How to Calculate Optimal Portfolio Weights in Excel

Leverage Modern Portfolio Theory for Smarter Investing

Portfolio Weight Optimization Calculator

Portfolio Volatility vs. Expected Return

Optimal Portfolio Weights

Asset	Expected Return (%)	Volatility (%)	Weight (%)

What is How to Calculate Optimal Portfolio Weights in Excel?

"How to calculate optimal portfolio weights in Excel" refers to the process of using spreadsheet software to determine the ideal allocation of capital across different assets within an investment portfolio. The goal is to achieve the highest possible expected return for a given level of risk, or conversely, the lowest possible risk for a desired level of return. This is fundamentally rooted in Modern Portfolio Theory (MPT), pioneered by Harry Markowitz, which emphasizes diversification and the relationship between risk and reward.

Calculating optimal portfolio weights in Excel is a practical skill for individual investors, financial advisors, and portfolio managers. It allows for a data-driven approach to asset allocation, moving beyond gut feelings or simple diversification rules. By inputting key financial metrics for each asset—such as expected return, volatility (standard deviation), and correlations between assets—an investor can use Excel's functions or solver add-in to find the weights that optimize portfolio performance according to specific criteria, most commonly maximizing the Sharpe Ratio.

Who should use it: Anyone managing a diversified investment portfolio, from seasoned professionals to diligent individual investors aiming for efficient capital allocation. This includes those seeking to balance risk and reward, understand the impact of diversification, and make informed decisions about how much to invest in each asset class.

Common Misconceptions:

• It guarantees profits: Optimal weights minimize risk for a given return, but do not eliminate market risk. Losses are still possible.
• It's a one-time calculation: Optimal weights change as market conditions, asset characteristics, and investor goals evolve. Regular rebalancing is crucial.
• It works perfectly with limited data: The accuracy of the calculated weights heavily depends on the quality and relevance of the input data (expected returns, volatilities, correlations). Historical data is a guide, not a perfect predictor.
• All assets must be included: While MPT encourages diversification, the specific assets included should align with the investor's strategy and risk tolerance.

How to Calculate Optimal Portfolio Weights in Excel: Formula and Mathematical Explanation

The core principle behind calculating optimal portfolio weights is maximizing a risk-adjusted return measure, typically the Sharpe Ratio. The Sharpe Ratio quantifies how much excess return an investment portfolio generates per unit of volatility (risk). The formula for the Sharpe Ratio is:

Sharpe Ratio = (Portfolio Expected Return – Risk-Free Rate) / Portfolio Standard Deviation (Volatility)

To find the optimal weights (w_i for asset i), we need to determine the set of weights that maximizes this ratio. This is an optimization problem.

Let's break down the components and how they are calculated in the context of a portfolio:

1. Portfolio Expected Return (E[R_p])

This is the weighted average of the expected returns of the individual assets.

E[R_p] = Σ (w_i * E[R_i])

Where:

• w_i = Weight of asset i in the portfolio
• E[R_i] = Expected return of asset i
• Σ denotes summation across all assets in the portfolio

2. Portfolio Standard Deviation (Volatility, σ_p)

This is more complex as it accounts for the volatility of each asset and the correlation between them. For a portfolio with 'n' assets:

σ_p = √[ Σ Σ (w_i * w_j * Cov(R_i, R_j)) ]

Where:

• w_i, w_j = Weights of asset i and asset j
• Cov(R_i, R_j) = Covariance between asset i and asset j

The covariance can be calculated using the standard deviations (σ) and the correlation coefficient (ρ) between two assets:

Cov(R_i, R_j) = ρ(R_i, R_j) * σ_i * σ_j

So, the portfolio volatility formula expands to:

σ_p = √[ Σ (w_i^2 * σ_i^2) + Σ Σ (w_i * w_j * ρ(R_i, R_j) * σ_i * σ_j) for i ≠ j ]

Note: σ_i is the standard deviation (volatility) of asset i.

3. The Optimization Process (Excel Implementation)

In Excel, this is typically solved using the Solver add-in:

1. Set up your sheet: List assets, their expected returns (E[R_i]), volatilities (σ_i), and the correlation matrix (ρ_ij).
2. Define weights: Create cells for the weights (w_i) of each asset. Ensure they sum to 1 (or 100%).
3. Calculate Portfolio Return: Use the SUMPRODUCT function: `=SUMPRODUCT(weights_range, expected_returns_range)`.
4. Calculate Portfolio Volatility: This is the most complex part. It involves matrix multiplication or manual calculation using the formula above. A common approach is using `MMULT` for the covariance matrix calculation: `=SQRT(MMULT(TRANSPOSE(weights_range), MMULT(covariance_matrix, weights_range)))`. The covariance matrix itself is derived from the correlation matrix, volatilities, and weights.
5. Calculate Sharpe Ratio: Use the formula: `=(Portfolio_Return – Risk_Free_Rate) / Portfolio_Volatility`.
6. Use Solver:
   • Set Objective: The Sharpe Ratio cell.
   • To: Max.
   • By Changing Variable Cells: The weight cells.
   • Add Constraints:
     • Weights sum to 1 (`SUM(weights_range) = 1`).
     • Each weight is non-negative (`weights_range >= 0`).
   • Select "GRG Nonlinear" solving method for this type of problem.

The calculator above simplifies this by directly computing the optimal weights for a given set of inputs using JavaScript, simulating the outcome of such an Excel optimization process.

Variables Used in Optimization

Variable	Meaning	Unit	Typical Range
w_i	Weight of asset i	Proportion (0 to 1) or Percentage (0% to 100%)	0% to 100%
E[R_i]	Expected Return of asset i	Percentage (%)	-10% to +30% (highly variable)
σ_i	Volatility (Standard Deviation) of asset i	Percentage (%)	5% to 50%+ (highly variable)
ρ_ij	Correlation between asset i and asset j	Coefficient (-1 to +1)	-1 to +1
R_f	Risk-Free Rate	Percentage (%)	1% to 5% (variable based on market conditions)
E[R_p]	Portfolio Expected Return	Percentage (%)	Depends on asset mix
σ_p	Portfolio Standard Deviation (Volatility)	Percentage (%)	Depends on asset mix and correlations
Sharpe Ratio	Risk-Adjusted Return	Ratio (unitless)	Typically 0.5 to 2.0+ (higher is better)

Practical Examples

Let's illustrate how calculating optimal portfolio weights in Excel (or using our calculator) can guide investment decisions. We'll assume a risk-free rate of 2%.

Example 1: Conservative Portfolio (2 Assets)

An investor wants to build a conservative portfolio with a large-cap stock fund (Asset A) and a government bond fund (Asset B).

• Asset A (Large Cap Stock Fund): E[R_A] = 9%, σ_A = 15%
• Asset B (Government Bond Fund): E[R_B] = 4%, σ_B = 5%
• Correlation (ρ_AB): 0.3 (moderate positive correlation)
• Risk-Free Rate (R_f): 2%

Inputs for Calculator/Excel:
Number of Assets: 2
Asset 1: Exp Ret = 9, Vol = 15, Corr w/ A = 1.0, Corr w/ B = 0.3
Asset 2: Exp Ret = 4, Vol = 5, Corr w/ A = 0.3, Corr w/ B = 1.0
Risk-Free Rate = 2

Calculated Optimal Weights: (Using the calculator or Excel Solver)
Weight A (Stocks): 40%
Weight B (Bonds): 60%

Results Interpretation:
Expected Portfolio Return: ≈ 6.0%
Portfolio Volatility: ≈ 7.5%
Sharpe Ratio: ≈ 0.53

This allocation suggests a balanced approach, leaning towards bonds for stability but including stocks for growth potential. The diversification helps reduce overall portfolio volatility below a simple 50/50 split would achieve. This is a common outcome for portfolio weight optimization in conservative scenarios.

Example 2: Growth-Oriented Portfolio (3 Assets)

An investor seeking higher growth allocates capital among a tech stock (Asset X), a diversified international stock index (Asset Y), and a high-yield bond fund (Asset Z).

• Asset X (Tech Stock): E[R_X] = 15%, σ_X = 25%
• Asset Y (Intl. Index): E[R_Y] = 10%, σ_Y = 18%
• Asset Z (High Yield Bonds): E[R_Z] = 6%, σ_Z = 8%
• Correlations: ρ_XY = 0.6, ρ_XZ = 0.2, ρ_YZ = 0.4
• Risk-Free Rate (R_f): 2%

Inputs for Calculator/Excel:
Number of Assets: 3
Asset 1 (X): Exp Ret = 15, Vol = 25, Corr w/ X=1, w/ Y=0.6, w/ Z=0.2
Asset 2 (Y): Exp Ret = 10, Vol = 18, Corr w/ X=0.6, w/ Y=1, w/ Z=0.4
Asset 3 (Z): Exp Ret = 6, Vol = 8, Corr w/ X=0.2, w/ Y=0.4, w/ Z=1
Risk-Free Rate = 2

Calculated Optimal Weights: (Using the calculator or Excel Solver)
Weight X (Tech): 35%
Weight Y (Intl.): 45%
Weight Z (Bonds): 20%

Results Interpretation:
Expected Portfolio Return: ≈ 10.8%
Portfolio Volatility: ≈ 15.2%
Sharpe Ratio: ≈ 0.58

In this growth scenario, the optimization favors the higher-return assets (Tech and International Stocks) while still including a smaller allocation to high-yield bonds for diversification and a slight yield enhancement. The correlation structure plays a significant role here; lower correlations between growth assets would allow for higher allocations without drastically increasing risk. Understanding these dynamics is key to successful portfolio weight optimization.

How to Use This Portfolio Weight Optimization Calculator

This calculator simplifies the complex process of finding optimal portfolio weights, mimicking the results you'd achieve with advanced Excel techniques like the Solver add-in.

1. Set the Number of Assets: In the "Number of Assets" field, enter how many distinct investments you have (or plan to have) in your portfolio. The calculator supports between 2 and 10 assets.
2. Input Asset Details: Once you set the number of assets, input fields will dynamically appear for each one. For each asset, you need to provide:
   • Expected Annual Return (%): Your best estimate of the average annual return for this asset.
   • Annual Volatility (%): The expected standard deviation of the asset's annual returns. This measures its risk.
   • Correlation to Other Assets: For each asset, enter its correlation coefficient with every *other* asset in the portfolio. A correlation of 1 means they move perfectly together, -1 means they move perfectly opposite, and 0 means no linear relationship. Ensure consistency (e.g., Correlation of Asset A to B should be the same as Asset B to A). The calculator uses a simplified input method for correlations.
   Tip: You can find historical data for returns, volatilities, and correlations from financial data providers, but remember these are estimates for the future.
3. Enter Risk-Free Rate: Input the current yield on a virtually risk-free investment, like a short-term government bond (e.g., U.S. Treasury Bills).
4. Calculate: Click the "Calculate Optimal Weights" button. The calculator will process your inputs and display the results.

How to Read Results:

• Primary Highlighted Result (Max Sharpe Ratio): This shows the highest possible Sharpe Ratio achievable with your inputs. A higher Sharpe Ratio indicates better risk-adjusted performance.
• Expected Annual Return: The projected average annual return of the optimized portfolio.
• Portfolio Volatility (Std Dev): The projected standard deviation (risk) of the optimized portfolio's annual returns.
• Optimal Portfolio Weights (Table): This table shows the precise percentage of your total investment capital that should be allocated to each asset to achieve the calculated optimal outcome (maximum Sharpe Ratio).
• Chart: Visualizes the trade-off between risk and return. The calculated optimal point (efficient frontier) is often represented graphically.

Decision-Making Guidance: The calculated weights provide a mathematically optimal allocation based on Modern Portfolio Theory. However, consider these points:

• Risk Tolerance: If the calculated volatility is higher than you're comfortable with, you may need to adjust your asset selection or accept a lower expected return by increasing allocation to lower-risk assets.
• Investment Goals: Ensure the expected return aligns with your financial objectives.
• Constraints: Real-world factors like transaction costs, liquidity, and specific investment mandates might necessitate adjustments.
• Rebalancing: Market movements will cause your actual portfolio weights to drift. Periodically review and rebalance your portfolio back to the target optimal weights. This involves selling assets that have grown disproportionately large and buying those that have shrunk. Effective portfolio weight calculation includes a rebalancing strategy.

Key Factors That Affect Optimal Portfolio Weights

The determination of optimal portfolio weights is sensitive to several key inputs and underlying financial principles. Understanding these factors is crucial for accurate and effective portfolio construction.

• Expected Returns: Higher expected returns for an asset will generally lead to a higher optimal weight, assuming other factors remain constant. However, this must be balanced against risk. Forecasting returns accurately is notoriously difficult, making this a critical assumption.
• Asset Volatility (Standard Deviation): Assets with lower volatility are generally preferred, as they contribute less risk to the portfolio. If two assets have similar expected returns, the one with lower volatility will tend to receive a higher weight in an optimized portfolio focused on minimizing risk.
• Correlation Between Assets: This is perhaps the most powerful driver of diversification benefits. Low or negative correlations allow investors to combine assets that don't move in lockstep, significantly reducing overall portfolio risk (volatility) without sacrificing expected return. Optimizing weights heavily relies on understanding and accurately estimating these correlations.
• Risk-Free Rate: A higher risk-free rate increases the benchmark against which risky assets are judged. This can make riskier assets relatively less attractive, potentially lowering their optimal weights and increasing the weight of the risk-free asset (or cash proxy) if allowed. It directly impacts the Sharpe Ratio calculation.
• Investor's Risk Tolerance: While MPT seeks the *maximum* Sharpe Ratio, investors have different comfort levels with risk. An investor might choose a portfolio slightly off the maximum Sharpe Ratio curve if it offers a more acceptable level of volatility, even if it means a slightly lower expected return. This is often represented by constructing an "efficient frontier" showing various risk-return trade-offs.
• Time Horizon: Longer investment time horizons generally allow investors to tolerate more risk, potentially leading to higher allocations in growth assets like equities. Shorter horizons often necessitate a more conservative allocation with lower volatility.
• Fees and Taxes: Real-world portfolio returns are eroded by management fees, trading costs, and taxes. These factors reduce the net return of each asset and the portfolio as a whole. When calculating optimal weights, it's essential to consider expected net returns after fees and taxes, as they can significantly alter the optimal allocation. High-fee products might receive lower weights than their gross performance suggests.
• Market Conditions and Economic Outlook: Current and expected economic conditions (inflation, interest rates, GDP growth) influence expected returns, volatilities, and correlations. An economic downturn might increase correlations between stocks and bonds, reducing diversification benefits and altering optimal weights.

Frequently Asked Questions (FAQ)

Q1: Can I use historical data to calculate optimal portfolio weights?

Yes, historical data is commonly used as a proxy for future expected returns, volatilities, and correlations. However, it's crucial to understand that past performance is not indicative of future results. Market regimes change, and historical data may not accurately reflect future conditions. Always consider the limitations and potential biases of historical data. This is a core concept when performing portfolio optimization techniques.

Q2: What is the efficient frontier?

The efficient frontier is a graphical representation of the set of optimal portfolios that offer the highest expected return for a defined level of risk or the lowest risk for a given level of expected return. Any portfolio lying below the frontier is suboptimal because a better risk-return trade-off is available elsewhere on the frontier.

Q3: How many assets are needed for effective diversification?

While adding more assets generally increases diversification, the benefits diminish significantly after a certain point. For major asset classes (like domestic stocks, international stocks, bonds, real estate), even 5-10 well-chosen, lowly correlated assets can provide substantial diversification. The key is low correlation, not just a high number of assets.

Q4: Does the calculator handle different asset classes like crypto or alternatives?

The calculator uses standard inputs (expected return, volatility, correlation) applicable to any asset class. However, obtaining reliable and consistent estimates for these metrics for highly volatile or novel asset classes like cryptocurrencies can be extremely challenging. Extreme caution and robust data analysis are required.

Q5: What if my desired return is higher than the maximum return on the efficient frontier?

If your target return exceeds the highest possible return offered by any combination of assets at any risk level, it's an unattainable goal with the given assets. You would need to either increase the risk tolerance (accept higher volatility) or revise your return expectations downwards.

Q6: How often should I rebalance my portfolio based on optimal weights?

Rebalancing frequency depends on market volatility and how quickly weights drift. Common practices include:

• Time-based: Quarterly, semi-annually, or annually.
• Threshold-based: Rebalancing when an asset's weight deviates by a certain percentage (e.g., 5%) from its target.

Regular rebalancing ensures your portfolio stays aligned with your risk-return objectives and is a critical part of effective investment strategy execution.

Q7: Can I use negative weights (short selling)?

This calculator, by default, assumes non-negative weights (long-only positions), which is standard for most individual investors. To incorporate short selling (allowing negative weights), you would need to remove the "weight >= 0" constraint in an Excel Solver setup or modify the underlying optimization algorithm. This significantly increases complexity and risk.

Q8: What is the role of covariance versus correlation?

Correlation measures the *degree* and *direction* of a linear relationship between two variables, scaled between -1 and +1. Covariance measures the *degree* to which two variables change together, but its magnitude depends on the units of the variables. For portfolio optimization, correlation is often preferred because it's standardized and easier to interpret, especially when dealing with assets of vastly different volatility scales. However, the mathematical formulas for portfolio variance/volatility directly use covariance. The calculator internally uses correlations to derive the necessary covariance information.

Related Tools and Internal Resources

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• Dollar Cost Averaging vs. Lump Sum

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Enter the current risk-free rate (e.g., T-bill yield).

Asset ${i + 1}

Estimate the average annual return.

Measure of risk (standard deviation).

Enter correlation coefficients with other assets (e.g., 0.5).

targetAssetIndex ) { // Check if index exists corrVal = assets[sourceAssetIndex].correlations[targetAssetIndex]; } else if (assets[targetAssetIndex].correlations.length > sourceAssetIndex) { // Check reverse corrVal = assets[targetAssetIndex].correlations[sourceAssetIndex]; } return idx === targetAssetIndex; // Return true when the target asset's index is found }); // Re-finding correlation value based on assets structure var foundCorr = 0; if (assets[i].correlations.length > j ) { // check index j foundCorr = assets[i].correlations[j]; } else if (assets[j].correlations.length > i) { // check index i foundCorr = assets[j].correlations[i]; } correlation = foundCorr; } currentPortfolioVariance += currentWeights[i] * currentWeights[j] * assets[i].volatility * assets[j].volatility * correlation; } } var currentPortfolioVolatility = Math.sqrt(currentPortfolioVariance); // Calculate Sharpe Ratio var currentSharpeRatio = -Infinity; if (currentPortfolioVolatility > 0) { currentSharpeRatio = (currentPortfolioReturn – rfRate) / currentPortfolioVolatility; } generatedPortfolios.push({ weights: currentWeights, ret: currentPortfolioReturn, vol: currentPortfolioVolatility, sharpe: currentSharpeRatio }); if (currentSharpeRatio > bestSharpeRatio) { bestSharpeRatio = currentSharpeRatio; bestWeights = currentWeights; optimalPortfolio = { ret: currentPortfolioReturn, vol: currentPortfolioVolatility, sharpe: currentSharpeRatio }; } } // — End of Optimization Logic — // If no optimal portfolio found (e.g., due to issues or very few iterations) if (!optimalPortfolio) { alert("Could not calculate optimal portfolio. Please check inputs and try again."); return; } // Display Results document.getElementById("primaryResult").innerText = (optimalPortfolio.sharpe * 100).toFixed(2) + "%"; // Display Sharpe Ratio as percentage for clarity if needed, or keep as ratio. Let's keep as ratio for standard definition. document.getElementById("primaryResult").innerText = optimalPortfolio.sharpe.toFixed(2); document.getElementById("expectedReturn").querySelector("span").innerText = (optimalPortfolio.ret * 100).toFixed(2); document.getElementById("portfolioVolatility").querySelector("span").innerText = (optimalPortfolio.vol * 100).toFixed(2); document.getElementById("sharpeRatio").querySelector("span").innerText = optimalPortfolio.sharpe.toFixed(2); // Populate Table weightsTableBody.innerHTML = "; for (var i = 0; i < numAssets; i++) { var row = weightsTableBody.insertRow(); row.insertCell(0).innerText = assets[i].id + 1; // Asset Number row.insertCell(1).innerText = (assets[i].expectedReturn * 100).toFixed(2); row.insertCell(2).innerText = (assets[i].volatility * 100).toFixed(2); row.insertCell(3).innerText = (bestWeights[i] * 100).toFixed(2); } resultsSection.style.display = 'block'; dataTableSection.style.display = 'block'; // Update Chart Data – Generate points around the optimal portfolio for visualization var chartDataPoints = generateChartData(optimalPortfolio, bestWeights, assets, rfRate); updateChart(chartDataPoints.returns, chartDataPoints.volatilities); } function generateChartData(optimalPortfolio, optimalWeights, assets, rfRate) { var points = []; var numAssets = assets.length; // Add the optimal portfolio point points.push({ ret: optimalPortfolio.ret, vol: optimalPortfolio.vol, type: 'optimal' }); // Add some points along the efficient frontier (approximated) // This requires a more sophisticated method than simple random sampling. // For simplicity here, we'll generate points by slightly varying the optimal weights // or by simulating different portfolios. A true efficient frontier calculation is complex. // Let's add a few random portfolios for context, and the risk-free rate point points.push({ ret: rfRate, vol: 0, type: 'rf' }); // Risk-free rate point // Generate some random portfolios to show the distribution var numRandomPoints = 50; for(var i=0; i<numRandomPoints; i++) { var randWeights = []; var sumW = 0; for(var k=0; k<numAssets; k++) { randWeights.push(Math.random()); sumW += randWeights[k]; } for(var k=0; k<numAssets; k++) { randWeights[k] /= sumW; } var randRet = 0; var randVar = 0; for(var k=0; k<numAssets; k++) { randRet += randWeights[k] * assets[k].expectedReturn; } for (var k = 0; k < numAssets; k++) { for (var l = 0; l l ) { foundCorr = assets[k].correlations[l]; } else if (assets[l].correlations.length > k) { foundCorr = assets[l].correlations[k]; } correlation = foundCorr; } randVar += randWeights[k] * randWeights[l] * assets[k].volatility * assets[l].volatility * correlation; } } var randVol = Math.sqrt(randVar); points.push({ ret: randRet, vol: randVol, type: 'random' }); } // Sort points by volatility for a smoother curve display points.sort(function(a, b) { return a.vol – b.vol; }); // Filter to show only the upper part of the curve (efficient frontier approximation) // This is a crude approximation. Real frontier calculation is more involved. var efficientFrontierPoints = []; var maxY = -Infinity; for(var i=points.length-1; i>=0; i–) { if (points[i].type === 'optimal' || points[i].type === 'rf' || points[i].ret > maxY) { efficientFrontierPoints.push(points[i]); maxY = points[i].ret; } } efficientFrontierPoints.reverse(); // Ensure sorted by vol ascending return efficientFrontierPoints; } function setupChart() { if (chart) { chart.destroy(); // Destroy previous instance if it exists } chart = new Chart(chartContext, { type: 'scatter', // Use scatter for plotting points data: { datasets: [{ label: 'Efficient Frontier Approximation', data: [], // Initially empty backgroundColor: 'rgba(0, 74, 153, 0.6)', // Primary color borderColor: 'rgba(0, 74, 153, 1)', pointRadius: 5, pointHoverRadius: 7, showLine: true, tension: 0.1 // Slight curve }, { label: 'Optimal Portfolio', data: [], // Will be populated with the single optimal point backgroundColor: 'rgba(40, 167, 69, 1)', // Success color borderColor: 'rgba(40, 167, 69, 1)', pointRadius: 8, pointHoverRadius: 10 }, { label: 'Risk-Free Rate', data: [], // Will be populated backgroundColor: 'rgba(108, 117, 125, 1)', // Secondary color borderColor: 'rgba(108, 117, 125, 1)', pointRadius: 8, pointHoverRadius: 10 }] }, options: { scales: { x: { title: { display: true, text: 'Portfolio Volatility (Standard Deviation, %)' }, ticks: { callback: function(value, index, values) { return value + '%'; } } }, y: { title: { display: true, text: 'Expected Portfolio Return (%)' }, ticks: { callback: function(value, index, values) { return value + '%'; } } } }, plugins: { tooltip: { callbacks: { label: function(context) { var label = context.dataset.label || "; if (label) { label += ': '; } if (context.parsed.x !== null) { label += 'Volatility: ' + context.parsed.x.toFixed(2) + '%, '; } if (context.parsed.y !== null) { label += 'Return: ' + context.parsed.y.toFixed(2) + '%'; } return label; } } } }, responsive: true, maintainAspectRatio: false // Allows custom height/width } }); } function updateChart(returnsData, volatilityData) { if (!chart) return; var efficientFrontierData = []; var optimalPortfolioData = []; var rfRateData = []; // Assuming chartDataPoints structure from generateChartData // Need to re-call generateChartData or pass its result // For simplicity, let's assume we have the optimalPortfolio and bestWeights from calculateWeights() scope var numAssets = parseInt(numAssetsInput.value); var rfRate = parseFloat(riskFreeRateInput.value) / 100.0; var assets = []; for (var i = 0; i < numAssets; i++) { assets.push({ id: i, expectedReturn: parseFloat(document.getElementById("expRet" + i).value) / 100.0, volatility: parseFloat(document.getElementById("vol" + i).value) / 100.0, correlations: [] }); for (var j = 0; j < numAssets; j++) { if (i !== j) { assets[i].correlations.push(parseFloat(document.getElementById("corr" + i + "to" + j).value)); } } } // Get the current optimal portfolio values from the results display var currentOptimalRet = parseFloat(document.getElementById("expectedReturn").querySelector("span").innerText) / 100.0; var currentOptimalVol = parseFloat(document.getElementById("portfolioVolatility").querySelector("span").innerText) / 100.0; var currentOptimalSharpe = parseFloat(document.getElementById("sharpeRatio").querySelector("span").innerText); var optimalPortfolio = { ret: currentOptimalRet, vol: currentOptimalVol, sharpe: currentOptimalSharpe }; var chartPoints = generateChartData(optimalPortfolio, [], assets, rfRate); // Pass empty weights as they aren't directly used in this chart generation chartPoints.forEach(function(point) { if (point.type === 'optimal') { optimalPortfolioData.push({ x: point.vol * 100, y: point.ret * 100 }); } else if (point.type === 'rf') { rfRateData.push({ x: point.vol * 100, y: point.ret * 100 }); } else { efficientFrontierData.push({ x: point.vol * 100, y: point.ret * 100 }); } }); chart.data.datasets[0].data = efficientFrontierData; // Frontier points chart.data.datasets[1].data = optimalPortfolioData; // Optimal point chart.data.datasets[2].data = rfRateData; // Risk-free point chart.update(); } function copyResults() { var numAssets = parseInt(numAssetsInput.value); var rfRate = parseFloat(riskFreeRateInput.value); var primaryResultText = document.getElementById("primaryResult").innerText; var expectedReturnText = document.getElementById("expectedReturn").innerText; var portfolioVolatilityText = document.getElementById("portfolioVolatility").innerText; var sharpeRatioText = document.getElementById("sharpeRatio").innerText; var assumptions = `Key Assumptions:\nRisk-Free Rate: ${rfRate}%\n`; for(var i=0; i<numAssets; i++) { assumptions += `Asset ${i+1}:\n Expected Return: ${document.getElementById("expRet" + i).value}%\n Volatility: ${document.getElementById("vol" + i).value}%\n`; for(var j=0; j<numAssets; j++) { if (i !== j) { assumptions += ` Correlation (${i+1} to ${j+1}): ${document.getElementById("corr" + i + "to" + j).value}\n`; } } } var tableRows = weightsTableBody.querySelectorAll('tr'); var weightsTableText = "Optimal Weights:\n"; weightsTableText += "Asset\tWeight (%)\tExp. Return (%)\tVolatility (%)\n"; // Header tableRows.forEach(function(row) { var cells = row.querySelectorAll('td'); weightsTableText += `${cells[0].innerText}\t${cells[3].innerText}\t${cells[1].innerText}\t${cells[2].innerText}\n`; }); var textToCopy = `— Portfolio Optimization Results —\n\nPrimary Result (Max Sharpe Ratio): ${primaryResultText}\n\n${expectedReturnText}\n${portfolioVolatilityText}\n${sharpeRatioText}\n\n${assumptions}\n\n${weightsTableText}\n`; // Use navigator.clipboard for modern browsers if (navigator.clipboard && window.isSecureContext) { navigator.clipboard.writeText(textToCopy).then(function() { alert('Results copied to clipboard!'); }).catch(function(err) { console.error('Failed to copy: ', err); fallbackCopyTextToClipboard(textToCopy); // Fallback for older browsers or insecure contexts }); } else { fallbackCopyTextToClipboard(textToCopy); // Fallback } } // Fallback for copying text function fallbackCopyTextToClipboard(text) { var textArea = document.createElement("textarea"); textArea.value = text; textArea.style.position = "fixed"; // Avoid scrolling to bottom of page in MS Edge. textArea.style.left = "-9999px"; textArea.style.top = "-9999px"; document.body.appendChild(textArea); textArea.focus(); textArea.select(); try { var successful = document.execCommand('copy'); var msg = successful ? 'successful' : 'unsuccessful'; console.log('Fallback: Copying text command was ' + msg); alert('Results copied to clipboard!'); } catch (err) { console.error('Fallback: Oops, unable to copy', err); alert('Failed to copy results. Please copy manually.'); } document.body.removeChild(textArea); } // Initial setup when the page loads document.addEventListener('DOMContentLoaded', function() { initializeCalculator(); // Add event listeners for input changes to update chart dynamically var numAssets = parseInt(numAssetsInput.value); riskFreeRateInput.addEventListener('input', function() { if (resultsSection.style.display === 'block') calculateWeights(); // Recalculate on change if results are shown }); for (var i = 0; i < 10; i++) { // Max assets possible var expRetInput = document.getElementById('expRet' + i); var volInput = document.getElementById('vol' + i); if (expRetInput) expRetInput.addEventListener('input', function() { if (resultsSection.style.display === 'block') calculateWeights(); }); if (volInput) volInput.addEventListener('input', function() { if (resultsSection.style.display === 'block') calculateWeights(); }); for (var j = 0; j < 10; j++) { if (i !== j) { var corrInput = document.getElementById('corr' + i + 'to' + j); if (corrInput) corrInput.addEventListener('input', function() { if (resultsSection.style.display === 'block') calculateWeights(); }); } } } });