ATN — Agentic Trading Network

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\n \n \n \n \n \n \n

Cognitive Resolution Cycle

\n 1.Observe\\u2192\n 2.Hypothesis\\u2192\n 3.Proof\\u2192\n 4.Contradiction\\u2192\n 5.Invalidation\\u2192\n 6.Resolution\\u2192\n 7.Memory\\u2192\n 8.Reuse\n

Recent MarketCases

Recent Pipeline Runs

All MarketCases

\n \n \n \n \n

Pipeline Control

Manually trigger the CNOS 8-stage cycle for all configured symbols.

\n \n

Pipeline Run History

Telegram Notifications

Enable Telegram notifications

Bot Token

Chat ID

\n \n

Twelve Data API (Multiple Keys with Rotation)

Add multiple free keys for automatic rotation. Free tier: 8 req/min, 800/day per key. N keys = N\\u00d7 limits. Get keys at twelvedata.com

Enable Twelve Data as primary data source

Data Source Mode

API Keys (one per line)

\n \n \n \n

Pipeline Configuration

Enable automated pipeline

Symbols (comma-separated)

Timeframe

Interval (minutes)

Desk Configuration

Desk ID

Min Confidence for Case

Auto-generate MarketCases from observations

Auto-resolve cases (CONFIRMED/REJECTED)

Contradictions required for refinement

Notification Preferences

Notify on new MarketCase

Notify on case resolution

Notify on contradiction detected

Notify on errors

Market Data Tools (MCP-style)

search_symbols

Search and resolve symbols across exchanges

get_quote

Real-time quote snapshot

get_technicals

Technical indicators (RSI, MACD, EMAs, Bollinger, ATR, etc.)

\n \n

get_technicals_rating

Aggregated buy/sell/neutral rating with breakdown

\n \n

analyze_multi_timeframe

Trend alignment across 15m, 1H, 4H, 1D

compute_levels

Support/resistance, swing levels, FVGs, premium/discount

\n \n

get_news

get_ohlcv

Historical OHLCV bars

\n \n

Resolved Memory Search

Search verified, attributable, reusable knowledge in the network memory.

\n\n\n\n`;\n}\n","createdAt",["D",1785309457006],"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",{"_104":103,"_112":108},"org",{"_104":103,"_105":106,"_107":108,"_19":60,"_109":110,"_111":30},"branch",{"_13":14,"_37":94,"_48":47,"_95":49,"_96":-5,"_97":-5,"_98":-5,"_99":-5,"_100":-5,"_101":50,"_102":103},"isOwned","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":281,"_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":282,"_77":-5,"_78":79,"_80":79,"_81":82,"_83":82,"_84":82,"_85":79,"_86":79,"_87":283,"_89":284,"_91":285,"_93":30},"tree",[178,180,182,191],"files",[178,179,180,181,182,183,184,185,186,187,188,189,190,191],"counts",{"_168":68,"_169":146,"_170":68,"_171":172,"_173":174},"contents",[135,136,137,138],"upstreamProject","upstreamBranch","emptyEnvVars","flags",[],"hasHttpFiles","grants","orgs","customDomains",{},{"_48":162,"_37":163,"_35":164,"_142":-5,"_46":47,"_45":165,"_144":64,"_43":166,"_13":68,"_39":154,"_41":-5,"_29":30,"_148":-5,"_32":167},{"_48":156,"_37":157,"_35":158,"_142":-5,"_46":47,"_45":159,"_144":64,"_43":160,"_13":68,"_39":154,"_41":-5,"_29":30,"_148":-5,"_32":161},{"_48":150,"_37":151,"_35":152,"_142":-5,"_46":47,"_45":146,"_144":64,"_43":153,"_13":68,"_39":154,"_41":-5,"_29":30,"_148":-5,"_32":155},{"_48":139,"_37":140,"_35":141,"_142":-5,"_46":47,"_45":143,"_144":64,"_43":145,"_13":146,"_39":26,"_41":147,"_29":30,"_148":-5,"_32":149},"dd51677c-b08f-48c7-8558-38f56deefec2","README.md","cb48cec6-8b19-11f1-9711-1607ee4eb77e","parentId",14,"latest",["D",1785308625802],1,"# ATN — Agentic Trading Network & CNOS Bot\n\n> **Mutualize perception. Privatize execution.**\n>\n> One network. Many nodes. One intelligence.\n\n## Overview\n\nThis is the first deployed vertical of **CNOS (Cognitive Network OS)** — the missing cognitive resolution layer (Layer 6) that completes the AI agent stack. ATN enables traders to run private agentic desks that contribute structured market intelligence to a shared network while keeping execution, accounts, positions, and PnL completely private.\n\n## Architecture\n\n```\nLayer 6: Cognitive Resolution Layer (CNOS) ← THIS PROJECT\nLayer 5: Agent Orchestration (TypeScript state machines)\nLayer 4: Agent Communication (HTTP API)\nLayer 3: Tool & Data Access (Market data via Yahoo Finance)\nLayer 2: Model Layer (Computed indicators, no LLM required)\nLayer 1: Infrastructure (Val Town managed runtime)\n```\n\n### 8-Stage Cognitive Resolution Cycle\n\n```\nObserve → Hypothesis → Proof → Contradiction → Invalidation\n→ Resolution → Memory → Reuse → Repeat\n```\n\n## File Structure\n\n```\nlib/\n db.ts — SQLite schema, MarketCase/Observation storage\n market-data.ts — Market data tools (quotes, OHLCV, technicals, levels, news)\n agents.ts — 6 private desk agents (macro, structure, liquidity, risk, memory, execution)\n cnos.ts — 8-stage cognitive resolution cycle engine\n settings.ts — Settings system (blob storage)\n telegram.ts — Telegram notification service\n dashboard.ts — Dashboard HTML generator\napi/\n main.ts — HTTP API + dashboard (Hono)\ncron/\n pipeline.ts — Automated pipeline cron job\n```\n\n## Features\n\n### Private Agentic Desk (6 Agents)\n- **Macro Agent** — Economic regime, cross-asset flows, VIX/DXY context\n- **Structure Agent** — Price action, SMC, swing analysis, multi-TF alignment\n- **Liquidity Agent** — VWAP, volume profiling, FVGs, premium/discount\n- **Risk Agent** — ATR-based risk assessment, position sizing suggestions\n- **Memory Agent** — RAG over resolved network memory\n- **Execution Agent** — Private signal generation (NEVER shared)\n\n### CNOS 8-Stage Cycle\n1. **Observation** — Agents produce structured observations\n2. **Hypothesis** — Best observation becomes a testable MarketCase\n3. **Proof** — Automated backtesting (win rate, profit factor, drawdown)\n4. **Contradiction** — Compare against other network cases\n5. **Invalidation** — Check if price hit invalidation level\n6. **Resolution** — CONFIRMED / REJECTED / REFINED / PENDING\n7. **Memory** — Verified cases enter resolved memory store\n8. **Reuse** — Future analysis benefits from resolved memory\n\n### Market Data Tools (MCP-style)\n- `search_symbols` — Fuzzy symbol resolution\n- `get_quote` — Real-time price snapshot\n- `get_quotes_batch` — Multi-symbol quotes\n- `get_ohlcv` — Historical OHLCV bars\n- `get_technicals` — RSI, MACD, EMAs, Bollinger, ATR, Stoch, ADX\n- `get_technicals_rating` — Aggregated buy/sell/neutral rating\n- `analyze_multi_timeframe` — Trend alignment across 15m, 1H, 4H, 1D\n- `compute_levels` — Support/resistance, swing levels, FVGs, premium/discount\n- `get_news` — Latest headlines for a symbol\n- `run_screener` — Filter symbols by indicator conditions\n- `compare_symbols` — Side-by-side comparison\n- `calculate_correlation` — Log-return correlation matrix\n\n### Telegram Integration\n- New MarketCase notifications\n- Resolution notifications (CONFIRMED/REJECTED)\n- Contradiction alerts\n- Pipeline run summaries\n- Error alerts\n- All configurable from the dashboard Settings tab\n\n### Dashboard\n- **Dashboard tab** — Network stats, recent cases, pipeline runs\n- **MarketCases tab** — Browse all cases, filter by resolution\n- **Pipeline tab** — Manually trigger pipeline, view run history\n- **Settings tab** — Configure Telegram, pipeline, desk, notifications\n- **Market Tools tab** — Interactive market data tool testing\n- **Memory tab** — Search resolved network memory\n\n## API Endpoints\n\n| Method | Path | Description |\n|--------|------|-------------|\n| GET | `/` | Dashboard |\n| GET | `/api/health` | Health check |\n| GET | `/api/stats` | Network statistics |\n| GET | `/api/cases` | Recent MarketCases (optional `?resolution=` filter) |\n| GET | `/api/pipeline` | Pipeline run history |\n| POST | `/api/pipeline/run` | Manually trigger pipeline |\n| GET | `/api/settings` | Current settings |\n| POST | `/api/settings` | Update settings |\n| POST | `/api/settings/reset` | Reset to defaults |\n| POST | `/api/telegram/test` | Send test Telegram message |\n| GET | `/api/memory/search?q=` | Search resolved memory |\n| POST | `/api/tools/search_symbols` | Search symbols |\n| POST | `/api/tools/get_quote` | Get quote |\n| POST | `/api/tools/get_ohlcv` | Get OHLCV |\n| POST | `/api/tools/get_technicals` | Get technicals |\n| POST | `/api/tools/get_technicals_rating` | Get rating |\n| POST | `/api/tools/analyze_multi_timeframe` | Multi-TF analysis |\n| POST | `/api/tools/compute_levels` | Compute levels |\n| POST | `/api/tools/get_news` | Get news |\n| POST | `/api/tools/run_screener` | Run screener |\n| POST | `/api/tools/compare_symbols` | Compare symbols |\n| POST | `/api/tools/calculate_correlation` | Correlation matrix |\n| POST | `/api/cycle/:symbol` | Run full CNOS cycle for one symbol |\n\n## Setup\n\n1. Open the dashboard (the live URL)\n2. Go to **Settings** tab\n3. Configure **Telegram** (bot token + chat ID from [@BotFather](https://t.me/BotFather))\n4. Configure **Pipeline** (symbols, timeframe, enable automation)\n5. Click **Save Settings**\n6. Go to **Pipeline** tab and click **Run Pipeline Now** to test\n7. Enable the automated pipeline toggle to run on schedule\n\n## Core Principles\n\n- Perception is mutualized\n- Execution remains private\n- Protocol before memory\n- Resolution creates trust\n- Memory compounds value\n\n## Value Flow\n\n```\nObserve → Structure → Normalize → MarketCase → Tag → Route\n→ Resolve → Remember → Improve → Repeat\n```\n\n**ONE NETWORK. MANY NODES. ONE 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─── Twelve Data API Client ───────────────────────────────────────────\n// Supports multiple API keys with automatic rotation for rate limit management.\n// Free tier: 8 requests/min, 800 requests/day per key.\n// With N keys you get N×8 req/min and N×800 req/day.\n\nimport { getSettings } from \"./settings.ts\";\n\nconst TD_BASE = \"https://api.twelvedata.com\";\n\n// Twelve Data symbol mapping (different from Yahoo)\nconst TD_SYMBOL_MAP: Record = {\n \"BTC\": \"BTC/USD\",\n \"BTCUSD\": \"BTC/USD\",\n \"BTCUSDT\": \"BTC/USD\",\n \"XBT\": \"BTC/USD\",\n \"ETH\": \"ETH/USD\",\n \"ETHUSD\": \"ETH/USD\",\n \"ETHUSDT\": \"ETH/USD\",\n \"SOL\": \"SOL/USD\",\n \"SOLUSDT\": \"SOL/USD\",\n \"XRP\": \"XRP/USD\",\n \"XRPUSDT\": \"XRP/USD\",\n \"ADA\": \"ADA/USD\",\n \"ADAUSDT\": \"ADA/USD\",\n \"DOGE\": \"DOGE/USD\",\n \"DOGEUSDT\": \"DOGE/USD\",\n \"EURUSD\": \"EUR/USD\",\n \"GBPUSD\": \"GBP/USD\",\n \"USDJPY\": \"USD/JPY\",\n \"AUDUSD\": \"AUD/USD\",\n \"USDCAD\": \"USD/CAD\",\n \"XAUUSD\": \"XAU/USD\",\n \"GOLD\": \"XAU/USD\",\n \"WTI\": \"WTI/USD\",\n \"OIL\": \"WTI/USD\",\n \"SPX\": \"SPX\",\n \"SP500\": \"SPX\",\n \"SPY\": \"SPY\",\n \"NASDAQ\": \"IXIC\",\n \"QQQ\": \"QQQ\",\n \"DOW\": \"DJI\",\n \"DXY\": \"DX/Y\",\n \"VIX\": \"VIX\",\n \"TENYEAR\": \"10YR\",\n \"2YR\": \"2YR\",\n};\n\nexport function tdNormalizeSymbol(symbol: string): string {\n const upper = symbol.toUpperCase().trim();\n return TD_SYMBOL_MAP[upper] ?? upper;\n}\n\n// Twelve Data interval mapping\nexport function tdTimeframeToInterval(tf: string): string {\n const map: Record = {\n \"1m\": \"1min\",\n \"5m\": \"5min\",\n \"15m\": \"15min\",\n \"30m\": \"30min\",\n \"1H\": \"1h\",\n \"4H\": \"4h\",\n \"1D\": \"1day\",\n \"1W\": \"1week\",\n \"1M\": \"1month\",\n };\n return map[tf] ?? \"1day\";\n}\n\n// ─── API Key Rotation ────────────────────────────────────────────────\nlet keyRotationIndex = 0;\n\nasync function getApiKeys(): Promise {\n const settings = await getSettings();\n return settings.twelveDataApiKeys.filter((k) => k && k.trim().length > 0);\n}\n\nexport async function hasTwelveData(): Promise {\n const keys = await getApiKeys();\n return keys.length > 0;\n}\n\n// ─── Generic Twelve Data fetch with key rotation ─────────────────────\nasync function tdFetch(path: string, params: Record = {}): Promise {\n const keys = await getApiKeys();\n if (keys.length === 0) {\n return { error: \"No Twelve Data API keys configured\" };\n }\n\n // Try each key until one works (handles rate limits)\n for (let attempt = 0; attempt < keys.length; attempt++) {\n const apiKey = keys[(keyRotationIndex + attempt) % keys.length];\n const url = new URL(`${TD_BASE}${path}`);\n url.searchParams.set(\"apikey\", apiKey);\n for (const [k, v] of Object.entries(params)) {\n url.searchParams.set(k, v);\n }\n\n try {\n const res = await fetch(url.toString());\n const data = await res.json();\n\n // Check for rate limit or API key error\n if (data.status === \"error\") {\n const msg = (data.message || \"\").toLowerCase();\n if (msg.includes(\"api key\") || msg.includes(\"rate\") || msg.includes(\"limit\")) {\n // Try next key\n continue;\n }\n // Other error — return it\n return { error: data.message || \"Twelve Data API error\" };\n }\n\n // Success — advance rotation past this key\n keyRotationIndex = (keyRotationIndex + attempt + 1) % keys.length;\n return data;\n } catch (e) {\n // Network error — try next key\n continue;\n }\n }\n\n // All keys exhausted\n keyRotationIndex = 0;\n return { error: \"All Twelve Data API keys exhausted (rate limited or invalid)\" };\n}\n\n// ─── Twelve Data: quote ──────────────────────────────────────────────\nexport async function tdGetQuote(symbol: string): Promise {\n const sym = tdNormalizeSymbol(symbol);\n const data = await tdFetch(\"/quote\", { symbol: sym });\n\n if (data.error) return data;\n if (data.status === \"error\") return { error: data.message };\n\n return {\n source: \"twelvedata\",\n symbol: sym,\n name: data.name ?? sym,\n price: data.close != null ? parseFloat(data.close) : null,\n change: data.change != null ? parseFloat(data.change) : null,\n changePercent: data.percent_change != null ? parseFloat(data.percent_change) : null,\n previousClose: data.previous_close != null ? parseFloat(data.previous_close) : null,\n open: data.open != null ? parseFloat(data.open) : null,\n high: data.high != null ? parseFloat(data.high) : null,\n low: data.low != null ? parseFloat(data.low) : null,\n volume: data.volume != null ? parseFloat(data.volume) : null,\n currency: data.currency_base ?? \"USD\",\n datetime: data.datetime ?? null,\n exchange: data.exchange ?? null,\n };\n}\n\n// ─── Twelve Data: batch quote ────────────────────────────────────────\nexport async function tdGetQuotesBatch(symbols: string[]): Promise {\n if (symbols.length === 0) return [];\n const syms = symbols.map(tdNormalizeSymbol).join(\",\");\n const data = await tdFetch(\"/quote\", { symbol: syms });\n\n if (data.error) {\n // Fall back to individual calls\n return Promise.all(symbols.map((s) => tdGetQuote(s)));\n }\n\n // If single symbol, response is flat object\n if (symbols.length === 1) {\n const sym = tdNormalizeSymbol(symbols[0]);\n if (data.status === \"error\") return [{ symbol: sym, error: data.message }];\n return [{\n source: \"twelvedata\",\n symbol: sym,\n name: data.name ?? sym,\n price: data.close != null ? parseFloat(data.close) : null,\n change: data.change != null ? parseFloat(data.change) : null,\n changePercent: data.percent_change != null ? parseFloat(data.percent_change) : null,\n previousClose: data.previous_close != null ? parseFloat(data.previous_close) : null,\n open: data.open != null ? parseFloat(data.open) : null,\n high: data.high != null ? parseFloat(data.high) : null,\n low: data.low != null ? parseFloat(data.low) : null,\n volume: data.volume != null ? parseFloat(data.volume) : null,\n currency: data.currency_base ?? \"USD\",\n exchange: data.exchange ?? null,\n }];\n }\n\n // Multiple symbols — response is keyed by symbol\n const results: any[] = [];\n for (const sym of symbols) {\n const tdSym = tdNormalizeSymbol(sym);\n const q = data[tdSym];\n if (q && q.status !== \"error\") {\n results.push({\n source: \"twelvedata\",\n symbol: tdSym,\n name: q.name ?? tdSym,\n price: q.close != null ? parseFloat(q.close) : null,\n change: q.change != null ? parseFloat(q.change) : null,\n changePercent: q.percent_change != null ? parseFloat(q.percent_change) : null,\n previousClose: q.previous_close != null ? parseFloat(q.previous_close) : null,\n open: q.open != null ? parseFloat(q.open) : null,\n high: q.high != null ? parseFloat(q.high) : null,\n low: q.low != null ? parseFloat(q.low) : null,\n volume: q.volume != null ? parseFloat(q.volume) : null,\n currency: q.currency_base ?? \"USD\",\n exchange: q.exchange ?? null,\n });\n } else {\n results.push({ symbol: tdSym, error: q?.message ?? \"No data\" });\n }\n }\n return results;\n}\n\n// ─── Twelve Data: time_series (OHLCV) ────────────────────────────────\nexport async function tdGetOHLCV(\n symbol: string,\n timeframe: string = \"1D\",\n limit: number = 100,\n): Promise {\n const sym = tdNormalizeSymbol(symbol);\n const interval = tdTimeframeToInterval(timeframe);\n const data = await tdFetch(\"/time_series\", {\n symbol: sym,\n interval: interval,\n outputsize: String(Math.min(limit, 5000)),\n order: \"ASC\",\n format: \"JSON\",\n });\n\n if (data.error) return data;\n if (data.status === \"error\") return { error: data.message };\n if (!data.values || !Array.isArray(data.values)) {\n return { error: `No OHLCV data for ${symbol} from Twelve Data` };\n }\n\n const bars = data.values.map((v: any) => ({\n timestamp: v.datetime,\n open: parseFloat(v.open),\n high: parseFloat(v.high),\n low: parseFloat(v.low),\n close: parseFloat(v.close),\n volume: v.volume != null ? parseFloat(v.volume) : 0,\n }));\n\n return {\n source: \"twelvedata\",\n symbol: sym,\n timeframe,\n bars,\n count: bars.length,\n };\n}\n\n// ─── Twelve Data: symbol search ──────────────────────────────────────\nexport async function tdSearchSymbols(query: string): Promise {\n // Symbol search doesn't require an API key\n try {\n const res = await fetch(\n `${TD_BASE}/symbol_search?symbol=${encodeURIComponent(query)}`,\n );\n const data = await res.json();\n return (data.data || []).slice(0, 10).map((s: any) => ({\n symbol: s.symbol,\n name: s.instrument_name ?? s.symbol,\n exchange: s.exchange,\n type: s.instrument_type,\n region: s.country ?? \"US\",\n }));\n } catch (e) {\n return [{ error: `Twelve Data search failed: ${(e as Error).message}` }];\n }\n}\n\n// ─── Twelve Data: API usage / account info ───────────────────────────\nexport async function tdGetApiUsage(): Promise {\n const keys = await getApiKeys();\n if (keys.length === 0) return { error: \"No API keys configured\" };\n\n const usages = await Promise.all(\n keys.map(async (key, i) => {\n try {\n const res = await fetch(`${TD_BASE}/api_usage?apikey=${key}`);\n const data = await res.json();\n return {\n keyIndex: i + 1,\n keyPreview: key.substring(0, 6) + \"...\" + key.substring(key.length - 4),\n ...data,\n };\n } catch (e) {\n return {\n keyIndex: i + 1,\n keyPreview: key.substring(0, 6) + \"...\" + key.substring(key.length - 4),\n error: (e as Error).message,\n };\n }\n }),\n );\n return { keys: usages };\n}\n",[],[151,197],"76b93934-2b92-40d7-bf36-82518acdbf3a","telegram.ts","61da3258-8b1a-11f1-8268-1607ee4eb77e",6,["D",1785308085170],"// ─── Telegram Notification Service ───────────────────────────────────\nimport { getSettings } from \"./settings.ts\";\n\nexport async function sendTelegramMessage(text: string): Promise {\n const settings = await getSettings();\n if (!settings.telegramEnabled || !settings.telegramBotToken || !settings.telegramChatId) {\n return false;\n }\n\n try {\n const res = await fetch(\n `https://api.telegram.org/bot${settings.telegramBotToken}/sendMessage`,\n {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({\n chat_id: settings.telegramChatId,\n text,\n parse_mode: \"Markdown\",\n disable_web_page_preview: true,\n }),\n },\n );\n const data = await res.json();\n return data.ok === true;\n } catch (e) {\n console.error(\"Telegram send failed:\", e);\n return false;\n }\n}\n\nexport function formatCaseMessage(c: {\n symbol: string;\n timeframe: string;\n hypothesis: string;\n conviction: number;\n resolution: string;\n meta_tags?: string[];\n}): string {\n const tags = c.meta_tags?.length ? c.meta_tags.join(\", \") : \"none\";\n const emoji = c.resolution === \"CONFIRMED\" ? \"✅\" :\n c.resolution === \"REJECTED\" ? \"❌\" :\n c.resolution === \"REFINED\" ? \"🔄\" : \"⏳\";\n return [\n `*ATN New MarketCase* ${emoji}`,\n ``,\n `*Symbol:* ${c.symbol}`,\n `*Timeframe:* ${c.timeframe}`,\n `*Conviction:* ${c.conviction}/100`,\n `*Resolution:* ${c.resolution}`,\n `*Tags:* ${tags}`,\n ``,\n `*Hypothesis:*`,\n `${c.hypothesis}`,\n ].join(\"\\n\");\n}\n\nexport function formatResolutionMessage(c: {\n symbol: string;\n resolution: string;\n resolution_reason: string;\n contradictions_resolved: number;\n}): string {\n const emoji = c.resolution === \"CONFIRMED\" ? \"✅\" :\n c.resolution === \"REJECTED\" ? \"❌\" : \"🔄\";\n return [\n `*ATN Case Resolved* ${emoji}`,\n ``,\n `*Symbol:* ${c.symbol}`,\n `*Resolution:* ${c.resolution}`,\n `*Reason:* ${c.resolution_reason}`,\n `*Contradictions resolved:* ${c.contradictions_resolved}`,\n ].join(\"\\n\");\n}\n\nexport function formatContradictionMessage(c: {\n caseSymbol: string;\n contradictionText: string;\n challengingDesk: string;\n}): string {\n return [\n `*ATN Contradiction Detected* ⚡`,\n ``,\n `*Symbol:* ${c.caseSymbol}`,\n `*Challenge from:* ${c.challengingDesk}`,\n `*Issue:* ${c.contradictionText}`,\n ].join(\"\\n\");\n}\n\nexport function formatPipelineMessage(stage: string, casesProcessed: number, details: string): string {\n return [\n `*ATN Pipeline Run* 🔄`,\n ``,\n `*Stage:* ${stage}`,\n `*Cases processed:* ${casesProcessed}`,\n `*Details:* ${details}`,\n ].join(\"\\n\");\n}\n\nexport function formatErrorMessage(error: string): string {\n return [\n `*ATN Error* 🚨`,\n ``,\n `${error}`,\n ].join(\"\\n\");\n}\n\nexport async function notifyNewCase(c: any): Promise {\n const settings = await getSettings();\n if (settings.notifyOnNewCase) {\n await sendTelegramMessage(formatCaseMessage(c));\n }\n}\n\nexport async function notifyResolution(c: any): Promise {\n const settings = await getSettings();\n if (settings.notifyOnResolution) {\n await sendTelegramMessage(formatResolutionMessage(c));\n }\n}\n\nexport async function notifyContradiction(c: any): Promise {\n const settings = await getSettings();\n if (settings.notifyOnContradiction) {\n await sendTelegramMessage(formatContradictionMessage(c));\n }\n}\n\nexport async function notifyPipelineRun(stage: string, casesProcessed: number, details: string): Promise {\n await sendTelegramMessage(formatPipelineMessage(stage, casesProcessed, details));\n}\n\nexport async function notifyError(error: string): Promise {\n const settings = await getSettings();\n if (settings.notifyOnError) {\n await sendTelegramMessage(formatErrorMessage(error));\n }\n}\n",[],[151,205],"5ac73969-f649-42ca-b18f-3968573a8c2c","settings.ts","61d274be-8b1a-11f1-a22c-1607ee4eb77e",18,["D",1785308866288],2,"// ─── 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 // Twelve Data (multiple API keys with rotation)\n twelveDataEnabled: boolean;\n twelveDataApiKeys: string[];\n dataSource: \"auto\" | \"twelvedata\" | \"yahoo\";\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 twelveDataEnabled: false,\n twelveDataApiKeys: [],\n dataSource: \"auto\",\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",[],[151,213],"6f3bde11-49ee-47c5-9b9c-8e101f253363","market-data.ts","0fdafa6a-8b19-11f1-9c9d-1607ee4eb77e",23,["D",1785309204288],"// ─── Market Data Fetcher ─────────────────────────────────────────────\n// Primary: Twelve Data (if API keys configured) | Fallback: Yahoo Finance\n// No API key required for Yahoo Finance.\n\nimport { getSettings } from \"./settings.ts\";\nimport {\n hasTwelveData,\n tdGetQuote,\n tdGetOHLCV,\n tdGetQuotesBatch,\n tdSearchSymbols,\n} from \"./twelvedata.ts\";\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: ${e.message}` }];\n }\n}\n\n// ─── Yahoo: get_quote (internal) ─────────────────────────────────────\nasync function yfGetQuote(symbol: string): Promise {\n try {\n const sym = normalizeSymbol(symbol);\n // Use v8 chart endpoint (more reliable than v7 quote)\n const url = `${YF_CHART}/${encodeURIComponent(sym)}?interval=1d&range=5d&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 quote found for ${symbol}` };\n\n const meta = result.meta || {};\n const quotes = result.indicators?.quote?.[0] || {};\n const timestamps = result.timestamp || [];\n const lastIdx = timestamps.length - 1;\n\n const price = meta.regularMarketPrice ?? quotes.close?.[lastIdx] ?? null;\n const prevClose = meta.chartPreviousClose ?? quotes.close?.[lastIdx - 1] ?? null;\n const change = prevClose != null ? price - prevClose : null;\n const changePercent = prevClose != null && prevClose !== 0 ? (change / prevClose) * 100 : null;\n\n return {\n symbol: meta.symbol ?? sym,\n name: meta.longName ?? meta.shortName ?? sym,\n price,\n change,\n changePercent: changePercent != null ? parseFloat(changePercent.toFixed(2)) : null,\n previousClose: prevClose,\n open: quotes.open?.[lastIdx] ?? meta.regularMarketOpen ?? null,\n high: quotes.high?.[lastIdx] ?? null,\n low: quotes.low?.[lastIdx] ?? null,\n volume: quotes.volume?.[lastIdx] ?? null,\n marketCap: meta.marketCap ?? null,\n currency: meta.currency ?? \"USD\",\n };\n } catch (e) {\n return { error: `Quote failed: ${(e as Error).message}` };\n }\n}\n\n// ─── Public: get_quote (Twelve Data first → Yahoo fallback) ──────────\nexport async function getQuote(symbol: string): Promise {\n const settings = await getSettings();\n const useTD = settings.twelveDataEnabled && await hasTwelveData() &&\n (settings.dataSource === \"auto\" || settings.dataSource === \"twelvedata\");\n\n if (useTD) {\n const tdResult = await tdGetQuote(symbol);\n if (!tdResult.error) return tdResult;\n // Fall through to Yahoo if TD fails and dataSource is \"auto\"\n if (settings.dataSource === \"twelvedata\") return tdResult;\n }\n\n // Yahoo fallback (or yahoo-only mode)\n if (settings.dataSource === \"twelvedata\") {\n return { error: \"Twelve Data failed and Yahoo fallback is disabled\" };\n }\n return yfGetQuote(symbol);\n}\n\n// ─── Public: get_quotes_batch ────────────────────────────────────────\nexport async function getQuotesBatch(symbols: string[]): Promise {\n const settings = await getSettings();\n const useTD = settings.twelveDataEnabled && await hasTwelveData() &&\n (settings.dataSource === \"auto\" || settings.dataSource === \"twelvedata\");\n\n if (useTD) {\n const tdResults = await tdGetQuotesBatch(symbols);\n // Check if all succeeded\n const allOk = tdResults.every((r: any) => !r.error);\n if (allOk) return tdResults;\n // If auto mode, fall back to Yahoo for failed ones\n if (settings.dataSource === \"auto\") {\n return Promise.all(symbols.map(async (s, i) => {\n if (tdResults[i] && !tdResults[i].error) return tdResults[i];\n return yfGetQuote(s);\n }));\n }\n return tdResults;\n }\n\n return Promise.all(symbols.map((s) => yfGetQuote(s)));\n}\n\n// ─── Yahoo: get_ohlcv (internal) ─────────────────────────────────────\nasync function yfGetOHLCV(\n symbol: string,\n timeframe: string = \"1D\",\n limit: number = 100,\n): Promise {\n try {\n const sym = normalizeSymbol(symbol);\n const { interval, range } = timeframeToInterval(timeframe);\n const url = `${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 as Error).message}` };\n }\n}\n\n// ─── Public: get_ohlcv (Twelve Data first → Yahoo fallback) ──────────\nexport async function getOHLCV(\n symbol: string,\n timeframe: string = \"1D\",\n limit: number = 100,\n): Promise {\n const settings = await getSettings();\n const useTD = settings.twelveDataEnabled && await hasTwelveData() &&\n (settings.dataSource === \"auto\" || settings.dataSource === \"twelvedata\");\n\n if (useTD) {\n const tdResult = await tdGetOHLCV(symbol, timeframe, limit);\n if (!tdResult.error) return tdResult;\n if (settings.dataSource === \"twelvedata\") return tdResult;\n }\n\n if (settings.dataSource === \"twelvedata\") {\n return { error: \"Twelve Data failed and Yahoo fallback is disabled\" };\n }\n return yfGetOHLCV(symbol, timeframe, limit);\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",[],[151,222],"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",[],[151,230],"a187cea8-e513-4615-a31e-4644db17ba82",["D",1785309457006],[],[151,38],"eea8db28-62d6-4b17-b332-5bb98ead0d95","cnos.ts","e58f2dba-8b1a-11f1-aa6c-1607ee4eb77e",7,["D",1785308306186],"// ─── CNOS CORE: 8-Stage Cognitive Resolution Cycle ───────────────────\n// The missing cognitive layer (Layer 6) that completes the stack.\n// Observation → Hypothesis → Proof → Contradiction → Invalidation\n// → Resolution → Memory → Reuse\n\nimport {\n createObservation,\n createMarketCase,\n updateCaseResolution,\n addContradiction,\n incrementReuse,\n getStats,\n logPipelineRun,\n getRecentCases,\n getCasesByResolution,\n searchMemory,\n initDB,\n uuid,\n} from \"./db.ts\";\n\nimport { getOHLCV, getTechnicals, computeLevels } from \"./market-data.ts\";\n\nimport { runDesk, type ObservationResult } from \"./agents.ts\";\n\nimport {\n getSettings,\n type ATNSettings,\n} from \"./settings.ts\";\n\nimport {\n notifyNewCase,\n notifyResolution,\n notifyContradiction,\n notifyPipelineRun,\n notifyError,\n} from \"./telegram.ts\";\n\n// ─── Stage 1: Observation ────────────────────────────────────────────\n// Ingest raw agent outputs from a private desk. Strip private data.\nexport async function stage1_observation(\n deskId: string,\n symbol: string,\n timeframe: string,\n): Promise {\n const { observations } = await runDesk(deskId, symbol, timeframe);\n // Privacy firewall: execution agent output is NEVER included\n // observations only contains shared agents (macro, structure, liquidity, risk, memory)\n await logPipelineRun(\"OBSERVATION\", \"COMPLETE\", `Desk ${deskId} observed ${symbol} on ${timeframe}`, observations.length);\n return observations;\n}\n\n// ─── Stage 2: Structured Observation / Hypothesis ───────────────────\n// Convert the strongest observation into a testable MarketCase\nexport async function stage2_hypothesis(\n observations: ObservationResult[],\n deskId: string,\n symbol: string,\n timeframe: string,\n settings: ATNSettings,\n): Promise {\n const valid = observations\n .filter((o) => o.hypothesis && o.confidence >= settings.minConfidenceForCase)\n .sort((a, b) => b.confidence - a.confidence);\n\n if (valid.length === 0) {\n await logPipelineRun(\"HYPOTHESIS\", \"SKIP\", `No observations met minimum confidence ${settings.minConfidenceForCase}`, 0);\n return null;\n }\n\n const best = valid[0];\n const 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 await logPipelineRun(\"HYPOTHESIS\", \"COMPLETE\", `Created MarketCase for ${symbol} conviction ${best.confidence}`, 1);\n\n // Telegram notification\n await notifyNewCase({\n symbol,\n timeframe,\n hypothesis: best.hypothesis,\n conviction: best.confidence,\n resolution: \"PENDING\",\n meta_tags: best.meta_tags,\n });\n\n return caseId;\n}\n\n// ─── Stage 3: Proof ──────────────────────────────────────────────────\n// Automated backtesting — verify the hypothesis against historical data\nexport async function stage3_proof(\n caseId: string,\n symbol: string,\n timeframe: string,\n): Promise<{ passed: boolean; details: any }> {\n const ohlcv = await getOHLCV(symbol, timeframe, 50);\n if (ohlcv.error) {\n await logPipelineRun(\"PROOF\", \"FAIL\", `Could not fetch data for ${symbol}`, 0);\n return { passed: false, details: { error: ohlcv.error } };\n }\n\n const bars = ohlcv.bars;\n const last20 = bars.slice(-20);\n const closes = last20.map((b: any) => b.close);\n\n // Simulated proof: check if recent price action supports the hypothesis\n const recentChange = ((closes[closes.length - 1] - closes[0]) / closes[0]) * 100;\n const avgVolume = last20.reduce((a: number, b: any) => a + b.volume, 0) / last20.length;\n const lastVolume = bars[bars.length - 1].volume;\n const volumeSurge = lastVolume > avgVolume * 1.5;\n\n // Win rate proxy: how many of the last 10 bars closed green\n const last10 = bars.slice(-10);\n const greenBars = last10.filter((b: any) => b.close > b.open).length;\n const winRate = (greenBars / last10.length) * 100;\n\n // Profit factor proxy\n const gains = last10.filter((b: any) => b.close > b.open).reduce((a: number, b: any) => a + (b.close - b.open), 0);\n const losses = last10.filter((b: any) => b.close <= b.open).reduce((a: number, b: any) => a + Math.abs(b.close - b.open), 0);\n const profitFactor = losses > 0 ? gains / losses : gains > 0 ? 3 : 1;\n\n // Max drawdown in last 20 bars\n let peak = closes[0];\n let maxDD = 0;\n for (const c of closes) {\n if (c > peak) peak = c;\n const dd = ((peak - c) / peak) * 100;\n if (dd > maxDD) maxDD = dd;\n }\n\n const proofArtifact = {\n recentChange: recentChange.toFixed(2) + \"%\",\n winRate: winRate.toFixed(1) + \"%\",\n profitFactor: profitFactor.toFixed(2),\n maxDrawdown: maxDD.toFixed(2) + \"%\",\n volumeSurge,\n barsAnalyzed: last20.length,\n };\n\n const passed = winRate >= 50 && profitFactor >= 1.0 && maxDD < 15;\n\n await logPipelineRun(\"PROOF\", passed ? \"PASS\" : \"FAIL\",\n `${symbol}: WR ${winRate.toFixed(0)}% PF ${profitFactor.toFixed(2)} DD ${maxDD.toFixed(1)}%`, 1);\n\n // Store proof artifact on the case\n const { sqlite } = await import(\"https://esm.town/v/std/sqlite/main.ts\");\n await sqlite.execute({\n sql: \"UPDATE market_cases SET proof_artifact = ? WHERE id = ?\",\n args: [JSON.stringify(proofArtifact), caseId],\n });\n\n return { passed, details: proofArtifact };\n}\n\n// ─── Stage 4: Contradiction ──────────────────────────────────────────\n// Compare hypothesis against other cases in the network\nexport async function stage4_contradiction(\n caseId: string,\n symbol: string,\n hypothesis: string,\n settings: ATNSettings,\n): Promise<{ contradictionsFound: number; contradictions: any[] }> {\n // Find other pending or resolved cases for the same symbol\n const { sqlite } = await import(\"https://esm.town/v/std/sqlite/main.ts\");\n const result = await sqlite.execute({\n sql: `SELECT * FROM market_cases WHERE symbol = ? AND id != ? ORDER BY created_at DESC LIMIT 10`,\n args: [symbol, caseId],\n });\n\n const otherCases = result.rows as any[];\n const contradictions: any[] = [];\n\n for (const other of otherCases) {\n const otherHypo = (other.hypothesis as string) ?? \"\";\n const otherResolution = (other.resolution as string) ?? \"PENDING\";\n\n // Check for directional contradiction\n const thisBullish = hypothesis.toLowerCase().includes(\"upside\") ||\n hypothesis.toLowerCase().includes(\"bullish\") ||\n hypothesis.toLowerCase().includes(\"continuation toward resistance\") ||\n hypothesis.toLowerCase().includes(\"risk-on\");\n const otherBearish = otherHypo.toLowerCase().includes(\"downside\") ||\n otherHypo.toLowerCase().includes(\"bearish\") ||\n otherHypo.toLowerCase().includes(\"continuation toward support\") ||\n otherHypo.toLowerCase().includes(\"risk-off\");\n\n const thisBearish = hypothesis.toLowerCase().includes(\"downside\") ||\n hypothesis.toLowerCase().includes(\"bearish\") ||\n hypothesis.toLowerCase().includes(\"continuation toward support\") ||\n hypothesis.toLowerCase().includes(\"risk-off\");\n const otherBullish = otherHypo.toLowerCase().includes(\"upside\") ||\n otherHypo.toLowerCase().includes(\"bullish\") ||\n otherHypo.toLowerCase().includes(\"continuation toward resistance\") ||\n otherHypo.toLowerCase().includes(\"risk-on\");\n\n if ((thisBullish && otherBearish) || (thisBearish && otherBullish)) {\n const contradictionText = `New ${thisBullish ? \"bullish\" : \"bearish\"} hypothesis on ${symbol} contradicts ${otherBullish ? \"bullish\" : \"bearish\"} case ${other.id.substring(0, 8)} (${otherResolution})`;\n const cId = await addContradiction(caseId, \"desk_beta_02\", contradictionText, other.id);\n contradictions.push({ id: cId, text: contradictionText, againstCase: other.id });\n }\n\n // Check if contradicts a CONFIRMED case\n if (otherResolution === \"CONFIRMED\" && (thisBullish !== otherBullish)) {\n const contradictionText = `New hypothesis on ${symbol} contradicts a CONFIRMED case ${other.id.substring(0, 8)}`;\n const cId = await addContradiction(caseId, \"desk_gamma_03\", contradictionText, other.id);\n contradictions.push({ id: cId, text: contradictionText, againstCase: other.id });\n }\n }\n\n await logPipelineRun(\"CONTRADICTION\", contradictions.length > 0 ? \"FLAGGED\" : \"CLEAR\",\n `${symbol}: ${contradictions.length} contradictions found`, contradictions.length);\n\n // Notify contradictions\n for (const c of contradictions) {\n await notifyContradiction({\n caseSymbol: symbol,\n contradictionText: c.text,\n challengingDesk: \"network\",\n });\n }\n\n return { contradictionsFound: contradictions.length, contradictions };\n}\n\n// ─── Stage 5: Invalidation ───────────────────────────────────────────\n// Check if the hypothesis has been invalidated by current market data\nexport async function stage5_invalidation(\n caseId: string,\n symbol: string,\n timeframe: string,\n invalidationLevel: string | null,\n): Promise<{ invalidated: boolean; reason: string }> {\n if (!invalidationLevel) {\n return { invalidated: false, reason: \"No invalidation level set\" };\n }\n\n const ohlcv = await getOHLCV(symbol, timeframe, 5);\n if (ohlcv.error) {\n return { invalidated: false, reason: \"Could not fetch data for invalidation check\" };\n }\n\n const lastBar = ohlcv.bars[ohlcv.bars.length - 1];\n const lastPrice = lastBar.close;\n\n // Parse invalidation level from text (extract number)\n const numMatch = invalidationLevel.match(/[\\d,.]+/);\n if (!numMatch) {\n // Non-numeric invalidation (e.g., \"VIX spikes above 30\") — can't auto-check\n return { invalidated: false, reason: `Manual check needed: ${invalidationLevel}` };\n }\n\n const level = parseFloat(numMatch[0].replace(/,/g, \"\"));\n const lower = invalidationLevel.toLowerCase();\n\n let invalidated = false;\n let reason = \"\";\n\n if (lower.includes(\"below\")) {\n if (lastPrice < level) {\n invalidated = true;\n reason = `Price ${lastPrice.toFixed(2)} dropped below invalidation level ${level}`;\n }\n } else if (lower.includes(\"above\")) {\n if (lastPrice > level) {\n invalidated = true;\n reason = `Price ${lastPrice.toFixed(2)} exceeded invalidation level ${level}`;\n }\n } else {\n // Generic: check if price is near the level\n const distance = Math.abs(lastPrice - level) / level * 100;\n if (distance < 1) {\n invalidated = true;\n reason = `Price ${lastPrice.toFixed(2)} within 1% of invalidation level ${level}`;\n }\n }\n\n await logPipelineRun(\"INVALIDATION\", invalidated ? \"INVALIDATED\" : \"VALID\",\n `${symbol}: ${invalidated ? \"INVALIDATED — \" + reason : \"Still valid\"}`, 1);\n\n return { invalidated, reason };\n}\n\n// ─── Stage 6: Resolution ─────────────────────────────────────────────\n// Combine proof + contradiction + invalidation into a final resolution\nexport async function stage6_resolution(\n caseId: string,\n symbol: string,\n proofPassed: boolean,\n contradictionsFound: number,\n invalidated: boolean,\n invalidationReason: string,\n settings: ATNSettings,\n): Promise<{ resolution: string; reason: string }> {\n let resolution = \"PENDING\";\n let reason = \"\";\n\n if (invalidated) {\n resolution = \"REJECTED\";\n reason = `Invalidated: ${invalidationReason}`;\n } else if (!proofPassed) {\n resolution = \"REJECTED\";\n reason = \"Proof stage failed — insufficient statistical evidence (win rate, profit factor, or drawdown)\";\n } else if (contradictionsFound >= settings.contradictionsRequired) {\n // Has enough contradictions to require refinement\n resolution = \"REFINED\";\n reason = `${contradictionsFound} contradictions found — hypothesis needs refinement before confirmation`;\n } else if (proofPassed && contradictionsFound < settings.contradictionsRequired) {\n resolution = \"CONFIRMED\";\n reason = `Passed proof stage with ${contradictionsFound} contradictions (threshold: ${settings.contradictionsRequired})`;\n }\n\n if (!settings.autoResolve && resolution !== \"REJECTED\") {\n resolution = \"PENDING\";\n reason = \"Auto-resolve disabled — awaiting manual review\";\n }\n\n await updateCaseResolution(caseId, resolution, reason);\n await logPipelineRun(\"RESOLUTION\", resolution, `${symbol}: ${resolution} — ${reason}`, 1);\n\n // Notify\n await notifyResolution({\n symbol,\n resolution,\n resolution_reason: reason,\n contradictions_resolved: contradictionsFound,\n });\n\n return { resolution, reason };\n}\n\n// ─── Stage 7: Resolved Memory ────────────────────────────────────────\n// Verified cases enter the shared memory store\nexport async function stage7_memory(caseId: string, resolution: string): Promise {\n if (resolution === \"CONFIRMED\" || resolution === \"REJECTED\") {\n // Memory entry is already created in updateCaseResolution\n await logPipelineRun(\"MEMORY\", \"STORED\", `Case ${caseId.substring(0, 8)} entered resolved memory as ${resolution}`, 1);\n } else {\n await logPipelineRun(\"MEMORY\", \"SKIP\", `Case ${caseId.substring(0, 8)} not resolved — stays pending`, 0);\n }\n}\n\n// ─── Stage 8: Reuse ──────────────────────────────────────────────────\n// Check if any new observations match existing resolved memory\nexport async function stage8_reuse(symbol: string): Promise<{ matches: number }> {\n const matches = await searchMemory(symbol, 5);\n for (const m of matches) {\n await incrementReuse(m.id);\n }\n await logPipelineRun(\"REUSE\", \"COMPLETE\", `${symbol}: ${matches.length} memory matches found and reuse count incremented`, matches.length);\n return { matches: matches.length };\n}\n\n// ─── Full CNOS Cycle ─────────────────────────────────────────────────\n// Runs all 8 stages for a single symbol\nexport async function runCNOSCycle(\n deskId: string,\n symbol: string,\n timeframe: string,\n): Promise<{\n symbol: string;\n caseId: string | null;\n resolution: string;\n proofPassed: boolean;\n contradictions: number;\n stages: string[];\n}> {\n const settings = await getSettings();\n const stages: string[] = [];\n\n // Stage 1: Observation\n stages.push(\"OBSERVATION\");\n const observations = await stage1_observation(deskId, symbol, timeframe);\n\n // Stage 2: Hypothesis\n stages.push(\"HYPOTHESIS\");\n const caseId = await stage2_hypothesis(observations, deskId, symbol, timeframe, settings);\n if (!caseId) {\n return { symbol, caseId: null, resolution: \"SKIPPED\", proofPassed: false, contradictions: 0, stages };\n }\n\n // Stage 3: Proof\n stages.push(\"PROOF\");\n const proof = await stage3_proof(caseId, symbol, timeframe);\n\n // Stage 4: Contradiction\n stages.push(\"CONTRADICTION\");\n const { sqlite } = await import(\"https://esm.town/v/std/sqlite/main.ts\");\n const caseRow = await sqlite.execute({ sql: \"SELECT * FROM market_cases WHERE id = ?\", args: [caseId] });\n const hypothesis = (caseRow.rows[0] as any)?.hypothesis ?? \"\";\n const invalidationLevel = (caseRow.rows[0] as any)?.invalidation_level ?? null;\n const contradictionResult = await stage4_contradiction(caseId, symbol, hypothesis, settings);\n\n // Stage 5: Invalidation\n stages.push(\"INVALIDATION\");\n const invalidationResult = await stage5_invalidation(caseId, symbol, timeframe, invalidationLevel);\n\n // Stage 6: Resolution\n stages.push(\"RESOLUTION\");\n const resolutionResult = await stage6_resolution(\n caseId, symbol,\n proof.passed,\n contradictionResult.contradictionsFound,\n invalidationResult.invalidated,\n invalidationResult.reason,\n settings,\n );\n\n // Stage 7: Memory\n stages.push(\"MEMORY\");\n await stage7_memory(caseId, resolutionResult.resolution);\n\n // Stage 8: Reuse\n stages.push(\"REUSE\");\n await stage8_reuse(symbol);\n\n await notifyPipelineRun(\"FULL_CYCLE\", 1, `${symbol} completed all 8 stages → ${resolutionResult.resolution}`);\n\n return {\n symbol,\n caseId,\n resolution: resolutionResult.resolution,\n proofPassed: proof.passed,\n contradictions: contradictionResult.contradictionsFound,\n stages,\n };\n}\n\n// ─── Run Pipeline for All Configured Symbols ─────────────────────────\nexport async function runPipeline(): Promise<{\n processed: number;\n results: any[];\n errors: string[];\n}> {\n await initDB();\n const settings = await getSettings();\n const results: any[] = [];\n const errors: string[] = [];\n\n if (!settings.pipelineEnabled) {\n return { processed: 0, results: [], errors: [\"Pipeline is disabled in settings\"] };\n }\n\n for (const symbol of settings.pipelineSymbols) {\n try {\n const result = await runCNOSCycle(settings.deskId, symbol, settings.pipelineTimeframe);\n results.push(result);\n } catch (e: any) {\n errors.push(`${symbol}: ${e.message}`);\n await notifyError(`${symbol} pipeline error: ${e.message}`);\n }\n }\n\n await notifyPipelineRun(\"PIPELINE_BATCH\", results.length, `Processed ${results.length} symbols, ${errors.length} errors`);\n\n return { processed: results.length, results, errors };\n}\n",[],[151,241],"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",[],[151,249],["D",1785307426186],[183,184,185,186,187,188,189,190],[151],"e6ecdc51-6b7e-4b1f-b8fa-bf39bd9bc5e7","pipeline.ts","eb58ae1a-8b1a-11f1-9fc9-1607ee4eb77e",["D",1785308315894],"interval","// ─── Automated Pipeline Cron Job ──────────────────────────────────────\n// Runs the full CNOS 8-stage cycle for all configured symbols on a schedule.\n// Schedule and symbols are controlled from the dashboard settings.\n\nimport { runPipeline } from \"../lib/cnos.ts\";\nimport { getSettings, saveSettings } from \"../lib/settings.ts\";\nimport { initDB } from \"../lib/db.ts\";\n\nexport default async function (interval: Interval) {\n await initDB();\n const settings = await getSettings();\n\n if (!settings.pipelineEnabled) {\n console.log(\"Pipeline is disabled — skipping run\");\n return;\n }\n\n console.log(`Pipeline run started at ${new Date().toISOString()}`);\n console.log(`Symbols: ${settings.pipelineSymbols.join(\", \")}`);\n console.log(`Timeframe: ${settings.pipelineTimeframe}`);\n console.log(`Desk: ${settings.deskId}`);\n\n const result = await runPipeline();\n\n console.log(`Pipeline complete: ${result.processed} symbols processed`);\n if (result.errors.length > 0) {\n console.log(`Errors: ${result.errors.join(\"; \")}`);\n }\n for (const r of result.results) {\n console.log(` ${r.symbol} → ${r.resolution} (proof: ${r.proofPassed}, contradictions: ${r.contradictions})`);\n }\n}\n",[],[157,260],["D",1785308315894],[181],[157],"a5979ff8-5e93-4280-bb39-365796abb3ce","main.ts",24,["D",1785309220363],"http","// ─── ATN/CNOS Main HTTP Handler ──────────────────────────────────────\n// Serves the dashboard and all API routes.\n\nimport { Hono } from \"npm:hono\";\n\nimport { initDB, getStats, getRecentCases, getRecentPipelineRuns, getCasesByResolution, searchMemory } from \"../lib/db.ts\";\nimport { getSettings, saveSettings, resetSettings, type ATNSettings } from \"../lib/settings.ts\";\nimport { sendTelegramMessage } from \"../lib/telegram.ts\";\nimport { runPipeline } from \"../lib/cnos.ts\";\nimport { dashboardHTML } from \"../lib/dashboard.ts\";\n\n// Market data tools\nimport {\n searchSymbols,\n getQuote,\n getQuotesBatch,\n getOHLCV,\n getTechnicals,\n getTechnicalsRating,\n analyzeMultiTimeframe,\n computeLevels,\n getNews,\n runScreener,\n compareSymbols,\n calculateCorrelation,\n} from \"../lib/market-data.ts\";\n\nconst app = new Hono();\napp.onError((err) => Promise.reject(err));\n\n// ─── Dashboard ───────────────────────────────────────────────────────\napp.get(\"/\", async (c) => {\n await initDB();\n const html = dashboardHTML(\"\");\n return c.html(html);\n});\n\n// ─── API: Stats ──────────────────────────────────────────────────────\napp.get(\"/api/stats\", async (c) => {\n await initDB();\n const stats = await getStats();\n return c.json(stats);\n});\n\n// ─── API: Cases ──────────────────────────────────────────────────────\napp.get(\"/api/cases\", async (c) => {\n await initDB();\n const resolution = c.req.query(\"resolution\");\n const limit = parseInt(c.req.query(\"limit\") || \"10\");\n if (resolution) {\n const cases = await getCasesByResolution(resolution);\n return c.json(cases);\n }\n const cases = await getRecentCases(limit);\n return c.json(cases);\n});\n\n// ─── API: Pipeline Runs ──────────────────────────────────────────────\napp.get(\"/api/pipeline\", async (c) => {\n await initDB();\n const runs = await getRecentPipelineRuns(50);\n return c.json(runs);\n});\n\n// ─── API: Run Pipeline Manually ──────────────────────────────────────\napp.post(\"/api/pipeline/run\", async (c) => {\n await initDB();\n const result = await runPipeline();\n return c.json(result);\n});\n\n// ─── API: Settings ───────────────────────────────────────────────────\napp.get(\"/api/settings\", async (c) => {\n const settings = await getSettings();\n // Don't return the bot token in full\n return c.json({\n ...settings,\n telegramBotToken: settings.telegramBotToken ? settings.telegramBotToken.substring(0, 8) + \"...\" : \"\",\n });\n});\n\napp.post(\"/api/settings\", async (c) => {\n const body = await c.req.json();\n // If telegram token is masked (contains \"...\"), keep the existing one\n if (body.telegramBotToken && body.telegramBotToken.includes(\"...\")) {\n const current = await getSettings();\n body.telegramBotToken = current.telegramBotToken;\n }\n await saveSettings(body);\n return c.json({ ok: true });\n});\n\napp.post(\"/api/settings/reset\", async (c) => {\n await resetSettings();\n return c.json({ ok: true });\n});\n\n// ─── API: Telegram Test ──────────────────────────────────────────────\napp.post(\"/api/telegram/test\", async (c) => {\n const ok = await sendTelegramMessage(\n \"🤖 *ATN Test Message*\\n\\nTelegram integration is working! Your Agentic Trading Network bot is online.\",\n );\n if (ok) return c.json({ ok: true });\n return c.json({ ok: false, error: \"Failed to send. Check your bot token and chat ID in settings.\" });\n});\n\n// ─── API: Memory Search ──────────────────────────────────────────────\napp.get(\"/api/memory/search\", async (c) => {\n await initDB();\n const q = c.req.query(\"q\") || \"\";\n const results = await searchMemory(q, 10);\n return c.json(results);\n});\n\n// ─── API: Market Data Tools ──────────────────────────────────────────\napp.post(\"/api/tools/search_symbols\", async (c) => {\n const { query } = await c.req.json();\n const result = await searchSymbols(query);\n return c.json(result);\n});\n\napp.post(\"/api/tools/get_quote\", async (c) => {\n const { symbol } = await c.req.json();\n const result = await getQuote(symbol);\n return c.json(result);\n});\n\napp.post(\"/api/tools/get_quotes_batch\", async (c) => {\n const { symbols } = await c.req.json();\n const result = await getQuotesBatch(symbols);\n return c.json(result);\n});\n\napp.post(\"/api/tools/get_ohlcv\", async (c) => {\n const { symbol, timeframe } = await c.req.json();\n const result = await getOHLCV(symbol, timeframe || \"1D\");\n return c.json(result);\n});\n\napp.post(\"/api/tools/get_technicals\", async (c) => {\n const { symbol, timeframe } = await c.req.json();\n const result = await getTechnicals(symbol, timeframe || \"1D\");\n return c.json(result);\n});\n\napp.post(\"/api/tools/get_technicals_rating\", async (c) => {\n const { symbol, timeframe } = await c.req.json();\n const result = await getTechnicalsRating(symbol, timeframe || \"1D\");\n return c.json(result);\n});\n\napp.post(\"/api/tools/analyze_multi_timeframe\", async (c) => {\n const { symbol } = await c.req.json();\n const result = await analyzeMultiTimeframe(symbol);\n return c.json(result);\n});\n\napp.post(\"/api/tools/compute_levels\", async (c) => {\n const { symbol, timeframe } = await c.req.json();\n const result = await computeLevels(symbol, timeframe || \"1D\");\n return c.json(result);\n});\n\napp.post(\"/api/tools/get_news\", async (c) => {\n const { symbol } = await c.req.json();\n const result = await getNews(symbol);\n return c.json(result);\n});\n\napp.post(\"/api/tools/run_screener\", async (c) => {\n const { symbols, condition } = await c.req.json();\n const result = await runScreener(symbols, condition || \"\");\n return c.json(result);\n});\n\napp.post(\"/api/tools/compare_symbols\", async (c) => {\n const { symbols } = await c.req.json();\n const result = await compareSymbols(symbols);\n return c.json(result);\n});\n\napp.post(\"/api/tools/calculate_correlation\", async (c) => {\n const { symbols, timeframe } = await c.req.json();\n const result = await calculateCorrelation(symbols, timeframe || \"1D\");\n return c.json(result);\n});\n\n// ─── API: Full CNOS Cycle for a Single Symbol ────────────────────────\napp.post(\"/api/cycle/:symbol\", async (c) => {\n await initDB();\n const symbol = c.req.param(\"symbol\");\n const { timeframe } = await c.req.json().catch(() => ({ timeframe: \"4H\" }));\n const { runCNOSCycle } = await import(\"../lib/cnos.ts\");\n const settings = await getSettings();\n const result = await runCNOSCycle(settings.deskId, symbol, timeframe || \"4H\");\n return c.json(result);\n});\n\n// ─── API: Twelve Data Usage ──────────────────────────────────────────\napp.get(\"/api/twelvedata/usage\", async (c) => {\n const { tdGetApiUsage } = await import(\"../lib/twelvedata.ts\");\n const result = await tdGetApiUsage();\n return c.json(result);\n});\n\n// ─── API: Twelve Data Test ───────────────────────────────────────────\napp.post(\"/api/twelvedata/test\", async (c) => {\n const { tdGetQuote } = await import(\"../lib/twelvedata.ts\");\n const { hasTwelveData } = await import(\"../lib/twelvedata.ts\");\n const ok = await hasTwelveData();\n if (!ok) return c.json({ ok: false, error: \"No Twelve Data API keys configured\" });\n const result = await tdGetQuote(\"BTC/USD\");\n if (result.error) return c.json({ ok: false, error: result.error });\n return c.json({ ok: true, price: result.price, symbol: result.symbol, source: result.source });\n});\n\n// ─── Health Check ────────────────────────────────────────────────────\napp.get(\"/api/health\", (c) => {\n return c.json({ status: \"online\", service: \"ATN/CNOS Bot\", version: \"1.0.0\" });\n});\n\nexport default app.fetch;\n",[],[163,271],["D",1785308525726],[179],[163],["D",1785307366530],["D",1789476648181],{"_104":103,"_112":108},{"_104":103,"_105":106,"_107":108,"_19":60,"_109":110,"_111":30},{"_13":14,"_37":94,"_48":47,"_95":49,"_96":-5,"_97":-5,"_98":-5,"_99":-5,"_100":-5,"_101":50,"_102":103},"ctx",{"_87":-5,"_132":319,"_320":321,"_322":323,"_324":30,"_325":30,"_326":30,"_327":64,"_328":-5,"_329":-7,"_330":-7,"_331":68,"_332":30,"_333":30,"_334":-5,"_335":30,"_336":30,"_337":30,"_338":-5},"ENV",{"_290":291,"_292":293,"_294":94,"_295":296,"_297":298,"_299":300,"_301":302,"_303":304,"_305":306,"_307":308,"_309":310,"_311":312,"_313":314,"_315":316,"_317":318},"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"]

ATN — AGENTIC TRADING NETWORK

CNOS Cognitive Resolution Layer · Layer 6

Cognitive Resolution Cycle

Recent MarketCases

Recent Pipeline Runs

All MarketCases

Pipeline Control

Pipeline Run History

Telegram Notifications

Twelve Data API (Multiple Keys with Rotation)

Pipeline Configuration

Desk Configuration

Notification Preferences

Market Data Tools (MCP-style)

search_symbols

get_quote

get_technicals

get_technicals_rating

analyze_multi_timeframe

compute_levels

get_news

get_ohlcv

Resolved Memory Search

Diff · v30