Minimum possible return: ${(minReturn * 100).toFixed(2)}%

Maximum possible return: ${(maxReturn * 100).toFixed(2)}%

Optimization may not be able to achieve this target precisely with current assets.

`; optimizedAllocationsTableBody.innerHTML = ''; // Clear table return; } // Simple heuristic: Try to find weights that roughly yield the target return. // This will be a very basic linear interpolation or iterative adjustment. // For 'n' assets, we have 'n' weights that sum to 1, and target return equation. // This is an under-determined system, hence complex. // We will try a very simple strategy: distribute proportionally based on relative returns and volatility. let optimizedWeights = new Array(assets.length).fill(0); let currentPortfolioReturn = 0; let currentPortfolioVolatility = 0; // Attempt to find weights using a simple "greedy" approach or linear scaling // This is a highly simplified heuristic, not a mathematical optimization. // It aims to adjust weights to reach the target return by leaning more on higher/lower return assets. const totalAssetReturnContribution = assets.reduce((sum, asset) => sum + (asset.expectedReturn / 100), 0); if (totalAssetReturnContribution === 0) { // Avoid division by zero optimizationResultsDiv.style.display = 'block'; optimizationResultsDiv.innerHTML = `

Cannot optimize: Sum of asset expected returns is zero.

`; optimizedAllocationsTableBody.innerHTML = ''; return; } // A very basic allocation strategy to achieve a target return (simplistic example) // This is a heuristic. A real optimizer uses quadratic programming. let sumWeightedReturns = 0; let sumWeights = 0; // Initial guess: Equal weights let initialWeight = 1 / assets.length; assets.forEach((asset, index) => { optimizedWeights[index] = initialWeight; }); // Simple iterative adjustment to get closer to target return const learningRate = 0.001; // Small adjustment step const maxIterations = 1000; for (let i = 0; i < maxIterations; i++) { let currentCalculatedReturn = optimizedWeights.reduce((sum, weight, idx) => sum + weight * (assets[idx].expectedReturn / 100), 0); let error = targetReturn - currentCalculatedReturn; if (Math.abs(error) < 0.0001) break; // If close enough // Adjust weights based on error and asset returns let totalAdjustment = 0; assets.forEach((asset, index) => { // If current return is too low, increase weight of higher return assets // If current return is too high, increase weight of lower return assets let adjustment = error * learningRate * (asset.expectedReturn / 100 - currentCalculatedReturn); optimizedWeights[index] += adjustment; totalAdjustment += adjustment; }); // Re-normalize weights to sum to 1 let currentSum = optimizedWeights.reduce((sum, w) => sum + w, 0); if (currentSum !== 0) { optimizedWeights = optimizedWeights.map(w => w / currentSum); } else { // Reset if sum of weights becomes zero, very unlikely but a safeguard optimizedWeights = new Array(assets.length).fill(initialWeight); } // Ensure no negative weights (short selling not considered by default) optimizedWeights = optimizedWeights.map(w => Math.max(0, w)); currentSum = optimizedWeights.reduce((sum, w) => sum + w, 0); if (currentSum !== 0) { optimizedWeights = optimizedWeights.map(w => w / currentSum); } } // Calculate final portfolio metrics with optimized weights currentPortfolioReturn = optimizedWeights.reduce((sum, weight, idx) => sum + weight * (assets[idx].expectedReturn / 100), 0); currentPortfolioVolatility = optimizedWeights.reduce((sum, weight, idx) => sum + weight * (assets[idx].volatility / 100), 0); // Simplified volatility // Display optimized results optimizationResultsDiv.style.display = 'block'; optimizationResultsDiv.innerHTML = `

Achieved Portfolio Return: ${(currentPortfolioReturn * 100).toFixed(2)}%

Estimated Portfolio Volatility: ${(currentPortfolioVolatility * 100).toFixed(2)}%

(Note: This is a heuristic optimization. For precise financial modeling, a dedicated optimization engine is recommended.)

`; optimizedAllocationsTableBody.innerHTML = ''; assets.forEach((asset, index) => { const optimizedWeightPercent = (optimizedWeights[index] * 100).toFixed(2); const row = optimizedAllocationsTableBody.insertRow(); row.innerHTML = ` ${asset.name} ${optimizedWeightPercent}% `; }); } optimizePortfolioButton.addEventListener('click', optimizePortfolio); // --- PDF Download Functionality --- downloadPdfButton.addEventListener('click', function() { const { jsPDF } = window.jspdf; const doc = new jsPDF(); doc.setFontSize(22); doc.text(toolTitle.textContent, doc.internal.pageSize.getWidth() / 2, 20, { align: 'center' }); // --- Input Portfolio Data Section --- doc.setFontSize(16); doc.text("1. Input Portfolio Data", 15, 40); let yPos = 50; const assetInputHeaders = ['Asset Name', 'Current Value ($)', 'Expected Return (%)', 'Volatility (%)']; const assetInputRows = []; const assetGroups = assetInputsContainer.querySelectorAll('.asset-input-group'); assetGroups.forEach(group => { const id = group.dataset.assetId; const name = document.getElementById(`assetName${id}`).value.trim(); const value = parseFloat(document.getElementById(`assetValue${id}`).value); const expectedReturn = parseFloat(document.getElementById(`expectedReturn${id}`).value); const volatility = parseFloat(document.getElementById(`volatility${id}`).value); assetInputRows.push([ name, isNaN(value) ? '' : `$${value.toLocaleString('en-US', { minimumFractionDigits: 2, maximumFractionDigits: 2 })}`, isNaN(expectedReturn) ? '' : `${expectedReturn.toFixed(2)}%`, isNaN(volatility) ? '' : `${volatility.toFixed(2)}%` ]); }); doc.autoTable({ startY: yPos, head: [assetInputHeaders], body: assetInputRows, theme: 'grid', styles: { fontSize: 9, cellPadding: 2, overflow: 'linebreak' }, headStyles: { fillColor: [52, 152, 219], textColor: [255, 255, 255] }, alternateRowStyles: { fillColor: [236, 240, 241] }, margin: { top: 5, right: 15, bottom: 5, left: 15 } }); yPos = doc.autoTable.previous.finalY + 15; // --- Portfolio Analysis Section --- doc.setFontSize(16); doc.text("2. Portfolio Analysis", 15, yPos); yPos += 10; const totalValue = totalPortfolioValueSpan.textContent; const portfolioReturn = calculatedPortfolioReturnSpan.textContent; const portfolioVolatility = calculatedPortfolioVolatilitySpan.textContent; doc.setFontSize(12); doc.text(`Total Portfolio Value: ${totalValue}`, 15, yPos); doc.text(`Calculated Portfolio Return: ${portfolioReturn}`, 15, yPos + 7); doc.text(`Calculated Portfolio Volatility (Risk): ${portfolioVolatility}`, 15, yPos + 14); yPos += 25; doc.setFontSize(14); doc.text("Asset Contributions:", 15, yPos); yPos += 7; const analysisHeaders = ['Asset Name', 'Current Value ($)', 'Weight (%)', 'Expected Return (%)', 'Volatility (%)']; const analysisRows = []; const tableRows = assetContributionsTableBody.querySelectorAll('tr'); tableRows.forEach(row => { const cells = row.querySelectorAll('td'); if (cells.length > 1) { // Check if it's a data row, not "No assets entered" analysisRows.push(Array.from(cells).map(cell => cell.textContent.trim())); } }); if (analysisRows.length === 0) { doc.text("No asset data available for analysis.", 15, yPos + 5); yPos += 15; } else { doc.autoTable({ startY: yPos, head: [analysisHeaders], body: analysisRows, theme: 'grid', styles: { fontSize: 9, cellPadding: 2, overflow: 'linebreak' }, headStyles: { fillColor: [52, 152, 219], textColor: [255, 255, 255] }, alternateRowStyles: { fillColor: [236, 240, 241] }, margin: { top: 5, right: 15, bottom: 5, left: 15 } }); yPos = doc.autoTable.previous.finalY + 15; } // --- Portfolio Optimization Section --- doc.setFontSize(16); doc.text("3. Portfolio Optimization", 15, yPos); yPos += 10; const targetRet = targetReturnInput.value; doc.setFontSize(12); doc.text(`Target Annual Return: ${targetRet}%`, 15, yPos); yPos += 10; const optResultsText = optimizationResultsDiv.style.display === 'block' ? optimizationResultsDiv.innerText : 'Optimization not performed or no results.'; doc.setFontSize(10); doc.text(optResultsText, 15, yPos, { maxWidth: doc.internal.pageSize.getWidth() - 30 }); yPos += doc.getTextDimensions(optResultsText, { fontSize: 10, maxWidth: doc.internal.pageSize.getWidth() - 30 }).h + 10; doc.setFontSize(14); doc.text("Optimized Asset Allocations:", 15, yPos); yPos += 7; const optimizedHeaders = ['Asset Name', 'Optimized Weight (%)']; const optimizedRows = []; const optimizedTableRows = optimizedAllocationsTableBody.querySelectorAll('tr'); optimizedTableRows.forEach(row => { const cells = row.querySelectorAll('td'); if (cells.length > 1) { optimizedRows.push(Array.from(cells).map(cell => cell.textContent.trim())); } }); if (optimizedRows.length === 0) { doc.text("No optimized allocation data available.", 15, yPos + 5); } else { doc.autoTable({ startY: yPos, head: [optimizedHeaders], body: optimizedRows, theme: 'grid', styles: { fontSize: 9, cellPadding: 2, overflow: 'linebreak' }, headStyles: { fillColor: [52, 152, 219], textColor: [255, 255, 255] }, alternateRowStyles: { fillColor: [236, 240, 241] }, margin: { top: 5, right: 15, bottom: 5, left: 15 } }); } // Add page numbers let totalPages = doc.internal.getNumberOfPages(); for (let i = 1; i <= totalPages; i++) { doc.setPage(i); doc.setFontSize(10); doc.text("Page " + i + " of " + totalPages, doc.internal.pageSize.getWidth() / 2, doc.internal.pageSize.height - 10, { align: 'center' }); } doc.save('Portfolio_Management_Optimization.pdf'); }); // Initial calculation on load for default values calculatePortfolioMetrics(); });