Conversational Portfolio Advisor

[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_152":153,"_154":155},"routes/_app.x.$handle.$name",{"_11":12,"_13":14,"_15":16,"_18":19,"_17":-7,"_115":-5,"_20":116,"_22":117,"_118":119,"_120":121,"_25":122,"_24":-5,"_123":-5,"_124":-5,"_125":53,"_126":127,"_128":44,"_129":-5,"_130":-5,"_131":132},"routes/_app.x.$handle.$name.branch.$branch.code.$",{"_11":12,"_13":14,"_15":16,"_17":-7,"_18":19,"_20":21,"_22":23,"_24":-7,"_25":26,"_27":-5,"_28":-5,"_29":-5,"_30":-5,"_31":-5},"handle","real","name","futurespast","branchName","main","version","path","","project",{"_33":61,"_13":14,"_45":62,"_63":-5,"_64":53,"_65":66,"_67":66,"_68":-5,"_69":-5,"_70":71,"_72":37,"_73":44,"_74":44,"_75":-5,"_76":77,"_78":79,"_80":-5,"_81":-5,"_82":-5,"_83":-5,"_84":85,"_86":85,"_87":88,"_89":88,"_90":88,"_91":85,"_92":85,"_93":94,"_95":96,"_97":98,"_99":53},"tree",[56],"file","contents",[32],"upstream","pull","ahead","behind","envVars",{"_33":34,"_13":35,"_36":37,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":46,"_17":47,"_48":49,"_50":51,"_52":53,"_54":-5,"_18":55},"id","e4f67eae-744a-11f0-893c-0224a6c84d84","main.tsx","fileId","36b91912-7424-11f0-826e-0224a6c84d84","parentId","branchId","36b69f98-7424-11f0-826e-0224a6c84d84","branchVersion",12,"latest",true,"createdAt",["D",1754652546611],11,"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', 'normalizedPerformance', and new '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** 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. **Normalized Performance & Technicals 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\n4. **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\n5. **Concluding Remarks:**\n * Offer a final analytical summary of the portfolio's historical behavior and any technical signals observed from 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: \"month\",\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 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 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\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 < 2) 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 const normalizedValues = prices.map((p) => (p / firstPrice) * 100);\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 analysis.keyMetrics[ticker] = {\n totalReturn: (totalReturn * 100).toFixed(2) + \"%\",\n annualizedVolatility: (volatility * 100).toFixed(2) + \"%\",\n };\n analysis.normalizedPerformance[ticker] = prices.map((price, i) => ({\n date: dates[i],\n value: normalizedValues[i],\n }));\n\n const smaRabbit = calculateSMA(normalizedValues, rabbit_window);\n const smaTurtle = calculateSMA(normalizedValues, turtle_window);\n const crossovers = findCrossovers(dates, smaRabbit, smaTurtle);\n\n analysis.technicalAnalysis[ticker] = {\n smaRabbit: dates.map((d, i) => ({ date: d, value: smaRabbit[i] })),\n smaTurtle: dates.map((d, i) => ({ date: d, value: smaTurtle[i] })),\n crossoverEvents: crossovers,\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 ---\nfunction generateHtml() {\n return `\n\n\n \n \n Conversational Portfolio Advisor\n \n \n \n \n \n \n\n\n\n

\n \n

Fetching data... Calculating technicals... Generating report...

Chat with Advisor

Advisor is thinking...

\n\n\n`;\n}\n\n// --- CORE ANALYSIS FUNCTION & ROUTER (Unchanged) ---\nasync function generateAnalysis(tickers, startDate, endDate) {\n debugLog(\"--- New Analysis Job Started ---\", { tickers, startDate, endDate });\n const { interval, annualizationFactor, unit, periodName } =\n getIntervalForDateRange(startDate, endDate);\n const tickerList = tickers.split(\",\").map((t) => t.trim().toUpperCase())\n .filter(Boolean);\n if (tickerList.length === 0) throw new Error(\"Please provide valid tickers.\");\n const promises = tickerList.map((ticker) =>\n fetchFromYahoo(ticker, startDate, endDate, interval).then((data) => ({\n ticker,\n data,\n }))\n );\n const results = await Promise.all(promises);\n const successfulResults = results.filter((r) => r.data && r.data.length > 1);\n if (successfulResults.length === 0) {\n throw new Error(\"Could not fetch valid historical data.\");\n }\n const analysis = processHistoricalData(\n successfulResults,\n annualizationFactor,\n );\n const userInput = `--- PORTFOLIO & PERIOD ---\\nTickers: ${\n successfulResults.map((r) => r.ticker).join(\", \")\n }\\nAnalysis Period: ${startDate} to ${endDate} (${periodName}ly data)\\n--- PROCESSED DATA ---\\n${\n JSON.stringify(analysis, null, 2)\n }`;\n const openai = new OpenAI();\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [{ role: \"system\", content: PORTFOLIO_ANALYST_PROMPT }, {\n role: \"user\",\n content: userInput,\n }],\n temperature: 0.1,\n });\n const report = completion.choices[0].message.content;\n return { report, analysis, timeUnit: unit };\n}\n\nconst app = new Hono();\napp.use(\"*\", cors());\napp.get(\"/\", (c) => c.html(generateHtml()));\napp.post(\"/api/report\", async (c) => {\n try {\n const { tickers, startDate, endDate } = await c.req.json();\n const analysisResult = await generateAnalysis(tickers, startDate, endDate);\n return c.json(analysisResult);\n } catch (error) {\n debugLog(\"FATAL ERROR in /api/report\", {\n error: error.message,\n stack: error.stack,\n });\n return c.json({\n error: error.message || \"An internal server error occurred.\",\n }, 500);\n }\n});\napp.post(\"/api/chat\", async (c) => {\n try {\n const { conversationHistory, currentState } = await c.req.json();\n const lastUserMessage = conversationHistory.slice(-1)[0]?.content;\n const messages = [\n { role: \"system\", content: ADVISOR_PROMPT },\n ...conversationHistory,\n {\n role: \"user\",\n content: `--- Current Analysis State ---\\nTickers: ${\n currentState.tickers.join(\", \")\n }\\nStart Date: ${currentState.startDate}\\nEnd Date: ${currentState.endDate}\\n\\n--- My Request ---\\n${lastUserMessage}`,\n },\n ];\n const openai = new OpenAI();\n const response = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: messages,\n tools: ADVISOR_TOOLS,\n tool_choice: \"auto\",\n });\n const responseMessage = response.choices[0].message;\n const toolCalls = responseMessage.tool_calls;\n if (toolCalls) {\n const toolCall = toolCalls[0];\n const functionArgs = JSON.parse(toolCall.function.arguments);\n const newAnalysis = await generateAnalysis(\n functionArgs.tickers.join(\",\"),\n functionArgs.startDate,\n functionArgs.endDate,\n );\n const confirmationMessage = `Of course. I've updated the analysis for **${\n functionArgs.tickers.join(\", \")\n }** from **${functionArgs.startDate}** to **${functionArgs.endDate}**. 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