2b:null 2c:["$","title",null,{"children":"India Conflict Risk Index | Adjacent | Observable"}] 31:T1f6c,// Improved function to fetch and process price histories for multiple markets async function fetchProcessedMarketHistoriesMax(marketIds, marketNames) { try { // Validate inputs if (!Array.isArray(marketIds) || !Array.isArray(marketNames) || marketIds.length !== marketNames.length) { throw new Error("marketIds and marketNames must be arrays of the same length"); } // Create a mapping between market IDs and their display names const marketNameMap = {}; marketIds.forEach((id, index) => { marketNameMap[id] = marketNames[index]; }); console.log("Fetching data for markets:", marketNames.join(", ")); // Fetch raw data for all markets - use max interval to ensure we get all available data const fetchPromises = marketIds.map(marketId => { // Using "max" interval to get all available data for this market const interval = "max"; const url = `https://clob.polymarket.com/prices-history?market=${marketId}&interval=${interval}`; return fetch(url, { headers: { "Content-Type": "application/json", "Cache-Control": "no-cache" } }) .then(response => { if (!response.ok) { throw new Error(`HTTP error! Status: ${response.status} for market ${marketId}`); } return response.json(); }) .then(data => ({ marketId, data })) .catch(error => { console.error(`Error fetching market ${marketId}:`, error); return { marketId, error: error.message }; }); }); // Wait for all fetches to complete const results = await Promise.all(fetchPromises); // Process each market's data to get daily closing prices const marketDailyPrices = {}; const marketCreationDates = {}; results.forEach(result => { const marketName = marketNameMap[result.marketId]; if (result.error || !result.data?.history || result.data.history.length === 0) { console.warn(`No valid data for market ${marketName}`); marketDailyPrices[marketName] = new Map(); return; } console.log(`Processing ${result.data.history.length} price points for ${marketName}`); // Find market creation date from earliest data point if (result.data.history.length > 0) { const timestamps = result.data.history.map(point => point.t); const earliestTimestamp = Math.min(...timestamps); const creationDate = new Date(earliestTimestamp * 1000); // Store creation date with no time component (UTC date) const creationDateUTC = new Date(Date.UTC( creationDate.getUTCFullYear(), creationDate.getUTCMonth(), creationDate.getUTCDate() )); marketCreationDates[marketName] = creationDateUTC; console.log(`Market ${marketName} creation date:`, creationDateUTC.toISOString().split('T')[0]); } // Group prices by day and get closing price for each day const pricesByDay = {}; result.data.history.forEach(point => { // Convert timestamp to date (assuming timestamp is in seconds) const date = new Date(point.t * 1000); // Create a UTC date key for consistent timezone handling const dayKey = `${date.getUTCFullYear()}-${String(date.getUTCMonth() + 1).padStart(2, '0')}-${String(date.getUTCDate()).padStart(2, '0')}`; if (!pricesByDay[dayKey]) { pricesByDay[dayKey] = []; } pricesByDay[dayKey].push({ timestamp: point.t, price: point.p }); }); // For each day, get the closing price (last price of the day) const dailyClosingPrices = new Map(); Object.entries(pricesByDay).forEach(([day, prices]) => { // Sort by timestamp (latest last) const sortedPrices = prices.sort((a, b) => a.timestamp - b.timestamp); // Take the latest price of the day const latestPrice = sortedPrices[sortedPrices.length - 1]; dailyClosingPrices.set(day, latestPrice.price); }); // Store the daily prices for this market marketDailyPrices[marketName] = dailyClosingPrices; console.log(`Processed ${dailyClosingPrices.size} daily prices for ${marketName}`); }); // Determine the full date range const today = new Date(); const todayUTC = new Date(Date.UTC(today.getUTCFullYear(), today.getUTCMonth(), today.getUTCDate())); // Find the earliest market creation date let startDate = todayUTC; Object.values(marketCreationDates).forEach(date => { if (date < startDate) { startDate = date; } }); console.log("Start date:", startDate.toISOString().split('T')[0]); console.log("End date (today):", todayUTC.toISOString().split('T')[0]); // Generate all dates in the range const allDates = []; const currentDate = new Date(startDate); while (currentDate <= todayUTC) { const dateStr = currentDate.toISOString().split('T')[0]; allDates.push(dateStr); // Move to next day currentDate.setUTCDate(currentDate.getUTCDate() + 1); } console.log(`Generated ${allDates.length} days in date range`); // Fill in missing prices by carrying forward the last known price const filledMarketPrices = {}; Object.entries(marketDailyPrices).forEach(([marketName, priceMap]) => { filledMarketPrices[marketName] = new Map(); const creationDate = marketCreationDates[marketName]; // If empty market, skip if (priceMap.size === 0) { console.warn(`No price data to fill for ${marketName}`); return; } // Sort the existing dates const existingDates = Array.from(priceMap.keys()).sort(); console.log(`${marketName} has data for ${existingDates.length} days, first:`, existingDates[0], "last:", existingDates[existingDates.length - 1]); // If we have no data, skip if (existingDates.length === 0) return; let lastPrice = null; // Fill in prices for all dates in our range allDates.forEach(dateStr => { const currentDate = new Date(dateStr); if (priceMap.has(dateStr)) { // We have data for this day lastPrice = priceMap.get(dateStr); filledMarketPrices[marketName].set(dateStr, lastPrice); } else if (lastPrice !== null && currentDate >= creationDate) { // Carry forward last known price if after market creation filledMarketPrices[marketName].set(dateStr, lastPrice); } else { // No data yet for this market filledMarketPrices[marketName].set(dateStr, null); } }); console.log(`Filled in prices for ${marketName}, now have ${filledMarketPrices[marketName].size} days`); }); // Create the final dataset in the requested format const processedHistory = allDates.map(dateStr => { // Create Date object for midnight UTC, then adjust to local time for display const utcDate = new Date(dateStr); // For display in Observable, adjust to local timezone const date = new Date(utcDate.getTime()); const result = { date: date }; // Add each market's price for this date Object.keys(filledMarketPrices).forEach(marketName => { const price = filledMarketPrices[marketName].get(dateStr); // Multiply by 100 to match your format result[marketName] = price !== null ? price * 100 : null; }); return result; }); console.log(`Final processed dataset has ${processedHistory.length} days`); return processedHistory; } catch (error) { console.error("Error in fetchProcessedMarketHistories:", error); return { error: error.message }; } }32:T22a7,// Updated function to fetch and process price histories for the last week with proper fidelity async function fetchProcessedMarketHistoriesLastWeek(marketIds, marketNames) { try { // Validate inputs if (!Array.isArray(marketIds) || !Array.isArray(marketNames) || marketIds.length !== marketNames.length) { throw new Error("marketIds and marketNames must be arrays of the same length"); } // Create a mapping between market IDs and their display names const marketNameMap = {}; marketIds.forEach((id, index) => { marketNameMap[id] = marketNames[index]; }); console.log("Fetching data for markets (last week only):", marketNames.join(", ")); // Fetch raw data for all markets - use 1w interval with fidelity=15 to get last week of data const fetchPromises = marketIds.map(marketId => { // Using "1w" interval with fidelity=15 (15-minute intervals) const interval = "1w"; const fidelity = 15; const url = `https://clob.polymarket.com/prices-history?market=${marketId}&interval=${interval}&fidelity=${fidelity}`; console.log(`Fetching from URL: ${url}`); return fetch(url, { headers: { "Content-Type": "application/json", "Cache-Control": "no-cache" } }) .then(response => { if (!response.ok) { throw new Error(`HTTP error! Status: ${response.status} for market ${marketId}`); } return response.json(); }) .then(data => { // Log the data size to verify we're getting results // Use marketNameMap to get the market name from the ID const mktName = marketNameMap[marketId]; console.log(`Received ${data.history ? data.history.length : 0} data points for ${mktName}`); return { marketId, data }; }) .catch(error => { console.error(`Error fetching market ${marketId}:`, error); return { marketId, error: error.message }; }); }); // Wait for all fetches to complete const results = await Promise.all(fetchPromises); // Process each market's data to get daily closing prices const marketDailyPrices = {}; const marketCreationDates = {}; results.forEach(result => { const marketName = marketNameMap[result.marketId]; if (result.error || !result.data?.history || result.data.history.length === 0) { console.warn(`No valid data for market ${marketName}`); marketDailyPrices[marketName] = new Map(); return; } console.log(`Processing ${result.data.history.length} price points for ${marketName}`); // For last week data, we'll still track the earliest date in our fetched data if (result.data.history.length > 0) { const timestamps = result.data.history.map(point => point.t); const earliestTimestamp = Math.min(...timestamps); const creationDate = new Date(earliestTimestamp * 1000); // Store creation date with no time component (UTC date) const creationDateUTC = new Date(Date.UTC( creationDate.getUTCFullYear(), creationDate.getUTCMonth(), creationDate.getUTCDate() )); marketCreationDates[marketName] = creationDateUTC; console.log(`Market ${marketName} earliest date in last week:`, creationDateUTC.toISOString().split('T')[0]); } // Group prices by day and get closing price for each day const pricesByDay = {}; result.data.history.forEach(point => { // Convert timestamp to date (assuming timestamp is in seconds) const date = new Date(point.t * 1000); // Create a UTC date key for consistent timezone handling const dayKey = `${date.getUTCFullYear()}-${String(date.getUTCMonth() + 1).padStart(2, '0')}-${String(date.getUTCDate()).padStart(2, '0')}`; if (!pricesByDay[dayKey]) { pricesByDay[dayKey] = []; } pricesByDay[dayKey].push({ timestamp: point.t, price: point.p }); }); // For each day, get the closing price (last price of the day) const dailyClosingPrices = new Map(); Object.entries(pricesByDay).forEach(([day, prices]) => { // Sort by timestamp (latest last) const sortedPrices = prices.sort((a, b) => a.timestamp - b.timestamp); // Take the latest price of the day const latestPrice = sortedPrices[sortedPrices.length - 1]; dailyClosingPrices.set(day, latestPrice.price); }); // Store the daily prices for this market marketDailyPrices[marketName] = dailyClosingPrices; console.log(`Processed ${dailyClosingPrices.size} daily prices for ${marketName}`); // Log a sample of the prices to verify data if (dailyClosingPrices.size > 0) { const sampleDay = Array.from(dailyClosingPrices.keys())[0]; console.log(`Sample price for ${marketName} on ${sampleDay}: ${dailyClosingPrices.get(sampleDay)}`); } }); // Determine the date range for the last week const today = new Date(); const todayUTC = new Date(Date.UTC(today.getUTCFullYear(), today.getUTCMonth(), today.getUTCDate())); // For last week, we calculate the start date as 7 days ago const oneWeekAgo = new Date(todayUTC); oneWeekAgo.setUTCDate(oneWeekAgo.getUTCDate() - 7); const startDate = oneWeekAgo; console.log("Start date (one week ago):", startDate.toISOString().split('T')[0]); console.log("End date (today):", todayUTC.toISOString().split('T')[0]); // Generate all dates in the range (last 7 days) const allDates = []; const currentDate = new Date(startDate); while (currentDate <= todayUTC) { const dateStr = currentDate.toISOString().split('T')[0]; allDates.push(dateStr); // Move to next day currentDate.setUTCDate(currentDate.getUTCDate() + 1); } console.log(`Generated ${allDates.length} days in date range`); // Fill in missing prices by carrying forward the last known price const filledMarketPrices = {}; Object.entries(marketDailyPrices).forEach(([marketName, priceMap]) => { filledMarketPrices[marketName] = new Map(); const earliestDate = marketCreationDates[marketName] || startDate; // If empty market, skip if (priceMap.size === 0) { console.warn(`No price data to fill for ${marketName}`); return; } // Sort the existing dates const existingDates = Array.from(priceMap.keys()).sort(); console.log(`${marketName} has data for ${existingDates.length} days, first:`, existingDates[0], "last:", existingDates[existingDates.length - 1]); // If we have no data, skip if (existingDates.length === 0) return; let lastPrice = null; // Fill in prices for all dates in our range allDates.forEach(dateStr => { const currentDate = new Date(dateStr); if (priceMap.has(dateStr)) { // We have data for this day lastPrice = priceMap.get(dateStr); filledMarketPrices[marketName].set(dateStr, lastPrice); } else if (lastPrice !== null && currentDate >= earliestDate) { // Carry forward last known price if after market creation filledMarketPrices[marketName].set(dateStr, lastPrice); } else { // No data yet for this market filledMarketPrices[marketName].set(dateStr, null); } }); console.log(`Filled in prices for ${marketName}, now have ${filledMarketPrices[marketName].size} days`); }); // Create the final dataset in the requested format const processedHistory = allDates.map(dateStr => { // Create Date object for midnight UTC, then adjust to local time for display const utcDate = new Date(dateStr); // For display in Observable, adjust to local timezone const date = new Date(utcDate.getTime()); const result = { date: date }; // Add each market's price for this date Object.keys(filledMarketPrices).forEach(marketName => { const price = filledMarketPrices[marketName].get(dateStr); // Multiply by 100 to match your format result[marketName] = price !== null ? price * 100 : null; }); return result; }); console.log(`Final processed dataset has ${processedHistory.length} days`); return processedHistory; } catch (error) { console.error("Error in fetchProcessedMarketHistoriesLastWeek:", error); return { error: error.message }; } }33:T1c79,viewof indiaPakistan = { // Function to determine width based on container size const getResponsiveWidth = () => { // Get the container width (default to 800 if not available) const containerWidth = width || 800; return Math.min(containerWidth, 800); // Cap at 800px max }; // Calculate responsive dimensions const chartWidth = getResponsiveWidth(); const chartHeight = chartWidth * 0.625; // Maintain aspect ratio (500/800 = 0.625) // Adjust font sizes based on width const titleSize = chartWidth < 500 ? 14 : 16; const subtitleSize = chartWidth < 500 ? 12 : 14; const labelSize = chartWidth < 500 ? 10 : 12; // Adjust margins for smaller screens const rightMargin = chartWidth < 500 ? 80 : 120; const leftMargin = chartWidth < 500 ? 40 : 60; const bottomMargin = chartWidth < 500 ? 30 : 40; const topMargin = chartWidth < 500 ? 30 : 40; // Adjust tick count for smaller screens const tickCount = chartWidth < 500 ? 4 : 8; // Create the basic chart using Plot const chart = Plot.plot({ title: "India and Pakistan Conflict Risk", subtitle: "Based on prediction market data", width: chartWidth, height: chartHeight, style: { fontSize: labelSize, ".plot-title": { fontSize: titleSize + "px" }, ".plot-subtitle": { fontSize: subtitleSize + "px" } }, x: { label: "Date", tickFormat: "%b %d", tickCount: tickCount, grid: true }, y: { label: "Probability (%)", domain: [0, 100], grid: true }, // Use responsive margins marginRight: rightMargin, marginLeft: leftMargin, marginBottom: bottomMargin, marginTop: topMargin, marks: [ Plot.ruleY([0, 25, 50, 75, 100], { stroke: "#ccc", strokeDasharray: "3,3" }), // Dynamically generate lines for each market with distinct colors ...marketNames.map((marketName, index) => { // Expanded color palette to ensure all lines have different colors const colors = ["#E63946", "#1D3557", "#2A9D8F", "#F4A261"]; return Plot.line(processedData, { x: "date", y: marketName, stroke: colors[index % colors.length], strokeWidth: chartWidth < 500 ? 1.5 : 2 }); }), // Dynamically generate dots for each market with matching colors ...marketNames.map((marketName, index) => { const colors = ["#E63946", "#1D3557", "#2A9D8F", "#F4A261"]; const dotFrequency = chartWidth < 500 ? 14 : 7; // Show fewer dots on small screens return Plot.dot( processedData.filter((d, i) => i % dotFrequency === 0), { x: "date", y: marketName, fill: colors[index % colors.length], r: chartWidth < 500 ? 2 : 3, opacity: 0.7 } ); }), // Dynamically generate end labels for each market with matching colors // Only show on larger screens ...(chartWidth >= 500 ? marketNames.map((marketName, index) => { const colors = ["#E63946", "#1D3557", "#2A9D8F", "#F4A261"]; return Plot.text( processedData.filter((d, i) => i === processedData.length - 1), { x: "date", y: marketName, text: (d) => `${d[marketName]?.toFixed(1)}%`, dx: chartWidth < 600 ? 30 : 50, fill: colors[index % colors.length], fontWeight: "bold", fontSize: chartWidth < 600 ? 12 : 14 } ); }) : []) ] }); // Determine legend font size const legendFontSize = chartWidth < 500 ? 11 : 10.5; // Calculate watermark position and size const watermarkTop = chartHeight / 2 + topMargin; const watermarkLeft = chartWidth / 2; const watermarkSize = chartWidth < 500 ? 30 : 60; // Create a container for the chart const chartContainer = html`

${chart}

ADJACENT

`; // Create legend items HTML (will be used in both desktop and mobile views) const legendItemsHTML = marketNames .map((name, index) => { const colors = ["#E63946", "#1D3557", "#2A9D8F", "#F4A261"]; const displayName = name.replace("?", ""); if (chartWidth < 500) { // Mobile legend items return `

${displayName}

`; } else { // Desktop legend items return `

${displayName}

`; } }) .join(""); // Add legend to the chart if (chartWidth < 500) { // Mobile: Create a wrapper that includes both chart and legend below it const wrapper = html`

${chartContainer}

${html([legendItemsHTML])}

`; wrapper.value = chart; // Create a resize observer if (typeof ResizeObserver !== "undefined") { const observer = new ResizeObserver((entries) => { const containerWidth = entries[0].contentRect.width; if (Math.abs(containerWidth - chartWidth) > 20) { invalidation.then(() => observer.disconnect()); return wrapper; } }); observer.observe(wrapper); } return wrapper; } else { // Desktop: Add legend as an overlay const legendElement = html`

${html([legendItemsHTML])}

${chart}

ADJACENT

`; // Create legend items HTML (will be used in both desktop and mobile views) const legendItemsHTML = chinaMarketNames .map((name, index) => { const colors = ["#E63946", "#1D3557", "#2A9D8F", "#F4A261"]; const displayName = name.replace("?", ""); if (chartWidth < 500) { // Mobile legend items return `

${displayName}

`; } else { // Desktop legend items return `

${displayName}

`; } }) .join(""); // Add legend to the chart if (chartWidth < 500) { // Mobile: Create a wrapper that includes both chart and legend below it const wrapper = html`

${chartContainer}

${html([legendItemsHTML])}

${chart}

ADJACENT

`; // Let's use a simpler approach for the first version - this version is the one that // has been working for our initial mobile responsive charts // Create legend items HTML (will be used in both desktop and mobile views) const legendItemsHTML = indiaRiskMarketNames .map((name, index) => { const colors = ["#E63946", "#1D3557", "#2A9D8F", "#F4A261"]; const displayName = name.replace("?", ""); if (chartWidth < 500) { // Mobile legend items return `

${displayName}

`; } else { // Desktop legend items return `

${displayName}

`; } }) .join(""); // Add legend to the chart if (chartWidth < 500) { // Mobile: Create a wrapper that includes both chart and legend below it const wrapper = html`

${chartContainer}

${html([legendItemsHTML])}

`; chartContainer.appendChild(legendElement); chartContainer.value = chart; // Create a resize observer if (typeof ResizeObserver !== "undefined") { const observer = new ResizeObserver((entries) => { const containerWidth = entries[0].contentRect.width; if (Math.abs(containerWidth - chartWidth) > 20) { invalidation.then(() => observer.disconnect()); return chartContainer; } }); observer.observe(chartContainer); } return chartContainer; } }36:T894,// Function to fetch liquidity data for multiple Adjacent market IDs async function fetchAdjacentMarketData(adjMarketIds) { try { // Validate input if (!Array.isArray(adjMarketIds) || adjMarketIds.length === 0) { throw new Error("adjMarketIds must be a non-empty array"); } console.log(`Fetching liquidity data for ${adjMarketIds.length} markets...`); // API token - this should be handled more securely in a production environment const apiToken = "38314d45-7899-4f51-a860-f6b898707a70"; // Create fetch promises for each market ID const fetchPromises = adjMarketIds.map(marketId => { const url = `https://api.data.adj.news/api/markets/${marketId}`; return fetch(url, { method: 'GET', headers: { 'Authorization': `Bearer ${apiToken}`, 'Content-Type': 'application/json' } }) .then(response => { if (!response.ok) { throw new Error(`HTTP error! Status: ${response.status} for market ${marketId}`); } return response.json(); }) .then(data => { // Extract the needed data return { marketId: marketId, ticker: data.data.ticker, liquidity: data.data.liquidity, volume: data.data.volume, probability: data.data.probability, end_date: data.data.end_date }; }) .catch(error => { console.error(`Error fetching liquidity for market ${marketId}:`, error); return { marketId: marketId, error: error.message, liquidity: null, volume: null, probability: null, end_date: null }; }); }); // Wait for all fetches to complete const results = await Promise.all(fetchPromises); // Log summary const successCount = results.filter(r => r.liquidity !== null).length; console.log(`Successfully fetched liquidity data for ${successCount} out of ${adjMarketIds.length} markets`); return results; } catch (error) { console.error("Error in fetchLiquidityData:", error); return { error: error.message }; } }37:Td10,// Updated function with more direct matching and better debugging function combineMarketDataWithPrices(adjacentMarketData, processedData) { // Create a mapping of market names to their metadata const marketMetadataMap = {}; // Debug: Print all market names from processedData const marketKeysInProcessedData = new Set(); processedData.forEach(dayData => { Object.keys(dayData).forEach(key => { if (key !== 'date') marketKeysInProcessedData.add(key); }); }); console.log("Market keys in processedData:", Array.from(marketKeysInProcessedData)); // Debug: Print all tickers from adjacentMarketData console.log("Tickers in adjacentMarketData:", adjacentMarketData.map(m => m.ticker)); // Create explicit mappings adjacentMarketData.forEach(metadata => { const ticker = metadata.ticker.toLowerCase(); if (ticker.includes("invade") || ticker.includes("will-india-invade")) { marketMetadataMap["India invade Pakistan before July?"] = metadata; console.log("Mapped: India invade Pakistan before July?"); } // More explicit matching for military action if (ticker.includes("military-action") || ticker.includes("military action")) { marketMetadataMap["India military action against Pakistan before June?"] = metadata; console.log("Mapped: India military action against Pakistan before June?"); } // More explicit matching for military strike if (ticker.includes("military-strike") || ticker.includes("military strike")) { marketMetadataMap["India military strike on Pakistan by Friday?"] = metadata; console.log("Mapped: India military strike on Pakistan by Friday?"); } if (ticker.includes("reopen") && ticker.includes("airspace")) { marketMetadataMap["Pakistan reopen airspace to India before May?"] = metadata; console.log("Mapped: Pakistan reopen airspace to India before May?"); } if (ticker.includes("clash") && ticker.includes("june")) { marketMetadataMap["China x India military clash by June 30"] = metadata; console.log("Mapped: China x India military clash by June 30"); } if (ticker.includes("clash") && ticker.includes("december")) { marketMetadataMap["China x India military clash in 2025"] = metadata; console.log("Mapped: China x India military clash in 2025"); } }); // For debugging - print the final mapping console.log("Market metadata mapping keys:", Object.keys(marketMetadataMap)); // Now enhance the processed data with metadata const enhancedData = processedData.map(dayData => { const enhancedDayData = { date: dayData.date, marketValues: [] }; // For each market in the daily data Object.keys(dayData).forEach(key => { // Skip the date field if (key === 'date') return; // Find corresponding metadata const metadata = marketMetadataMap[key]; if (metadata) { enhancedDayData.marketValues.push({ name: key, probability: dayData[key], // The probability value from price history metadata: metadata // All the metadata }); } else { console.warn(`No metadata found for market: ${key}`); } }); return enhancedDayData; }); return enhancedData; }38:T5f2,// Calculate the weighted conflict risk index function calculateConflictRiskIndex(enhancedData) { return enhancedData.map(dayData => { const date = dayData.date; let totalWeight = 0; let weightedSum = 0; // Calculate total liquidity across all markets for this day const totalLiquidity = dayData.marketValues.reduce( (sum, marketData) => sum + (marketData.metadata.liquidity || 0), 0 ); // For each market on this day dayData.marketValues.forEach(marketData => { // Skip if probability is null if (marketData.probability === null) return; const timeWeight = calculateTimeHorizonWeight(marketData.metadata.end_date, date); const liquidityWeight = calculateLiquidityWeight(marketData.metadata.liquidity, totalLiquidity); const severityWeight = calculateSeverityWeight(marketData.name); // Combine weights (you can adjust this formula based on your preference) const combinedWeight = (timeWeight * 0.4) + (liquidityWeight * 0.3) + (severityWeight * 0.3); // Weight the probability by the combined weight weightedSum += marketData.probability * combinedWeight; totalWeight += combinedWeight; }); // Calculate the final index value for this day const indexValue = totalWeight > 0 ? weightedSum / totalWeight : null; return { date: date, indexValue: indexValue, // Include raw data for debugging rawData: dayData.marketValues }; }); }39:T17d4,// Create the India Conflict Risk Index Chart viewof indiaConflictRiskChart = { // Function to determine width based on container size const getResponsiveWidth = () => { // Get the container width (default to 800 if not available) const containerWidth = width || 800; return Math.min(containerWidth, 800); // Cap at 800px max }; // Calculate responsive dimensions const chartWidth = getResponsiveWidth(); const chartHeight = chartWidth * 0.625; // Maintain aspect ratio (500/800 = 0.625) // Adjust font sizes based on width const titleSize = chartWidth < 500 ? 14 : 16; const subtitleSize = chartWidth < 500 ? 12 : 14; const labelSize = chartWidth < 500 ? 10 : 12; // Adjust margins for smaller screens const rightMargin = chartWidth < 500 ? 80 : 120; const leftMargin = chartWidth < 500 ? 40 : 60; const bottomMargin = chartWidth < 500 ? 30 : 40; const topMargin = chartWidth < 500 ? 30 : 40; // Adjust tick count for smaller screens const tickCount = chartWidth < 500 ? 4 : 8; // Create the basic chart using Plot const chart = Plot.plot({ title: "India Conflict Weighted Risk Index", subtitle: "Based on weighted prediction markets for India-Pakistan and India-China conflicts", width: chartWidth, height: chartHeight, style: { fontSize: labelSize, ".plot-title": { fontSize: titleSize + "px" }, ".plot-subtitle": { fontSize: subtitleSize + "px" } }, x: { label: "Date", tickFormat: "%b %d", tickCount: tickCount, grid: true }, y: { label: "Risk Index (%)", domain: [0, 100], grid: true }, // Use responsive margins marginRight: rightMargin, marginLeft: leftMargin, marginBottom: bottomMargin, marginTop: topMargin, marks: [ Plot.ruleY([0, 25, 50, 75, 100], { stroke: "#ccc", strokeDasharray: "3,3" }), // Line for the index - thinner on mobile Plot.line(indiaConflictRiskIndex, { x: "date", y: "indexValue", stroke: "#E63946", // Red color for the index strokeWidth: chartWidth < 500 ? 2 : 3 }), // Dots for the index - smaller on mobile and fewer Plot.dot( indiaConflictRiskIndex.filter( (d, i) => i % (chartWidth < 500 ? 10 : 5) === 0 ), { x: "date", y: "indexValue", fill: "#E63946", r: chartWidth < 500 ? 3 : 4, opacity: 0.8 } ), // End label for the index - only on larger screens ...(chartWidth >= 500 ? [ Plot.text( indiaConflictRiskIndex.filter( (d, i) => i === indiaConflictRiskIndex.length - 1 ), { x: "date", y: "indexValue", text: (d) => `${d.indexValue.toFixed(1)}%`, dx: chartWidth < 600 ? 30 : 50, fill: "#E63946", fontWeight: "bold", fontSize: chartWidth < 600 ? 12 : 14 } ) ] : []) ] }); // Determine legend font size const legendFontSize = chartWidth < 500 ? 11 : 10.5; // Calculate watermark position and size const watermarkTop = chartHeight / 2 + topMargin; const watermarkLeft = chartWidth / 2; const watermarkSize = chartWidth < 500 ? 30 : 60; // Create a container for the chart const chartContainer = html`

${chart}

ADJACENT

`; // Create legend items HTML for consistency with other charts const legendItemsHTML = `

India Conflict Risk Index

`; // Add legend to the chart if (chartWidth < 500) { // Mobile: Create a wrapper that includes both chart and legend below it const wrapper = html`

${chartContainer}

${html([legendItemsHTML])}

India Conflict Risk Index

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":5,"value":"overview = md`The goal of the following is to build an index concerning India's conflict risk using China and Pakistan-related prediction markets to assess the changing risks associated with a potential conflict. After charting out and overlaying these markets upon one another, I conclude with a weighted risk index re: India Conflict Risk likelihood`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":25,"value":"First, let's assign market IDs to variables","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":7,"value":"indiaInvadePakistanJuly = \"23019469721164329581998410097229538654312623220643058925130223412597439912703\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":9,"value":"indiaMilitaryActionJune = \"106112879205164947095986936372001671477383165205130802795190592060604325851007\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":12,"value":"indiaMilitaryStrikeFriday = \"97648518381693298716594693413647903080758694909302724969118337870514701307896\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":14,"value":"pakistanAirspaceMay = \"40584055668244370196434601223770627107533669235311146721519053118718362640398\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":36,"value":"Next, we'll populate the marketIds and marketNames arrays with the IDs and names of the markets we will be getting price histories for","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":27,"value":"marketIds = [indiaInvadePakistanJuly, indiaMilitaryActionJune, indiaMilitaryStrikeFriday, pakistanAirspaceMay]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":30,"value":"marketNames = [\"India invade Pakistan before July?\", \"India military action against Pakistan before June?\", \"India military strike on Pakistan by Friday?\", \"Pakistan reopen airspace to India before May?\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":38,"value":"We'll now use the `fetchProcessedMarketHistories` function to get price histories for all of these markets","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":91,"value":"This function gets pricing history for the `max` time interval - i.e., from market inception","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":33,"value":"$31","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":96,"value":"This function gets pricing history for the last week","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":93,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":43,"value":"We'll then process the price history data for these markets and use it for charting purposes","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":40,"value":"processedData = {\n return await fetchProcessedMarketHistoriesMax(marketIds, marketNames);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":45,"value":"We'll no go ahead and chart the data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":47,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":53,"value":"The risk of conflict between India and China has also increased of late. Let's chart that data, too.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":58,"value":"chinaIndiaJune30 = \"82814855654672504061247670166274349069234747344604118638495723199148512283270\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":62,"value":"chinaIndiaDec31 = \"62499647704758013801224496519154801919449219197687180369505612744147504118777\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":65,"value":"chinaMarketIds = [chinaIndiaJune30, chinaIndiaDec31]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":67,"value":"chinaMarketNames = [\"China x India military clash by June 30\", \"China x India military clash in 2025\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":73,"value":"processedData2 = {\n return await fetchProcessedMarketHistoriesMax(chinaMarketIds, chinaMarketNames);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":79,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":84,"value":"Next, I'd like to chart out all of this India risk data in a single chart, before ultimately developing the \"India Conflict Risk Index\"","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":107,"value":"indiaRiskMarketsId = [...marketIds, ...chinaMarketIds]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":111,"value":"indiaRiskMarketNames = [...marketNames, ...chinaMarketNames]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":117,"value":"processedData3 = {\n return await fetchProcessedMarketHistoriesLastWeek(indiaRiskMarketsId, indiaRiskMarketNames);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":121,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":314,"value":"title = html`

India Conflict Risk Index

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":136,"value":"riskOverview = md`Lastly, we want to develop a weighted index that captures India's conflict risk when it comes to both Pakistan and China. The index uses the following for weighting:\n- Time Horizon: Near-term events weighted higher\n- Impact Severity: More significant impacts weighted higher\n- Market Liquidity and Volume: Markets with more liquidity and volume given preference`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":176,"value":"Adjacent Market Data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":180,"value":"We'll use the Adjacent API to get market end dates, liquidity, and volume data. First, we'll assign the corresponding Adjacent market IDs to variables and add them to an array.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":182,"value":"indiaInvadePakistanJulyAdjId = \"adj_polymarket_will_india_invade_pakistan_before_j\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":185,"value":"indiaMilitaryActionJuneAdjId = \"adj_polymarket_india_military_action_against_pakis\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":187,"value":"indiaMilitaryStrikeFridayAdjId = \"adj_polymarket_india_military_strike_on_pakistan_b\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":191,"value":"pakistanAirspaceAdjId= \"adj_polymarket_will_pakistan_reopen_its_airspace_t\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":193,"value":"chinaIndiaMilitaryJuneAdjId = \"adj_polymarket_china_x_india_military_clash_by_jun\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":197,"value":"chinaIndiaMilitaryConflict2025AdjId = \"adj_polymarket_china_x_india_military_clash_by_dec\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":222,"value":"Add the variables to an array that we can pass to `fetchAdjacentMarketData` function","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":205,"value":"adjMarketIds = [indiaInvadePakistanJulyAdjId, indiaMilitaryActionJuneAdjId, indiaMilitaryStrikeFridayAdjId, pakistanAirspaceAdjId, chinaIndiaMilitaryJuneAdjId, chinaIndiaMilitaryConflict2025AdjId]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":208,"value":"Let's build a function to get the data we need to build our index (i.e., end dates, volume, and liquidity)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":210,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":213,"value":"adjacentMarketData = fetchAdjacentMarketData(adjMarketIds)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":238,"value":"We need a function that merges this market metadata with the corresponding price histories for each market","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":240,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":245,"value":"processedDataStructure = combineMarketDataWithPrices(adjacentMarketData, processedData3)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":256,"value":"Weighting Functions","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":254,"value":"Let's define our weighting functions:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":259,"value":"// Calculate time horizon weight - markets closer to expiry get higher weight\nfunction calculateTimeHorizonWeight(endDateStr, currentDate) {\n const endDate = new Date(endDateStr);\n const today = currentDate || new Date();\n \n // Calculate days until expiration\n const daysUntilExpiration = Math.max(0, Math.floor((endDate - today) / (1000 * 60 * 60 * 24)));\n \n // Weight formula: Higher weight for closer expiration dates\n // Using an exponential decay function\n const maxDays = 365; // Max days to consider\n return Math.exp(-daysUntilExpiration / maxDays);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":262,"value":"// Calculate liquidity weight - markets with higher liquidity get higher weight\nfunction calculateLiquidityWeight(liquidity, totalLiquidity) {\n if (totalLiquidity === 0) return 0;\n return liquidity / totalLiquidity;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":266,"value":"// Calculate impact severity weight - define severity levels for different market types\nfunction calculateSeverityWeight(marketName) {\n // Severity mapping based on market description\n const severityMap = {\n 'invade': 5, // Invasion is most severe\n 'military action': 4, // Military action is severe\n 'military strike': 4, // Strike is severe\n 'military clash': 3, // Clash is moderately severe\n 'reopen airspace': 1 // Diplomatic action is less severe\n };\n \n // Find the highest matching severity for this market\n let maxSeverity = 1; // Default severity\n \n Object.keys(severityMap).forEach(key => {\n if (marketName.toLowerCase().includes(key.toLowerCase()) && \n severityMap[key] > maxSeverity) {\n maxSeverity = severityMap[key];\n }\n });\n \n return maxSeverity / 5; // Normalize to 0-1 range\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":271,"value":"Calculate the Weighted Index","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":269,"value":"Now, let's create the function that calculates the weighted index for each day:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":273,"value":"$38","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":287,"value":"Finally, let's combine these functions to generate the conflict risk index:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":289,"value":"// Generate the India Conflict Risk Index\nasync function generateIndiaConflictRiskIndex(processedData, adjacentMarketData) {\n // Step 1: Combine market data with prices\n const enhancedData = combineMarketDataWithPrices(adjacentMarketData, processedData);\n \n // Step 2: Calculate the weighted index\n const indexData = calculateConflictRiskIndex(enhancedData);\n \n // Convert to a simpler format for charting\n const chartData = indexData.map(day => ({\n date: day.date,\n indexValue: day.indexValue\n }));\n \n return chartData;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":294,"value":"India Conflict Risk Index","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":292,"value":"Finally, we'll now chart the India Conflict Risk Index\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":299,"value":"indiaConflictRiskIndex = generateIndiaConflictRiskIndex(processedData, adjacentMarketData);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":303,"value":"$39","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W3a","stdlib":"1","titling":"auto"}} 30:"$2f:body"