${corrText}

${betaText}

`; } /** * Creates or updates the Chart.js scatter plot. */ function createOrUpdateChart(data, primaryLabel, secondaryLabel) { const ctx = chartCanvas.getContext('2d'); if (correlationChart) { correlationChart.destroy(); } correlationChart = new Chart(ctx, { type: 'scatter', data: { datasets: [{ label: 'Monthly Returns', data: data, backgroundColor: 'rgba(79, 70, 229, 0.6)', borderColor: 'rgba(79, 70, 229, 1)', }] }, options: { responsive: true, maintainAspectRatio: false, scales: { x: { title: { display: true, text: `${primaryLabel} Monthly Return (%)`, font: { weight: 'bold' } }, ticks: { callback: val => val + '%' } }, y: { title: { display: true, text: `${secondaryLabel} Monthly Return (%)`, font: { weight: 'bold' } }, ticks: { callback: val => val + '%' } } }, plugins: { legend: { display: false }, tooltip: { callbacks: { label: (context) => `(${context.parsed.x.toFixed(2)}%, ${context.parsed.y.toFixed(2)}%)` } } } } }); } // --- Helper functions for calculation (based on simulated data) --- function sum(arr) { return arr.reduce((acc, val) => acc + val, 0); } function mean(arr) { return sum(arr) / arr.length; } function stdDev(arr) { const mu = mean(arr); return Math.sqrt(sum(arr.map(x => (x - mu) ** 2)) / arr.length); } function covariance(arr1, arr2) { const mean1 = mean(arr1); const mean2 = mean(arr2); let covar = 0; for (let i = 0; i < arr1.length; i++) { covar += (arr1[i] - mean1) * (arr2[i] - mean2); } return covar / arr1.length; } function calculateCorrelation(arr1, arr2) { return covariance(arr1, arr2) / (stdDev(arr1) * stdDev(arr2)); } function calculateBeta(arr1, arr2) { return covariance(arr1, arr2) / (stdDev(arr1) ** 2); } /** * Handles the PDF generation process. * Adheres to Specification II.C. */ async function generatePDF() { const contentToPrint = document.getElementById('pdf-content'); if (!contentToPrint || resultsContainer.classList.contains('hidden')) { alert("Please run an analysis first to generate results."); return; } pdfBtn.disabled = true; pdfSpinner.classList.remove('hidden'); pdfBtn.querySelector('span').textContent = 'Generating...'; try { const canvas = await html2canvas(contentToPrint, { scale: 2, backgroundColor: '#ffffff' }); const imgData = canvas.toDataURL('image/png'); const { jsPDF } = window.jspdf; const pdf = new jsPDF({ orientation: 'p', unit: 'mm', format: 'a4' }); const pdfWidth = pdf.internal.pageSize.getWidth(); const pdfHeight = pdf.internal.pageSize.getHeight(); const imgProps = pdf.getImageProperties(imgData); const imgRatio = imgProps.height / imgProps.width; let imgWidth = pdfWidth - 20; let imgHeight = imgWidth * imgRatio; if (imgHeight > pdfHeight - 20) { imgHeight = pdfHeight - 20; imgWidth = imgHeight / imgRatio; } const x = (pdfWidth - imgWidth) / 2; const y = 10; pdf.addImage(imgData, 'PNG', x, y, imgWidth, imgHeight); const primaryTicker = primaryAssetSelect.value; const secondaryTicker = secondaryAssetSelect.value; pdf.save(`Risk-Correlation-Analysis-${primaryTicker}-vs-${secondaryTicker}.pdf`); } catch (error) { console.error("Error generating PDF:", error); alert("An error occurred while generating the PDF."); } finally { pdfBtn.disabled = false; pdfSpinner.classList.add('hidden'); pdfBtn.querySelector('span').textContent = 'Download Results as PDF'; } } });