MOMENTUM

[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_168":169,"_170":171},"routes/_app.x.$handle.$name",{"_99":98,"_32":45,"_107":85,"_27":108,"_13":14,"_109":-5,"_11":110,"_111":112,"_113":114,"_115":116,"_117":118,"_20":-5,"_119":120,"_121":122,"_123":25,"_124":125,"_126":57,"_127":-5,"_128":-5,"_129":130},"routes/_app.x.$handle.$name.branch.$branch.version.$version.diff",{"_11":12,"_13":14,"_15":16,"_17":-5,"_18":-5},"project",{"_43":44,"_32":45,"_38":46,"_47":-5,"_48":25,"_49":50,"_51":50,"_52":-5,"_53":-5,"_18":54,"_55":31,"_56":57,"_58":57,"_59":-5,"_60":61,"_62":63,"_64":-5,"_65":-5,"_66":-5,"_67":-5,"_68":69,"_70":69,"_71":72,"_73":72,"_74":72,"_75":69,"_76":69,"_77":78,"_79":80,"_81":82,"_83":25},"version",9,"changes",[19],"aiChatInfo","userId",{"_20":21,"_22":23,"_24":25,"_26":25,"_27":28,"_29":28},"file",{"_30":31,"_32":28,"_33":34,"_35":37,"_38":39,"_40":14,"_41":42,"_24":25},"previous",{"_30":31,"_32":28,"_33":34,"_35":36},"deleted",false,"deletedHere","path","main.tsx","latestPath","fileId","01988b90-b4ab-7657-8ecc-b7edef5e9955","name","fileType","http","content","// @ts-ignore\nimport { OpenAI } from \"https://esm.town/v/std/openai?v=4\";\n// @ts-ignore\nimport { Hono } from \"npm:hono@4.4.12\";\n// @ts-ignore\nimport { cors } from \"npm:hono@4.4.12/cors\";\n\n// --- DEBUG LOGGING SETUP ---\nfunction debugLog(message, data) {\n if (Deno.env.get(\"DEBUG_MODE\") === \"true\") {\n console.log(`[DEBUG] ${new Date().toISOString()}: ${message}`);\n if (data) {\n console.log(JSON.stringify(data, null, 2));\n }\n }\n}\n\n// --- AI BEHAVIORAL GUIDELINES ---\nconst PORTFOLIO_ANALYST_PROMPT = `\nYou are a sophisticated AI quantitative portfolio analyst. Your task is to generate a \"Historical Performance Report\" based on a user's list of tickers and a payload of processed historical data and key metrics. Your tone must be empirical, objective, and clear. You are to interpret the provided data, not invent new information.\n\n**CRITICAL INSTRUCTION: Your entire analysis must be derived from the provided 'PROCESSED HISTORICAL DATA'. Use the 'keyMetrics' and 'technicalAnalysis' data to drive your conclusions.**\n\n**Your report MUST follow this exact structure:**\n\n1. **Header & Summary:** State the tickers in the portfolio and the analysis period. Provide a 2-3 sentence executive summary of the overall findings.\n\n2. **Performance Analysis:**\n * Using the \\`keyMetrics\\` data, create a Markdown table summarizing the **Total Return (Buy & Hold)** and **Annualized Volatility** for each ticker.\n * Analyze the table. Identify the best and worst-performing assets based on both absolute return and risk-adjusted performance (by comparing return to volatility).\n\n3. **Rabbit & Turtle Strategy Analysis:**\n * Using the \\`keyMetrics\\` data, create a Markdown table comparing the \\`totalReturn\\` (Buy & Hold) to the \\`strategyReturn\\` (Rabbit & Turtle Crossover Strategy).\n * Analyze the table. For each ticker, state whether the Rabbit & Turtle strategy outperformed or underperformed a simple buy-and-hold approach during this period.\n\n4. **Technical Chart Interpretation:**\n * The user will be shown a chart with performance and a set of technical indicators that can be toggled on/off. The strategy uses a fast 'Rabbit' (5-period) moving average and a slow 'Turtle' (60-period) moving average.\n * Crossover events are marked with a 🚀 for a 'Golden Cross' (buy signal) and an ⚓ for a 'Death Cross' (sell signal).\n * Explain what these signals signify as potential shifts in momentum. Reference the \\`crossoverEvents\\` data to mention any specific signals that occurred for any tickers during the period.\n\n5. **Correlation & Diversification Insights:**\n * Based on the performance data, make qualitative inferences about how these stocks likely move in relation to one another.\n * Provide a brief comment on the diversification benefits (or lack thereof) of this specific portfolio.\n\n6. **Concluding Remarks:**\n * Offer a final analytical summary of the portfolio's historical behavior and the results of the Rabbit and Turtle crossover strategy.\n`;\nconst ADVISOR_PROMPT = `\nYou are a helpful and conversational AI portfolio advisor. Your goal is to help the user analyze their list of stocks. - **Be Conversational:** Chat with the user in a natural, helpful, and friendly tone. - **Analyze Data:** When the user asks for insights, refer to the provided analysis data. - **Execute Commands:** The user can ask you to modify the analysis (e.g., \"add a ticker,\" \"remove a ticker,\" \"change the date\"). When they do, you MUST use the \\`update_analysis\\` tool to fulfill their request. - **Clarify When Needed:** If a user's request is ambiguous (e.g., \"what about Tesla?\"), ask for clarification (e.g., \"Do you mean you want to add TSLA to the analysis?\"). - **Current State Awareness:** The user's message will include the current state of the analysis (tickers, dates). Use this to inform your responses and tool calls.\n`;\nconst ADVISOR_TOOLS = [{\n type: \"function\",\n function: {\n name: \"update_analysis\",\n description:\n \"Updates the portfolio analysis by adding or removing tickers, or changing the date range. Use this tool whenever the user asks to modify the view.\",\n parameters: {\n type: \"object\",\n properties: {\n tickers: {\n type: \"array\",\n items: { type: \"string\" },\n description:\n \"The complete, updated list of tickers for the new analysis. This should be based on the previous list plus or minus the user's request.\",\n },\n startDate: {\n type: \"string\",\n description: \"The new start date in YYYY-MM-DD format.\",\n },\n endDate: {\n type: \"string\",\n description: \"The new end date in YYYY-MM-DD format.\",\n },\n },\n required: [\"tickers\", \"startDate\", \"endDate\"],\n },\n },\n}];\n\n// --- DATA ANALYSIS HELPERS ---\nfunction getIntervalForDateRange(startDate, endDate) {\n const start = new Date(startDate);\n const end = new Date(endDate);\n const diffDays = (end - start) / (1000 * 60 * 60 * 24);\n if (diffDays > 365 * 7) {\n return {\n interval: \"1mo\",\n annualizationFactor: 12,\n unit: \"year\",\n periodName: \"Month\",\n };\n }\n if (diffDays > 365 * 2) {\n return {\n interval: \"1wk\",\n annualizationFactor: 52,\n unit: \"month\",\n periodName: \"Week\",\n };\n }\n return {\n interval: \"1d\",\n annualizationFactor: 252,\n unit: \"day\",\n periodName: \"Day\",\n };\n}\n\nfunction getPeriodReturns(prices) {\n const returns = [];\n for (let i = 1; i < prices.length; i++) {\n if (prices[i] !== null && prices[i - 1] !== null) {\n returns.push((prices[i] / prices[i - 1]) - 1);\n }\n }\n return returns;\n}\n\nfunction calculateStdDev(values) {\n const n = values.length;\n if (n === 0) return 0;\n const mean = values.reduce((a, b) => a + b) / n;\n const variance =\n values.map((x) => Math.pow(x - mean, 2)).reduce((a, b) => a + b) / n;\n return Math.sqrt(variance);\n}\n\nfunction calculateSMA(data, windowSize) {\n if (data.length < windowSize) return Array(data.length).fill(null);\n const sma = Array(windowSize - 1).fill(null);\n let sum = 0;\n for (let i = 0; i < data.length; i++) {\n sum += data[i];\n if (i >= windowSize) {\n sum -= data[i - windowSize];\n sma.push(sum / windowSize);\n } else if (i === windowSize - 1) {\n sma.push(sum / windowSize);\n }\n }\n return sma;\n}\n\nfunction findCrossovers(dates, shortMA, longMA) {\n const events = [];\n for (let i = 1; i < dates.length; i++) {\n if (\n shortMA[i - 1] === null || longMA[i - 1] === null ||\n shortMA[i] === null || longMA[i] === null\n ) continue;\n // Golden Cross: Short MA crosses ABOVE Long MA\n if (shortMA[i - 1] <= longMA[i - 1] && shortMA[i] > longMA[i]) {\n events.push({ date: dates[i], type: \"Golden Cross\", value: shortMA[i] });\n }\n // Death Cross: Short MA crosses BELOW Long MA\n if (shortMA[i - 1] >= longMA[i - 1] && shortMA[i] < longMA[i]) {\n events.push({ date: dates[i], type: \"Death Cross\", value: shortMA[i] });\n }\n }\n return events;\n}\n\n/**\n * NEW: Simulates a trading strategy based on crossover events.\n * Buys on Golden Cross, Sells on Death Cross.\n */\nfunction simulateRabbitTurtleStrategy(prices, dates, crossoverEvents) {\n if (crossoverEvents.length === 0) return 0;\n\n let capital = 1.0;\n let shares = 0;\n let inMarket = false;\n const priceMap = new Map(dates.map((date, i) => [date, prices[i]]));\n\n for (const event of crossoverEvents) {\n const eventPrice = priceMap.get(event.date);\n if (!eventPrice) continue;\n\n if (event.type === \"Golden Cross\" && !inMarket) {\n shares = capital / eventPrice;\n capital = 0;\n inMarket = true;\n } else if (event.type === \"Death Cross\" && inMarket) {\n capital = shares * eventPrice;\n shares = 0;\n inMarket = false;\n }\n }\n\n if (inMarket) {\n const finalPrice = prices[prices.length - 1];\n capital = shares * finalPrice;\n }\n\n return capital - 1; // Return the profit factor (e.g., 0.15 for 15% return)\n}\n\n/**\n * REFACTORED: Calculates metrics and technicals from raw prices first,\n * then normalizes all data for consistent charting.\n */\nfunction processHistoricalData(results, annualizationFactor) {\n const analysis = {\n keyMetrics: {},\n normalizedPerformance: {},\n technicalAnalysis: {},\n };\n const rabbit_window = 5;\n const turtle_window = 60;\n\n for (const result of results) {\n if (!result.data || result.data.length < turtle_window) continue;\n const ticker = result.ticker;\n const dates = result.data.map((d) => d.date);\n const prices = result.data.map((d) => d.close);\n const firstPrice = prices[0];\n\n const totalReturn = (prices[prices.length - 1] / prices[0]) - 1;\n const periodReturns = getPeriodReturns(prices);\n const volatility = calculateStdDev(periodReturns) *\n Math.sqrt(annualizationFactor);\n\n const smaRabbit = calculateSMA(prices, rabbit_window);\n const smaTurtle = calculateSMA(prices, turtle_window);\n const crossovers = findCrossovers(dates, smaRabbit, smaTurtle);\n const strategyReturn = simulateRabbitTurtleStrategy(\n prices,\n dates,\n crossovers,\n );\n\n analysis.keyMetrics[ticker] = {\n totalReturn: (totalReturn * 100).toFixed(2) + \"%\",\n strategyReturn: (strategyReturn * 100).toFixed(2) + \"%\",\n annualizedVolatility: (volatility * 100).toFixed(2) + \"%\",\n };\n\n const normalizedValues = prices.map((p) => (p / firstPrice) * 100);\n const normalizedSmaRabbit = smaRabbit.map((v) =>\n v === null ? null : (v / firstPrice) * 100\n );\n const normalizedSmaTurtle = smaTurtle.map((v) =>\n v === null ? null : (v / firstPrice) * 100\n );\n const normalizedCrossovers = crossovers.map((e) => ({\n ...e,\n value: (e.value / firstPrice) * 100,\n }));\n\n analysis.normalizedPerformance[ticker] = dates.map((date, i) => ({\n date: date,\n value: normalizedValues[i],\n }));\n\n analysis.technicalAnalysis[ticker] = {\n smaRabbit: dates.map((d, i) => ({\n date: d,\n value: normalizedSmaRabbit[i],\n })),\n smaTurtle: dates.map((d, i) => ({\n date: d,\n value: normalizedSmaTurtle[i],\n })),\n crossoverEvents: normalizedCrossovers,\n };\n }\n return analysis;\n}\n\nasync function fetchFromYahoo(ticker, startDate, endDate, interval = \"1d\") {\n const period1 = Math.floor(new Date(startDate).getTime() / 1000);\n const period2 = Math.floor(new Date(endDate).getTime() / 1000);\n const url =\n `https://query1.finance.yahoo.com/v8/finance/chart/${ticker}?period1=${period1}&period2=${period2}&interval=${interval}`;\n const response = await fetch(url, {\n headers: { \"User-Agent\": \"Mozilla/5.0\" },\n });\n if (!response.ok) return null;\n const json = await response.json();\n const chartData = json.chart.result[0];\n if (!chartData || !chartData.timestamp) return null;\n return chartData.timestamp.map((ts, i) => ({\n date: new Date(ts * 1000).toISOString().split(\"T\")[0],\n close: chartData.indicators.quote[0].close[i],\n })).filter((d) => d.close !== null);\n}\n\n// --- HTML & FRONTEND ---\n// --- HTML & FRONTEND ---\n// --- HTML & FRONTEND ---\n// --- HTML & FRONTEND ---\nfunction generateHtml() {\n return `\n\n\n \n \n MOMENTUM\n \n \n \n

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