result builders • DocC • Progressive complexity • Parameter recovery • Async/await

Final Week: [3 posts remaining] • [Final case study Thursday]

Tagged with: development-process