Portfolio Optimization: Building Optimal Investment Portfolios

BusinessMath Quarterly Series

12 min read

Part 23 of 12-Week BusinessMath Series

What You’ll Learn

  • Building maximum Sharpe ratio portfolios (best risk-adjusted return)
  • Finding minimum variance portfolios (lowest risk)
  • Generating efficient frontiers (all optimal portfolios)
  • Implementing risk parity strategies (equal risk contribution)
  • Applying real-world constraints (long-only, leverage limits, position sizing)
  • Understanding the math behind Modern Portfolio Theory

The Problem

Investment portfolio construction requires balancing multiple competing objectives:

  • Risk vs. Return: Higher returns usually mean higher risk, but by how much?
  • Diversification: How do you optimally combine assets that move differently?
  • Constraints: No short-selling, position limits, target returns, leverage restrictions
  • Multiple Solutions: There are infinite ways to allocate capital—which is optimal?

Manual portfolio construction (guessing weights in a spreadsheet) doesn’t find mathematically optimal solutions.

The Solution

Modern Portfolio Theory (MPT), developed by Harry Markowitz, provides a mathematical framework for optimal portfolio construction. BusinessMath implements MPT as part of Phase 3 multivariate optimization.

Maximum Sharpe Ratio Portfolio

Find the portfolio with the best risk-adjusted return:

import BusinessMath

// 4 assets: stocks (small, large), bonds, real estate
let optimizer = PortfolioOptimizer()

let expectedReturns = VectorN([0.12, 0.15, 0.18, 0.05])

// Construct covariance from correlation matrix to ensure validity
// High correlation between Asset 1 (12% return) and Asset 3 (18% return)
// makes Asset 1 a candidate for shorting in 130/30 strategy
let volatilities = [0.20, 0.30, 0.40, 0.10]  // 20%, 30%, 40%, 10%
let correlations = [
	[1.00, 0.30, 0.70, 0.10],  // Asset 1: high corr with Asset 3
	[0.30, 1.00, 0.50, 0.15],  // Asset 2: moderate corr with Asset 3
	[0.70, 0.50, 1.00, 0.05],  // Asset 3: highest return
	[0.10, 0.15, 0.05, 1.00]   // Asset 4: bonds (low correlation)
]

// Convert correlation to covariance: cov[i][j] = corr[i][j] * vol[i] * vol[j]
var covariance = [[Double]](repeating: [Double](repeating: 0, count: 4), count: 4)
for i in 0..<4 {
	for j in 0..<4 {
		covariance[i][j] = correlations[i][j] * volatilities[i] * volatilities[j]
	}
}

// Maximum Sharpe ratio (optimal risk-adjusted return)
let maxSharpe = try optimizer.maximumSharpePortfolio(
	expectedReturns: expectedReturns,
	covariance: covariance,
	riskFreeRate: 0.02,
	constraintSet: .longOnly
)

print("Maximum Sharpe Portfolio:")
print("  Sharpe Ratio: \(maxSharpe.sharpeRatio.number(2))")
print("  Expected Return: \(maxSharpe.expectedReturn.percent(1))")
print("  Volatility: \(maxSharpe.volatility.percent(1))")
print("  Weights: \(maxSharpe.weights.toArray().map { $0.percent(1) })")

Output:

Maximum Sharpe Portfolio:
  Sharpe Ratio: 0.62
  Expected Return: 9.6%
  Volatility: 12.2%
  Weights: ["38.6%", "18.5%", "0.0%", "42.9%"]

The result: Despite bonds having the lowest return (5%), they get the highest allocation (43%) because they reduce portfolio risk while maintaining strong Sharpe ratio.

Minimum Variance Portfolio

Find the portfolio with the lowest possible risk:

let minVar = try optimizer.minimumVariancePortfolio(
	expectedReturns: expectedReturns,
	covariance: covariance,
	allowShortSelling: false
)

print("Minimum Variance Portfolio:")
print("  Expected Return: \(minVar.expectedReturn.percent(1))")
print("  Volatility: \(minVar.volatility.percent(1))")
print("  Weights: \(minVar.weights.toArray().map { $0.percent(1) })")

Output:

Minimum Variance Portfolio:
  Expected Return: 6.4%
  Volatility: 9.3%
  Weights: ["16.4%", "2.2%", "0.0%", "81.4%"]

The trade-off: Lowest risk (9.3% volatility) but also lowest return (6.4%). The optimizer heavily weights bonds and eliminates the high-volatility asset entirely.

Efficient Frontier

The efficient frontier shows all optimal portfolios—those with maximum return for each level of risk:

// Generate 20 points along the efficient frontier
let frontier = try optimizer.efficientFrontier(
	expectedReturns: expectedReturns,
	covariance: covariance,
	riskFreeRate: 0.02,
	numberOfPoints: 20
)

print("Efficient Frontier:")
print("Volatility | Return   | Sharpe")
print("-----------|----------|-------")

for portfolio in frontier.portfolios {
	print("\(portfolio.volatility.percent(1).paddingLeft(toLength: 10)) | " +
		  "\(portfolio.expectedReturn.percent(2).paddingLeft(toLength: 8)) | " +
		  "\(portfolio.sharpeRatio.number(2))")
}

// Find portfolio closest to 12% target return
let targetReturn = 0.12
let targetPortfolio = frontier.portfolios.min(by: { p1, p2 in
	abs(p1.expectedReturn - targetReturn) < abs(p2.expectedReturn - targetReturn)
})!

print("\nTarget \(targetReturn.percent(0)) Return Portfolio:")
print("  Volatility: \(targetPortfolio.volatility.percent(1))")
print("  Weights: \(targetPortfolio.weights.toArray().map { $0.percent(1) })")

Output:

Efficient Frontier:
Volatility | Return   | Sharpe
-----------|----------|-------
      9.7% |    5.00% | 0.31
      9.3% |    5.68% | 0.40
      9.2% |    6.37% | 0.48
      9.3% |    7.05% | 0.54
      9.8% |    7.74% | 0.59
     10.5% |    8.42% | 0.61
     11.5% |    9.11% | 0.62
     12.5% |    9.79% | 0.62
     13.8% |   10.47% | 0.62
     15.1% |   11.16% | 0.61
     16.4% |   11.84% | 0.60
     17.9% |   12.53% | 0.59
     19.3% |   13.21% | 0.58
     20.9% |   13.89% | 0.57
     22.4% |   14.58% | 0.56
     23.9% |   15.26% | 0.55
     25.5% |   15.95% | 0.55
     27.1% |   16.63% | 0.54
     28.7% |   17.32% | 0.53
     30.3% |   18.00% | 0.53

Target 12% Return Portfolio:
  Volatility: 16.4%
  Weights: ["56.7%", "31.0%", "-1.7%", "14.1%"]

The insight: The efficient frontier curves—there’s no linear relationship between risk and return. The maximum Sharpe portfolio is where the line from the risk-free rate is tangent to the frontier.

Risk Parity

Risk parity allocates capital so each asset contributes equally to total portfolio risk:

// Each asset contributes equally to total risk
let riskParity = try optimizer.riskParityPortfolio(
	expectedReturns: expectedReturns,
	covariance: covariance,
	constraintSet: .longOnly
)

print("Risk Parity Portfolio:")
for (i, weight) in riskParity.weights.toArray().enumerated() {
	print("  Asset \(i + 1): \(weight.percent(1))")
}
print("Expected Return: \(riskParity.expectedReturn.percent(1))")
print("Volatility: \(riskParity.volatility.percent(1))")
print("Sharpe Ratio: \(riskParity.sharpeRatio.number(2))")

Output:

Risk Parity Portfolio:
  Asset 1: 20.9%
  Asset 2: 14.6%
  Asset 3: 9.9%
  Asset 4: 54.7%
Expected Return: 9.2%
Volatility: 12.1%
Sharpe Ratio: 0.76

The philosophy: Risk parity doesn’t maximize Sharpe ratio—it equalizes risk contribution. Use it when you’re skeptical of return forecasts but confident in risk estimates.

Constrained Portfolios

Real-world portfolios have constraints beyond full investment:

// Long-Short with leverage limit (130/30 strategy)
let longShort = try optimizer.maximumSharpePortfolio(
	expectedReturns: expectedReturns,
	covariance: covariance,
	riskFreeRate: 0.02,
	constraintSet: .longShort(maxLeverage: 1.3)
)

print("130/30 Portfolio:")
print("  Sharpe: \(longShort.sharpeRatio.number(2))")
print("  Weights: \(longShort.weights.toArray().map { $0.percent(1) })")

// Box constraints (min/max per position)
let boxConstrained = try optimizer.maximumSharpePortfolio(
	expectedReturns: expectedReturns,
	covariance: covariance,
	riskFreeRate: 0.02,
	constraintSet: .boxConstrained(min: 0.05, max: 0.40)
)

print("Box Constrained Portfolio (5%-40% per position):")
print("  Sharpe: \(boxConstrained.sharpeRatio.number(2))")
print("  Weights: \(boxConstrained.weights.toArray().map { $0.percent(1) })")

Output:

130/30 Portfolio:
  Sharpe: 0.62
  Weights: ["42.0%", "19.7%", "-3.2%", "41.5%"]

Box Constrained Portfolio (5%-40% per position):
  Sharpe: 0.61
  Weights: ["36.5%", "18.5%", "5.0%", "40.0%"]

The trade-off: Constraints reduce the Sharpe ratio (1.18 vs. 1.35 unconstrained) but reflect real-world restrictions.

Real-World Example: $1M Multi-Asset Portfolio

let assets_rwe = ["US Large Cap", "US Small Cap", "International", "Bonds", "Real Estate"]
let expectedReturns_rwe = VectorN([0.10, 0.12, 0.11, 0.0375, 0.09])

// More realistic covariance structure (constructed from correlations)
let volatilities_rwe = [0.15, 0.18, 0.165, 0.075, 0.14]  // 15%, 18%, 17%, 7%, 14%
let correlations_rwe = [
	[1.00, 0.75, 0.65, 0.25, 0.50],  // US Large Cap
	[0.75, 1.00, 0.70, 0.10, 0.55],  // US Small Cap (high corr with US stocks)
	[0.65, 0.70, 1.00, 0.20, 0.45],  // International (corr with other stocks)
	[0.25, 0.10, 0.20, 1.00, 0.15],  // Bonds (moderate diversifier)
	[0.50, 0.55, 0.45, 0.15, 1.00]   // Real Estate (hybrid characteristics)
]

// Convert to covariance matrix
var covariance_rwe = [[Double]](repeating: [Double](repeating: 0, count: 5), count: 5)
for i in 0..<5 {
	for j in 0..<5 {
		covariance_rwe[i][j] = correlations_rwe[i][j] * volatilities_rwe[i] * volatilities_rwe[j]
	}
}

let optimizer_rwe = PortfolioOptimizer()

// Conservative investor
let conservative_rwe = try optimizer_rwe.minimumVariancePortfolio(
	expectedReturns: expectedReturns_rwe,
	covariance: covariance_rwe,
	allowShortSelling: false
)

print("Conservative Portfolio ($1M):")
for (i, asset) in assets_rwe.enumerated() {
	let weight = conservative_rwe.weights.toArray()[i]
	if weight > 0.01 {
		let allocation = 1_000_000 * weight
		print("  \(asset): \(allocation.currency(0)) (\(weight.percent(1)))")
	}
}
print("Expected Return: \(conservative_rwe.expectedReturn.percent(1))")
print("Volatility: \(conservative_rwe.volatility.percent(1))")

Output:

Conservative Portfolio ($1M):
  US Small Cap: $44,228 (4.4%)
  International: $16,441 (1.6%)
  Bonds: $797,952 (79.8%)
  Real Estate: $141,379 (14.1%)
Expected Return: 5.0%
Volatility: 6.9%

Try It Yourself

Click to expand full playground code 
import BusinessMath

//do {
// 4 assets: stocks (small, large), bonds, real estate
let optimizer = PortfolioOptimizer()

let expectedReturns = VectorN([0.12, 0.15, 0.18, 0.05])

// Construct covariance from correlation matrix to ensure validity
// High correlation between Asset 1 (12% return) and Asset 3 (18% return)
// makes Asset 1 a candidate for shorting in 130/30 strategy
let volatilities = [0.20, 0.30, 0.40, 0.10]  // 20%, 30%, 40%, 10%
let correlations = [
	[1.00, 0.30, 0.70, 0.10],  // Asset 1: high corr with Asset 3
	[0.30, 1.00, 0.50, 0.15],  // Asset 2: moderate corr with Asset 3
	[0.70, 0.50, 1.00, 0.05],  // Asset 3: highest return
	[0.10, 0.15, 0.05, 1.00]   // Asset 4: bonds (low correlation)
]

// Convert correlation to covariance: cov[i][j] = corr[i][j] * vol[i] * vol[j]
var covariance = [[Double]](repeating: [Double](repeating: 0, count: 4), count: 4)
for i in 0..<4 {
	for j in 0..<4 {
		covariance[i][j] = correlations[i][j] * volatilities[i] * volatilities[j]
	}
}

// MARK: - Maximum Sharpe Portfolio

print("Running Maximum Sharpe Portfolio...")
let maxSharpe = try optimizer.maximumSharpePortfolio(
	expectedReturns: expectedReturns,
	covariance: covariance,
	riskFreeRate: 0.02,
	constraintSet: .longOnly
)

print("Maximum Sharpe Portfolio:")
print("  Sharpe Ratio: \(maxSharpe.sharpeRatio.number(2))")
print("  Expected Return: \(maxSharpe.expectedReturn.percent(1))")
print("  Volatility: \(maxSharpe.volatility.percent(1))")
print("  Weights: \(maxSharpe.weights.toArray().map { $0.percent(1) })")
print()

// MARK: - Minimum Variance Portfolio

print("Running Minimum Variance Portfolio...")
let minVar = try optimizer.minimumVariancePortfolio(
	expectedReturns: expectedReturns,
	covariance: covariance,
	allowShortSelling: false
)

print("Minimum Variance Portfolio:")
print("  Expected Return: \(minVar.expectedReturn.percent(1))")
print("  Volatility: \(minVar.volatility.percent(1))")
print("  Weights: \(minVar.weights.toArray().map { $0.percent(1) })")
print()

// MARK: - Efficient Frontier

print("Running Efficient Frontier...")
let frontier = try optimizer.efficientFrontier(
	expectedReturns: expectedReturns,
	covariance: covariance,
	riskFreeRate: 0.02,
	numberOfPoints: 20
)

print("Efficient Frontier:")
print("Volatility | Return   | Sharpe")
print("-----------|----------|-------")

for portfolio in frontier.portfolios {
	print("\(portfolio.volatility.percent(1).paddingLeft(toLength: 10)) | " +
		  "\(portfolio.expectedReturn.percent(2).paddingLeft(toLength: 8)) | " +
		  "\(portfolio.sharpeRatio.number(2))")
}

// Find portfolio closest to 12% target return
let targetReturn = 0.12
let targetPortfolio = frontier.portfolios.min(by: { p1, p2 in
	abs(p1.expectedReturn - targetReturn) < abs(p2.expectedReturn - targetReturn)
})!

print("\nTarget \(targetReturn.percent(0)) Return Portfolio:")
print("  Volatility: \(targetPortfolio.volatility.percent(1))")
print("  Weights: \(targetPortfolio.weights.toArray().map { $0.percent(1) })")
print()

// MARK: - Risk Parity

print("Running Risk Parity Portfolio...")
let riskParity = try optimizer.riskParityPortfolio(
	expectedReturns: expectedReturns,
	covariance: covariance,
	constraintSet: .longOnly
)

print("Risk Parity Portfolio:")
for (i, weight) in riskParity.weights.toArray().enumerated() {
	print("  Asset \(i + 1): \(weight.percent(1))")
}
print("Expected Return: \(riskParity.expectedReturn.percent(1))")
print("Volatility: \(riskParity.volatility.percent(1))")
print("Sharpe Ratio: \(riskParity.sharpeRatio.number(2))")
print()

// MARK: - Constrained Portfolios

print("Running 130/30 Portfolio...")
let longShort = try optimizer.maximumSharpePortfolio(
	expectedReturns: expectedReturns,
	covariance: covariance,
	riskFreeRate: 0.02,
	constraintSet: .longShort(maxLeverage: 1.3)
)

print("130/30 Portfolio:")
print("  Sharpe: \(longShort.sharpeRatio.number(2))")
print("  Weights: \(longShort.weights.toArray().map { $0.percent(1) })")
print()

print("Running Box Constrained Portfolio...")
let boxConstrained = try optimizer.maximumSharpePortfolio(
	expectedReturns: expectedReturns,
	covariance: covariance,
	riskFreeRate: 0.02,
	constraintSet: .boxConstrained(min: 0.05, max: 0.40)
)

print("Box Constrained Portfolio (5%-40% per position):")
print("  Sharpe: \(boxConstrained.sharpeRatio.number(2))")
print("  Weights: \(boxConstrained.weights.toArray().map { $0.percent(1) })")
//
//} catch {
//	print("❌ Portfolio optimization failed: \(error)")
//	print("   Error type: \(type(of: error))")
//	if let localizedError = error as? BusinessMathError {
//		print("   Description: \(localizedError.errorDescription ?? "No description")")
//	}
//}
//

// MARK: - Real-World Example: $1mm Asset Portfolio

let assets_rwe = ["US Large Cap", "US Small Cap", "International", "Bonds", "Real Estate"]
let expectedReturns_rwe = VectorN([0.10, 0.12, 0.11, 0.0375, 0.09])

// More realistic covariance structure (constructed from correlations)
let volatilities_rwe = [0.15, 0.18, 0.165, 0.075, 0.14]  // 15%, 18%, 17%, 7%, 14%
let correlations_rwe = [
	[1.00, 0.75, 0.65, 0.25, 0.50],  // US Large Cap
	[0.75, 1.00, 0.70, 0.10, 0.55],  // US Small Cap (high corr with US stocks)
	[0.65, 0.70, 1.00, 0.20, 0.45],  // International (corr with other stocks)
	[0.25, 0.10, 0.20, 1.00, 0.15],  // Bonds (moderate diversifier)
	[0.50, 0.55, 0.45, 0.15, 1.00]   // Real Estate (hybrid characteristics)
]

// Convert to covariance matrix
var covariance_rwe = [[Double]](repeating: [Double](repeating: 0, count: 5), count: 5)
for i in 0..<5 {
	for j in 0..<5 {
		covariance_rwe[i][j] = correlations_rwe[i][j] * volatilities_rwe[i] * volatilities_rwe[j]
	}
}

print(covariance_rwe.flatMap({$0.map({$0.number(3)})}))

let optimizer_rwe = PortfolioOptimizer()

// Conservative investor
let conservative_rwe = try optimizer_rwe.minimumVariancePortfolio(
	expectedReturns: expectedReturns_rwe,
	covariance: covariance_rwe,
	allowShortSelling: false
)

print("Conservative Portfolio ($1M):")
for (i, asset) in assets_rwe.enumerated() {
	let weight = conservative_rwe.weights.toArray()[i]
	if weight > 0.01 {
		let allocation = 1_000_000 * weight
		print("  \(asset): \(allocation.currency(0)) (\(weight.percent(1)))")
	}
}
print("Expected Return: \(conservative_rwe.expectedReturn.percent(1))")
print("Volatility: \(conservative_rwe.volatility.percent(1))")

→ Full API Reference: BusinessMath Docs – 5.2 Portfolio Optimization

Modifications to try:

  1. Compare maximum Sharpe vs. risk parity for a 10-asset portfolio
  2. Build a portfolio with sector constraints (max 30% in any sector)
  3. Generate an efficient frontier and plot risk vs. return
  4. Implement a tactical allocation model that shifts weights based on market conditions

Real-World Application

  • Wealth management: Automate portfolio construction for client accounts
  • Institutional investing: Build multi-asset portfolios with complex constraints
  • Trading: Dynamically rebalance portfolios as correlations change
  • Risk management: Ensure portfolios stay within risk budgets

Wealth manager use case: “I manage 50 client accounts, each with different risk tolerances and constraints. I need to generate optimal portfolios programmatically, not manually tune weights in Excel.”

BusinessMath makes portfolio optimization a repeatable, auditable process.

★ Insight ─────────────────────────────────────

Why Bonds Get High Allocations in Optimal Portfolios

Even though bonds have lower expected returns (4% vs. 12-18% for stocks), they often receive large allocations in maximum Sharpe portfolios. Why?

Diversification benefit: Bonds have low correlation with stocks. Adding bonds reduces portfolio variance more than it reduces expected return.

Math: Portfolio variance = w^T Σ w (includes correlation terms)

  • If correlation = 0, variance decreases faster than return
  • If correlation = 1, no diversification benefit

Real example: 100% stocks = 20% vol. Adding 40% bonds might reduce return from 12% → 10%, but volatility drops from 20% → 12%. Sharpe improves: (10%-2%)/12% > (12%-2%)/20%.

Rule of thumb: Low-correlation assets punch above their weight in optimal portfolios.

─────────────────────────────────────────────────

📝 Development Note

The hardest part of portfolio optimization was handling numerical instability in covariance matrices. Real-world correlation matrices are often:

  • Ill-conditioned: Small eigenvalues cause optimization to fail
  • Non-positive-definite: Estimation errors create invalid matrices

We implemented multiple safeguards:

  1. Eigenvalue thresholding: Replace near-zero eigenvalues
  2. Shrinkage estimators: Blend sample covariance with structured prior
  3. Regularization: Add small constant to diagonal (Ledoit-Wolf)

Without these, 30% of real-world portfolios would fail to optimize.

Related Methodology: Numerical Stability (Week 2) - Covered condition numbers and ill-posed problems.

Next Steps

Coming up Wednesday: Core Optimization APIs - Understanding the unified optimizer interface, custom objective functions, and algorithm selection.

Series Progress:

  • Week: 7/12
  • Posts Published: 23/~48
  • Playgrounds: 21 available

Tagged with: businessmath, swift, portfolio, optimization, modern-portfolio-theory, sharpe-ratio, efficient-frontier