[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_210":211,"_212":213},"routes/_app.x.$handle.$name",{"_105":104,"_37":50,"_114":95,"_32":115,"_13":14,"_116":-5,"_11":117,"_118":119,"_120":121,"_122":123,"_124":125,"_26":-5,"_126":-5,"_127":-5,"_128":30,"_129":130,"_131":30,"_132":-5,"_133":-5,"_134":135},"routes/_app.x.$handle.$name.branch.$branch.version.$version.diff",{"_11":12,"_13":14,"_15":16,"_17":18,"_19":-5},"project",{"_48":49,"_37":50,"_43":51,"_52":-5,"_53":30,"_54":55,"_56":55,"_57":58,"_59":-5,"_19":60,"_61":62,"_63":64,"_65":-5,"_66":-5,"_67":68,"_69":70,"_71":72,"_73":74,"_75":76,"_77":-5,"_78":79,"_80":79,"_81":82,"_83":82,"_84":82,"_85":79,"_86":79,"_87":88,"_89":90,"_91":92,"_93":30},"version",5,"changes",[25],"aiChatInfo",{"_20":21,"_22":-5,"_23":-5,"_24":-5},"userId","aiChatMessageId","576e2032-b2d4-4075-8f4e-003e485a5250","aiChatThreadId","aiChatThreadUserId","aiChatThreadTitle",{"_26":27,"_28":-7,"_29":30,"_31":30,"_32":33,"_34":33},"file",{"_35":36,"_37":38,"_39":40,"_41":42,"_43":44,"_45":14,"_46":47,"_29":30},"previous","deleted",false,"deletedHere","path","lib/settings.ts","latestPath","fileId","61d274be-8b1a-11f1-a22c-1607ee4eb77e","name","settings.ts","fileType","script","content","// ─── Settings System ─────────────────────────────────────────────────\n// Settings stored in blob storage, editable from the dashboard.\n\nimport { blob } from \"https://esm.town/v/std/blob/main.ts\";\n\nexport interface ATNSettings {\n // Telegram\n telegramBotToken: string;\n telegramChatId: string;\n telegramEnabled: boolean;\n\n // Pipeline\n pipelineEnabled: boolean;\n pipelineSymbols: string[];\n pipelineTimeframe: string;\n pipelineIntervalMinutes: number;\n\n // Desk\n deskId: string;\n autoGenerateCases: boolean;\n autoResolve: boolean;\n minConfidenceForCase: number;\n contradictionsRequired: number;\n\n // Notifications\n notifyOnNewCase: boolean;\n notifyOnResolution: boolean;\n notifyOnContradiction: boolean;\n notifyOnError: boolean;\n\n // Network\n networkName: string;\n vertical: string;\n}\n\nconst DEFAULT_SETTINGS: ATNSettings = {\n telegramBotToken: \"\",\n telegramChatId: \"\",\n telegramEnabled: false,\n\n pipelineEnabled: false,\n pipelineSymbols: [\"BTC\", \"ETH\", \"SPY\", \"EURUSD\", \"GOLD\"],\n pipelineTimeframe: \"4H\",\n pipelineIntervalMinutes: 60,\n\n deskId: \"desk_alpha_01\",\n autoGenerateCases: true,\n autoResolve: true,\n minConfidenceForCase: 50,\n contradictionsRequired: 2,\n\n notifyOnNewCase: true,\n notifyOnResolution: true,\n notifyOnContradiction: true,\n notifyOnError: true,\n\n networkName: \"ATN\",\n vertical: \"trading\",\n};\n\nconst SETTINGS_KEY = \"atn_settings\";\n\nexport async function getSettings(): Promise {\n const data = await blob.getJSON(SETTINGS_KEY);\n if (!data) return DEFAULT_SETTINGS;\n return { ...DEFAULT_SETTINGS, ...data };\n}\n\nexport async function saveSettings(settings: Partial): Promise {\n const current = await getSettings();\n const updated = { ...current, ...settings };\n await blob.setJSON(SETTINGS_KEY, updated);\n return updated;\n}\n\nexport async function resetSettings(): Promise {\n await blob.setJSON(SETTINGS_KEY, DEFAULT_SETTINGS);\n return DEFAULT_SETTINGS;\n}\n","createdAt",["D",1785308085169],"branchVersion","branchId","7f4fd07b-175a-4243-9d9e-b81c40f32513","id","a9d8b9e9-d031-4c55-a731-b847098d7926","atn-cnos-bot",["D",1785307366530],"deletedAt","legacyEnvVars","privacy","public","httpPrivacy","description","Agentic Trading Network bot with CNOS cognitive resolution layer","imageUrl","fb553982-e0f4-426a-964b-f9954390755a","pinnedFileId","686ae364-8b1b-11f1-be27-1607ee4eb77e","pinPreview",true,"pinMigrated","screenshotVersion","likeCount",0,"orgId","019fab8b-9f9e-774c-8b18-dc32359ac260","tursoName","a9d8b9e9d0314c55a731b847098d7926","tursoHostname","a9d8b9e9d0314c55a731b847098d7926-val-town.aws-us-east-2.turso.io","tursoMeasuredAt",["D",1789476648181],"pinned","userPerm","r","valPerm","blobPerm","rw","emailPerm","sqlitePerm","projectPerm","telemetryPerm","user",{"_105":104,"_113":109},"org",{"_105":104,"_106":107,"_108":109,"_19":60,"_110":111,"_112":30},"branch",{"_13":94,"_37":95,"_48":47,"_96":49,"_97":-5,"_98":-5,"_99":-5,"_100":-5,"_101":-5,"_102":50,"_103":104},"isOwned",30,"main","projectId","forkedBranchId","forkedBranchVersion","forkedProjectPrivacy","forkedProjectId","forkedProjectOrgId","projectName","userHandle","hitter2","handle","displayName","Hitter","avatar","https://lh3.googleusercontent.com/a/ACg8ocJ8jWQdlaCgat_lteLBEdqQMeHm85EOjYF-UtAmj4WyqMLMkQ=s96-c","tier","free","canRestrictVals","profileImageUrl","branchName","","adoptOrgId",{"_48":49,"_37":50,"_43":205,"_52":-5,"_53":30,"_54":55,"_56":55,"_57":58,"_59":-5,"_19":60,"_61":62,"_63":64,"_65":-5,"_66":-5,"_67":68,"_69":70,"_71":72,"_73":74,"_75":206,"_77":-5,"_78":79,"_80":79,"_81":82,"_83":82,"_84":82,"_85":79,"_86":79,"_87":207,"_89":208,"_91":209,"_93":30},"tree",[165,170],"files",[165,166,167,168,169,170],"counts",{"_155":68,"_156":151,"_157":68,"_158":159,"_160":161},"contents",[136,137],"upstreamProject","upstreamBranch","emptyEnvVars","flags",[],"hasHttpFiles","grants","orgs","customDomains",{},{"_48":148,"_37":149,"_35":150,"_141":-5,"_46":47,"_45":151,"_143":64,"_43":152,"_13":68,"_39":153,"_41":-5,"_29":30,"_146":-5,"_32":154},{"_48":138,"_37":139,"_35":140,"_141":-5,"_46":47,"_45":142,"_143":30,"_43":144,"_13":68,"_39":26,"_41":145,"_29":30,"_146":-5,"_32":147},"3310d570-516b-4ecd-96c2-3faafa44fb51","README.md","cb48cec6-8b19-11f1-9711-1607ee4eb77e","parentId",3,"latest",["D",1785307832603],"# atn-cnos-bot","label",[139],"fa6ed36c-77e7-408a-b21f-b7e68e58f55f","lib","28a98165-a9c7-443c-a3db-b41db2dce7b9",1,["D",1785307426186],"directory",[149],"pulls","branches","forks","envVars",{"_162":68,"_164":68},"connections",{"_162":68,"_163":68},"count","unfilledCount","emptyCount",{"_48":148,"_37":149,"_35":150,"_141":-5,"_46":47,"_45":151,"_143":64,"_43":202,"_13":68,"_39":153,"_41":-5,"_29":30,"_146":-5,"_172":203,"_32":204},{"_48":194,"_37":195,"_35":196,"_141":150,"_46":47,"_45":197,"_143":64,"_43":198,"_13":68,"_39":40,"_41":199,"_29":30,"_146":-5,"_172":200,"_32":201},{"_48":187,"_37":188,"_35":189,"_141":150,"_46":47,"_45":151,"_143":64,"_43":190,"_13":68,"_39":40,"_41":191,"_29":30,"_146":-5,"_172":192,"_32":193},{"_48":179,"_37":180,"_35":181,"_141":150,"_46":47,"_45":182,"_143":30,"_43":183,"_13":68,"_39":40,"_41":184,"_29":30,"_146":-5,"_172":185,"_32":186},{"_48":175,"_37":38,"_35":36,"_141":150,"_46":47,"_45":14,"_143":30,"_43":176,"_13":68,"_39":40,"_41":42,"_29":30,"_146":-5,"_172":177,"_32":178},{"_48":138,"_37":139,"_35":140,"_141":-5,"_46":47,"_45":142,"_143":30,"_43":171,"_13":68,"_39":26,"_41":145,"_29":30,"_146":-5,"_172":173,"_32":174},["D",1785307832603],"children",[],[139],"5b6c62ba-c256-4ff2-9f7a-3b0a1f78d478",["D",1785308085169],[],[149,38],"5f3754be-e7d6-4771-bd82-eca67f95ea0d","market-data.ts","0fdafa6a-8b19-11f1-9c9d-1607ee4eb77e",2,["D",1785307518151],"// ─── Market Data Fetcher ─────────────────────────────────────────────\n// Uses free public APIs (Yahoo Finance, Stooq) for market data.\n// No API key required.\n\nconst YF_BASE = \"https://query1.finance.yahoo.com/v8/finance\";\nconst YF_CHART = \"https://query1.finance.yahoo.com/v8/finance/chart\";\n\n// Symbol normalization map\nconst SYMBOL_MAP: Record = {\n \"BTC\": \"BTC-USD\",\n \"BTCUSDT\": \"BTC-USD\",\n \"XBT\": \"BTC-USD\",\n \"ETH\": \"ETH-USD\",\n \"ETHUSDT\": \"ETH-USD\",\n \"SOL\": \"SOL-USD\",\n \"SOLUSDT\": \"SOL-USD\",\n \"EURUSD\": \"EURUSD=X\",\n \"GBPUSD\": \"GBPUSD=X\",\n \"USDJPY\": \"USDJPY=X\",\n \"XAUUSD\": \"GC=F\",\n \"GOLD\": \"GC=F\",\n \"SPX\": \"^GSPC\",\n \"SP500\": \"^GSPC\",\n \"NASDAQ\": \"^IXIC\",\n \"DOW\": \"^DJI\",\n \"DXY\": \"DX-Y.NYB\",\n \"VIX\": \"^VIX\",\n \"WTI\": \"CL=F\",\n \"OIL\": \"CL=F\",\n \"TENYEAR\": \"^TNX\",\n \"2YR\": \"^FVX\",\n};\n\nexport function normalizeSymbol(symbol: string): string {\n const upper = symbol.toUpperCase().trim();\n return SYMBOL_MAP[upper] ?? upper;\n}\n\nexport function timeframeToInterval(tf: string): { interval: string; range: string } {\n const map: Record = {\n \"1m\": { interval: \"1m\", range: \"1d\" },\n \"5m\": { interval: \"5m\", range: \"5d\" },\n \"15m\": { interval: \"15m\", range: \"1mo\" },\n \"1H\": { interval: \"60m\", range: \"3mo\" },\n \"4H\": { interval: \"1h\", range: \"6mo\" },\n \"1D\": { interval: \"1d\", range: \"1y\" },\n \"1W\": { interval: \"1wk\", range: \"2y\" },\n \"1M\": { interval: \"1mo\", range: \"5y\" },\n };\n return map[tf] ?? { interval: \"1d\", range: \"6mo\" };\n}\n\n// ─── Tool: search_symbols ────────────────────────────────────────────\nexport async function searchSymbols(query: string): Promise {\n try {\n const res = await fetch(\n `${YF_BASE}/search?q=${encodeURIComponent(query)}"esCount=10&newsCount=0`,\n { headers: { \"User-Agent\": \"Mozilla/5.0\" } },\n );\n const data = await res.json();\n return (data.quotes || []).map((q: any) => ({\n symbol: q.symbol,\n name: q.shortname || q.longname || q.symbol,\n exchange: q.exchange,\n type: q.quoteType,\n region: q.region ?? \"US\",\n }));\n } catch (e) {\n return [{ error: `Search failed: ${e.message}` }];\n }\n}\n\n// ─── Tool: get_quote ─────────────────────────────────────────────────\nexport async function getQuote(symbol: string): Promise {\n try {\n const sym = normalizeSymbol(symbol);\n const res = await fetch(\n `https://query1.finance.yahoo.com/v7/finance/quote?symbols=${encodeURIComponent(sym)}`,\n { headers: { \"User-Agent\": \"Mozilla/5.0\" } },\n );\n const data = await res.json();\n const q = data.quoteResponse?.result?.[0];\n if (!q) return { error: `No quote found for ${symbol}` };\n return {\n symbol: q.symbol,\n name: q.shortName ?? q.longName ?? q.symbol,\n price: q.regularMarketPrice,\n change: q.regularMarketChange,\n changePercent: q.regularMarketChangePercent,\n previousClose: q.regularMarketPreviousClose,\n open: q.regularMarketOpen,\n high: q.regularMarketDayHigh,\n low: q.regularMarketDayLow,\n volume: q.regularMarketVolume,\n marketCap: q.marketCap,\n currency: q.currency,\n };\n } catch (e) {\n return { error: `Quote failed: ${e.message}` };\n }\n}\n\n// ─── Tool: get_quotes_batch ──────────────────────────────────────────\nexport async function getQuotesBatch(symbols: string[]): Promise {\n const results = await Promise.all(symbols.map((s) => getQuote(s)));\n return results;\n}\n\n// ─── Tool: get_ohlcv ─────────────────────────────────────────────────\nexport async function getOHLCV(\n symbol: string,\n timeframe: string = \"1D\",\n limit: number = 100,\n): Promise {\n try {\n const sym = normalizeSymbol(symbol);\n const { interval, range } = timeframeToInterval(timeframe);\n const url = `${YF_CHART}/${encodeURIComponent(sym)}?interval=${interval}&range=${range}&includePrePost=false`;\n const res = await fetch(url, { headers: { \"User-Agent\": \"Mozilla/5.0\" } });\n const data = await res.json();\n const result = data.chart?.result?.[0];\n if (!result) return { error: `No OHLCV data for ${symbol}` };\n\n const timestamps = result.timestamp || [];\n const quotes = result.indicators?.quote?.[0] || {};\n const bars = [];\n const startIdx = Math.max(0, timestamps.length - limit);\n for (let i = startIdx; i < timestamps.length; i++) {\n if (quotes.open?.[i] != null) {\n bars.push({\n timestamp: new Date(timestamps[i] * 1000).toISOString(),\n open: quotes.open[i],\n high: quotes.high[i],\n low: quotes.low[i],\n close: quotes.close[i],\n volume: quotes.volume?.[i] ?? 0,\n });\n }\n }\n return { symbol: sym, timeframe, bars, count: bars.length };\n } catch (e) {\n return { error: `OHLCV failed: ${e.message}` };\n }\n}\n\n// ─── Tool: get_technicals ────────────────────────────────────────────\nexport async function getTechnicals(symbol: string, timeframe: string = \"1D\"): Promise {\n try {\n const ohlcv = await getOHLCV(symbol, timeframe, 200);\n if (ohlcv.error) return ohlcv;\n const bars = ohlcv.bars;\n const closes = bars.map((b: any) => b.close);\n const highs = bars.map((b: any) => b.high);\n const lows = bars.map((b: any) => b.low);\n const volumes = bars.map((b: any) => b.volume);\n\n const last = closes[closes.length - 1];\n\n // Simple Moving Averages\n const sma = (period: number) => {\n if (closes.length < period) return null;\n const slice = closes.slice(-period);\n return slice.reduce((a: number, b: number) => a + b, 0) / period;\n };\n // Exponential Moving Averages\n const ema = (period: number) => {\n if (closes.length < period) return null;\n const k = 2 / (period + 1);\n let emaVal = closes.slice(0, period).reduce((a: number, b: number) => a + b, 0) / period;\n for (let i = period; i < closes.length; i++) {\n emaVal = closes[i] * k + emaVal * (1 - k);\n }\n return emaVal;\n };\n // RSI\n const rsi = (period: number = 14) => {\n if (closes.length < period + 1) return null;\n let gains = 0, losses = 0;\n for (let i = closes.length - period; i < closes.length; i++) {\n const diff = closes[i] - closes[i - 1];\n if (diff > 0) gains += diff; else losses -= diff;\n }\n const avgGain = gains / period;\n const avgLoss = losses / period;\n if (avgLoss === 0) return 100;\n const rs = avgGain / avgLoss;\n return 100 - (100 / (1 + rs));\n };\n // MACD\n const macd = () => {\n const ema12 = ema(12);\n const ema26 = ema(26);\n if (!ema12 || !ema26) return null;\n const macdLine = ema12 - ema26;\n const signalLine = macdLine * 0.9; // simplified\n return { macd: macdLine, signal: signalLine, histogram: macdLine - signalLine };\n };\n // Bollinger Bands\n const bollinger = (period: number = 20) => {\n if (closes.length < period) return null;\n const slice = closes.slice(-period);\n const smaVal = slice.reduce((a: number, b: number) => a + b, 0) / period;\n const variance = slice.reduce((a: number, b: number) => a + (b - smaVal) ** 2, 0) / period;\n const std = Math.sqrt(variance);\n return {\n middle: smaVal,\n upper: smaVal + 2 * std,\n lower: smaVal - 2 * std,\n };\n };\n // ATR\n const atr = (period: number = 14) => {\n if (bars.length < period + 1) return null;\n let trSum = 0;\n for (let i = bars.length - period; i < bars.length; i++) {\n const tr = Math.max(\n highs[i] - lows[i],\n Math.abs(highs[i] - closes[i - 1]),\n Math.abs(lows[i] - closes[i - 1]),\n );\n trSum += tr;\n }\n return trSum / period;\n };\n // Stochastic\n const stoch = (period: number = 14) => {\n if (bars.length < period) return null;\n const slice = bars.slice(-period);\n const highestHigh = Math.max(...slice.map((b: any) => b.high));\n const lowestLow = Math.min(...slice.map((b: any) => b.low));\n const currentClose = closes[closes.length - 1];\n if (highestHigh === lowestLow) return { k: 50, d: 50 };\n const k = ((currentClose - lowestLow) / (highestHigh - lowestLow)) * 100;\n return { k, d: k * 0.9 };\n };\n // ADX (simplified)\n const adx = (period: number = 14) => {\n if (bars.length < period * 2) return null;\n let plusDM = 0, minusDM = 0, tr = 0;\n for (let i = bars.length - period; i < bars.length; i++) {\n const upMove = highs[i] - highs[i - 1];\n const downMove = lows[i - 1] - lows[i];\n if (upMove > downMove && upMove > 0) plusDM += upMove;\n if (downMove > upMove && downMove > 0) minusDM += downMove;\n tr += Math.max(highs[i] - lows[i], Math.abs(highs[i] - closes[i - 1]));\n }\n const di_plus = (plusDM / tr) * 100;\n const di_minus = (minusDM / tr) * 100;\n const dx = Math.abs(di_plus - di_minus) / (di_plus + di_minus) * 100;\n return dx;\n };\n\n return {\n symbol: ohlcv.symbol,\n timeframe,\n lastPrice: last,\n indicators: {\n sma: { sma20: sma(20), sma50: sma(50), sma200: sma(200) },\n ema: { ema12: ema(12), ema20: ema(20), ema50: ema(50), ema200: ema(200) },\n rsi: rsi(14),\n macd: macd(),\n bollinger: bollinger(20),\n atr: atr(14),\n stochastic: stoch(14),\n adx: adx(14),\n volume: volumes[volumes.length - 1],\n avgVolume20: volumes.slice(-20).reduce((a: number, b: number) => a + b, 0) / Math.min(20, volumes.length),\n },\n };\n } catch (e) {\n return { error: `Technicals failed: ${e.message}` };\n }\n}\n\n// ─── Tool: get_technicals_rating ─────────────────────────────────────\nexport async function getTechnicalsRating(symbol: string, timeframe: string = \"1D\"): Promise {\n const tech = await getTechnicals(symbol, timeframe);\n if (tech.error) return tech;\n\n const i = tech.indicators;\n let buySignals = 0;\n let sellSignals = 0;\n const breakdown: any[] = [];\n\n // RSI\n if (i.rsi != null) {\n if (i.rsi < 30) { buySignals++; breakdown.push({ indicator: \"RSI\", signal: \"BUY\", value: i.rsi }); }\n else if (i.rsi > 70) { sellSignals++; breakdown.push({ indicator: \"RSI\", signal: \"SELL\", value: i.rsi }); }\n else breakdown.push({ indicator: \"RSI\", signal: \"NEUTRAL\", value: i.rsi });\n }\n // MACD\n if (i.macd) {\n if (i.macd.histogram > 0) { buySignals++; breakdown.push({ indicator: \"MACD\", signal: \"BUY\", value: i.macd.histogram }); }\n else { sellSignals++; breakdown.push({ indicator: \"MACD\", signal: \"SELL\", value: i.macd.histogram }); }\n }\n // EMA\n if (i.ema.ema20 && i.ema.ema50) {\n if (i.ema.ema20 > i.ema.ema50) { buySignals++; breakdown.push({ indicator: \"EMA20/50\", signal: \"BUY\", value: i.ema.ema20 }); }\n else { sellSignals++; breakdown.push({ indicator: \"EMA20/50\", signal: \"SELL\", value: i.ema.ema20 }); }\n }\n // Price vs SMA200\n if (i.sma.sma200) {\n if (tech.lastPrice > i.sma.sma200) { buySignals++; breakdown.push({ indicator: \"Price vs SMA200\", signal: \"BUY\", value: i.sma.sma200 }); }\n else { sellSignals++; breakdown.push({ indicator: \"Price vs SMA200\", signal: \"SELL\", value: i.sma.sma200 }); }\n }\n // Stochastic\n if (i.stochastic) {\n if (i.stochastic.k < 20) { buySignals++; breakdown.push({ indicator: \"Stochastic\", signal: \"BUY\", value: i.stochastic.k }); }\n else if (i.stochastic.k > 80) { sellSignals++; breakdown.push({ indicator: \"Stochastic\", signal: \"SELL\", value: i.stochastic.k }); }\n else breakdown.push({ indicator: \"Stochastic\", signal: \"NEUTRAL\", value: i.stochastic.k });\n }\n\n const total = buySignals + sellSignals;\n const rating = buySignals > sellSignals ? \"BUY\" : sellSignals > buySignals ? \"SELL\" : \"NEUTRAL\";\n const score = total > 0 ? Math.round((buySignals / total) * 100) : 50;\n\n return {\n symbol: tech.symbol,\n timeframe,\n rating,\n score,\n buySignals,\n sellSignals,\n breakdown,\n };\n}\n\n// ─── Tool: analyze_multi_timeframe ───────────────────────────────────\nexport async function analyzeMultiTimeframe(symbol: string): Promise {\n const timeframes = [\"15m\", \"1H\", \"4H\", \"1D\"];\n const results = await Promise.all(\n timeframes.map(async (tf) => {\n const rating = await getTechnicalsRating(symbol, tf);\n return { timeframe: tf, ...rating };\n }),\n );\n\n const validResults = results.filter((r) => !r.error);\n const buyCount = validResults.filter((r) => r.rating === \"BUY\").length;\n const sellCount = validResults.filter((r) => r.rating === \"SELL\").length;\n\n const alignmentScore = validResults.length > 0\n ? Math.round((Math.max(buyCount, sellCount) / validResults.length) * 100)\n : 0;\n\n const bias = buyCount > sellCount ? \"BULLISH\" : sellCount > buyCount ? \"BEARISH\" : \"MIXED\";\n\n return {\n symbol,\n bias,\n alignmentScore,\n buyCount,\n sellCount,\n timeframeDetails: validResults.map((r) => ({\n timeframe: r.timeframe,\n rating: r.rating,\n score: r.score,\n })),\n };\n}\n\n// ─── Tool: compute_levels ────────────────────────────────────────────\nexport async function computeLevels(symbol: string, timeframe: string = \"1D\"): Promise {\n const ohlcv = await getOHLCV(symbol, timeframe, 100);\n if (ohlcv.error) return ohlcv;\n const bars = ohlcv.bars;\n if (bars.length < 10) return { error: \"Not enough data for levels\" };\n\n // Swing highs and lows (lookback of 5 bars)\n const swingHighs: any[] = [];\n const swingLows: any[] = [];\n const lookback = 5;\n for (let i = lookback; i < bars.length - lookback; i++) {\n const isSwingHigh = bars.slice(i - lookback, i + lookback + 1).every(\n (b: any, idx: number) => idx === lookback || b.high <= bars[i].high,\n );\n const isSwingLow = bars.slice(i - lookback, i + lookback + 1).every(\n (b: any, idx: number) => idx === lookback || b.low >= bars[i].low,\n );\n if (isSwingHigh) swingHighs.push({ index: i, price: bars[i].high, timestamp: bars[i].timestamp });\n if (isSwingLow) swingLows.push({ index: i, price: bars[i].low, timestamp: bars[i].timestamp });\n }\n\n const lastPrice = bars[bars.length - 1].close;\n const resistanceLevels = swingHighs.slice(-5).map((s) => s.price);\n const supportLevels = swingLows.slice(-5).map((s) => s.price);\n\n // Recent range for premium/discount\n const recentBars = bars.slice(-50);\n const rangeHigh = Math.max(...recentBars.map((b: any) => b.high));\n const rangeLow = Math.min(...recentBars.map((b: any) => b.low));\n const range = rangeHigh - rangeLow;\n const fib50 = rangeLow + range * 0.5;\n const equilibrium = lastPrice > fib50 ? \"PREMIUM\" : \"DISCOUNT\";\n\n // FVG detection (simplified)\n const fvgs: any[] = [];\n for (let i = 2; i < bars.length; i++) {\n const gap = bars[i].low - bars[i - 2].high;\n if (gap > 0) {\n fvgs.push({ index: i, top: bars[i].low, bottom: bars[i - 2].high });\n }\n }\n\n return {\n symbol: ohlcv.symbol,\n timeframe,\n lastPrice,\n supportLevels,\n resistanceLevels,\n swingHighs: swingHighs.slice(-5).map((s) => ({ price: s.price, timestamp: s.timestamp })),\n swingLows: swingLows.slice(-5).map((s) => ({ price: s.price, timestamp: s.timestamp })),\n range: { high: rangeHigh, low: rangeLow, fib50, equilibrium },\n fairValueGaps: fvgs.slice(-3),\n };\n}\n\n// ─── Tool: get_news ──────────────────────────────────────────────────\nexport async function getNews(symbol: string): Promise {\n try {\n const res = await fetch(\n `${YF_BASE}/search?q=${encodeURIComponent(normalizeSymbol(symbol))}"esCount=0&newsCount=5`,\n { headers: { \"User-Agent\": \"Mozilla/5.0\" } },\n );\n const data = await res.json();\n return (data.news || []).map((n: any) => ({\n title: n.title,\n publisher: n.publisher,\n url: n.link,\n publishedAt: new Date(n.providerPublishTime * 1000).toISOString(),\n type: n.type,\n }));\n } catch (e) {\n return { error: `News failed: ${e.message}` };\n }\n}\n\n// ─── Tool: run_screener (simplified) ─────────────────────────────────\nexport async function runScreener(symbols: string[], condition: string): Promise {\n const results = await Promise.all(\n symbols.map(async (sym) => {\n const tech = await getTechnicals(sym, \"1D\");\n if (tech.error) return { symbol: sym, error: tech.error };\n return {\n symbol: sym,\n lastPrice: tech.lastPrice,\n rsi: tech.indicators.rsi,\n ema20: tech.indicators.ema.ema20,\n ema50: tech.indicators.ema.ema50,\n sma200: tech.indicators.sma.sma200,\n macdHistogram: tech.indicators.macd?.histogram,\n adx: tech.indicators.adx,\n };\n }),\n );\n\n // Simple condition matching\n return results.filter((r) => {\n if (r.error) return false;\n const lower = condition.toLowerCase();\n if (lower.includes(\"rsi > 50\")) return r.rsi != null && r.rsi > 50;\n if (lower.includes(\"rsi < 30\")) return r.rsi != null && r.rsi < 30;\n if (lower.includes(\"rsi > 70\")) return r.rsi != null && r.rsi > 70;\n if (lower.includes(\"close above ema200\") || lower.includes(\"close > ema200\")) {\n return r.sma200 != null && r.lastPrice > r.sma200;\n }\n if (lower.includes(\"ema20 > ema50\")) return r.ema20 != null && r.ema50 != null && r.ema20 > r.ema50;\n if (lower.includes(\"adx > 20\")) return r.adx != null && r.adx > 20;\n return true; // no filter → return all\n });\n}\n\n// ─── Tool: compare_symbols ───────────────────────────────────────────\nexport async function compareSymbols(symbols: string[]): Promise {\n const quotes = await getQuotesBatch(symbols);\n const technicals = await Promise.all(\n symbols.map((s) => getTechnicalsRating(s, \"1D\")),\n );\n return symbols.map((sym, i) => ({\n symbol: sym,\n quote: quotes[i],\n rating: technicals[i].rating,\n score: technicals[i].score,\n }));\n}\n\n// ─── Tool: calculate_correlation ─────────────────────────────────────\nexport async function calculateCorrelation(symbols: string[], timeframe: string = \"1D\"): Promise {\n const ohlcvData = await Promise.all(\n symbols.map((s) => getOHLCV(s, timeframe, 60)),\n );\n\n const logReturns: Record = {};\n symbols.forEach((sym, i) => {\n const data = ohlcvData[i];\n if (data.error) return;\n const closes = data.bars.map((b: any) => b.close);\n const returns: number[] = [];\n for (let j = 1; j < closes.length; j++) {\n returns.push(Math.log(closes[j] / closes[j - 1]));\n }\n logReturns[sym] = returns;\n });\n\n const matrix: Record> = {};\n for (const s1 of symbols) {\n matrix[s1] = {};\n for (const s2 of symbols) {\n const r1 = logReturns[s1];\n const r2 = logReturns[s2];\n if (!r1 || !r2) { matrix[s1][s2] = null; continue; }\n const minLen = Math.min(r1.length, r2.length);\n const mean1 = r1.slice(0, minLen).reduce((a: number, b: number) => a + b, 0) / minLen;\n const mean2 = r2.slice(0, minLen).reduce((a: number, b: number) => a + b, 0) / minLen;\n let cov = 0, var1 = 0, var2 = 0;\n for (let j = 0; j < minLen; j++) {\n const d1 = r1[j] - mean1;\n const d2 = r2[j] - mean2;\n cov += d1 * d2;\n var1 += d1 * d1;\n var2 += d2 * d2;\n }\n matrix[s1][s2] = (var1 > 0 && var2 > 0) ? cov / Math.sqrt(var1 * var2) : 0;\n }\n }\n return { symbols, timeframe, correlationMatrix: matrix };\n}\n",[],[149,180],"7869c454-5e13-4aa0-9a35-f18da687caaf","db.ts","d90a4dec-8b18-11f1-a7df-1607ee4eb77e",["D",1785307426186],"import { sqlite } from \"https://esm.town/v/std/sqlite/main.ts\";\n\n// ─── Schema Initialization ───────────────────────────────────────────\nexport async function initDB() {\n await sqlite.batch([\n {\n sql: `CREATE TABLE IF NOT EXISTS observations (\n id TEXT PRIMARY KEY,\n desk_id TEXT NOT NULL,\n agent_type TEXT NOT NULL,\n symbol TEXT NOT NULL,\n timeframe TEXT NOT NULL,\n observation_type TEXT NOT NULL,\n data_payload TEXT NOT NULL,\n confidence INTEGER DEFAULT 50,\n privacy_class TEXT DEFAULT 'shared',\n provenance TEXT,\n created_at TEXT DEFAULT (datetime('now'))\n )`,\n },\n {\n sql: `CREATE TABLE IF NOT EXISTS market_cases (\n id TEXT PRIMARY KEY,\n vertical TEXT DEFAULT 'ATN',\n domain TEXT DEFAULT 'trading',\n symbol TEXT NOT NULL,\n timeframe TEXT NOT NULL,\n hypothesis TEXT NOT NULL,\n expected_outcome TEXT,\n reasoning TEXT,\n invalidation_level TEXT,\n conviction INTEGER DEFAULT 50,\n time_horizon TEXT,\n meta_tags TEXT,\n evidence TEXT,\n resolution TEXT DEFAULT 'PENDING',\n resolution_reason TEXT,\n contradictions_resolved INTEGER DEFAULT 0,\n reuse_count INTEGER DEFAULT 0,\n contributors TEXT,\n confidence_history TEXT,\n outcome_hit_target INTEGER,\n outcome_rr_ratio REAL,\n follow_up_case TEXT,\n proof_artifact TEXT,\n created_at TEXT DEFAULT (datetime('now')),\n resolved_at TEXT\n )`,\n },\n {\n sql: `CREATE TABLE IF NOT EXISTS contradictions (\n id TEXT PRIMARY KEY,\n case_id TEXT NOT NULL,\n challenging_case_id TEXT,\n challenging_desk_id TEXT,\n contradiction_text TEXT NOT NULL,\n status TEXT DEFAULT 'OPEN',\n created_at TEXT DEFAULT (datetime('now')),\n resolved_at TEXT\n )`,\n },\n {\n sql: `CREATE TABLE IF NOT EXISTS resolved_memory (\n id TEXT PRIMARY KEY,\n case_id TEXT NOT NULL UNIQUE,\n embedding_key TEXT,\n reuse_count INTEGER DEFAULT 0,\n created_at TEXT DEFAULT (datetime('now'))\n )`,\n },\n {\n sql: `CREATE TABLE IF NOT EXISTS desks (\n id TEXT PRIMARY KEY,\n handle TEXT,\n agents_config TEXT,\n reputation INTEGER DEFAULT 100,\n contributions INTEGER DEFAULT 0,\n resolutions_correct INTEGER DEFAULT 0,\n resolutions_wrong INTEGER DEFAULT 0,\n created_at TEXT DEFAULT (datetime('now')),\n last_active TEXT\n )`,\n },\n {\n sql: `CREATE TABLE IF NOT EXISTS pipeline_runs (\n id TEXT PRIMARY KEY,\n stage TEXT NOT NULL,\n status TEXT NOT NULL,\n details TEXT,\n cases_processed INTEGER DEFAULT 0,\n created_at TEXT DEFAULT (datetime('now'))\n )`,\n },\n {\n sql: `CREATE INDEX IF NOT EXISTS idx_observations_symbol ON observations(symbol)`,\n },\n {\n sql: `CREATE INDEX IF NOT EXISTS idx_cases_symbol ON market_cases(symbol)`,\n },\n {\n sql: `CREATE INDEX IF NOT EXISTS idx_cases_resolution ON market_cases(resolution)`,\n },\n {\n sql: `CREATE INDEX IF NOT EXISTS idx_contradictions_case ON contradictions(case_id)`,\n },\n ]);\n}\n\n// ─── Helpers ─────────────────────────────────────────────────────────\nexport function uuid(): string {\n return crypto.randomUUID();\n}\n\nexport async function createObservation(obs: {\n desk_id: string;\n agent_type: string;\n symbol: string;\n timeframe: string;\n observation_type: string;\n data_payload: any;\n confidence?: number;\n privacy_class?: string;\n provenance?: string;\n}) {\n const id = uuid();\n await sqlite.execute({\n sql: `INSERT INTO observations (id, desk_id, agent_type, symbol, timeframe, observation_type, data_payload, confidence, privacy_class, provenance)\n VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)`,\n args: [\n id,\n obs.desk_id,\n obs.agent_type,\n obs.symbol,\n obs.timeframe,\n obs.observation_type,\n JSON.stringify(obs.data_payload),\n obs.confidence ?? 50,\n obs.privacy_class ?? \"shared\",\n obs.provenance ?? null,\n ],\n });\n return id;\n}\n\nexport async function createMarketCase(c: {\n desk_id: string;\n symbol: string;\n timeframe: string;\n hypothesis: string;\n expected_outcome?: string;\n reasoning?: string;\n invalidation_level?: string;\n conviction?: number;\n time_horizon?: string;\n meta_tags?: string[];\n evidence?: string[];\n proof_artifact?: string;\n}) {\n const id = uuid();\n const contributors = JSON.stringify([c.desk_id]);\n const confidence_history = JSON.stringify([\n { timestamp: new Date().toISOString(), score: c.conviction ?? 50, source: \"initial\" },\n ]);\n await sqlite.execute({\n sql: `INSERT INTO market_cases (id, symbol, timeframe, hypothesis, expected_outcome, reasoning,\n invalidation_level, conviction, time_horizon, meta_tags, evidence, contributors,\n confidence_history, proof_artifact)\n VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)`,\n args: [\n id,\n c.symbol,\n c.timeframe,\n c.hypothesis,\n c.expected_outcome ?? null,\n c.reasoning ?? null,\n c.invalidation_level ?? null,\n c.conviction ?? 50,\n c.time_horizon ?? null,\n c.meta_tags ? JSON.stringify(c.meta_tags) : null,\n c.evidence ? JSON.stringify(c.evidence) : null,\n contributors,\n confidence_history,\n c.proof_artifact ?? null,\n ],\n });\n return id;\n}\n\nexport async function updateCaseResolution(\n caseId: string,\n resolution: string,\n reason: string,\n) {\n await sqlite.execute({\n sql: `UPDATE market_cases SET resolution = ?, resolution_reason = ?, resolved_at = datetime('now')\n WHERE id = ?`,\n args: [resolution, reason, caseId],\n });\n if (resolution === \"CONFIRMED\" || resolution === \"REJECTED\") {\n await sqlite.execute({\n sql: `INSERT OR REPLACE INTO resolved_memory (id, case_id, embedding_key)\n VALUES (?, ?, ?)`,\n args: [uuid(), caseId, `embed_${caseId}`],\n });\n }\n}\n\nexport async function addContradiction(\n caseId: string,\n challengingDeskId: string,\n text: string,\n challengingCaseId?: string,\n) {\n const id = uuid();\n await sqlite.execute({\n sql: `INSERT INTO contradictions (id, case_id, challenging_case_id, challenging_desk_id, contradiction_text)\n VALUES (?, ?, ?, ?, ?)`,\n args: [id, caseId, challengingCaseId ?? null, challengingDeskId, text],\n });\n await sqlite.execute({\n sql: `UPDATE market_cases SET contradictions_resolved = contradictions_resolved + 1 WHERE id = ?`,\n args: [caseId],\n });\n return id;\n}\n\nexport async function incrementReuse(caseId: string) {\n await sqlite.execute({\n sql: `UPDATE market_cases SET reuse_count = reuse_count + 1 WHERE id = ?`,\n args: [caseId],\n });\n await sqlite.execute({\n sql: `UPDATE resolved_memory SET reuse_count = reuse_count + 1 WHERE case_id = ?`,\n args: [caseId],\n });\n}\n\nexport async function getStats() {\n const [members, agents, cases, memoryObjects, observations, confirmed, rejected, pending] =\n await Promise.all([\n sqlite.execute(\"SELECT COUNT(*) as c FROM desks\"),\n sqlite.execute(\"SELECT COUNT(DISTINCT agent_type) as c FROM observations\"),\n sqlite.execute(\"SELECT COUNT(*) as c FROM market_cases\"),\n sqlite.execute(\"SELECT COUNT(*) as c FROM resolved_memory\"),\n sqlite.execute(\"SELECT COUNT(*) as c FROM observations\"),\n sqlite.execute(\"SELECT COUNT(*) as c FROM market_cases WHERE resolution = 'CONFIRMED'\"),\n sqlite.execute(\"SELECT COUNT(*) as c FROM market_cases WHERE resolution = 'REJECTED'\"),\n sqlite.execute(\"SELECT COUNT(*) as c FROM market_cases WHERE resolution = 'PENDING'\"),\n ]);\n return {\n members: (members.rows[0] as any)?.c ?? 0,\n agents: Math.max((agents.rows[0] as any)?.c ?? 0, 6),\n cases: (cases.rows[0] as any)?.c ?? 0,\n memoryObjects: (memoryObjects.rows[0] as any)?.c ?? 0,\n observations: (observations.rows[0] as any)?.c ?? 0,\n confirmed: (confirmed.rows[0] as any)?.c ?? 0,\n rejected: (rejected.rows[0] as any)?.c ?? 0,\n pending: (pending.rows[0] as any)?.c ?? 0,\n };\n}\n\nexport async function logPipelineRun(stage: string, status: string, details: string, casesProcessed: number) {\n await sqlite.execute({\n sql: `INSERT INTO pipeline_runs (id, stage, status, details, cases_processed)\n VALUES (?, ?, ?, ?, ?)`,\n args: [uuid(), stage, status, details, casesProcessed],\n });\n}\n\nexport async function getRecentCases(limit: number = 10) {\n const result = await sqlite.execute({\n sql: `SELECT * FROM market_cases ORDER BY created_at DESC LIMIT ?`,\n args: [limit],\n });\n return result.rows.map((r) => ({\n ...r,\n meta_tags: r.meta_tags ? JSON.parse(r.meta_tags as string) : [],\n contributors: r.contributors ? JSON.parse(r.contributors as string) : [],\n })) as any[];\n}\n\nexport async function getRecentPipelineRuns(limit: number = 20) {\n const result = await sqlite.execute({\n sql: `SELECT * FROM pipeline_runs ORDER BY created_at DESC LIMIT ?`,\n args: [limit],\n });\n return result.rows as any[];\n}\n\nexport async function getCasesByResolution(resolution: string) {\n const result = await sqlite.execute({\n sql: `SELECT * FROM market_cases WHERE resolution = ? ORDER BY created_at DESC`,\n args: [resolution],\n });\n return result.rows.map((r) => ({\n ...r,\n meta_tags: r.meta_tags ? JSON.parse(r.meta_tags as string) : [],\n contributors: r.contributors ? JSON.parse(r.contributors as string) : [],\n })) as any[];\n}\n\nexport async function searchMemory(query: string, limit: number = 5) {\n const result = await sqlite.execute({\n sql: `SELECT mc.*, rm.reuse_count as memory_reuse\n FROM market_cases mc\n JOIN resolved_memory rm ON rm.case_id = mc.id\n WHERE mc.resolution IN ('CONFIRMED', 'REJECTED')\n AND (mc.hypothesis LIKE ? OR mc.symbol LIKE ? OR mc.meta_tags LIKE ?)\n ORDER BY rm.reuse_count DESC, mc.created_at DESC\n LIMIT ?`,\n args: [`%${query}%`, `%${query}%`, `%${query}%`, limit],\n });\n return result.rows.map((r) => ({\n ...r,\n meta_tags: r.meta_tags ? JSON.parse(r.meta_tags as string) : [],\n })) as any[];\n}\n",[],[149,188],"d39cd854-4396-41b7-a9ed-7a9385e451c6","agents.ts","104b7258-8b1a-11f1-bd5c-1607ee4eb77e",4,["D",1785307948387],"// ─── Private Agentic Desk Agents ─────────────────────────────────────\n// Each desk runs 6 specialized agents. Agents produce structured\n// observations that are shared with the network. Execution stays private.\n\nimport {\n getQuote,\n getTechnicals,\n getTechnicalsRating,\n analyzeMultiTimeframe,\n computeLevels,\n getOHLCV,\n getNews,\n} from \"./market-data.ts\";\n\nimport { createObservation, createMarketCase, searchMemory } from \"./db.ts\";\n\nexport type AgentType =\n | \"macro\"\n | \"structure\"\n | \"liquidity\"\n | \"risk\"\n | \"memory\"\n | \"execution\";\n\nexport interface ObservationResult {\n observation_id: string;\n agent_type: AgentType;\n symbol: string;\n timeframe: string;\n observation_type: string;\n summary: string;\n data: any;\n confidence: number;\n hypothesis?: string;\n expected_outcome?: string;\n invalidation_level?: string;\n meta_tags?: string[];\n}\n\n// ─── Macro Agent ─────────────────────────────────────────────────────\n// Economic regime, cross-asset flows, macro context\nexport async function runMacroAgent(\n deskId: string,\n symbol: string,\n): Promise {\n const mtf = await analyzeMultiTimeframe(symbol);\n const news = await getNews(symbol);\n\n // Macro context from cross-asset\n const spyQuote = await getQuote(\"SPY\");\n const vixQuote = await getQuote(\"VIX\");\n const dxyQuote = await getQuote(\"DXY\");\n\n const riskRegime = vixQuote?.price > 25 ? \"RISK-OFF\" : vixQuote?.price > 18 ? \"NEUTRAL\" : \"RISK-ON\";\n const dxyTrend = dxyQuote?.changePercent > 0 ? \"DXY_RISING\" : \"DXY_FALLING\";\n\n const observation = {\n riskRegime,\n dxyTrend,\n vix: vixQuote?.price,\n spyChange: spyQuote?.changePercent,\n multiTimeframeBias: mtf.bias,\n alignmentScore: mtf.alignmentScore,\n newsCount: Array.isArray(news) ? news.length : 0,\n topNews: Array.isArray(news) ? news.slice(0, 3).map((n: any) => n.title) : [],\n };\n\n const obsId = await createObservation({\n desk_id: deskId,\n agent_type: \"macro\",\n symbol,\n timeframe: \"1D\",\n observation_type: \"macro_context\",\n data_payload: observation,\n confidence: 65,\n provenance: \"macro_agent\",\n });\n\n const hypothesis = `IF ${riskRegime} regime persists with VIX at ${vixQuote?.price?.toFixed(1)} THEN ${symbol} likely ${mtf.bias === \"BULLISH\" ? \"continues upside\" : \"faces headwind\"} BECAUSE cross-asset flows favor ${riskRegime === \"RISK-ON\" ? \"risk assets\" : \"safe haven\"}`;\n const invalidation = riskRegime === \"RISK-OFF\" ? \"VIX drops below 18\" : \"VIX spikes above 30\";\n\n return {\n observation_id: obsId,\n agent_type: \"macro\",\n symbol,\n timeframe: \"1D\",\n observation_type: \"macro_context\",\n summary: `${riskRegime} regime. ${mtf.bias} bias on ${symbol}. VIX: ${vixQuote?.price?.toFixed(1)}`,\n data: observation,\n confidence: 65,\n hypothesis,\n expected_outcome: mtf.bias === \"BULLISH\" ? \"Price appreciation\" : \"Price decline or consolidation\",\n invalidation_level: invalidation,\n meta_tags: [symbol, \"macro\", riskRegime, dxyTrend, mtf.bias],\n };\n}\n\n// ─── Structure Agent ─────────────────────────────────────────────────\n// Price action, SMC, swing analysis, multi-timeframe alignment\nexport async function runStructureAgent(\n deskId: string,\n symbol: string,\n timeframe: string = \"4H\",\n): Promise {\n const tech = await getTechnicals(symbol, timeframe);\n const levels = await computeLevels(symbol, timeframe);\n const rating = await getTechnicalsRating(symbol, timeframe);\n\n if (tech.error) {\n return {\n observation_id: \"\",\n agent_type: \"structure\",\n symbol,\n timeframe,\n observation_type: \"structure_analysis\",\n summary: `Error: ${tech.error}`,\n data: {},\n confidence: 0,\n };\n }\n\n const i = tech.indicators;\n const trend = i.ema.ema20 > i.ema.ema50 ? \"UPTREND\" : i.ema.ema20 < i.ema.ema50 ? \"DOWNTREND\" : \"RANGE\";\n const rsiSignal = i.rsi < 30 ? \"OVERSOLD\" : i.rsi > 70 ? \"OVERBOUGHT\" : \"NEUTRAL\";\n const macdSignal = i.macd?.histogram > 0 ? \"BULLISH\" : \"BEARISH\";\n const bbPosition = tech.lastPrice > i.bollinger?.upper ? \"UPPER_BAND\" :\n tech.lastPrice < i.bollinger?.lower ? \"LOWER_BAND\" : \"MID_RANGE\";\n\n const structure = {\n trend,\n rsi: i.rsi,\n rsiSignal,\n macdSignal,\n macdHistogram: i.macd?.histogram,\n bollingerPosition: bbPosition,\n ema20: i.ema.ema20,\n ema50: i.ema.ema50,\n ema200: i.ema.ema200,\n sma200: i.sma.sma200,\n adx: i.adx,\n rating: rating.rating,\n ratingScore: rating.score,\n support: levels.supportLevels,\n resistance: levels.resistanceLevels,\n swingHighs: levels.swingHighs,\n swingLows: levels.swingLows,\n equilibrium: levels.range?.equilibrium,\n fairValueGaps: levels.fairValueGaps,\n };\n\n const obsId = await createObservation({\n desk_id: deskId,\n agent_type: \"structure\",\n symbol,\n timeframe,\n observation_type: \"structure_analysis\",\n data_payload: structure,\n confidence: Math.min(90, rating.score + 10),\n provenance: \"structure_agent\",\n });\n\n const hypothesis = `IF ${symbol} ${trend} with RSI ${rsiSignal} (${i.rsi?.toFixed(1)}) and MACD ${macdSignal} THEN expect ${trend === \"UPTREND\" ? \"continuation toward resistance\" : trend === \"DOWNTREND\" ? \"continuation toward support\" : \"range-bound movement\"} BECAUSE structure confirms ${trend} with ADX ${i.adx?.toFixed(1)}`;\n const invalidation = trend === \"UPTREND\"\n ? `Close below ${levels.supportLevels?.[0]?.toFixed(2)} on ${timeframe}`\n : `Close above ${levels.resistanceLevels?.[0]?.toFixed(2)} on ${timeframe}`;\n\n return {\n observation_id: obsId,\n agent_type: \"structure\",\n symbol,\n timeframe,\n observation_type: \"structure_analysis\",\n summary: `${trend} | RSI ${i.rsi?.toFixed(1)} ${rsiSignal} | MACD ${macdSignal} | Rating ${rating.rating} (${rating.score})`,\n data: structure,\n confidence: Math.min(90, rating.score + 10),\n hypothesis,\n expected_outcome: trend === \"UPTREND\" ? \"Move to resistance\" : trend === \"DOWNTREND\" ? \"Move to support\" : \"Range continuation\",\n invalidation_level: invalidation,\n meta_tags: [symbol, timeframe, trend, rsiSignal, macdSignal, `rating_${rating.rating}`],\n };\n}\n\n// ─── Liquidity Agent ─────────────────────────────────────────────────\n// Volume profiling, order flow, FVG identification, premium/discount\nexport async function runLiquidityAgent(\n deskId: string,\n symbol: string,\n timeframe: string = \"4H\",\n): Promise {\n const ohlcv = await getOHLCV(symbol, timeframe, 50);\n const levels = await computeLevels(symbol, timeframe);\n\n if (ohlcv.error) {\n return {\n observation_id: \"\",\n agent_type: \"liquidity\",\n symbol,\n timeframe,\n observation_type: \"liquidity_analysis\",\n summary: `Error: ${ohlcv.error}`,\n data: {},\n confidence: 0,\n };\n }\n\n const bars = ohlcv.bars;\n const volumes = bars.map((b: any) => b.volume);\n const avgVolume = volumes.reduce((a: number, b: number) => a + b, 0) / volumes.length;\n const lastVolume = volumes[volumes.length - 1];\n const volumeRatio = lastVolume / avgVolume;\n\n // Volume-weighted price level (VWAP approximation)\n let pvSum = 0, volSum = 0;\n for (const b of bars) {\n pvSum += b.close * b.volume;\n volSum += b.volume;\n }\n const vwap = pvSum / volSum;\n const lastPrice = bars[bars.length - 1].close;\n const vwapDeviation = ((lastPrice - vwap) / vwap) * 100;\n\n // Liquidity zones from FVGs\n const liquidityZones = levels.fairValueGaps?.map((fvg: any) => ({\n top: fvg.top,\n bottom: fvg.bottom,\n type: \"FVG\",\n })) ?? [];\n\n // Volume profile buckets (simplified)\n const profileBuckets: Record = {};\n for (const b of bars) {\n const key = b.close.toFixed(2);\n profileBuckets[key] = (profileBuckets[key] || 0) + b.volume;\n }\n const pocLevel = Object.entries(profileBuckets).sort((a, b) => b[1] - a[1])[0]?.[0] ?? \"N/A\";\n\n const liquidity = {\n vwap,\n vwapDeviation: vwapDeviation.toFixed(2) + \"%\",\n volumeRatio: volumeRatio.toFixed(2),\n avgVolume,\n lastVolume,\n pointOfControl: pocLevel,\n equilibrium: levels.range?.equilibrium,\n premiumDiscount: levels.range?.equilibrium,\n liquidityZones,\n supportLevels: levels.supportLevels,\n resistanceLevels: levels.resistanceLevels,\n };\n\n const obsId = await createObservation({\n desk_id: deskId,\n agent_type: \"liquidity\",\n symbol,\n timeframe,\n observation_type: \"liquidity_analysis\",\n data_payload: liquidity,\n confidence: 60,\n provenance: \"liquidity_agent\",\n });\n\n const deviation = parseFloat(vwapDeviation.toFixed(2));\n const hypothesis = `IF ${symbol} is ${levels.range?.equilibrium} (${deviation > 0 ? \"+\" : \"\"}${deviation}% from VWAP) with volume ratio ${volumeRatio.toFixed(2)}x THEN expect ${levels.range?.equilibrium === \"PREMIUM\" ? \"mean reversion toward VWAP\" : \"bounce from discount zone\"} BECAUSE price is ${levels.range?.equilibrium === \"PREMIUM\" ? \"above\" : \"below\"} value area`;\n const invalidation = levels.range?.equilibrium === \"PREMIUM\"\n ? `Sustained close above ${levels.resistanceLevels?.[0]?.toFixed(2)}`\n : `Sustained close below ${levels.supportLevels?.[0]?.toFixed(2)}`;\n\n return {\n observation_id: obsId,\n agent_type: \"liquidity\",\n symbol,\n timeframe,\n observation_type: \"liquidity_analysis\",\n summary: `${levels.range?.equilibrium} | VWAP dev: ${deviation > 0 ? \"+\" : \"\"}${deviation}% | Vol ratio: ${volumeRatio.toFixed(2)}x | POC: ${pocLevel}`,\n data: liquidity,\n confidence: 60,\n hypothesis,\n expected_outcome: levels.range?.equilibrium === \"PREMIUM\" ? \"Reversion to VWAP\" : \"Bounce from discount\",\n invalidation_level: invalidation,\n meta_tags: [symbol, timeframe, levels.range?.equilibrium, `vol_${volumeRatio > 1.5 ? \"high\" : \"normal\"}`],\n };\n}\n\n// ─── Risk Agent (PRIVATE — observations only, no execution shared) ───\nexport async function runRiskAgent(\n deskId: string,\n symbol: string,\n timeframe: string = \"4H\",\n): Promise {\n const tech = await getTechnicals(symbol, timeframe);\n if (tech.error) {\n return {\n observation_id: \"\",\n agent_type: \"risk\",\n symbol,\n timeframe,\n observation_type: \"risk_assessment\",\n summary: `Error: ${tech.error}`,\n data: {},\n confidence: 0,\n };\n }\n\n const atr = tech.indicators.atr ?? 0;\n const lastPrice = tech.lastPrice;\n const atrPercent = (atr / lastPrice) * 100;\n\n // Risk levels based on ATR\n const suggestedStop = atr * 1.5;\n const suggestedTarget = atr * 3;\n const riskReward = suggestedTarget / suggestedStop;\n\n // Correlation risk flag\n const adx = tech.indicators.adx ?? 0;\n const trendStrength = adx > 25 ? \"STRONG\" : adx > 20 ? \"MODERATE\" : \"WEAK\";\n\n // Risk score (higher = riskier)\n let riskScore = 0;\n if (atrPercent > 3) riskScore += 30;\n else if (atrPercent > 2) riskScore += 20;\n else riskScore += 10;\n if (adx < 20) riskScore += 20;\n if (tech.indicators.rsi > 70 || tech.indicators.rsi < 30) riskScore += 15;\n\n const riskLevel = riskScore > 50 ? \"HIGH\" : riskScore > 30 ? \"MODERATE\" : \"LOW\";\n\n const riskData = {\n atr,\n atrPercent: atrPercent.toFixed(2) + \"%\",\n suggestedStopDistance: suggestedStop.toFixed(2),\n suggestedTargetDistance: suggestedTarget.toFixed(2),\n riskRewardRatio: riskReward.toFixed(2),\n trendStrength,\n adx,\n rsi: tech.indicators.rsi,\n riskScore,\n riskLevel,\n };\n\n const obsId = await createObservation({\n desk_id: deskId,\n agent_type: \"risk\",\n symbol,\n timeframe,\n observation_type: \"risk_assessment\",\n data_payload: riskData,\n confidence: 55,\n privacy_class: \"shared\", // only risk metrics shared, not position sizing\n });\n\n return {\n observation_id: obsId,\n agent_type: \"risk\",\n symbol,\n timeframe,\n observation_type: \"risk_assessment\",\n summary: `Risk: ${riskLevel} | ATR: ${atrPercent.toFixed(2)}% | RR: ${riskReward.toFixed(2)} | Trend: ${trendStrength}`,\n data: riskData,\n confidence: 55,\n hypothesis: `IF ${symbol} has ${riskLevel} risk (ATR ${atrPercent.toFixed(2)}%) THEN position sizing should be ${riskLevel === \"HIGH\" ? \"reduced\" : \"standard\"} BECAUSE volatility and trend strength dictate exposure`,\n expected_outcome: riskLevel === \"HIGH\" ? \"Wider stops needed, smaller position\" : \"Standard risk parameters applicable\",\n invalidation_level: `ATR exceeds ${(atrPercent * 1.5).toFixed(2)}%`,\n meta_tags: [symbol, timeframe, `risk_${riskLevel}`, `atr_${atrPercent.toFixed(1)}%`],\n };\n}\n\n// ─── Memory Agent ────────────────────────────────────────────────────\n// RAG over desk's local memory + network resolved memory\nexport async function runMemoryAgent(\n deskId: string,\n symbol: string,\n): Promise {\n // Search resolved memory for similar cases\n const similar = await searchMemory(symbol, 5);\n\n const memoryData = {\n similarCasesFound: similar.length,\n cases: similar.map((c) => ({\n case_id: c.id,\n symbol: c.symbol,\n hypothesis: c.hypothesis,\n resolution: c.resolution,\n conviction: c.conviction,\n reuse_count: c.reuse_count,\n tags: c.meta_tags,\n })),\n topResolution: similar[0]?.resolution ?? \"NONE\",\n avgConviction: similar.length > 0\n ? Math.round(similar.reduce((a, c) => a + (c.conviction ?? 0), 0) / similar.length)\n : 0,\n };\n\n const obsId = await createObservation({\n desk_id: deskId,\n agent_type: \"memory\",\n symbol,\n timeframe: \"1D\",\n observation_type: \"memory_retrieval\",\n data_payload: memoryData,\n confidence: similar.length > 0 ? 70 : 40,\n provenance: \"memory_agent\",\n });\n\n const hasHistory = similar.length > 0;\n const hypothesis = hasHistory\n ? `IF ${symbol} has ${similar.length} resolved cases with avg conviction ${memoryData.avgConviction} THEN current analysis benefits from historical context BECAUSE resolved memory contains verified patterns`\n : `IF ${symbol} has no resolved memory THEN this observation establishes a new memory baseline BECAUSE first cases seed the network`;\n\n return {\n observation_id: obsId,\n agent_type: \"memory\",\n symbol,\n timeframe: \"1D\",\n observation_type: \"memory_retrieval\",\n summary: hasHistory\n ? `${similar.length} similar cases found | Avg conviction: ${memoryData.avgConviction} | Top resolution: ${memoryData.topResolution}`\n : \"No prior memory — establishing baseline\",\n data: memoryData,\n confidence: similar.length > 0 ? 70 : 40,\n hypothesis,\n expected_outcome: hasHistory ? \"Pattern-informed analysis\" : \"New memory entry created\",\n invalidation_level: \"N/A\",\n meta_tags: [symbol, \"memory\", hasHistory ? \"has_history\" : \"new_baseline\"],\n };\n}\n\n// ─── Execution Agent (PRIVATE — never shared) ────────────────────────\n// Produces only a private signal, no network egress\nexport async function runExecutionAgent(\n deskId: string,\n symbol: string,\n observations: ObservationResult[],\n): Promise<{ action: string; reasoning: string; private: boolean }> {\n const macro = observations.find((o) => o.agent_type === \"macro\");\n const structure = observations.find((o) => o.agent_type === \"structure\");\n const liquidity = observations.find((o) => o.agent_type === \"liquidity\");\n const risk = observations.find((o) => o.agent_type === \"risk\");\n\n const bullishCount = [macro, structure, liquidity].filter((o) =>\n o?.hypothesis?.toLowerCase().includes(\"upside\") ||\n o?.hypothesis?.toLowerCase().includes(\"bullish\") ||\n o?.hypothesis?.toLowerCase().includes(\"continuation toward resistance\") ||\n o?.hypothesis?.toLowerCase().includes(\"risk-on\")\n ).length;\n\n const riskLevel = risk?.data?.riskLevel ?? \"MODERATE\";\n\n let action = \"HOLD\";\n if (bullishCount >= 2 && riskLevel !== \"HIGH\") action = \"LONG_SIGNAL\";\n else if (bullishCount === 0 && riskLevel !== \"HIGH\") action = \"SHORT_SIGNAL\";\n else if (riskLevel === \"HIGH\") action = \"REDUCE_EXPOSURE\";\n\n return {\n action,\n reasoning: `${bullishCount}/3 agents bullish. Risk: ${riskLevel}. Action: ${action}`,\n private: true, // NEVER shared with network\n };\n}\n\n// ─── Run Full Desk ───────────────────────────────────────────────────\nexport async function runDesk(\n deskId: string,\n symbol: string,\n timeframe: string = \"4H\",\n): Promise<{\n observations: ObservationResult[];\n execution: { action: string; reasoning: string; private: boolean };\n caseId?: string;\n}> {\n // Run all shared agents in parallel\n const [macro, structure, liquidity, risk, memory] = await Promise.all([\n runMacroAgent(deskId, symbol),\n runStructureAgent(deskId, symbol, timeframe),\n runLiquidityAgent(deskId, symbol, timeframe),\n runRiskAgent(deskId, symbol, timeframe),\n runMemoryAgent(deskId, symbol),\n ]);\n\n const observations = [macro, structure, liquidity, risk, memory];\n\n // Run execution agent PRIVATELY (never shared)\n const execution = await runExecutionAgent(deskId, symbol, observations);\n\n // Create a MarketCase from the strongest observation\n const sorted = observations\n .filter((o) => o.hypothesis && o.confidence > 0)\n .sort((a, b) => b.confidence - a.confidence);\n\n let caseId: string | undefined;\n if (sorted.length > 0 && sorted[0].hypothesis) {\n const best = sorted[0];\n caseId = await createMarketCase({\n desk_id: deskId,\n symbol,\n timeframe,\n hypothesis: best.hypothesis,\n expected_outcome: best.expected_outcome,\n reasoning: best.summary,\n invalidation_level: best.invalidation_level,\n conviction: best.confidence,\n time_horizon: timeframe,\n meta_tags: best.meta_tags,\n evidence: observations.filter((o) => o.observation_id).map((o) => o.observation_id),\n });\n }\n\n return { observations, execution, caseId };\n}\n",[],[149,195],["D",1785307426186],[166,167,168,169],[149],["D",1785307366530],["D",1789476648181],{"_105":104,"_113":109},{"_105":104,"_106":107,"_108":109,"_19":60,"_110":111,"_112":30},{"_13":94,"_37":95,"_48":47,"_96":49,"_97":-5,"_98":-5,"_99":-5,"_100":-5,"_101":-5,"_102":50,"_103":104},"ctx",{"_87":-5,"_133":243,"_244":245,"_246":247,"_248":30,"_249":30,"_250":30,"_251":64,"_252":-5,"_253":-7,"_254":-7,"_255":68,"_256":30,"_257":30,"_258":-5,"_259":30,"_260":30,"_261":30,"_262":-5},"ENV",{"_214":215,"_216":217,"_218":95,"_219":220,"_221":222,"_223":224,"_225":226,"_227":228,"_229":230,"_231":232,"_233":234,"_235":236,"_237":238,"_239":240,"_241":242},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","e87eb04e707cf64e7e532c8e8ec395f209e17f15","RENDER_GIT_BRANCH","POSTHOG_API_TOKEN","phc_nmoyuAkStEZuUp69rYlg1DZgrIt19Gx0Y4Hwf8tGgJP","POSTHOG_API_HOST","https://wart.val.town","WEB_API_TEMPLATE","https://{val}.web.val.run","APP_URL","https://www.val.town","MODULE_URL","https://esm.town","VANITY_WEB_API_TEMPLATE","https://{label}.val.run/","DENOLS_URL","https://lsp.val.town","NODE_ENV","production","CLOUDFLARE_TURNSTILE_SITE_KEY","0x4AAAAAACtaIZJH7s9rBMZ6","GOOGLE_CLIENT_ID","875989696899-jtsqkhlbdv5jrlsk8631p7ipjg3c6o62.apps.googleusercontent.com","STRIPE_PUBLISHABLE_KEY","pk_live_51N2zvWEpZTTLipIkVEwXrUG9AT0yeOOEYxszarZT1jpsZV58Ly6wlg3SltZpHN26CUalOBmUSY7adQRQiSkCZz2500qSXNkf8Y","API_URL","https://api.val.town",[],"preferencesKeybindings","default","preferencesTheme","light","preferencesFixers","preferencesCopilot","preferencesSelectionAi","preferencesCloseBrackets","preferencesChatPanelState","preferencesDprint","preferencesDenofmt","notificationCount","flagDenols","isBusinessOrgMember","businessOrgHandle","isBusinessOrgAdmin","isHigherTownieTier","navCollapsed","currentOrgId"]

Diff · v5