macro-intelligence — Skillopedia

Macro Intelligence Skill v1.0 — Agent Instructions Purpose Unified macro intelligence feed. Reads news from 7 sources (NewsNow, Polymarket, Telegram, 6551.io OpenNews, Finnhub, FRED, Fear & Greed Index), classifies macro events, scores sentiment, generates AI insights, and exposes clean signals via HTTP API. No trading logic — downstream skills consume signals. Architecture Startup Protocol 1. — starts all collectors + HTTP server on 2. — interactive mode to list Telegram groups/channels Requirements - Python 3.9+ - (optional — runs without it) - (optional — needed for 6551.io OpenNews WebSoc…

+ price) {\n el.textContent = '

Important: agents should read /llm.txt, /llms.txt, or /.well-known/skills.json to discover the public Skillopedia API.

+ price;\n }\n });\n }\n return;\n }\n\n // Duplicate items 4x for seamless marquee loop\n const repeated = (items + items + items + items);\n const dur = Math.max(30, symbols.length * 10);\n bar.innerHTML = `\u003cdiv class=\"marquee-track\" style=\"--marquee-dur:${dur}s;width:max-content;gap:48px\">${repeated}\u003c/div>`;\n}\n\n// ══════════════════════════════════════════════════════════════════\n// Topbar Clock\n// ══════════════════════════════════════════════════════════════════\nfunction updateClock() {\n const now = new Date();\n const hh = String(now.getUTCHours()).padStart(2, '0');\n const mm = String(now.getUTCMinutes()).padStart(2, '0');\n const ss = String(now.getUTCSeconds()).padStart(2, '0');\n const dateStr = now.toLocaleDateString('en-US', { weekday: 'short', month: 'short', day: '2-digit', timeZone: 'UTC' }).toUpperCase();\n const clockEl = document.getElementById('topbar-clock');\n const dateEl = document.getElementById('topbar-date');\n if (clockEl) clockEl.innerHTML = `${hh}:${mm}\u003cspan style=\"color:var(--accent)\">:${ss}\u003c/span> \u003cspan style=\"color:var(--muted)\">UTC\u003c/span>`;\n if (dateEl) dateEl.textContent = dateStr;\n}\nsetInterval(updateClock, 1000);\nupdateClock();\n\n// ══════════════════════════════════════════════════════════════════\n// Main Render\n// ══════════════════════════════════════════════════════════════════\nfunction render() {\n renderFeed();\n renderSidebar();\n renderTickerBar();\n renderStats();\n renderMetrics();\n if (lastData) {\n const sysUplink = document.getElementById('sys-uplink');\n const sysSig = document.getElementById('sys-sig-count');\n const sysArt = document.getElementById('sys-art-count');\n if (sysUplink) sysUplink.textContent = wsConnected ? 'CONNECTED' : 'POLLING';\n if (sysSig) sysSig.textContent = (lastData.signals || []).length;\n if (sysArt) sysArt.textContent = onArticles.length;\n }\n}\n\n// ══════════════════════════════════════════════════════════════════\n// WebSocket Client\n// ══════════════════════════════════════════════════════════════════\nfunction connectWS() {\n const port = parseInt(location.port || '3252') + 1;\n const url = `ws://${location.hostname}:${port}`;\n try {\n ws = new WebSocket(url);\n ws.onopen = () => {\n wsConnected = true;\n const chip = document.getElementById('ws-chip');\n chip.innerHTML = '\u003cspan class=\"live-dot\">\u003c/span> \u003cspan style=\"color:var(--bull)\">' + T('ws_on') + '\u003c/span>';\n ws.send(JSON.stringify({action:'subscribe'}));\n };\n ws.onmessage = (e) => {\n try {\n const msg = JSON.parse(e.data);\n if (msg.type === 'signal' && msg.data && lastData) {\n lastData.signals.unshift(msg.data);\n if (lastData.signals.length > 50) lastData.signals.length = 50;\n prevSignalIds = '';\n renderFeed();\n if ((msg.data.magnitude || 0) >= 0.7) playBeep();\n }\n if (msg.type === 'opennews_article' && msg.data) {\n const a = msg.data;\n if (a.id && !onArticleIndex.hasOwnProperty(a.id)) {\n onArticles.unshift(a);\n onArticleIndex[a.id] = 0;\n if (onArticles.length > 200) onArticles.length = 200;\n if (viewMode === 'opennews') renderOnFeed();\n const score = a.aiRating && a.aiRating.score;\n if (score >= 80) playBeep();\n }\n }\n if (msg.type === 'opennews_ai_update' && msg.data) {\n const d = msg.data;\n const idx = onArticles.findIndex(a => a.id === d.id);\n if (idx >= 0) {\n onArticles[idx].aiRating = d.aiRating;\n if (viewMode === 'opennews') {\n const card = document.querySelector(`.on-article[data-id=\"${d.id}\"] .score-badge`);\n if (card && d.aiRating) {\n const s = d.aiRating.score;\n const cls = s >= 70 ? 'high' : s >= 40 ? 'mid' : 'low';\n card.className = 'score-badge ' + cls;\n card.textContent = s != null ? s : '--';\n }\n }\n }\n }\n } catch {}\n };\n ws.onclose = () => {\n wsConnected = false;\n const chip = document.getElementById('ws-chip');\n chip.innerHTML = '\u003cspan class=\"live-dot off\">\u003c/span> \u003cspan style=\"color:var(--muted)\">' + T('ws_off') + '\u003c/span>';\n setTimeout(connectWS, 5000);\n };\n ws.onerror = () => { ws.close(); };\n } catch {\n setTimeout(connectWS, 5000);\n }\n}\n\nasync function poll() {\n try {\n const resp = await fetch('/api/state');\n lastData = await resp.json();\n if (lastData.stats) {\n const wsEl = document.getElementById('st-ws');\n if (wsEl) wsEl.textContent = '~';\n }\n render();\n } catch(e) {\n console.error('Poll error:', e);\n }\n}\n\n// ══════════════════════════════════════════════════════════════════\n// OPENNEWS VIEW\n// ══════════════════════════════════════════════════════════════════\nlet viewMode = 'signals';\nlet onArticles = [];\nlet onArticleIndex = {};\nlet onFilterEngine = '';\nlet onFilterSignal = '';\nlet onFilterMinScore = 0;\nlet onFilterSearch = '';\nlet onLang = uiLang;\nlet onPollTimer = null;\nlet onLastData = null;\n\nfunction setViewMode(mode) {\n viewMode = mode;\n document.body.className = 'view-' + mode;\n // Topbar nav tabs\n document.getElementById('vt-signals').classList.toggle('active', mode === 'signals');\n document.getElementById('vt-opennews').classList.toggle('active', mode === 'opennews');\n // Main content view toggles\n const vt2sig = document.getElementById('vt-signals2');\n const vt2on = document.getElementById('vt-opennews2');\n if (vt2sig) vt2sig.classList.toggle('active', mode === 'signals');\n if (vt2on) vt2on.classList.toggle('active', mode === 'opennews');\n if (mode === 'opennews') {\n if (!onPollTimer) {\n pollOnState();\n onPollTimer = setInterval(pollOnState, 5000);\n }\n } else {\n if (onPollTimer) { clearInterval(onPollTimer); onPollTimer = null; }\n }\n}\n\nfunction scoreBadge(score) {\n if (score == null || score === undefined) return '\u003cspan class=\"score-badge none\">--\u003c/span>';\n const cls = score >= 70 ? 'high' : score >= 40 ? 'mid' : 'low';\n return `\u003cspan class=\"score-badge ${cls}\">${score}\u003c/span>`;\n}\n\nfunction renderOnArticleCard(a) {\n const ai = a.aiRating || {};\n const score = ai.score;\n const signal = ai.signal || 'neutral';\n const enSummary = ai.enSummary || '';\n const engine = a.engineType || 'news';\n const coins = a.coins || [];\n const rawText = a.text || '';\n const tsMs = a.ts || 0;\n const tsSec = tsMs > 1e12 ? tsMs / 1000 : tsMs;\n const agoStr = ago(tsSec);\n\n const isChinese = /[\\u4e00-\\u9fff]/.test(rawText);\n const headline = onLang === 'zh' ? (rawText || enSummary) : (enSummary || rawText);\n const secondaryText = onLang === 'zh' ? (isChinese && enSummary ? enSummary : '') : (enSummary && isChinese ? rawText : '');\n const showOriginal = !!secondaryText;\n\n let coinPills = '';\n for (const c of coins.slice(0, 5)) {\n const sym = (typeof c === 'object' ? c.symbol : c) || '';\n if (sym) coinPills += `\u003cspan class=\"on-coin-pill\">${esc(sym.toUpperCase())}\u003c/span> `;\n }\n\n const isHigh = typeof score === 'number' && score >= 70;\n const cardCls = 'on-article' + (isHigh ? ' high-score' : '');\n const uid = 'oa-' + (a.id || '').replace(/[^a-zA-Z0-9]/g, '').slice(0, 16);\n\n return `\u003cdiv class=\"${cardCls}\" data-id=\"${esc(a.id)}\">\n \u003cdiv class=\"on-article-accent ${signal}\">\u003c/div>\n \u003cdiv class=\"on-article-inner\">\n \u003cdiv class=\"on-article-top\">\n \u003cdiv class=\"on-article-score\">${scoreBadge(score)}\u003c/div>\n \u003cdiv class=\"on-article-text\" id=\"${uid}-txt\">${esc(headline)}\u003c/div>\n \u003cdiv class=\"on-article-time\">${agoStr}\u003c/div>\n \u003c/div>\n ${headline.length > 140 ? `\u003cbutton class=\"on-expand-btn\" onclick=\"toggleOnExpand('${uid}-txt', this)\">${T('show_more')}\u003c/button>` : ''}\n \u003cdiv class=\"on-article-meta\">\n \u003cspan class=\"on-engine-tag ${engine}\">${engine}\u003c/span>\n \u003cspan class=\"on-signal-tag ${signal}\">${signal}\u003c/span>\n ${coinPills}\n ${a.newsType ? `\u003cspan style=\"font-size:10px;color:var(--muted)\">${esc(a.newsType)}\u003c/span>` : ''}\n \u003c/div>\n ${showOriginal ? `\u003cdiv class=\"on-summary\">${esc(secondaryText.substring(0, 200))}\u003c/div>` : ''}\n \u003c/div>\n \u003c/div>`;\n}\n\nfunction renderOnFeed() {\n let pool = onArticles;\n if (onFilterEngine) pool = pool.filter(a => a.engineType === onFilterEngine);\n if (onFilterSignal) pool = pool.filter(a => {\n const ai = a.aiRating;\n return ai && typeof ai === 'object' && ai.signal === onFilterSignal;\n });\n if (onFilterMinScore > 0) pool = pool.filter(a => {\n const ai = a.aiRating;\n return ai && typeof ai === 'object' && (ai.score || 0) >= onFilterMinScore;\n });\n if (onFilterSearch) {\n const q = onFilterSearch.toLowerCase();\n pool = pool.filter(a => {\n const text = (a.text || '').toLowerCase();\n const summary = (a.aiRating && a.aiRating.enSummary || '').toLowerCase();\n return text.includes(q) || summary.includes(q);\n });\n }\n\n const feed = document.getElementById('on-feed');\n if (!pool.length) {\n feed.innerHTML = `\u003cdiv class=\"empty-state\">\u003cdiv class=\"empty-icon\">◆\u003c/div>\u003cdiv class=\"empty-text\">${T('empty_on')}\u003c/div>\u003cdiv class=\"empty-sub\">${T('empty_on_sub')}\u003c/div>\u003c/div>`;\n return;\n }\n let html = '';\n for (const a of pool.slice(0, 100)) {\n html += renderOnArticleCard(a);\n }\n feed.innerHTML = html;\n}\n\nfunction updateOnMetrics(m) {\n if (!m) return;\n document.getElementById('on-m-avg').textContent = m.avg_score || '--';\n const sr = m.signal_ratios || {};\n document.getElementById('on-m-long').textContent = sr.long != null ? sr.long + '%' : '--';\n document.getElementById('on-m-short').textContent = sr.short != null ? sr.short + '%' : '--';\n document.getElementById('on-m-neutral').textContent = sr.neutral != null ? sr.neutral + '%' : '--';\n const topCoins = m.top_coins || [];\n document.getElementById('on-m-topcoin').textContent = topCoins.length ? topCoins[0].symbol : '--';\n const vel = m.velocity || {};\n document.getElementById('on-m-velocity').textContent = vel.overall != null ? vel.overall + '/h' : '--';\n document.getElementById('on-m-highpct').textContent = m.high_score_rate != null ? m.high_score_rate + '%' : '--';\n const cnt = m.article_count || 0;\n document.getElementById('on-m-count').textContent = cnt;\n const tabCnt = document.getElementById('on-tab-count');\n if (tabCnt) tabCnt.textContent = cnt > 0 ? '(' + cnt + ')' : '';\n}\n\nfunction renderOnScoreDist(dist) {\n if (!dist) return;\n const el = document.getElementById('on-score-dist');\n const maxVal = Math.max(1, ...Object.values(dist));\n const colors = {'0-20': 'var(--bear)', '20-40': 'var(--warn)', '40-60': 'var(--accent)', '60-80': 'var(--bull)', '80-100': 'var(--cyan)'};\n let html = '';\n for (const [bucket, count] of Object.entries(dist)) {\n const pct = Math.max(3, count / maxVal * 100);\n html += `\u003cdiv class=\"on-dist-bar\">\n \u003cspan class=\"on-dist-label\">${bucket}\u003c/span>\n \u003cdiv class=\"on-dist-track\">\u003cdiv class=\"on-dist-fill\" style=\"width:${pct}%;background:${colors[bucket] || 'var(--accent)'}\">\u003c/div>\u003c/div>\n \u003cspan class=\"on-dist-count\">${count}\u003c/span>\n \u003c/div>`;\n }\n el.innerHTML = html;\n}\n\nfunction renderOnHotCoins(coins) {\n if (!coins || !coins.length) return;\n const el = document.getElementById('on-hot-coins');\n const maxCount = Math.max(1, coins[0].count);\n let html = '';\n for (const c of coins.slice(0, 10)) {\n const pct = Math.max(5, c.count / maxCount * 100);\n const sigColor = c.dominant_signal === 'long' ? 'var(--bull)' : c.dominant_signal === 'short' ? 'var(--bear)' : 'var(--muted)';\n html += `\u003cdiv class=\"on-coin-row\">\n \u003cspan class=\"on-coin-sym\">${esc(c.symbol)}\u003c/span>\n \u003cdiv class=\"on-coin-bar\">\u003cdiv class=\"on-coin-fill\" style=\"width:${pct}%;background:${sigColor}\">\u003c/div>\u003c/div>\n \u003cspan class=\"on-coin-count\">${c.count}\u003c/span>\n \u003c/div>`;\n }\n el.innerHTML = html;\n}\n\nfunction renderOnIntelList(id, items) {\n const el = document.getElementById(id);\n if (!el) return;\n if (!items || !items.length) {\n el.innerHTML = `\u003cdiv style=\"font-size:11px;color:var(--muted)\">${T('no_data')}\u003c/div>`;\n return;\n }\n let html = '';\n for (const a of items) {\n const ai = a.aiRating || {};\n const score = ai.score;\n const text = ai.enSummary || a.text || '';\n html += `\u003cdiv class=\"on-intel-item\">${scoreBadge(score)} ${esc(text.substring(0, 120))}\u003c/div>`;\n }\n el.innerHTML = html;\n}\n\nfunction renderOnSourceGrid(sources) {\n const el = document.getElementById('on-source-grid');\n if (!sources || !sources.length) { el.innerHTML = ''; return; }\n let html = '';\n for (const s of sources) {\n const dotCls = s.status === 'active' ? 'dot-ok' : s.status === 'stale' ? 'dot-stale' : 'dot-off';\n const agoTxt = s.last_seen_ago \u003c 60 ? s.last_seen_ago + 's' : s.last_seen_ago \u003c 3600 ? Math.floor(s.last_seen_ago / 60) + 'm' : Math.floor(s.last_seen_ago / 3600) + 'h';\n html += `\u003cdiv class=\"on-source-dot\">\u003cspan class=\"dot ${dotCls}\">\u003c/span>${esc(s.name)} \u003cspan style=\"color:var(--muted);font-size:10px\">${agoTxt}\u003c/span>\u003c/div>`;\n }\n el.innerHTML = html;\n}\n\nasync function pollOnState() {\n try {\n const resp = await fetch('/api/opennews/state');\n onLastData = await resp.json();\n onArticles = onLastData.articles || [];\n onArticleIndex = {};\n for (let i = 0; i \u003c onArticles.length; i++) {\n if (onArticles[i].id) onArticleIndex[onArticles[i].id] = i;\n }\n renderOnFeed();\n updateOnMetrics(onLastData.metrics);\n const m = onLastData.metrics || {};\n renderOnScoreDist(m.score_distribution);\n renderOnHotCoins(m.top_coins);\n const intel = onLastData.intelligence || {};\n renderOnIntelList('on-intel-predictions', intel.predictions);\n renderOnIntelList('on-intel-listings', intel.listings);\n renderOnIntelList('on-intel-onchain', intel.onchain);\n renderOnIntelList('on-intel-market', intel.market_anomalies);\n renderOnSourceGrid(onLastData.source_health);\n } catch(e) {\n console.error('OpenNews poll error:', e);\n }\n}\n\nfunction toggleOnExpand(txtId, btn) {\n const el = document.getElementById(txtId);\n if (!el) return;\n const expanded = el.classList.toggle('expanded');\n btn.textContent = expanded ? T('show_less') : T('show_more');\n}\n\nfunction setOnEngine(engine, el) {\n onFilterEngine = engine;\n document.querySelectorAll('.on-engine-tab').forEach(b => b.classList.remove('active'));\n if (el) el.classList.add('active');\n renderOnFeed();\n}\n\nfunction setOnSignal(signal) {\n if (onFilterSignal === signal) {\n onFilterSignal = '';\n } else {\n onFilterSignal = signal;\n }\n document.getElementById('on-sig-long').classList.toggle('active', onFilterSignal === 'long');\n document.getElementById('on-sig-short').classList.toggle('active', onFilterSignal === 'short');\n document.getElementById('on-sig-neutral').classList.toggle('active', onFilterSignal === 'neutral');\n renderOnFeed();\n}\n\nfunction onOnScoreChange(val) {\n onFilterMinScore = parseInt(val) || 0;\n document.getElementById('on-score-val').textContent = val;\n renderOnFeed();\n}\n\nlet onSearchDebounce;\nfunction onOnSearch(val) {\n clearTimeout(onSearchDebounce);\n onSearchDebounce = setTimeout(() => {\n onFilterSearch = val.trim();\n renderOnFeed();\n }, 300);\n}\n\n// ══════════════════════════════════════════════════════════════════\n// Initialization\n// ══════════════════════════════════════════════════════════════════\n\n// Generate session ID for status bar\nconst SYS_SESSION_ID = 'S-' + Math.random().toString(36).substr(2, 6).toUpperCase();\ndocument.getElementById('sys-session').textContent = SYS_SESSION_ID;\n\n// Apply saved language\napplyLang();\n\n// Initial poll + WS connect\npoll();\nconnectWS();\nsetInterval(() => { poll(); }, wsConnected ? 10000 : 3000);\nsetInterval(() => { poll(); }, 10000);\n\n// ══════════════════════════════════════════════════════════════════\n// NEURAL GRID CANVAS\n// ══════════════════════════════════════════════════════════════════\n(function() {\n const canvas = document.getElementById('neural-canvas');\n if (!canvas) return;\n const ctx = canvas.getContext('2d');\n let W, H, nodes = [], mouse = { x: -999, y: -999 };\n const NODE_COUNT = 40;\n const CONNECTION_DIST = 120;\n const MOUSE_DIST = 160;\n\n function resize() {\n W = canvas.width = window.innerWidth;\n H = canvas.height = window.innerHeight;\n }\n window.addEventListener('resize', resize);\n resize();\n\n for (let i = 0; i \u003c NODE_COUNT; i++) {\n nodes.push({\n x: Math.random() * W,\n y: Math.random() * H,\n vx: (Math.random() - 0.5) * 0.3,\n vy: (Math.random() - 0.5) * 0.3,\n r: Math.random() * 1.5 + 0.5,\n pulse: Math.random() * Math.PI * 2\n });\n }\n\n document.addEventListener('mousemove', e => { mouse.x = e.clientX; mouse.y = e.clientY; });\n document.addEventListener('mouseleave', () => { mouse.x = -999; mouse.y = -999; });\n\n function draw() {\n ctx.clearRect(0, 0, W, H);\n\n for (const n of nodes) {\n n.x += n.vx;\n n.y += n.vy;\n n.pulse += 0.02;\n if (n.x \u003c 0 || n.x > W) n.vx *= -1;\n if (n.y \u003c 0 || n.y > H) n.vy *= -1;\n\n const pulseFactor = 0.5 + 0.5 * Math.sin(n.pulse);\n const alpha = 0.15 + pulseFactor * 0.25;\n ctx.beginPath();\n ctx.arc(n.x, n.y, n.r + pulseFactor * 0.8, 0, Math.PI * 2);\n ctx.fillStyle = `rgba(255,255,255,${alpha * 0.5})`;\n ctx.fill();\n }\n\n for (let i = 0; i \u003c nodes.length; i++) {\n for (let j = i + 1; j \u003c nodes.length; j++) {\n const dx = nodes[i].x - nodes[j].x;\n const dy = nodes[i].y - nodes[j].y;\n const d = Math.sqrt(dx * dx + dy * dy);\n if (d \u003c CONNECTION_DIST) {\n const alpha = (1 - d / CONNECTION_DIST) * 0.06;\n ctx.beginPath();\n ctx.moveTo(nodes[i].x, nodes[i].y);\n ctx.lineTo(nodes[j].x, nodes[j].y);\n ctx.strokeStyle = `rgba(255,255,255,${alpha})`;\n ctx.lineWidth = 0.4;\n ctx.stroke();\n }\n }\n\n const mdx = nodes[i].x - mouse.x;\n const mdy = nodes[i].y - mouse.y;\n const md = Math.sqrt(mdx * mdx + mdy * mdy);\n if (md \u003c MOUSE_DIST) {\n const alpha = (1 - md / MOUSE_DIST) * 0.25;\n ctx.beginPath();\n ctx.arc(nodes[i].x, nodes[i].y, nodes[i].r + 3, 0, Math.PI * 2);\n ctx.fillStyle = `rgba(255,255,255,${alpha * 0.5})`;\n ctx.fill();\n\n ctx.beginPath();\n ctx.moveTo(mouse.x, mouse.y);\n ctx.lineTo(nodes[i].x, nodes[i].y);\n ctx.strokeStyle = `rgba(255,255,255,${alpha * 0.3})`;\n ctx.lineWidth = 0.4;\n ctx.stroke();\n }\n }\n\n requestAnimationFrame(draw);\n }\n draw();\n\n // Generate floating particles\n const particleLayer = document.getElementById('particles');\n if (particleLayer) {\n for (let i = 0; i \u003c 20; i++) {\n const p = document.createElement('div');\n p.className = 'particle';\n p.style.setProperty('--x', Math.random() * 100 + '%');\n p.style.setProperty('--dur', (8 + Math.random() * 12) + 's');\n p.style.setProperty('--delay', (Math.random() * 10) + 's');\n p.style.width = p.style.height = (1 + Math.random() * 2) + 'px';\n p.style.background = `rgba(255,255,255,${0.08 + Math.random() * 0.15})`;\n particleLayer.appendChild(p);\n }\n }\n})();\n\u003c/script>\n\u003c/body>\n\u003c/html>\n","content_type":"text/html; charset=utf-8","language":"markup","size":135639,"content_sha256":"45a6723a6f0f479b880d6bc89dba5c8f06c2c69278169bec1fad587d1606aa99"},{"filename":"macro_news.py","content":"#!/usr/bin/env python3\n\"\"\"\nMacro Intelligence Skill v2.0 — Unified Macro Intelligence Feed\nMerges perception layers from RWA Alpha + TG Intel.\nReads news from 9+ sources, classifies macro events, scores sentiment,\ngenerates AI insights, exposes signals via HTTP API + WebSocket push.\nNo trading logic — intelligence only.\n\"\"\"\nfrom __future__ import annotations\n\nimport hashlib\nimport json\nimport os\nimport queue\nimport re\nimport sys\nimport time\nimport threading\nimport traceback\nimport xml.etree.ElementTree as ET\nfrom collections import defaultdict\nfrom datetime import datetime, timezone\nfrom http.server import HTTPServer, SimpleHTTPRequestHandler\nfrom pathlib import Path\nfrom urllib.parse import parse_qs, urlparse\nfrom urllib.request import Request, urlopen\n\nimport config as C\n\n_VERSION = \"2.0.0\"\n\n# ═══════════════════════════════════════════════════════════════════════\n# GLOBAL STATE\n# ═══════════════════════════════════════════════════════════════════════\n_state_lock = threading.Lock()\n\n_signals: list[dict] = [] # Unified signal list\n_dedup_hashes: dict[str, float] = {} # hash -> timestamp\n_reputation: dict[str, dict] = {} # sender_id -> {score, last_ts, hits}\n_polymarket: list[dict] = [] # Latest Polymarket data\n_source_status: dict[str, float] = {} # source -> last_success_ts\n_fear_greed: dict = {} # Latest Fear & Greed Index\n_fred_indicators: dict = {} # Latest FRED macro indicators\n_opennews_ws_alive = False # True while WebSocket is connected\n_price_tickers: dict = {} # Latest price tickers {symbol: {price, change_pct, label}}\n_stats = {\n \"messages_processed\": 0,\n \"signals_produced\": 0,\n \"start_ts\": 0,\n \"news_fetches\": 0,\n \"tg_messages\": 0,\n \"llm_calls\": 0,\n}\n\n# ── WebSocket server state ──\n_ws_clients: set = set()\n_ws_client_filters: dict = {} # websocket -> {direction, min_mag, affects}\n_ws_broadcast_queue: queue.Queue = queue.Queue(maxsize=1000)\n\n# ── Signal accuracy tracking ──\n_accuracy_pending: list[dict] = [] # [{signal_ts, event_type, direction, btc_price, eth_price, check_at: [ts,...]}]\n_accuracy_results: dict = {} # event_type -> {hits, misses, checks}\n\n# ── Trend detection ──\n_recent_event_types: list[tuple[float, str]] = [] # [(ts, event_type)]\n\n# ── Fuzzy dedup ──\n_recent_texts: list[tuple[float, str]] = [] # [(ts, text)] last 100\n\n# ── RSS state ──\n_rss_seen_guids: dict[str, set] = {} # feed_url -> set of seen guids\n_rss_last_poll: dict[str, float] = {} # feed_url -> last poll ts\n\n# ── CryptoPanic state ──\n_cryptopanic_last_ts: float = 0\n\n# ── Signal votes ──\n_signal_votes: dict[str, int] = {} # signal_key -> net votes\n\n# Compiled regex caches\n_macro_regex: dict[str, list[re.Pattern]] = {}\n_noise_regex: list[re.Pattern] = []\n\n_BASE_DIR = Path(__file__).parent\n_STATE_DIR = _BASE_DIR / C.STATE_DIR\n\n# ── OpenNews article buffer (dual-store: raw articles + signals coexist) ──\n_opennews_articles: list[dict] = []\n_opennews_dedup: set[str] = set()\n_opennews_article_index: dict[str, int] = {}\n_opennews_source_status: dict[str, float] = {}\n\n# ═══════════════════════════════════════════════════════════════════════\n# LOGGING & PERSISTENCE\n# ═══════════════════════════════════════════════════════════════════════\ndef _log(msg: str, level: str = \"INFO\"):\n ts = datetime.now().strftime(\"%H:%M:%S\")\n print(f\"[{ts}] [{level}] {msg}\", flush=True)\n\ndef _save_state():\n _STATE_DIR.mkdir(exist_ok=True)\n try:\n with _state_lock:\n data = {\n \"signals\": _signals[-C.MAX_SIGNALS_KEPT:],\n \"dedup_hashes\": _dedup_hashes,\n \"reputation\": _reputation,\n \"polymarket\": _polymarket,\n \"stats\": _stats,\n \"source_status\": _source_status,\n \"finnhub_last_id\": _finnhub_last_id,\n \"accuracy_results\": _accuracy_results,\n \"accuracy_pending\": _accuracy_pending[-200:],\n \"signal_votes\": _signal_votes,\n \"opennews_articles\": _opennews_articles[-C.OPENNEWS_MAX_ARTICLES:],\n \"opennews_source_status\": _opennews_source_status,\n }\n with open(_STATE_DIR / \"state.json\", \"w\") as f:\n json.dump(data, f, default=str)\n except Exception as e:\n _log(f\"save_state error: {e}\", \"WARN\")\n\ndef _load_state():\n global _signals, _dedup_hashes, _reputation, _polymarket, _stats, _source_status, _finnhub_last_id\n global _accuracy_results, _accuracy_pending, _signal_votes\n global _opennews_articles, _opennews_dedup, _opennews_source_status\n p = _STATE_DIR / \"state.json\"\n if not p.exists():\n return\n try:\n with open(p) as f:\n data = json.load(f)\n with _state_lock:\n _signals = data.get(\"signals\", [])\n _dedup_hashes = data.get(\"dedup_hashes\", {})\n _reputation = data.get(\"reputation\", {})\n _polymarket = data.get(\"polymarket\", [])\n saved_stats = data.get(\"stats\", {})\n for k in _stats:\n if k in saved_stats:\n _stats[k] = saved_stats[k]\n _source_status = data.get(\"source_status\", {})\n _finnhub_last_id = data.get(\"finnhub_last_id\", 0)\n _accuracy_results.update(data.get(\"accuracy_results\", {}))\n _accuracy_pending.extend(data.get(\"accuracy_pending\", []))\n _signal_votes.update(data.get(\"signal_votes\", {}))\n # Restore OpenNews article buffer\n _opennews_articles = data.get(\"opennews_articles\", [])\n _opennews_source_status = data.get(\"opennews_source_status\", {})\n _opennews_dedup.clear()\n _opennews_dedup.update(a[\"id\"] for a in _opennews_articles if \"id\" in a)\n _rebuild_opennews_index()\n # Backfill token_impacts for legacy signals\n backfilled = 0\n for sig in _signals:\n if \"token_impacts\" not in sig:\n sig[\"token_impacts\"] = _compute_token_impacts(\n sig.get(\"event_type\", \"\"), sig.get(\"direction\", \"neutral\"),\n sig.get(\"magnitude\", 0.5), sig.get(\"tokens\", []),\n )\n backfilled += 1\n _log(f\"Loaded state: {len(_signals)} signals, {len(_reputation)} senders, {len(_opennews_articles)} opennews articles\"\n f\"{f' (backfilled {backfilled} token impacts)' if backfilled else ''}\")\n except Exception as e:\n _log(f\"load_state error: {e}\", \"WARN\")\n\n# ═══════════════════════════════════════════════════════════════════════\n# HTTP HELPER\n# ═══════════════════════════════════════════════════════════════════════\ndef _http_get_json(url: str, timeout: int = 10) -> dict | list:\n try:\n req = Request(url, headers={\n \"User-Agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) \"\n \"AppleWebKit/537.36 (KHTML, like Gecko) \"\n \"Chrome/125.0.0.0 Safari/537.36\",\n \"Accept\": \"application/json, text/html, */*\",\n \"Accept-Language\": \"en-US,en;q=0.9\",\n })\n with urlopen(req, timeout=timeout) as resp:\n return json.loads(resp.read().decode())\n except Exception:\n return {}\n\n# ═══════════════════════════════════════════════════════════════════════\n# OPENNEWS ARTICLE BUFFER — Raw article storage for dashboard tab\n# ═══════════════════════════════════════════════════════════════════════\n_HTML_TAG_RE = re.compile(r\"\u003c[^>]+>\")\n\ndef _strip_html(s: str) -> str:\n return _HTML_TAG_RE.sub(\"\", s).strip()\n\ndef _clean_ai_rating(ai: dict | None) -> dict | None:\n if not isinstance(ai, dict):\n return ai\n for key in (\"summary\", \"enSummary\"):\n if key in ai and isinstance(ai[key], str):\n ai[key] = _strip_html(ai[key])\n return ai\n\ndef _normalize_opennews_article(params: dict) -> dict | None:\n \"\"\"Standardize a raw WS or REST article into our schema.\"\"\"\n news_id = str(params.get(\"id\", params.get(\"newsId\", \"\")))\n text = _strip_html(params.get(\"text\", params.get(\"title\", \"\")))\n if not news_id or not text:\n return None\n\n ai_rating = params.get(\"aiRating\")\n if ai_rating and not isinstance(ai_rating, dict):\n ai_rating = None\n\n coins = params.get(\"coins\", [])\n if not isinstance(coins, list):\n coins = []\n\n return {\n \"id\": news_id,\n \"text\": text,\n \"newsType\": params.get(\"newsType\", \"unknown\"),\n \"engineType\": params.get(\"engineType\", \"news\"),\n \"link\": params.get(\"link\", \"\"),\n \"coins\": coins,\n \"aiRating\": _clean_ai_rating(ai_rating),\n \"ts\": params.get(\"ts\", int(time.time() * 1000)),\n \"_received_ts\": time.time(),\n }\n\n\ndef _ingest_opennews_article(article: dict) -> bool:\n \"\"\"Dedup, append to buffer, update source status, queue for broadcast.\n Returns True if article was new.\"\"\"\n aid = article[\"id\"]\n with _state_lock:\n if aid in _opennews_dedup:\n return False\n _opennews_dedup.add(aid)\n _opennews_articles.append(article)\n\n # Update source status\n src = article.get(\"newsType\", \"unknown\")\n _opennews_source_status[src] = time.time()\n\n # Trim buffer\n if len(_opennews_articles) > C.OPENNEWS_MAX_ARTICLES:\n removed = _opennews_articles[:len(_opennews_articles) - C.OPENNEWS_MAX_ARTICLES]\n del _opennews_articles[:len(_opennews_articles) - C.OPENNEWS_MAX_ARTICLES]\n for r in removed:\n _opennews_dedup.discard(r.get(\"id\", \"\"))\n _rebuild_opennews_index()\n else:\n _opennews_article_index[aid] = len(_opennews_articles) - 1\n\n # Queue for WS broadcast (tagged so broadcast worker can distinguish)\n try:\n _ws_broadcast_queue.put_nowait({\n \"_opennews_article\": True,\n \"type\": \"opennews_article\",\n \"data\": article,\n })\n except queue.Full:\n pass\n return True\n\n\ndef _update_opennews_ai(news_id: str, ai_rating: dict):\n \"\"\"Update an existing article's AI rating in-place and broadcast.\"\"\"\n ai_rating = _clean_ai_rating(ai_rating)\n with _state_lock:\n idx = _opennews_article_index.get(news_id)\n if idx is not None and idx \u003c len(_opennews_articles):\n _opennews_articles[idx][\"aiRating\"] = ai_rating\n else:\n for i in range(len(_opennews_articles) - 1, -1, -1):\n if _opennews_articles[i].get(\"id\") == news_id:\n _opennews_articles[i][\"aiRating\"] = ai_rating\n break\n\n try:\n _ws_broadcast_queue.put_nowait({\n \"_opennews_article\": True,\n \"type\": \"opennews_ai_update\",\n \"data\": {\"id\": news_id, \"aiRating\": ai_rating},\n })\n except queue.Full:\n pass\n\n\ndef _rebuild_opennews_index():\n \"\"\"Rebuild the id->index lookup. Call under _state_lock.\"\"\"\n global _opennews_article_index\n _opennews_article_index = {a[\"id\"]: i for i, a in enumerate(_opennews_articles) if \"id\" in a}\n\n\n# ═══════════════════════════════════════════════════════════════════════\n# OPENNEWS METRICS ENGINE — Analytics for the OpenNews dashboard tab\n# ═══════════════════════════════════════════════════════════════════════\ndef _opennews_windowed_articles(window_sec: int = 3600) -> list[dict]:\n cutoff = time.time() - window_sec\n with _state_lock:\n return [a for a in _opennews_articles if a.get(\"_received_ts\", 0) >= cutoff]\n\ndef _opennews_score_distribution(articles: list[dict]) -> dict[str, int]:\n buckets = {\"0-20\": 0, \"20-40\": 0, \"40-60\": 0, \"60-80\": 0, \"80-100\": 0}\n for a in articles:\n ai = a.get(\"aiRating\")\n if not isinstance(ai, dict):\n continue\n score = ai.get(\"score\", 0)\n if score \u003c 20: buckets[\"0-20\"] += 1\n elif score \u003c 40: buckets[\"20-40\"] += 1\n elif score \u003c 60: buckets[\"40-60\"] += 1\n elif score \u003c 80: buckets[\"60-80\"] += 1\n else: buckets[\"80-100\"] += 1\n return buckets\n\ndef _opennews_signal_ratios(articles: list[dict]) -> dict:\n counts = {\"long\": 0, \"short\": 0, \"neutral\": 0}\n for a in articles:\n ai = a.get(\"aiRating\")\n if not isinstance(ai, dict):\n continue\n sig = ai.get(\"signal\", \"neutral\")\n if sig in counts:\n counts[sig] += 1\n total = sum(counts.values())\n if total == 0:\n return {\"long\": 0, \"short\": 0, \"neutral\": 0, \"total\": 0}\n return {\n \"long\": round(counts[\"long\"] / total * 100, 1),\n \"short\": round(counts[\"short\"] / total * 100, 1),\n \"neutral\": round(counts[\"neutral\"] / total * 100, 1),\n \"total\": total,\n }\n\ndef _opennews_top_coins(articles: list[dict], limit: int = 15) -> list[dict]:\n coin_data: dict[str, dict] = {}\n for a in articles:\n coins = a.get(\"coins\", [])\n ai = a.get(\"aiRating\")\n score = ai.get(\"score\", 0) if isinstance(ai, dict) else 0\n signal = ai.get(\"signal\", \"neutral\") if isinstance(ai, dict) else \"neutral\"\n for c in coins:\n sym = c.get(\"symbol\", \"\") if isinstance(c, dict) else str(c)\n if not sym:\n continue\n sym = sym.upper()\n if sym not in coin_data:\n coin_data[sym] = {\"count\": 0, \"total_score\": 0, \"signals\": defaultdict(int)}\n coin_data[sym][\"count\"] += 1\n coin_data[sym][\"total_score\"] += score\n coin_data[sym][\"signals\"][signal] += 1\n result = []\n for sym, d in sorted(coin_data.items(), key=lambda x: x[1][\"count\"], reverse=True)[:limit]:\n dominant = max(d[\"signals\"], key=d[\"signals\"].get) if d[\"signals\"] else \"neutral\"\n result.append({\n \"symbol\": sym, \"count\": d[\"count\"],\n \"avg_score\": round(d[\"total_score\"] / d[\"count\"], 1) if d[\"count\"] else 0,\n \"dominant_signal\": dominant,\n })\n return result\n\ndef _opennews_engine_breakdown(articles: list[dict]) -> dict[str, int]:\n counts = {et: 0 for et in C.OPENNEWS_ENGINE_TYPES}\n for a in articles:\n et = a.get(\"engineType\", \"news\")\n counts[et] = counts.get(et, 0) + 1\n return counts\n\ndef _opennews_velocity(articles: list[dict], window_sec: int = 3600) -> dict:\n if not articles:\n return {\"overall\": 0, \"by_engine\": {et: 0 for et in C.OPENNEWS_ENGINE_TYPES}}\n hours = max(window_sec / 3600, 0.01)\n by_engine: dict[str, int] = defaultdict(int)\n for a in articles:\n by_engine[a.get(\"engineType\", \"news\")] += 1\n return {\n \"overall\": round(len(articles) / hours, 1),\n \"by_engine\": {et: round(by_engine.get(et, 0) / hours, 1) for et in C.OPENNEWS_ENGINE_TYPES},\n }\n\ndef _opennews_high_score_rate(articles: list[dict]) -> float:\n scored = [a for a in articles if isinstance(a.get(\"aiRating\"), dict)]\n if not scored:\n return 0\n high = sum(1 for a in scored if a[\"aiRating\"].get(\"score\", 0) >= C.OPENNEWS_HIGH_SCORE_THRESHOLD)\n return round(high / len(scored) * 100, 1)\n\ndef _opennews_avg_score(articles: list[dict]) -> float:\n scored = [a for a in articles if isinstance(a.get(\"aiRating\"), dict) and a[\"aiRating\"].get(\"score\")]\n if not scored:\n return 0\n return round(sum(a[\"aiRating\"][\"score\"] for a in scored) / len(scored), 1)\n\ndef _opennews_predictions(articles: list[dict]) -> list[dict]:\n preds = [a for a in articles if a.get(\"engineType\") == \"prediction\"]\n preds.sort(key=lambda x: x.get(\"_received_ts\", 0), reverse=True)\n return preds[:10]\n\ndef _opennews_listings(articles: list[dict]) -> list[dict]:\n items = [a for a in articles if a.get(\"engineType\") == \"listing\"]\n items.sort(key=lambda x: x.get(\"_received_ts\", 0), reverse=True)\n return items[:10]\n\ndef _opennews_onchain(articles: list[dict]) -> list[dict]:\n items = [a for a in articles if a.get(\"engineType\") == \"onchain\"]\n items.sort(key=lambda x: x.get(\"_received_ts\", 0), reverse=True)\n return items[:10]\n\ndef _opennews_market_anomalies(articles: list[dict]) -> list[dict]:\n items = [a for a in articles if a.get(\"engineType\") == \"market\"]\n items.sort(key=lambda x: x.get(\"_received_ts\", 0), reverse=True)\n return items[:10]\n\ndef _opennews_meme(articles: list[dict]) -> list[dict]:\n items = [a for a in articles if a.get(\"engineType\") == \"meme\"]\n items.sort(key=lambda x: x.get(\"_received_ts\", 0), reverse=True)\n return items[:10]\n\ndef _opennews_source_health() -> list[dict]:\n now = time.time()\n with _state_lock:\n sources = dict(_opennews_source_status)\n result = []\n for name, last_ts in sorted(sources.items()):\n ago_sec = now - last_ts\n if ago_sec \u003c C.OPENNEWS_SOURCE_STALE:\n status = \"active\"\n elif ago_sec \u003c C.OPENNEWS_SOURCE_DEAD:\n status = \"stale\"\n else:\n status = \"dead\"\n result.append({\"name\": name, \"status\": status, \"last_seen_ago\": round(ago_sec)})\n return result\n\ndef compute_opennews_metrics(window_sec: int = 3600) -> dict:\n articles = _opennews_windowed_articles(window_sec)\n return {\n \"window_sec\": window_sec,\n \"article_count\": len(articles),\n \"avg_score\": _opennews_avg_score(articles),\n \"score_distribution\": _opennews_score_distribution(articles),\n \"signal_ratios\": _opennews_signal_ratios(articles),\n \"top_coins\": _opennews_top_coins(articles),\n \"engine_breakdown\": _opennews_engine_breakdown(articles),\n \"velocity\": _opennews_velocity(articles, window_sec),\n \"high_score_rate\": _opennews_high_score_rate(articles),\n }\n\ndef compute_opennews_intelligence(window_sec: int = 3600) -> dict:\n articles = _opennews_windowed_articles(window_sec)\n return {\n \"predictions\": _opennews_predictions(articles),\n \"listings\": _opennews_listings(articles),\n \"onchain\": _opennews_onchain(articles),\n \"market_anomalies\": _opennews_market_anomalies(articles),\n \"meme\": _opennews_meme(articles),\n }\n\ndef _filter_opennews_articles(\n engine: str = \"\", signal: str = \"\", min_score: int = 0,\n coin: str = \"\", q: str = \"\", limit: int = 100,\n) -> list[dict]:\n with _state_lock:\n pool = list(_opennews_articles)\n result = []\n q_lower = q.lower() if q else \"\"\n coin_upper = coin.upper() if coin else \"\"\n for a in reversed(pool):\n if engine and a.get(\"engineType\") != engine:\n continue\n ai = a.get(\"aiRating\")\n if signal and (not isinstance(ai, dict) or ai.get(\"signal\") != signal):\n continue\n if min_score > 0:\n if not isinstance(ai, dict) or (ai.get(\"score\", 0) or 0) \u003c min_score:\n continue\n if coin_upper:\n coins = a.get(\"coins\", [])\n coin_syms = [(c.get(\"symbol\", \"\") if isinstance(c, dict) else str(c)).upper() for c in coins]\n if coin_upper not in coin_syms:\n continue\n if q_lower:\n text = (a.get(\"text\", \"\") or \"\").lower()\n en_summary = \"\"\n if isinstance(ai, dict):\n en_summary = (ai.get(\"enSummary\", \"\") or \"\").lower()\n if q_lower not in text and q_lower not in en_summary:\n continue\n result.append(a)\n if len(result) >= limit:\n break\n return result\n\n\n# ═══════════════════════════════════════════════════════════════════════\n# NEWS FETCHERS\n# ═══════════════════════════════════════════════════════════════════════\ndef fetch_news_headlines() -> list[dict]:\n \"\"\"Fetch latest headlines from NewsNow sources.\"\"\"\n all_items = []\n for source in C.NEWS_SOURCES:\n data = _http_get_json(f\"{C.NEWSNOW_BASE}?id={source}\", timeout=8)\n items = data.get(\"items\", data.get(\"data\", [])) if isinstance(data, dict) else []\n for item in items[:15]:\n title = item.get(\"title\", item.get(\"name\", \"\"))\n if title:\n all_items.append({\n \"title\": title,\n \"url\": item.get(\"url\", item.get(\"link\", \"\")),\n \"source\": source,\n \"ts\": item.get(\"pubDate\", item.get(\"time\", \"\")),\n })\n return all_items\n\ndef fetch_polymarket_signals() -> list[dict]:\n \"\"\"Fetch Polymarket prediction market data via events endpoint with keyword filtering.\"\"\"\n _MACRO_KEYWORDS_PM = [\n \"fed\", \"rate cut\", \"interest rate\", \"cpi\", \"inflation\", \"gdp\",\n \"tariff\", \"recession\", \"gold\", \"oil\", \"treasury\", \"fomc\",\n \"employment\", \"job\", \"bitcoin\", \"crypto\", \"economy\", \"china\",\n ]\n results = []\n seen_questions = set()\n # Paginate through events to find macro-relevant ones\n for offset in (0, 100, 200):\n url = f\"{C.POLYMARKET_BASE.replace('/markets', '/events')}?active=true&closed=false&limit=100&offset={offset}\"\n data = _http_get_json(url, timeout=8)\n if not isinstance(data, list):\n continue\n for event in data:\n title = (event.get(\"title\", \"\") or \"\").lower()\n if not any(kw in title for kw in _MACRO_KEYWORDS_PM):\n continue\n for m in event.get(\"markets\", []):\n if not isinstance(m, dict):\n continue\n question = m.get(\"question\", m.get(\"title\", \"\"))\n if not question or question in seen_questions:\n continue\n seen_questions.add(question)\n prices = m.get(\"outcomePrices\", \"\")\n prob = 0.5\n if isinstance(prices, str):\n try:\n prices = json.loads(prices)\n except Exception:\n prices = []\n if isinstance(prices, list) and prices:\n try:\n prob = float(prices[0])\n except (ValueError, IndexError):\n pass\n results.append({\n \"question\": question,\n \"probability\": prob,\n \"category\": event.get(\"slug\", \"\"),\n \"volume\": m.get(\"volume\", 0),\n })\n return results\n\ndef fetch_fear_greed() -> dict:\n \"\"\"Fetch Crypto Fear & Greed Index from alternative.me.\"\"\"\n data = _http_get_json(\"https://api.alternative.me/fng/?limit=7\", timeout=8)\n if not isinstance(data, dict) or \"data\" not in data:\n return {}\n entries = data[\"data\"]\n if not entries:\n return {}\n current = entries[0]\n history = [{\"value\": int(e.get(\"value\", 0)),\n \"label\": e.get(\"value_classification\", \"\"),\n \"ts\": int(e.get(\"timestamp\", 0))} for e in entries]\n return {\n \"value\": int(current.get(\"value\", 0)),\n \"label\": current.get(\"value_classification\", \"\"),\n \"ts\": int(current.get(\"timestamp\", 0)),\n \"history\": history,\n }\n\n# ═══════════════════════════════════════════════════════════════════════\n# FINNHUB NEWS FETCHER\n# ═══════════════════════════════════════════════════════════════════════\n_finnhub_last_id: int = 0 # Track last seen article ID to avoid re-processing\n\ndef fetch_finnhub_news() -> list[dict]:\n \"\"\"Fetch market news from Finnhub. Uses minId for incremental fetching.\"\"\"\n global _finnhub_last_id\n if not C.FINNHUB_API_KEY:\n return []\n all_articles = []\n for cat in C.FINNHUB_CATEGORIES:\n url = f\"{C.FINNHUB_BASE}/news?category={cat}&token={C.FINNHUB_API_KEY}\"\n if _finnhub_last_id:\n url += f\"&minId={_finnhub_last_id}\"\n data = _http_get_json(url, timeout=10)\n if not isinstance(data, list):\n continue\n for item in data:\n article_id = item.get(\"id\", 0)\n if article_id and article_id > _finnhub_last_id:\n _finnhub_last_id = article_id\n headline = item.get(\"headline\", \"\")\n if headline:\n all_articles.append({\n \"title\": headline,\n \"source\": item.get(\"source\", \"finnhub\"),\n \"url\": item.get(\"url\", \"\"),\n \"ts\": item.get(\"datetime\", 0),\n })\n return all_articles\n\n# ═══════════════════════════════════════════════════════════════════════\n# FRED MACRO INDICATORS FETCHER\n# ═══════════════════════════════════════════════════════════════════════\n# Thresholds for significant change detection (emit signals)\n_FRED_CHANGE_THRESHOLDS = {\n \"FEDFUNDS\": 0.10, # 10 bps change in Fed Funds Rate\n \"CPIAUCSL\": 0.3, # 0.3% CPI change\n \"GDP\": 0.5, # 0.5% GDP change\n \"UNRATE\": 0.2, # 0.2% unemployment change\n \"T10Y2Y\": 0.15, # 15 bps spread change\n \"DGS10\": 0.15, # 15 bps yield change\n}\n\ndef fetch_fred_indicators() -> dict:\n \"\"\"Fetch latest macro indicators from FRED. Returns dict of series data with change detection.\"\"\"\n if not C.FRED_API_KEY:\n return {}\n results = {}\n for series_id, label in C.FRED_SERIES.items():\n url = (f\"{C.FRED_BASE}/series/observations\"\n f\"?series_id={series_id}&api_key={C.FRED_API_KEY}\"\n f\"&file_type=json&limit=2&sort_order=desc\")\n data = _http_get_json(url, timeout=10)\n if not isinstance(data, dict):\n continue\n obs = data.get(\"observations\", [])\n if not obs:\n continue\n try:\n current_val = float(obs[0].get(\"value\", \"0\"))\n except (ValueError, TypeError):\n continue\n current_date = obs[0].get(\"date\", \"\")\n prev_val = None\n change = None\n if len(obs) > 1:\n try:\n prev_val = float(obs[1].get(\"value\", \"0\"))\n change = round(current_val - prev_val, 4)\n except (ValueError, TypeError):\n pass\n results[series_id] = {\n \"value\": current_val,\n \"date\": current_date,\n \"label\": label,\n \"prev_value\": prev_val,\n \"change\": change,\n }\n return results\n\n# ═══════════════════════════════════════════════════════════════════════\n# 6551.io OPENNEWS REST FALLBACK\n# ═══════════════════════════════════════════════════════════════════════\ndef fetch_opennews_rest() -> list[dict]:\n \"\"\"Fetch articles via 6551.io REST /open/news_search POST.\n Dual-store: raw articles go to _opennews_articles buffer,\n high-score articles returned as signal candidates.\"\"\"\n if not C.OPENNEWS_TOKEN:\n return []\n import urllib.error\n engine_filter = {et: [] for et in C.OPENNEWS_ENGINE_TYPES}\n body = json.dumps({\"engineTypes\": engine_filter, \"limit\": 50, \"hasCoin\": False}).encode()\n headers = {\n \"Content-Type\": \"application/json\",\n \"Authorization\": f\"Bearer {C.OPENNEWS_TOKEN}\",\n }\n req = Request(f\"{C.OPENNEWS_API_BASE}/open/news_search\",\n data=body, headers=headers, method=\"POST\")\n try:\n with urlopen(req, timeout=15) as resp:\n data = json.loads(resp.read())\n except Exception as e:\n _log(f\"OpenNews REST error: {e}\", \"WARN\")\n return []\n items = data.get(\"data\", [])\n if not isinstance(items, list):\n return []\n if items:\n with _state_lock:\n _source_status[\"opennews_rest\"] = time.time()\n results = []\n for item in items:\n # Dual-store: ingest every article into raw buffer\n article = _normalize_opennews_article(item)\n if article:\n _ingest_opennews_article(article)\n # High-score articles also become signal candidates\n score = item.get(\"aiRating\", {}).get(\"score\", 0) if isinstance(item.get(\"aiRating\"), dict) else 0\n if score \u003c C.OPENNEWS_MIN_SCORE:\n continue\n text = (item.get(\"enSummary\") or item.get(\"summary\") or\n item.get(\"title\") or item.get(\"text\", \"\"))\n if not text:\n continue\n results.append({\n \"text\": _strip_html(text),\n \"source\": item.get(\"newsType\", \"opennews\"),\n \"score\": score,\n \"signal\": item.get(\"aiRating\", {}).get(\"signal\", \"neutral\") if isinstance(item.get(\"aiRating\"), dict) else \"neutral\",\n })\n return results\n\n# ═══════════════════════════════════════════════════════════════════════\n# PRICE TICKER FETCHER\n# ═══════════════════════════════════════════════════════════════════════\ndef fetch_price_tickers() -> dict:\n \"\"\"Fetch live prices for dashboard ticker bar.\n Finnhub for stocks/ETFs (SPY, GLD, SLV), CoinGecko for crypto (BTC, ETH).\n \"\"\"\n results = {}\n\n # Finnhub stock/ETF quotes\n if C.FINNHUB_API_KEY:\n for symbol, label in C.FINNHUB_PRICE_SYMBOLS.items():\n data = _http_get_json(\n f\"{C.FINNHUB_BASE}/quote?symbol={symbol}&token={C.FINNHUB_API_KEY}\",\n timeout=5,\n )\n if isinstance(data, dict) and data.get(\"c\"):\n price = data[\"c\"] # Current price\n prev_close = data.get(\"pc\", price) # Previous close\n change_pct = ((price - prev_close) / prev_close * 100) if prev_close else 0\n results[symbol] = {\n \"price\": price,\n \"change_pct\": round(change_pct, 2),\n \"label\": label,\n }\n\n # CoinGecko crypto quotes (free, no key)\n cg_data = _http_get_json(\n \"https://api.coingecko.com/api/v3/simple/price\"\n \"?ids=bitcoin,ethereum&vs_currencies=usd&include_24hr_change=true\",\n timeout=5,\n )\n if isinstance(cg_data, dict):\n if \"bitcoin\" in cg_data:\n results[\"BTC\"] = {\n \"price\": cg_data[\"bitcoin\"].get(\"usd\", 0),\n \"change_pct\": round(cg_data[\"bitcoin\"].get(\"usd_24h_change\", 0), 2),\n \"label\": \"BTC\",\n }\n if \"ethereum\" in cg_data:\n results[\"ETH\"] = {\n \"price\": cg_data[\"ethereum\"].get(\"usd\", 0),\n \"change_pct\": round(cg_data[\"ethereum\"].get(\"usd_24h_change\", 0), 2),\n \"label\": \"ETH\",\n }\n\n return results\n\n# ═══════════════════════════════════════════════════════════════════════\n# CRYPTOPANIC NEWS FETCHER\n# ═══════════════════════════════════════════════════════════════════════\ndef fetch_cryptopanic_news() -> list[dict]:\n \"\"\"Fetch news from CryptoPanic API with community vote data.\"\"\"\n if not C.CRYPTOPANIC_TOKEN:\n return []\n filt = C.CRYPTOPANIC_FILTER\n url = f\"{C.CRYPTOPANIC_BASE}?auth_token={C.CRYPTOPANIC_TOKEN}&public=true\"\n if filt and filt != \"all\":\n url += f\"&filter={filt}\"\n data = _http_get_json(url, timeout=10)\n if not isinstance(data, dict):\n return []\n results_list = data.get(\"results\", [])\n if not isinstance(results_list, list):\n return []\n articles = []\n for item in results_list[:20]:\n title = item.get(\"title\", \"\")\n if not title:\n continue\n # Parse source timestamp\n created_at = item.get(\"created_at\", \"\")\n src_ts = 0.0\n if created_at:\n try:\n # ISO 8601: \"2025-04-17T10:30:00Z\"\n from datetime import datetime as _dt\n dt = _dt.fromisoformat(created_at.replace(\"Z\", \"+00:00\"))\n src_ts = dt.timestamp()\n except Exception:\n pass\n # Extract community votes\n votes = item.get(\"votes\", {})\n positive = votes.get(\"positive\", 0) if isinstance(votes, dict) else 0\n negative = votes.get(\"negative\", 0) if isinstance(votes, dict) else 0\n important = votes.get(\"important\", 0) if isinstance(votes, dict) else 0\n source_info = item.get(\"source\", {})\n source_name = source_info.get(\"title\", \"cryptopanic\") if isinstance(source_info, dict) else \"cryptopanic\"\n articles.append({\n \"title\": title,\n \"source\": source_name,\n \"source_ts\": src_ts,\n \"votes_positive\": positive,\n \"votes_negative\": negative,\n \"votes_important\": important,\n })\n return articles\n\n\n# ═══════════════════════════════════════════════════════════════════════\n# RSS / ATOM FEED FETCHER\n# ═══════════════════════════════════════════════════════════════════════\ndef _parse_rss_date(date_str: str) -> float:\n \"\"\"Try common RSS/Atom date formats. Returns unix timestamp or 0.\"\"\"\n formats = [\n \"%a, %d %b %Y %H:%M:%S %z\", # RSS 2.0: \"Thu, 17 Apr 2025 10:30:00 +0000\"\n \"%a, %d %b %Y %H:%M:%S %Z\", # RSS 2.0 with TZ name\n \"%Y-%m-%dT%H:%M:%S%z\", # Atom / ISO 8601\n \"%Y-%m-%dT%H:%M:%SZ\", # Atom without offset\n \"%Y-%m-%d %H:%M:%S\", # Fallback\n ]\n for fmt in formats:\n try:\n dt = datetime.strptime(date_str.strip(), fmt)\n if dt.tzinfo is None:\n dt = dt.replace(tzinfo=timezone.utc)\n return dt.timestamp()\n except (ValueError, TypeError):\n continue\n return 0.0\n\n\ndef fetch_rss_feed(feed_config: dict) -> list[dict]:\n \"\"\"Fetch and parse an RSS 2.0 or Atom feed. Returns list of articles.\"\"\"\n url = feed_config.get(\"url\", \"\")\n label = feed_config.get(\"label\", url[:40])\n if not url:\n return []\n\n try:\n req = Request(url, headers={\n \"User-Agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) \"\n \"AppleWebKit/537.36 (KHTML, like Gecko) \"\n \"Chrome/125.0.0.0 Safari/537.36\",\n })\n with urlopen(req, timeout=10) as resp:\n raw = resp.read()\n except Exception as e:\n _log(f\"RSS fetch error ({label}): {e}\", \"WARN\")\n return []\n\n try:\n root = ET.fromstring(raw)\n except ET.ParseError as e:\n _log(f\"RSS parse error ({label}): {e}\", \"WARN\")\n return []\n\n # Initialize seen guids for this feed\n if url not in _rss_seen_guids:\n _rss_seen_guids[url] = set()\n seen = _rss_seen_guids[url]\n\n articles = []\n ns = {\"atom\": \"http://www.w3.org/2005/Atom\"}\n\n # Try RSS 2.0 format first\n items = root.findall(\".//item\")\n if items:\n for item in items[:20]:\n title_el = item.find(\"title\")\n title = title_el.text.strip() if title_el is not None and title_el.text else \"\"\n guid_el = item.find(\"guid\")\n link_el = item.find(\"link\")\n guid = (guid_el.text if guid_el is not None and guid_el.text else\n link_el.text if link_el is not None and link_el.text else title)\n if not title or guid in seen:\n continue\n seen.add(guid)\n pub_el = item.find(\"pubDate\")\n src_ts = _parse_rss_date(pub_el.text) if pub_el is not None and pub_el.text else 0.0\n articles.append({\"title\": title, \"source\": label, \"source_ts\": src_ts})\n else:\n # Try Atom format\n entries = root.findall(\"atom:entry\", ns) or root.findall(\"entry\")\n for entry in (entries or [])[:20]:\n title_el = entry.find(\"atom:title\", ns) or entry.find(\"title\")\n title = title_el.text.strip() if title_el is not None and title_el.text else \"\"\n id_el = entry.find(\"atom:id\", ns) or entry.find(\"id\")\n guid = id_el.text if id_el is not None and id_el.text else title\n if not title or guid in seen:\n continue\n seen.add(guid)\n pub_el = (entry.find(\"atom:published\", ns) or entry.find(\"published\") or\n entry.find(\"atom:updated\", ns) or entry.find(\"updated\"))\n src_ts = _parse_rss_date(pub_el.text) if pub_el is not None and pub_el.text else 0.0\n articles.append({\"title\": title, \"source\": label, \"source_ts\": src_ts})\n\n # Cap seen guids per feed\n if len(seen) > 500:\n _rss_seen_guids[url] = set(list(seen)[-500:])\n\n return articles\n\n\n# ═══════════════════════════════════════════════════════════════════════\n# NOISE FILTER (Telegram only)\n# ═══════════════════════════════════════════════════════════════════════\ndef _count_emoji(text: str) -> int:\n # Count chars in common emoji ranges\n count = 0\n for ch in text:\n cp = ord(ch)\n if (0x1F600 \u003c= cp \u003c= 0x1F64F or 0x1F300 \u003c= cp \u003c= 0x1F5FF or\n 0x1F680 \u003c= cp \u003c= 0x1F6FF or 0x1F900 \u003c= cp \u003c= 0x1F9FF or\n 0x2600 \u003c= cp \u003c= 0x26FF or 0x2700 \u003c= cp \u003c= 0x27BF or\n 0xFE00 \u003c= cp \u003c= 0xFE0F or 0x200D == cp):\n count += 1\n return count\n\ndef _is_noise(text: str, sender_id: str = \"\", sender_name: str = \"\",\n is_reply: bool = False, is_forward_from_bot: bool = False,\n is_deep_reply: bool = False) -> bool:\n \"\"\"Return True if message should be dropped as noise.\"\"\"\n # VIP bypass\n if sender_name in C.VIP_SENDERS or sender_id in C.VIP_SENDERS:\n return False\n\n stripped = text.strip()\n\n # Min length\n if len(stripped) \u003c C.NOISE_MIN_LENGTH:\n return True\n\n # Bot forward\n if C.NOISE_SKIP_BOT_FORWARDS and is_forward_from_bot:\n return True\n\n # Deep reply\n if C.NOISE_SKIP_DEEP_REPLIES and is_deep_reply:\n return True\n\n # Emoji ratio\n emoji_count = _count_emoji(stripped)\n if len(stripped) > 0 and emoji_count / len(stripped) > C.NOISE_MAX_EMOJI_RATIO:\n return True\n\n # Pattern match\n for pat in _noise_regex:\n if pat.search(stripped):\n return True\n\n return False\n\n# ═══════════════════════════════════════════════════════════════════════\n# DEDUP (cross-source, MD5 hash, time window)\n# ═══════════════════════════════════════════════════════════════════════\ndef _dedup_hash(text: str) -> str:\n n = C.DEDUP_SIMILARITY_CHARS\n snippet = re.sub(r'\\s+', ' ', text[:n].lower().strip())\n return hashlib.md5(snippet.encode()).hexdigest()[:12]\n\ndef _is_duplicate(text: str) -> bool:\n h = _dedup_hash(text)\n now = time.time()\n window = C.DEDUP_WINDOW_HOURS * 3600\n with _state_lock:\n # Clean old hashes\n expired = [k for k, ts in _dedup_hashes.items() if now - ts > window]\n for k in expired:\n del _dedup_hashes[k]\n if h in _dedup_hashes:\n return True\n _dedup_hashes[h] = now\n return False\n\n# ═══════════════════════════════════════════════════════════════════════\n# 3-LAYER CLASSIFIER\n# ═══════════════════════════════════════════════════════════════════════\ndef _match_macro_keywords(text: str) -> tuple[str, float] | None:\n \"\"\"Layer 1: Regex keyword match. Returns (event_type, confidence) or None.\"\"\"\n text_lower = text.lower()\n for event_type, patterns in _macro_regex.items():\n for pat in patterns:\n if pat.search(text_lower) or pat.search(text):\n return (event_type, 0.85)\n return None\n\ndef _match_classifier_rules(text: str) -> dict | None:\n \"\"\"Layer 1b: TG-style classifier rules. Returns matched rule dict or None.\"\"\"\n text_lower = text.lower()\n for rule in C.CLASSIFIER_RULES:\n # Check keywords_any (at least one must match)\n any_match = False\n for kw in rule[\"keywords_any\"]:\n if kw.lower() in text_lower:\n any_match = True\n break\n if not any_match:\n continue\n # Check keywords_not (none must match)\n not_match = False\n for kw in rule.get(\"keywords_not\", []):\n if kw.lower() in text_lower:\n not_match = True\n break\n if not_match:\n continue\n return rule\n return None\n\ndef _llm_classify(text: str, source_type: str) -> dict | None:\n \"\"\"Layer 2/3: LLM classification using Haiku. Returns signal dict or None.\"\"\"\n if not C.LLM_ENABLED:\n return None\n\n api_key = os.environ.get(\"ANTHROPIC_API_KEY\", \"\")\n if not api_key:\n return None\n\n # Pre-screen: check if text has any relevant keywords\n text_lower = text.lower()\n has_keyword = False\n for kw in C.LLM_PRESCREEN_KEYWORDS:\n if kw.startswith(r\"\\$\"):\n if re.search(kw, text):\n has_keyword = True\n break\n elif kw.lower() in text_lower:\n has_keyword = True\n break\n if not has_keyword:\n return None\n\n event_types = list(C.MACRO_PLAYBOOK.keys())\n prompt = f\"\"\"Classify this {source_type} message into a macro event type.\n\nEvent types: {', '.join(event_types)}\n\nIf the message matches an event type, respond with JSON:\n{{\"event_type\": \"...\", \"direction\": \"bullish|bearish|neutral\", \"confidence\": 0.0-1.0}}\n\nIf not relevant to macro/crypto, respond: {{\"event_type\": \"none\"}}\n\nMessage: {text[:500]}\"\"\"\n\n try:\n import urllib.request\n body = json.dumps({\n \"model\": C.LLM_MODEL,\n \"max_tokens\": C.LLM_MAX_TOKENS,\n \"messages\": [{\"role\": \"user\", \"content\": prompt}],\n }).encode()\n req = urllib.request.Request(\n \"https://api.anthropic.com/v1/messages\",\n data=body,\n headers={\n \"Content-Type\": \"application/json\",\n \"x-api-key\": api_key,\n \"anthropic-version\": \"2023-06-01\",\n },\n )\n with _state_lock:\n _stats[\"llm_calls\"] += 1\n with urllib.request.urlopen(req, timeout=C.LLM_TIMEOUT_SEC) as resp:\n result = json.loads(resp.read().decode())\n content = result.get(\"content\", [{}])[0].get(\"text\", \"\")\n # Extract JSON from response\n match = re.search(r'\\{[^}]+\\}', content)\n if not match:\n return None\n data = json.loads(match.group())\n if data.get(\"event_type\", \"none\") == \"none\":\n return None\n confidence = float(data.get(\"confidence\", 0.5))\n if confidence \u003c C.LLM_CONFIDENCE_BAND[0]:\n return None\n return {\n \"event_type\": data[\"event_type\"],\n \"direction\": data.get(\"direction\", \"neutral\"),\n \"confidence\": confidence,\n \"classify_method\": \"llm_confirm\" if confidence >= C.LLM_CONFIDENCE_BAND[1] else \"llm_discover\",\n }\n except Exception as e:\n _log(f\"LLM classify error: {e}\", \"WARN\")\n return None\n\ndef classify_text(text: str, source_type: str) -> dict:\n \"\"\"Unified 3-layer classification. Returns classification result.\"\"\"\n # Layer 1a: Macro keyword regex\n kw_match = _match_macro_keywords(text)\n if kw_match:\n event_type, confidence = kw_match\n playbook = C.MACRO_PLAYBOOK.get(event_type, {})\n return {\n \"event_type\": event_type,\n \"direction\": playbook.get(\"direction\", \"neutral\"),\n \"magnitude\": playbook.get(\"magnitude\", 0.5),\n \"urgency\": playbook.get(\"urgency\", 0.5),\n \"affects\": playbook.get(\"affects\", []),\n \"classify_method\": \"keyword\",\n }\n\n # Layer 1b: Classifier rules (TG-style)\n rule = _match_classifier_rules(text)\n if rule:\n return {\n \"event_type\": rule[\"event_type\"],\n \"direction\": rule[\"direction\"],\n \"magnitude\": rule[\"magnitude\"],\n \"urgency\": C.MACRO_PLAYBOOK.get(rule[\"event_type\"], {}).get(\"urgency\", 0.5),\n \"affects\": rule[\"affects\"],\n \"classify_method\": \"keyword\",\n }\n\n # Layer 2/3: LLM classification\n llm_result = _llm_classify(text, source_type)\n if llm_result:\n event_type = llm_result[\"event_type\"]\n playbook = C.MACRO_PLAYBOOK.get(event_type, {})\n return {\n \"event_type\": event_type,\n \"direction\": llm_result.get(\"direction\", playbook.get(\"direction\", \"neutral\")),\n \"magnitude\": playbook.get(\"magnitude\", 0.5),\n \"urgency\": playbook.get(\"urgency\", 0.5),\n \"affects\": playbook.get(\"affects\", []),\n \"classify_method\": llm_result[\"classify_method\"],\n }\n\n return {\n \"event_type\": \"unclassified\",\n \"direction\": \"neutral\",\n \"magnitude\": 0.0,\n \"urgency\": 0.0,\n \"affects\": [],\n \"classify_method\": \"none\",\n }\n\n# ═══════════════════════════════════════════════════════════════════════\n# LLM INSIGHT GENERATOR\n# ═══════════════════════════════════════════════════════════════════════\ndef _generate_insight(headline: str, event_type: str, direction: str,\n affects: list[str]) -> str:\n \"\"\"Call Haiku to produce a 2-3 sentence insight explaining what the headline\n means for specific asset classes. Returns insight text or empty string.\"\"\"\n if not C.LLM_INSIGHT_ENABLED:\n return \"\"\n api_key = os.environ.get(\"ANTHROPIC_API_KEY\", \"\")\n if not api_key:\n return \"\"\n\n affects_str = \", \".join(affects) if affects else \"broad crypto market\"\n prompt = (\n f\"You are a macro analyst. Given this headline and its classification, \"\n f\"write 2-3 concise sentences explaining:\\n\"\n f\"1) The key takeaway from this event\\n\"\n f\"2) How it is likely to affect specific assets or sectors \"\n f\"({affects_str})\\n\\n\"\n f\"Headline: {headline[:400]}\\n\"\n f\"Event type: {event_type}\\n\"\n f\"Direction: {direction}\\n\\n\"\n f\"Be specific about which assets benefit or suffer and why. \"\n f\"No preamble — start directly with the analysis.\"\n )\n\n try:\n import urllib.request\n body = json.dumps({\n \"model\": C.LLM_MODEL,\n \"max_tokens\": C.LLM_INSIGHT_MAX_TOKENS,\n \"messages\": [{\"role\": \"user\", \"content\": prompt}],\n }).encode()\n req = urllib.request.Request(\n \"https://api.anthropic.com/v1/messages\",\n data=body,\n headers={\n \"Content-Type\": \"application/json\",\n \"x-api-key\": api_key,\n \"anthropic-version\": \"2023-06-01\",\n },\n )\n with _state_lock:\n _stats[\"llm_calls\"] += 1\n with urllib.request.urlopen(req, timeout=C.LLM_INSIGHT_TIMEOUT_SEC) as resp:\n result = json.loads(resp.read().decode())\n content = result.get(\"content\", [{}])[0].get(\"text\", \"\")\n return content.strip()\n except Exception as e:\n _log(f\"Insight generation error: {e}\", \"WARN\")\n return \"\"\n\n# ═══════════════════════════════════════════════════════════════════════\n# SENTIMENT SCORING\n# ═══════════════════════════════════════════════════════════════════════\ndef _score_sentiment(text: str) -> float:\n \"\"\"Score sentiment from -1.0 to +1.0 using weighted lexicon.\"\"\"\n words = re.findall(r'[\\w\\u4e00-\\u9fff]+', text.lower())\n total_weight = 0.0\n word_count = 0\n for w in words:\n if w in C.POSITIVE_WORDS:\n total_weight += C.POSITIVE_WORDS[w]\n word_count += 1\n elif w in C.NEGATIVE_WORDS:\n total_weight += C.NEGATIVE_WORDS[w]\n word_count += 1\n if word_count == 0:\n return 0.0\n return max(-1.0, min(1.0, total_weight / word_count))\n\n# ═══════════════════════════════════════════════════════════════════════\n# TOKEN EXTRACTION\n# ═══════════════════════════════════════════════════════════════════════\ndef _extract_tokens(text: str) -> list[str]:\n \"\"\"Extract ticker symbols from text.\"\"\"\n dollar_tickers = re.findall(r'\\$([A-Za-z]{2,10})', text)\n caps_tickers = re.findall(r'\\b([A-Z]{3,5})\\b', text)\n all_tickers = set(t.upper() for t in dollar_tickers)\n all_tickers.update(t for t in caps_tickers if t not in C.TICKER_NOISE_WORDS)\n return sorted(all_tickers)\n\n# ═══════════════════════════════════════════════════════════════════════\n# TOKEN IMPACT ENGINE\n# ═══════════════════════════════════════════════════════════════════════\ndef _compute_token_impacts(event_type: str, direction: str, magnitude: float,\n extracted_tokens: list[str]) -> list[dict]:\n \"\"\"Compute per-token impact scores from event type and extracted tokens.\n\n Returns list of {symbol, impact, direction} sorted by abs(impact) desc.\n \"\"\"\n impacts = {} # symbol → impact score\n\n # 1. Map from event type (high confidence — curated correlations)\n base_map = C.TOKEN_IMPACT_MAP.get(event_type, [])\n if not base_map and direction != \"neutral\":\n # Unknown event type but has direction → use generic crypto correlation\n base_map = C.TOKEN_IMPACT_GENERIC\n if direction == \"bearish\":\n base_map = [(sym, -abs(score)) for sym, score in base_map]\n\n for sym, base_score in base_map:\n impacts[sym] = round(base_score * magnitude, 3)\n\n # 2. Extracted tokens not already covered get directional score\n for tok in extracted_tokens:\n tok_up = tok.upper()\n if tok_up in impacts or tok_up in C.TICKER_NOISE_WORDS:\n continue\n # Assign based on direction with lower confidence\n if direction == \"bullish\":\n impacts[tok_up] = round(magnitude * 0.45, 3)\n elif direction == \"bearish\":\n impacts[tok_up] = round(-magnitude * 0.45, 3)\n else:\n impacts[tok_up] = 0.0\n\n # 3. Convert to sorted list\n result = []\n for sym, score in sorted(impacts.items(), key=lambda x: abs(x[1]), reverse=True):\n result.append({\n \"symbol\": sym,\n \"impact\": round(score, 2),\n \"direction\": \"bullish\" if score > 0.01 else \"bearish\" if score \u003c -0.01 else \"neutral\",\n })\n return result[:8] # Cap at 8 tokens max\n\n\n# ═══════════════════════════════════════════════════════════════════════\n# REPUTATION SYSTEM\n# ═══════════════════════════════════════════════════════════════════════\ndef _update_sender_rep(sender_id: str, event_type: str):\n \"\"\"Update sender reputation based on signal quality.\"\"\"\n if not C.REPUTATION_ENABLED or not sender_id:\n return\n with _state_lock:\n if sender_id not in _reputation:\n _reputation[sender_id] = {\"score\": 0.0, \"last_ts\": time.time(), \"hits\": 0}\n\n rep = _reputation[sender_id]\n now = time.time()\n\n # Time decay\n days_elapsed = (now - rep[\"last_ts\"]) / 86400\n if days_elapsed > 0 and C.REPUTATION_DECAY_DAYS > 0:\n decay = 1.0 - (min(days_elapsed, C.REPUTATION_DECAY_DAYS) / C.REPUTATION_DECAY_DAYS) * 0.1\n rep[\"score\"] *= max(0.0, decay)\n\n # Boost/penalty\n if event_type in (\"alpha_call\", \"whale_buy\", \"whale_sell\"):\n rep[\"score\"] += C.REPUTATION_BOOST_ALPHA\n elif event_type == \"unclassified\":\n rep[\"score\"] += C.REPUTATION_PENALTY_NOISE\n else:\n rep[\"score\"] += C.REPUTATION_BOOST_NEWS\n\n rep[\"score\"] = max(C.REPUTATION_MIN_SCORE, min(C.REPUTATION_MAX_SCORE, rep[\"score\"]))\n rep[\"last_ts\"] = now\n rep[\"hits\"] = rep.get(\"hits\", 0) + 1\n\ndef _get_sender_rep(sender_id: str) -> float:\n with _state_lock:\n return _reputation.get(sender_id, {}).get(\"score\", 0.0)\n\ndef _decay_reputations():\n \"\"\"Periodic decay of all reputations.\"\"\"\n if not C.REPUTATION_ENABLED:\n return\n now = time.time()\n with _state_lock:\n for sid, rep in _reputation.items():\n days = (now - rep[\"last_ts\"]) / 86400\n if days > C.REPUTATION_DECAY_DAYS:\n rep[\"score\"] *= 0.5\n\n# ═══════════════════════════════════════════════════════════════════════\n# UNIFIED PIPELINE — single entry point for all sources\n# ═══════════════════════════════════════════════════════════════════════\ndef process_signal(text: str, source_type: str, source_name: str,\n sender: str = \"\", group_category: str = \"\",\n is_reply: bool = False, is_forward_from_bot: bool = False,\n is_deep_reply: bool = False,\n source_ts: float = 0.0) -> dict | None:\n \"\"\"\n Unified signal processing pipeline.\n source_type: \"newsnow\" | \"polymarket\" | \"telegram\" | \"opennews\" | \"finnhub\" | \"fred\" | \"cryptopanic\" | \"rss\"\n source_ts: original publication timestamp (for latency tracking)\n Returns UnifiedSignal dict or None if filtered.\n \"\"\"\n with _state_lock:\n _stats[\"messages_processed\"] += 1\n\n # 1. Noise filter (TG only)\n if source_type == \"telegram\":\n if _is_noise(text, sender, sender, is_reply, is_forward_from_bot, is_deep_reply):\n _update_sender_rep(sender, \"unclassified\")\n return None\n\n # 2. Dedup — exact MD5 match\n if _is_duplicate(text):\n return None\n\n # 2b. Fuzzy dedup — Jaccard similarity\n if _is_fuzzy_duplicate(text):\n return None\n\n # 3. Classify\n classification = classify_text(text, source_type)\n if classification[\"event_type\"] == \"unclassified\" and classification[\"magnitude\"] == 0.0:\n if source_type != \"telegram\":\n return None\n _update_sender_rep(sender, \"unclassified\")\n return None\n\n # 4. Sentiment\n sentiment = _score_sentiment(text)\n\n # 5. Token extraction\n tokens = _extract_tokens(text)\n\n # 6. Reputation\n sender_rep = 0.0\n if sender:\n _update_sender_rep(sender, classification[\"event_type\"])\n sender_rep = _get_sender_rep(sender)\n if sender_rep >= C.REPUTATION_HIGH_SIGNAL:\n classification[\"magnitude\"] = min(1.0, classification[\"magnitude\"] * 1.3)\n\n # 6b. Trend detection — boost urgency/magnitude if trending\n urgency = classification.get(\"urgency\", 0.5)\n magnitude = classification[\"magnitude\"]\n trend_meta = None\n if classification[\"event_type\"] != \"unclassified\":\n ub, mb, trend_meta = _detect_trend(classification[\"event_type\"], urgency, magnitude)\n urgency = min(1.0, urgency + ub)\n magnitude = min(1.0, magnitude + mb)\n classification[\"urgency\"] = urgency\n classification[\"magnitude\"] = magnitude\n\n # 7. Generate AI insight (for classified signals only)\n insight = \"\"\n if classification[\"event_type\"] != \"unclassified\":\n insight = _generate_insight(\n text, classification[\"event_type\"],\n classification[\"direction\"],\n classification.get(\"affects\", []),\n )\n\n # 8. Build signal\n now = time.time()\n latency_ms = int((now - source_ts) * 1000) if source_ts > 0 else 0\n signal = {\n \"ts\": int(now),\n \"ts_human\": datetime.now().strftime(\"%m-%d %H:%M:%S\"),\n \"source_type\": source_type,\n \"source_name\": source_name,\n \"event_type\": classification[\"event_type\"],\n \"direction\": classification[\"direction\"],\n \"magnitude\": round(classification[\"magnitude\"], 2),\n \"urgency\": round(classification.get(\"urgency\", 0.5), 2),\n \"affects\": classification.get(\"affects\", []),\n \"tokens\": tokens,\n \"sentiment\": round(sentiment, 3),\n \"text\": text[:400],\n \"insight\": insight,\n \"sender\": sender or source_name,\n \"sender_rep\": round(sender_rep, 2),\n \"classify_method\": classification[\"classify_method\"],\n \"group_category\": group_category or (\"http_news\" if source_type == \"newsnow\" else source_type),\n \"source_ts\": source_ts if source_ts > 0 else 0,\n \"latency_ms\": latency_ms,\n }\n\n # 8b. Token impact analysis — map event to specific crypto tokens\n signal[\"token_impacts\"] = _compute_token_impacts(\n signal[\"event_type\"], signal[\"direction\"],\n signal[\"magnitude\"], tokens,\n )\n\n # 9. Store\n with _state_lock:\n _signals.append(signal)\n if len(_signals) > C.MAX_SIGNALS_KEPT:\n _signals[:] = _signals[-C.MAX_SIGNALS_KEPT:]\n _stats[\"signals_produced\"] += 1\n _source_status[source_name] = now\n\n _log(f\"SIGNAL [{source_type}] {classification['event_type']} \"\n f\"{classification['direction']} mag={classification['magnitude']:.2f} \"\n f\"from={source_name} method={classification['classify_method']}\"\n f\"{f' latency={latency_ms}ms' if latency_ms > 0 else ''}\")\n\n # 10. WebSocket broadcast\n try:\n _ws_broadcast_queue.put_nowait(signal)\n except queue.Full:\n pass\n\n # 11. Webhook push\n _webhook_push(signal)\n\n # 12. Accuracy tracking\n _record_accuracy_checkpoint(signal)\n\n # 13. Emit trend meta-signal (if 3rd occurrence detected in step 6b)\n if trend_meta:\n now_ts = time.time()\n meta_signal = {\n \"ts\": int(now_ts),\n \"ts_human\": datetime.now().strftime(\"%m-%d %H:%M:%S\"),\n \"source_type\": source_type,\n \"source_name\": \"trend_detector\",\n \"event_type\": trend_meta[\"event_type\"],\n \"direction\": trend_meta[\"direction\"],\n \"magnitude\": round(trend_meta[\"magnitude\"], 2),\n \"urgency\": round(trend_meta[\"urgency\"], 2),\n \"affects\": trend_meta[\"affects\"],\n \"tokens\": [],\n \"sentiment\": 0.0,\n \"text\": trend_meta[\"text\"],\n \"insight\": \"\",\n \"sender\": \"trend_detector\",\n \"sender_rep\": 0.0,\n \"classify_method\": \"trend\",\n \"group_category\": \"macro\",\n \"source_ts\": 0,\n \"latency_ms\": 0,\n }\n with _state_lock:\n _signals.append(meta_signal)\n _stats[\"signals_produced\"] += 1\n try:\n _ws_broadcast_queue.put_nowait(meta_signal)\n except queue.Full:\n pass\n _webhook_push(meta_signal)\n _log(f\"TREND [{trend_meta['event_type']}] {trend_meta['text']}\")\n\n return signal\n\n# ═══════════════════════════════════════════════════════════════════════\n# NEWS COLLECTOR THREAD\n# ═══════════════════════════════════════════════════════════════════════\ndef _news_collector_loop():\n \"\"\"Background thread: polls NewsNow + Polymarket + Finnhub + FRED + OpenNews REST on intervals.\"\"\"\n global _polymarket, _fear_greed, _fred_indicators, _price_tickers\n _log(\"NewsNow + Polymarket + Finnhub + FRED + CryptoPanic + RSS collector started\")\n news_last = 0\n poly_last = 0\n fng_last = 0\n finnhub_last = 0\n fred_last = 0\n opennews_rest_last = 0\n prices_last = 0\n cryptopanic_last = 0\n\n while True:\n try:\n now = time.time()\n\n # NewsNow headlines\n if now - news_last >= C.NEWS_POLL_SEC:\n news_last = now\n headlines = fetch_news_headlines()\n with _state_lock:\n _stats[\"news_fetches\"] += 1\n for h in headlines:\n # Parse pubDate for latency tracking\n src_ts = 0.0\n raw_ts = h.get(\"ts\", \"\")\n if isinstance(raw_ts, (int, float)) and raw_ts > 1e9:\n src_ts = float(raw_ts)\n process_signal(\n text=h[\"title\"],\n source_type=\"newsnow\",\n source_name=h[\"source\"],\n group_category=\"http_news\",\n source_ts=src_ts,\n )\n if headlines:\n _log(f\"NewsNow: fetched {len(headlines)} headlines\")\n\n # Polymarket\n if now - poly_last >= C.POLYMARKET_POLL_SEC:\n poly_last = now\n markets = fetch_polymarket_signals()\n if markets:\n with _state_lock:\n _polymarket = markets\n _source_status[\"polymarket\"] = now\n # Group markets by event category — emit ONE signal per group\n by_cat: dict[str, list] = {}\n for m in markets:\n cat = m.get(\"category\", \"\") or \"other\"\n by_cat.setdefault(cat, []).append(m)\n for cat, cat_markets in by_cat.items():\n # Pick the most notable market (highest prob divergence from 50%)\n best = max(cat_markets, key=lambda x: abs(x.get(\"probability\", 0.5) - 0.5))\n prob = best.get(\"probability\", 0.5)\n q = best.get(\"question\", \"\")\n if abs(prob - 0.5) \u003c 0.15:\n continue # Skip near-50/50 markets\n n_related = len(cat_markets)\n summary = f\"{q} — currently at {prob:.0%} probability\"\n if n_related > 1:\n summary += f\". {n_related} related prediction markets tracking this event.\"\n process_signal(\n text=summary,\n source_type=\"polymarket\",\n source_name=\"polymarket\",\n group_category=\"polymarket\",\n )\n _log(f\"Polymarket: fetched {len(markets)} markets in {len(by_cat)} groups\")\n\n # Fear & Greed Index (every 5 min — updates daily but cheap to poll)\n if now - fng_last >= 300:\n fng_last = now\n fng = fetch_fear_greed()\n if fng:\n with _state_lock:\n _fear_greed = fng\n _log(f\"Fear & Greed: {fng['value']} ({fng['label']})\")\n\n # Price tickers (every 60s)\n if now - prices_last >= C.PRICE_TICKER_POLL_SEC:\n prices_last = now\n tickers = fetch_price_tickers()\n if tickers:\n with _state_lock:\n _price_tickers = tickers\n parts = [f\"{v['label']}=${v['price']:,.1f}\" for v in tickers.values()]\n _log(f\"Prices: {', '.join(parts)}\")\n\n # Finnhub market news\n if C.FINNHUB_ENABLED and C.FINNHUB_API_KEY and now - finnhub_last >= C.FINNHUB_POLL_SEC:\n finnhub_last = now\n articles = fetch_finnhub_news()\n for a in articles:\n src_ts = float(a.get(\"ts\", 0)) if a.get(\"ts\") else 0.0\n process_signal(\n text=a[\"title\"],\n source_type=\"finnhub\",\n source_name=a.get(\"source\", \"finnhub\"),\n group_category=\"http_news\",\n source_ts=src_ts,\n )\n if articles:\n _log(f\"Finnhub: fetched {len(articles)} articles\")\n\n # FRED macro indicators\n if C.FRED_ENABLED and C.FRED_API_KEY and now - fred_last >= C.FRED_POLL_SEC:\n fred_last = now\n indicators = fetch_fred_indicators()\n if indicators:\n with _state_lock:\n _fred_indicators = indicators\n _source_status[\"fred\"] = now\n _log(f\"FRED: updated {len(indicators)} indicators\")\n # Significant change detection — emit signals\n for series_id, data in indicators.items():\n if data[\"change\"] is not None:\n threshold = _FRED_CHANGE_THRESHOLDS.get(series_id, 0.2)\n if abs(data[\"change\"]) >= threshold:\n process_signal(\n text=f\"FRED {data['label']}: {data['value']} (prev: {data['prev_value']}, change: {data['change']:+.2f})\",\n source_type=\"fred\",\n source_name=\"fred\",\n group_category=\"macro_data\",\n )\n\n # 6551.io OpenNews REST (always poll — WS returns 403 on free tier)\n if (C.OPENNEWS_ENABLED and C.OPENNEWS_TOKEN\n and now - opennews_rest_last >= C.OPENNEWS_POLL_SEC):\n opennews_rest_last = now\n articles = fetch_opennews_rest()\n for a in articles:\n process_signal(\n text=a[\"text\"],\n source_type=\"opennews\",\n source_name=a.get(\"source\", \"opennews\"),\n group_category=\"opennews\",\n )\n if articles:\n _log(f\"OpenNews REST: fetched {len(articles)} signal articles ({len(_opennews_articles)} in buffer)\")\n\n # CryptoPanic\n if (C.CRYPTOPANIC_ENABLED and C.CRYPTOPANIC_TOKEN\n and now - cryptopanic_last >= C.CRYPTOPANIC_POLL_SEC):\n cryptopanic_last = now\n cp_articles = fetch_cryptopanic_news()\n for a in cp_articles:\n # Use community votes to boost magnitude\n vote_hint = \"\"\n pos = a.get(\"votes_positive\", 0)\n neg = a.get(\"votes_negative\", 0)\n imp = a.get(\"votes_important\", 0)\n if pos + neg + imp > 0:\n vote_hint = f\" [votes: +{pos}/-{neg} imp:{imp}]\"\n process_signal(\n text=a[\"title\"] + vote_hint,\n source_type=\"cryptopanic\",\n source_name=a.get(\"source\", \"cryptopanic\"),\n group_category=\"crypto_news\",\n source_ts=a.get(\"source_ts\", 0),\n )\n if cp_articles:\n with _state_lock:\n _source_status[\"cryptopanic\"] = now\n _log(f\"CryptoPanic: fetched {len(cp_articles)} articles\")\n\n # RSS feeds\n if C.RSS_ENABLED and C.RSS_FEEDS:\n for feed_cfg in C.RSS_FEEDS:\n feed_url = feed_cfg.get(\"url\", \"\")\n poll_sec = feed_cfg.get(\"poll_sec\", C.RSS_DEFAULT_POLL_SEC)\n last_poll = _rss_last_poll.get(feed_url, 0)\n if now - last_poll >= poll_sec:\n _rss_last_poll[feed_url] = now\n rss_articles = fetch_rss_feed(feed_cfg)\n category = feed_cfg.get(\"category\", \"crypto_news\")\n for a in rss_articles:\n process_signal(\n text=a[\"title\"],\n source_type=\"rss\",\n source_name=a.get(\"source\", \"rss\"),\n group_category=category,\n source_ts=a.get(\"source_ts\", 0),\n )\n if rss_articles:\n label = feed_cfg.get(\"label\", feed_url[:30])\n with _state_lock:\n _source_status[f\"rss:{label}\"] = now\n _log(f\"RSS ({label}): fetched {len(rss_articles)} articles\")\n\n # Periodic save + reputation decay + accuracy check\n _save_state()\n _decay_reputations()\n _check_accuracy()\n\n except Exception as e:\n _log(f\"News collector error: {e}\", \"ERROR\")\n traceback.print_exc()\n\n time.sleep(10)\n\n# ═══════════════════════════════════════════════════════════════════════\n# TELEGRAM COLLECTOR (Telethon)\n# ═══════════════════════════════════════════════════════════════════════\n_telethon_available = False\ntry:\n from telethon import TelegramClient, events\n from telethon.tl.types import User, Channel, Chat\n _telethon_available = True\nexcept ImportError:\n pass\n\ndef _build_group_map() -> dict:\n \"\"\"Build identifier -> category mapping from config.\"\"\"\n gmap = {}\n for category, identifiers in C.GROUPS.items():\n for ident in identifiers:\n gmap[ident] = category\n for category, identifiers in C.CHANNELS.items():\n for ident in identifiers:\n gmap[ident] = category\n return gmap\n\nasync def _telethon_monitor():\n \"\"\"Async Telethon event loop — runs in a dedicated thread.\"\"\"\n api_id = C.TELETHON_API_ID or int(os.environ.get(\"TG_API_ID\", \"0\"))\n api_hash = C.TELETHON_API_HASH or os.environ.get(\"TG_API_HASH\", \"\")\n if not api_id or not api_hash:\n _log(\"Telethon: no API credentials — Telegram monitoring disabled\", \"WARN\")\n return\n\n session_path = str(_BASE_DIR / C.SESSION_NAME)\n client = TelegramClient(session_path, api_id, api_hash)\n await client.start()\n _log(\"Telethon: connected\")\n\n group_map = _build_group_map()\n resolved_chats = []\n chat_categories = {}\n\n for identifier, category in group_map.items():\n try:\n entity = await client.get_entity(identifier)\n eid = entity.id\n resolved_chats.append(eid)\n chat_name = getattr(entity, 'title', getattr(entity, 'username', str(eid)))\n chat_categories[eid] = (category, chat_name)\n _log(f\"Telethon: resolved {identifier} → {chat_name} ({category})\")\n except Exception as e:\n _log(f\"Telethon: failed to resolve {identifier}: {e}\", \"WARN\")\n\n if not resolved_chats:\n _log(\"Telethon: no chats resolved — monitoring disabled\", \"WARN\")\n await client.disconnect()\n return\n\n @client.on(events.NewMessage(chats=resolved_chats))\n async def _on_message(event):\n text = event.text or \"\"\n if not text.strip():\n return\n\n with _state_lock:\n _stats[\"tg_messages\"] += 1\n\n # Extract sender info\n sender = await event.get_sender()\n sender_id = str(getattr(sender, 'id', ''))\n sender_name = \"\"\n is_bot = False\n if isinstance(sender, User):\n sender_name = sender.username or f\"{sender.first_name or ''} {sender.last_name or ''}\".strip()\n is_bot = sender.bot or False\n elif hasattr(sender, 'title'):\n sender_name = sender.title\n\n # Reply/forward info\n is_reply = event.is_reply\n is_forward_from_bot = False\n is_deep_reply = False\n if event.forward and hasattr(event.forward, 'sender') and event.forward.sender:\n is_forward_from_bot = getattr(event.forward.sender, 'bot', False)\n if is_reply:\n try:\n reply_msg = await event.get_reply_message()\n if reply_msg and reply_msg.is_reply:\n is_deep_reply = True\n except Exception:\n pass\n\n # Chat category\n chat_id = event.chat_id\n category, chat_name = chat_categories.get(chat_id, (\"general\", \"unknown\"))\n\n tg_source_ts = event.date.timestamp() if event.date else 0.0\n process_signal(\n text=text,\n source_type=\"telegram\",\n source_name=chat_name,\n sender=sender_name or sender_id,\n group_category=category,\n is_reply=is_reply,\n is_forward_from_bot=is_forward_from_bot,\n is_deep_reply=is_deep_reply,\n source_ts=tg_source_ts,\n )\n\n _log(f\"Telethon: monitoring {len(resolved_chats)} chats\")\n await client.run_until_disconnected()\n\ndef _start_telethon_thread():\n \"\"\"Start Telethon in a dedicated thread with its own event loop.\"\"\"\n import asyncio\n def _run():\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)\n try:\n loop.run_until_complete(_telethon_monitor())\n except Exception as e:\n _log(f\"Telethon thread error: {e}\", \"ERROR\")\n traceback.print_exc()\n t = threading.Thread(target=_run, daemon=True, name=\"telethon\")\n t.start()\n return t\n\n# ═══════════════════════════════════════════════════════════════════════\n# 6551.io OPENNEWS WEBSOCKET COLLECTOR\n# ═══════════════════════════════════════════════════════════════════════\nasync def _opennews_monitor():\n \"\"\"WebSocket listener for 6551.io OpenNews — runs in dedicated thread.\"\"\"\n global _opennews_ws_alive\n import asyncio\n try:\n import websockets\n except ImportError:\n _log(\"OpenNews: websockets not installed — run: pip install websockets\", \"WARN\")\n return\n\n backoff_secs = [5, 10, 30, 60]\n attempt = 0\n\n while True:\n ws_url = f\"{C.OPENNEWS_WSS_URL}?token={C.OPENNEWS_TOKEN}\"\n try:\n async with websockets.connect(ws_url, ping_interval=30, ping_timeout=10) as ws:\n _opennews_ws_alive = True\n attempt = 0\n _log(\"OpenNews: WebSocket connected\")\n\n # Subscribe to news updates\n engine_filter = {et: [] for et in C.OPENNEWS_ENGINE_TYPES}\n subscribe_msg = json.dumps({\n \"method\": \"news.subscribe\",\n \"params\": {\"engineTypes\": engine_filter, \"hasCoin\": False},\n })\n await ws.send(subscribe_msg)\n _log(f\"OpenNews: subscribed to {C.OPENNEWS_ENGINE_TYPES}\")\n\n # Pending articles waiting for AI rating\n pending: dict[str, dict] = {} # news_id -> article data\n\n async for raw in ws:\n try:\n msg = json.loads(raw)\n except (json.JSONDecodeError, TypeError):\n continue\n\n method = msg.get(\"method\", \"\")\n\n if method == \"news.update\":\n # New article arrived\n params = msg.get(\"params\", {})\n news_id = str(params.get(\"id\", params.get(\"newsId\", \"\")))\n text = params.get(\"text\", params.get(\"title\", \"\"))\n news_type = params.get(\"newsType\", \"opennews\")\n engine_type = params.get(\"engineType\", \"\")\n link = params.get(\"link\", \"\")\n\n # Dual-store: raw article buffer for OpenNews tab\n article = _normalize_opennews_article(params)\n if article:\n _ingest_opennews_article(article)\n\n if text and news_id:\n pending[news_id] = {\n \"text\": text,\n \"newsType\": news_type,\n \"engineType\": engine_type,\n \"link\": link,\n \"coins\": params.get(\"coins\", []),\n \"arrival_ts\": time.time(),\n }\n # Evict old pending entries (keep last 200)\n if len(pending) > 200:\n oldest = list(pending.keys())[:100]\n for k in oldest:\n del pending[k]\n\n elif method == \"news.ai_update\":\n # AI rating for a previously received article\n params = msg.get(\"params\", {})\n news_id = str(params.get(\"id\", params.get(\"newsId\", \"\")))\n ai_rating = params.get(\"aiRating\", {})\n score = ai_rating.get(\"score\", 0)\n signal = ai_rating.get(\"signal\", \"neutral\")\n en_summary = ai_rating.get(\"enSummary\", \"\")\n\n # Dual-store: update raw article buffer\n if news_id and isinstance(ai_rating, dict):\n _update_opennews_ai(news_id, ai_rating)\n\n article = pending.pop(news_id, None)\n if article and score >= C.OPENNEWS_MIN_SCORE:\n display_text = en_summary or article[\"text\"]\n with _state_lock:\n _source_status[\"opennews_ws\"] = time.time()\n process_signal(\n text=display_text,\n source_type=\"opennews\",\n source_name=article[\"newsType\"],\n group_category=\"opennews\",\n source_ts=article.get(\"arrival_ts\", 0),\n )\n _log(f\"OpenNews WS: score={score} signal={signal} src={article['newsType']}\")\n\n except Exception as e:\n _opennews_ws_alive = False\n delay = backoff_secs[min(attempt, len(backoff_secs) - 1)]\n _log(f\"OpenNews WS disconnected: {e} — reconnecting in {delay}s\", \"WARN\")\n attempt += 1\n await asyncio.sleep(delay)\n\n\ndef _start_opennews_thread():\n \"\"\"Start 6551.io WebSocket in a dedicated thread.\"\"\"\n import asyncio\n def _run():\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)\n try:\n loop.run_until_complete(_opennews_monitor())\n except Exception as e:\n _log(f\"OpenNews thread error: {e}\", \"ERROR\")\n traceback.print_exc()\n t = threading.Thread(target=_run, daemon=True, name=\"opennews_ws\")\n t.start()\n return t\n\n# ═══════════════════════════════════════════════════════════════════════\n# WEBSOCKET SERVER (real-time signal push)\n# ═══════════════════════════════════════════════════════════════════════\nasync def _ws_handler(websocket):\n \"\"\"Handle a single WebSocket client connection.\"\"\"\n _ws_clients.add(websocket)\n _ws_client_filters[websocket] = {}\n _log(f\"WS: client connected ({len(_ws_clients)} total)\")\n try:\n async for raw in websocket:\n try:\n msg = json.loads(raw)\n except (json.JSONDecodeError, TypeError):\n continue\n action = msg.get(\"action\", \"\")\n if action == \"subscribe\":\n _ws_client_filters[websocket] = {\n \"direction\": msg.get(\"direction\", \"\"),\n \"min_mag\": float(msg.get(\"min_mag\", 0)),\n \"affects\": msg.get(\"affects\", []),\n }\n await websocket.send(json.dumps({\"status\": \"subscribed\", \"filters\": _ws_client_filters[websocket]}))\n elif action == \"ping\":\n await websocket.send(json.dumps({\"status\": \"pong\"}))\n except Exception:\n pass\n finally:\n _ws_clients.discard(websocket)\n _ws_client_filters.pop(websocket, None)\n _log(f\"WS: client disconnected ({len(_ws_clients)} total)\")\n\n\ndef _ws_signal_matches(signal: dict, filters: dict) -> bool:\n \"\"\"Check if a signal matches a client's subscription filters.\"\"\"\n if not filters:\n return True\n if filters.get(\"direction\") and signal.get(\"direction\") != filters[\"direction\"]:\n return False\n if filters.get(\"min_mag\", 0) > 0 and signal.get(\"magnitude\", 0) \u003c filters[\"min_mag\"]:\n return False\n if filters.get(\"affects\"):\n sig_affects = set(signal.get(\"affects\", []))\n if not sig_affects.intersection(filters[\"affects\"]):\n return False\n return True\n\n\nasync def _ws_broadcast_worker():\n \"\"\"Pull signals from queue and broadcast to matching WS clients.\"\"\"\n import asyncio\n while True:\n try:\n item = await asyncio.get_event_loop().run_in_executor(\n None, lambda: _ws_broadcast_queue.get(timeout=1)\n )\n except queue.Empty:\n continue\n except Exception:\n await asyncio.sleep(0.5)\n continue\n\n # OpenNews article broadcasts go to ALL clients (no signal filter matching)\n if item.get(\"_opennews_article\"):\n payload = json.dumps({\"type\": item[\"type\"], \"data\": item[\"data\"]}, default=str)\n dead = set()\n for ws in list(_ws_clients):\n try:\n await ws.send(payload)\n except Exception:\n dead.add(ws)\n for ws in dead:\n _ws_clients.discard(ws)\n _ws_client_filters.pop(ws, None)\n else:\n # Regular signal broadcast with filter matching\n signal = item\n payload = json.dumps({\"type\": \"signal\", \"data\": signal}, default=str)\n dead = set()\n for ws in list(_ws_clients):\n filters = _ws_client_filters.get(ws, {})\n if not _ws_signal_matches(signal, filters):\n continue\n try:\n await ws.send(payload)\n except Exception:\n dead.add(ws)\n for ws in dead:\n _ws_clients.discard(ws)\n _ws_client_filters.pop(ws, None)\n\n\nasync def _ws_server_loop():\n \"\"\"Run WebSocket server + broadcast worker.\"\"\"\n import asyncio\n try:\n import websockets\n except ImportError:\n _log(\"WS server: websockets not installed — disabled\", \"WARN\")\n return\n\n server = await websockets.serve(\n _ws_handler, \"0.0.0.0\", C.WS_PORT,\n ping_interval=C.WS_PING_INTERVAL,\n ping_timeout=C.WS_PING_TIMEOUT,\n max_size=2**16,\n )\n _log(f\"WS server listening on :{C.WS_PORT}\")\n broadcast_task = asyncio.create_task(_ws_broadcast_worker())\n try:\n await asyncio.Future() # run forever\n finally:\n broadcast_task.cancel()\n server.close()\n\n\ndef _start_ws_server_thread():\n \"\"\"Start WebSocket server in a dedicated daemon thread.\"\"\"\n import asyncio\n def _run():\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)\n try:\n loop.run_until_complete(_ws_server_loop())\n except Exception as e:\n _log(f\"WS server thread error: {e}\", \"ERROR\")\n traceback.print_exc()\n t = threading.Thread(target=_run, daemon=True, name=\"ws_server\")\n t.start()\n return t\n\n\n# ═══════════════════════════════════════════════════════════════════════\n# WEBHOOK PUSH (fire-and-forget POST)\n# ═══════════════════════════════════════════════════════════════════════\ndef _webhook_push(signal: dict):\n \"\"\"POST signal JSON to configured webhook URLs (non-blocking, daemon thread).\"\"\"\n if not C.WEBHOOK_URLS:\n return\n # Filter by magnitude\n if signal.get(\"magnitude\", 0) \u003c C.WEBHOOK_MIN_MAGNITUDE:\n return\n # Filter by event type\n if C.WEBHOOK_EVENTS and signal.get(\"event_type\") not in C.WEBHOOK_EVENTS:\n return\n\n payload = json.dumps(signal, default=str).encode()\n\n def _post(url):\n try:\n req = Request(url, data=payload, method=\"POST\",\n headers={\"Content-Type\": \"application/json\",\n \"User-Agent\": \"MacroIntelligence/2.0\"})\n with urlopen(req, timeout=C.WEBHOOK_TIMEOUT_SEC):\n pass\n except Exception as e:\n _log(f\"Webhook POST to {url[:50]} failed: {e}\", \"WARN\")\n\n for url in C.WEBHOOK_URLS:\n threading.Thread(target=_post, args=(url,), daemon=True).start()\n\n\n# ═══════════════════════════════════════════════════════════════════════\n# SIGNAL ACCURACY TRACKING\n# ═══════════════════════════════════════════════════════════════════════\ndef _record_accuracy_checkpoint(signal: dict):\n \"\"\"Record BTC/ETH price at signal time for later accuracy check.\"\"\"\n if not C.ACCURACY_ENABLED:\n return\n direction = signal.get(\"direction\")\n if direction not in (\"bullish\", \"bearish\"):\n return\n with _state_lock:\n btc = _price_tickers.get(\"BTC\", {}).get(\"price\", 0)\n eth = _price_tickers.get(\"ETH\", {}).get(\"price\", 0)\n if not btc:\n return\n now = time.time()\n check_at = [now + h * 3600 for h in C.ACCURACY_CHECK_HOURS]\n _accuracy_pending.append({\n \"signal_ts\": signal[\"ts\"],\n \"event_type\": signal[\"event_type\"],\n \"direction\": direction,\n \"btc_price\": btc,\n \"eth_price\": eth,\n \"check_at\": check_at,\n })\n # Cap pending list\n if len(_accuracy_pending) > 500:\n _accuracy_pending[:] = _accuracy_pending[-500:]\n\n\ndef _check_accuracy():\n \"\"\"Background check: compare current prices to signal-time prices.\"\"\"\n if not C.ACCURACY_ENABLED or not _accuracy_pending:\n return\n now = time.time()\n with _state_lock:\n btc_now = _price_tickers.get(\"BTC\", {}).get(\"price\", 0)\n eth_now = _price_tickers.get(\"ETH\", {}).get(\"price\", 0)\n if not btc_now:\n return\n\n still_pending = []\n for entry in _accuracy_pending:\n remaining_checks = []\n for check_ts in entry[\"check_at\"]:\n if now >= check_ts:\n # Evaluate accuracy\n direction = entry[\"direction\"]\n btc_moved_up = btc_now > entry[\"btc_price\"]\n hit = (direction == \"bullish\" and btc_moved_up) or \\\n (direction == \"bearish\" and not btc_moved_up)\n et = entry[\"event_type\"]\n with _state_lock:\n if et not in _accuracy_results:\n _accuracy_results[et] = {\"hits\": 0, \"misses\": 0, \"checks\": 0}\n _accuracy_results[et][\"checks\"] += 1\n if hit:\n _accuracy_results[et][\"hits\"] += 1\n else:\n _accuracy_results[et][\"misses\"] += 1\n else:\n remaining_checks.append(check_ts)\n if remaining_checks:\n entry[\"check_at\"] = remaining_checks\n still_pending.append(entry)\n _accuracy_pending[:] = still_pending\n\n\ndef get_accuracy() -> dict:\n \"\"\"Return hit rates by event_type and overall.\"\"\"\n with _state_lock:\n results = {}\n total_hits = 0\n total_checks = 0\n for et, data in _accuracy_results.items():\n checks = data[\"checks\"]\n hits = data[\"hits\"]\n rate = hits / checks if checks > 0 else 0\n results[et] = {\"hits\": hits, \"misses\": data[\"misses\"],\n \"checks\": checks, \"hit_rate\": round(rate, 3)}\n total_hits += hits\n total_checks += checks\n overall_rate = total_hits / total_checks if total_checks > 0 else 0\n return {\"by_event_type\": results, \"overall_hit_rate\": round(overall_rate, 3),\n \"total_checks\": total_checks}\n\n\n# ═══════════════════════════════════════════════════════════════════════\n# TREND DETECTION\n# ═══════════════════════════════════════════════════════════════════════\ndef _detect_trend(event_type: str, urgency: float, magnitude: float) -> tuple[float, float, dict | None]:\n \"\"\"Check for trending event types (3+ in last hour).\n Returns (urgency_boost, magnitude_boost, meta_signal_or_None).\"\"\"\n now = time.time()\n hour_ago = now - 3600\n\n # Add current event\n _recent_event_types.append((now, event_type))\n\n # Prune old entries\n _recent_event_types[:] = [(ts, et) for ts, et in _recent_event_types if ts > hour_ago]\n\n # Count occurrences of this event type in last hour\n count = sum(1 for ts, et in _recent_event_types if et == event_type)\n\n if count >= 3:\n urgency_boost = 0.2\n magnitude_boost = 0.1\n meta_signal = None\n if count == 3:\n # Emit synthetic trend signal on the 3rd occurrence\n meta_signal = {\n \"event_type\": f\"trend_{event_type}\",\n \"text\": f\"Trend detected: {event_type} appeared {count} times in the last hour\",\n \"direction\": \"bullish\" if event_type in C.MACRO_PLAYBOOK and\n C.MACRO_PLAYBOOK[event_type].get(\"direction\") == \"bullish\" else \"bearish\",\n \"magnitude\": 0.8,\n \"urgency\": 0.9,\n \"affects\": C.MACRO_PLAYBOOK.get(event_type, {}).get(\"affects\", []),\n }\n return (urgency_boost, magnitude_boost, meta_signal)\n return (0, 0, None)\n\n\n# ═══════════════════════════════════════════════════════════════════════\n# FUZZY DEDUP (Jaccard similarity)\n# ═══════════════════════════════════════════════════════════════════════\ndef _jaccard_similarity(text_a: str, text_b: str) -> float:\n \"\"\"Compute Jaccard similarity between tokenized texts.\"\"\"\n tokens_a = set(re.findall(r'\\w+', text_a.lower()))\n tokens_b = set(re.findall(r'\\w+', text_b.lower()))\n if not tokens_a or not tokens_b:\n return 0.0\n intersection = tokens_a & tokens_b\n union = tokens_a | tokens_b\n return len(intersection) / len(union)\n\n\ndef _is_fuzzy_duplicate(text: str) -> bool:\n \"\"\"Check if text is too similar to recent signals (Jaccard).\"\"\"\n if not C.DEDUP_FUZZY_ENABLED:\n return False\n now = time.time()\n window = C.DEDUP_WINDOW_HOURS * 3600\n\n # Prune old entries\n _recent_texts[:] = [(ts, t) for ts, t in _recent_texts if now - ts \u003c window]\n\n for _, prev_text in _recent_texts[-100:]:\n if _jaccard_similarity(text, prev_text) >= C.DEDUP_FUZZY_THRESHOLD:\n return True\n\n _recent_texts.append((now, text))\n # Cap at 100\n if len(_recent_texts) > 100:\n _recent_texts[:] = _recent_texts[-100:]\n return False\n\n\n# ═══════════════════════════════════════════════════════════════════════\n# PUBLIC API — query functions\n# ═══════════════════════════════════════════════════════════════════════\ndef get_latest_signals(hours: float = 6, affects: str = \"\", direction: str = \"\",\n min_mag: float = 0.0, limit: int = 50) -> list[dict]:\n \"\"\"Get filtered signals.\"\"\"\n cutoff = time.time() - hours * 3600\n with _state_lock:\n results = []\n for s in reversed(_signals):\n if s[\"ts\"] \u003c cutoff:\n break\n if affects and affects not in s.get(\"affects\", []):\n continue\n if direction and s.get(\"direction\") != direction:\n continue\n if s.get(\"magnitude\", 0) \u003c min_mag:\n continue\n results.append(s)\n if len(results) >= limit:\n break\n return results\n\ndef get_sentiment(hours: float = 6) -> dict:\n \"\"\"Get aggregate sentiment over time window.\"\"\"\n cutoff = time.time() - hours * 3600\n sentiments = []\n with _state_lock:\n for s in reversed(_signals):\n if s[\"ts\"] \u003c cutoff:\n break\n sentiments.append(s.get(\"sentiment\", 0))\n if not sentiments:\n return {\"sentiment\": 0.0, \"regime\": \"neutral\", \"count\": 0}\n avg = sum(sentiments) / len(sentiments)\n regime = \"bullish\" if avg > 0.15 else (\"bearish\" if avg \u003c -0.15 else \"neutral\")\n return {\"sentiment\": round(avg, 3), \"regime\": regime, \"count\": len(sentiments)}\n\ndef get_regime(hours: float = 6) -> dict:\n \"\"\"Get market regime based on recent signals.\"\"\"\n s = get_sentiment(hours)\n return {\"regime\": s[\"regime\"], \"sentiment\": s[\"sentiment\"]}\n\ndef get_event_counts(hours: float = 6) -> dict:\n \"\"\"Count event types in time window.\"\"\"\n cutoff = time.time() - hours * 3600\n counts = defaultdict(int)\n with _state_lock:\n for s in reversed(_signals):\n if s[\"ts\"] \u003c cutoff:\n break\n counts[s[\"event_type\"]] += 1\n return dict(counts)\n\ndef get_polymarket() -> list[dict]:\n with _state_lock:\n return list(_polymarket)\n\ndef get_signals_summary(hours: float = 6) -> dict:\n \"\"\"All-in-one summary for downstream skills.\"\"\"\n sigs = get_latest_signals(hours=hours, limit=100)\n sent = get_sentiment(hours)\n events = get_event_counts(hours)\n return {\n \"sentiment\": sent[\"sentiment\"],\n \"regime\": sent[\"regime\"],\n \"signal_count\": len(sigs),\n \"event_counts\": events,\n \"top_events\": sorted(events.items(), key=lambda x: -x[1])[:5],\n \"polymarket\": get_polymarket(),\n \"latest_signals\": sigs[:10],\n }\n\ndef get_top_senders(limit: int = 10) -> list[dict]:\n \"\"\"Reputation leaderboard.\"\"\"\n with _state_lock:\n items = [(sid, r[\"score\"], r.get(\"hits\", 0)) for sid, r in _reputation.items()]\n items.sort(key=lambda x: -x[1])\n return [{\"sender\": s, \"score\": round(sc, 2), \"hits\": h}\n for s, sc, h in items[:limit]]\n\ndef get_source_breakdown() -> dict:\n \"\"\"Active sources with last-seen timestamps.\"\"\"\n with _state_lock:\n return {k: {\"last_seen\": int(v), \"ago_sec\": int(time.time() - v)}\n for k, v in _source_status.items()}\n\n# ═══════════════════════════════════════════════════════════════════════\n# DASHBOARD HTTP SERVER\n# ═══════════════════════════════════════════════════════════════════════\ndef _diverse_recent_signals(max_total: int = 0, per_source: int = 0) -> list:\n \"\"\"Return recent signals with per-source diversity quotas.\n\n Ensures minority sources (Finnhub, Polymarket, etc.) aren't buried\n by high-volume sources (OpenNews).\n \"\"\"\n if max_total \u003c= 0:\n max_total = getattr(C, \"DASHBOARD_MAX_SIGNALS\", 80)\n if per_source \u003c= 0:\n per_source = getattr(C, \"DASHBOARD_SOURCE_QUOTA\", 5)\n\n # Group signals by source_type (newest first)\n by_source: dict[str, list] = {}\n for s in reversed(_signals):\n src = s.get(\"source_type\", \"unknown\")\n by_source.setdefault(src, []).append(s)\n\n # Phase 1: Guarantee quota per source\n result = []\n used_ts = set()\n for src, sigs in by_source.items():\n for s in sigs[:per_source]:\n result.append(s)\n used_ts.add(s[\"ts\"])\n\n # Phase 2: Fill remaining slots by recency\n remaining = max_total - len(result)\n if remaining > 0:\n for s in reversed(_signals):\n if s[\"ts\"] not in used_ts:\n result.append(s)\n remaining -= 1\n if remaining \u003c= 0:\n break\n\n # Sort by timestamp descending (newest first)\n result.sort(key=lambda s: s[\"ts\"], reverse=True)\n return result[:max_total]\n\n\ndef _dashboard_api_data() -> dict:\n \"\"\"Full dashboard state.\"\"\"\n now = time.time()\n sent = get_sentiment(6)\n with _state_lock:\n recent = _diverse_recent_signals()\n stats_copy = dict(_stats)\n sources = dict(_source_status)\n return {\n \"ts\": int(now),\n \"uptime_sec\": int(now - stats_copy.get(\"start_ts\", now)),\n \"regime\": sent[\"regime\"],\n \"sentiment\": sent[\"sentiment\"],\n \"signals\": recent,\n \"stats\": stats_copy,\n \"polymarket\": get_polymarket(),\n \"event_counts\": get_event_counts(6),\n \"top_senders\": get_top_senders(10),\n \"source_status\": {k: {\"last_seen\": int(v), \"ago_sec\": int(now - v)}\n for k, v in sources.items()},\n \"telethon_active\": _telethon_available,\n \"fear_greed\": _fear_greed,\n \"fred_indicators\": _fred_indicators,\n \"price_tickers\": _price_tickers,\n \"opennews\": {\n \"article_count\": len(_opennews_articles),\n \"avg_score\": _opennews_avg_score(_opennews_windowed_articles(3600)),\n \"velocity\": _opennews_velocity(_opennews_windowed_articles(3600), 3600).get(\"overall\", 0),\n \"ws_alive\": _opennews_ws_alive,\n },\n }\n\nclass _DashboardHandler(SimpleHTTPRequestHandler):\n def log_message(self, format, *args):\n pass # Suppress HTTP logs\n\n def _json_response(self, data, status=200):\n body = json.dumps(data, default=str).encode()\n self.send_response(status)\n self.send_header(\"Content-Type\", \"application/json\")\n self.send_header(\"Access-Control-Allow-Origin\", \"*\")\n self.send_header(\"Content-Length\", str(len(body)))\n self.end_headers()\n self.wfile.write(body)\n\n def do_GET(self):\n parsed = urlparse(self.path)\n path = parsed.path\n params = parse_qs(parsed.query)\n\n def _p(key, default):\n return params.get(key, [default])[0]\n\n if path == \"/\" or path == \"/index.html\":\n html_path = _BASE_DIR / \"dashboard.html\"\n if html_path.exists():\n self.send_response(200)\n self.send_header(\"Content-Type\", \"text/html\")\n self.end_headers()\n self.wfile.write(html_path.read_bytes())\n else:\n self.send_response(404)\n self.end_headers()\n self.wfile.write(b\"dashboard.html not found\")\n return\n\n if path == \"/api/state\":\n self._json_response(_dashboard_api_data())\n elif path == \"/api/signals\":\n sigs = get_latest_signals(\n hours=float(_p(\"hours\", \"6\")),\n affects=_p(\"affects\", \"\"),\n direction=_p(\"direction\", \"\"),\n min_mag=float(_p(\"min_mag\", \"0\")),\n limit=int(_p(\"limit\", \"50\")),\n )\n self._json_response(sigs)\n elif path == \"/api/sentiment\":\n self._json_response(get_sentiment(float(_p(\"hours\", \"6\"))))\n elif path == \"/api/regime\":\n self._json_response(get_regime(float(_p(\"hours\", \"6\"))))\n elif path == \"/api/polymarket\":\n self._json_response(get_polymarket())\n elif path == \"/api/fng\":\n self._json_response(_fear_greed)\n elif path == \"/api/fred\":\n with _state_lock:\n self._json_response(dict(_fred_indicators))\n elif path == \"/api/prices\":\n with _state_lock:\n self._json_response(dict(_price_tickers))\n elif path == \"/api/senders\":\n self._json_response(get_top_senders(int(_p(\"limit\", \"10\"))))\n elif path == \"/api/events\":\n self._json_response(get_event_counts(float(_p(\"hours\", \"6\"))))\n elif path == \"/api/summary\":\n self._json_response(get_signals_summary(float(_p(\"hours\", \"6\"))))\n elif path == \"/api/health\":\n now = time.time()\n with _state_lock:\n last_sig_ts = _signals[-1][\"ts\"] if _signals else 0\n sig_total = len(_signals)\n sources = dict(_source_status)\n self._json_response({\n \"status\": \"ok\",\n \"version\": _VERSION,\n \"uptime_sec\": int(now - _stats.get(\"start_ts\", now)),\n \"last_signal_ts\": last_sig_ts,\n \"last_signal_ago_sec\": int(now - last_sig_ts) if last_sig_ts else None,\n \"signals_total\": sig_total,\n \"ws_clients\": len(_ws_clients),\n \"sources\": {k: {\"last_seen\": int(v), \"ago_sec\": int(now - v)}\n for k, v in sources.items()},\n \"telethon_active\": _telethon_available,\n \"opennews_ws_alive\": _opennews_ws_alive,\n \"opennews_article_count\": len(_opennews_articles),\n })\n elif path == \"/api/accuracy\":\n self._json_response(get_accuracy())\n elif path == \"/api/opennews/state\":\n window = int(_p(\"window\", \"3600\"))\n with _state_lock:\n recent = list(_opennews_articles[-100:])\n recent.reverse()\n self._json_response({\n \"articles\": recent,\n \"metrics\": compute_opennews_metrics(window),\n \"intelligence\": compute_opennews_intelligence(window),\n \"source_health\": _opennews_source_health(),\n \"ws_alive\": _opennews_ws_alive,\n })\n elif path == \"/api/opennews/articles\":\n arts = _filter_opennews_articles(\n engine=_p(\"engine\", \"\"),\n signal=_p(\"signal\", \"\"),\n min_score=int(_p(\"min_score\", \"0\")),\n coin=_p(\"coin\", \"\"),\n q=_p(\"q\", \"\"),\n limit=int(_p(\"limit\", \"100\")),\n )\n self._json_response(arts)\n elif path == \"/api/opennews/metrics\":\n window = int(_p(\"window\", \"3600\"))\n self._json_response(compute_opennews_metrics(window))\n elif path == \"/api/opennews/sources\":\n self._json_response(_opennews_source_health())\n else:\n self.send_response(404)\n self.end_headers()\n\n def do_POST(self):\n parsed = urlparse(self.path)\n path = parsed.path\n content_len = int(self.headers.get(\"Content-Length\", 0))\n body = self.rfile.read(content_len) if content_len else b\"\"\n\n if path == \"/api/vote\":\n try:\n data = json.loads(body) if body else {}\n key = data.get(\"signal_key\", \"\")\n vote = int(data.get(\"vote\", 0)) # +1 or -1\n if key and vote in (1, -1):\n with _state_lock:\n _signal_votes[key] = _signal_votes.get(key, 0) + vote\n self._json_response({\"status\": \"ok\", \"net_votes\": _signal_votes.get(key, 0)})\n else:\n self._json_response({\"error\": \"need signal_key and vote (+1/-1)\"}, 400)\n except Exception as e:\n self._json_response({\"error\": str(e)}, 400)\n else:\n self.send_response(404)\n self.end_headers()\n\n def do_OPTIONS(self):\n self.send_response(200)\n self.send_header(\"Access-Control-Allow-Origin\", \"*\")\n self.send_header(\"Access-Control-Allow-Methods\", \"GET, POST, OPTIONS\")\n self.send_header(\"Access-Control-Allow-Headers\", \"Content-Type\")\n self.end_headers()\n\n# ═══════════════════════════════════════════════════════════════════════\n# SETUP MODE — list Telegram groups\n# ═══════════════════════════════════════════════════════════════════════\nasync def _setup_mode():\n \"\"\"Interactive: list all Telegram groups/channels for config.\"\"\"\n api_id = C.TELETHON_API_ID or int(os.environ.get(\"TG_API_ID\", \"0\"))\n api_hash = C.TELETHON_API_HASH or os.environ.get(\"TG_API_HASH\", \"\")\n if not api_id or not api_hash:\n print(\"Set TELETHON_API_ID and TELETHON_API_HASH in config.py first.\")\n return\n\n client = TelegramClient(str(_BASE_DIR / C.SESSION_NAME), api_id, api_hash)\n await client.start()\n print(\"\\nYour Telegram Groups & Channels:\\n\")\n print(f\"{'Type':\u003c10} {'ID':\u003c20} {'Title'}\")\n print(\"-\" * 60)\n\n async for dialog in client.iter_dialogs():\n entity = dialog.entity\n if isinstance(entity, (Channel, Chat)):\n dtype = \"channel\" if getattr(entity, 'broadcast', False) else \"group\"\n print(f\"{dtype:\u003c10} {entity.id:\u003c20} {dialog.name}\")\n\n await client.disconnect()\n print(\"\\nAdd IDs or usernames to config.py GROUPS/CHANNELS dicts.\")\n\n# ═══════════════════════════════════════════════════════════════════════\n# COMPILE REGEX CACHES\n# ═══════════════════════════════════════════════════════════════════════\ndef _compile_patterns():\n \"\"\"Pre-compile all regex patterns for performance.\"\"\"\n global _macro_regex, _noise_regex\n for event_type, patterns in C.MACRO_KEYWORDS.items():\n _macro_regex[event_type] = [re.compile(p) for p in patterns]\n _noise_regex = [re.compile(p, re.IGNORECASE) for p in C.NOISE_SKIP_PATTERNS]\n\n# ═══════════════════════════════════════════════════════════════════════\n# MAIN\n# ═══════════════════════════════════════════════════════════════════════\ndef main():\n # Setup mode\n if len(sys.argv) > 1 and sys.argv[1] == \"setup\":\n if not _telethon_available:\n print(\"Install telethon first: pip install telethon\")\n return\n import asyncio\n asyncio.run(_setup_mode())\n return\n\n _compile_patterns()\n _load_state()\n _stats[\"start_ts\"] = time.time()\n\n _log(\"=\" * 60)\n _log(f\"Macro Intelligence Skill v{_VERSION} — Intelligence Feed\")\n _log(f\"Dashboard: http://localhost:{C.DASHBOARD_PORT}\")\n _log(f\"WebSocket: {'ws://localhost:' + str(C.WS_PORT) if C.WS_ENABLED else 'disabled'}\")\n _log(f\"Telethon: {'available' if _telethon_available else 'NOT installed'}\")\n _log(f\"OpenNews: {'enabled' if C.OPENNEWS_ENABLED and C.OPENNEWS_TOKEN else 'disabled'}\")\n _log(f\"Finnhub: {'enabled' if C.FINNHUB_ENABLED and C.FINNHUB_API_KEY else 'disabled'}\")\n _log(f\"FRED: {'enabled' if C.FRED_ENABLED and C.FRED_API_KEY else 'disabled'}\")\n _log(f\"CryptoPanic: {'enabled' if C.CRYPTOPANIC_ENABLED and C.CRYPTOPANIC_TOKEN else 'disabled'}\")\n _log(f\"RSS feeds: {len(C.RSS_FEEDS)} configured\" if C.RSS_ENABLED else \"RSS: disabled\")\n _log(f\"Webhooks: {len(C.WEBHOOK_URLS)} configured\" if C.WEBHOOK_URLS else \"Webhooks: none\")\n _log(f\"Accuracy: {'enabled' if C.ACCURACY_ENABLED else 'disabled'}\")\n _log(f\"Fuzzy dedup: {'enabled' if C.DEDUP_FUZZY_ENABLED else 'disabled'}\")\n _log(\"=\" * 60)\n\n # Start WebSocket server (if enabled)\n if C.WS_ENABLED:\n _start_ws_server_thread()\n\n # Start news collector thread\n news_thread = threading.Thread(target=_news_collector_loop, daemon=True, name=\"news_collector\")\n news_thread.start()\n\n # Start Telegram collector (if available)\n if _telethon_available:\n _start_telethon_thread()\n else:\n _log(\"Telethon not installed — run: pip install telethon\", \"WARN\")\n\n # Start 6551.io OpenNews WebSocket (if configured)\n if C.OPENNEWS_ENABLED and C.OPENNEWS_TOKEN:\n _start_opennews_thread()\n _log(\"OpenNews: WebSocket thread started\")\n else:\n _log(\"OpenNews: disabled (no OPENNEWS_TOKEN)\", \"WARN\")\n\n # Start HTTP dashboard\n server = HTTPServer((\"0.0.0.0\", C.DASHBOARD_PORT), _DashboardHandler)\n _log(f\"HTTP server listening on :{C.DASHBOARD_PORT}\")\n\n try:\n server.serve_forever()\n except KeyboardInterrupt:\n _log(\"Shutting down...\")\n _save_state()\n server.shutdown()\n\nif __name__ == \"__main__\":\n main()\n","content_type":"text/x-python; charset=utf-8","language":"python","size":115437,"content_sha256":"2f3feb1ed23a09a01640523016070d0ae32d23fdfb7c327827e2606aabffd3a2"},{"filename":"plugin.yaml","content":"schema_version: 1\nname: \"macro-intelligence\"\nversion: \"1.0.0\"\ndescription: \"Unified macro intelligence feed — reads 7 sources, classifies events, scores sentiment, generates AI insights, exposes signals via HTTP API\"\nauthor:\n name: \"victorlee\"\n github: \"VibeCodeDaddy69\"\nlicense: MIT\ncategory: strategy\ntags:\n - macro\n - news-aggregator\n - sentiment\n - signals\n - fred\n - finnhub\n\ncomponents:\n skill:\n dir: \".\"\n\napi_calls: []\ntype: community-developer\n","content_type":"application/yaml; charset=utf-8","language":"yaml","size":466,"content_sha256":"b6f55e0c12f5c0b798f858afaf71c14251cb1578c53f6651e59fc33d954a79eb"},{"filename":"README.md","content":"# Macro Intelligence\n\nMacro Intelligence trading skill\n\n## Prerequisites\n\n- **onchainos CLI** >= 2.1.0 — [install](https://docs.onchainos.com)\n- **Python** >= 3.9\n- Agentic wallet logged in: `onchainos wallet login`\n\n## Install\n\n```bash\npip install -r requirements.txt\n```\n\n## Quick Start\n\n```bash\n# 1. Login to wallet\nonchainos wallet login\n\n# 2. Start the dashboard\npython3 macro_news.py\n# Open http://localhost:3252\n```\n\n## Configuration\n\nEdit `config.py` to adjust parameters. Hot-reload supported (no restart needed).\n\n**Safe defaults**: The skill starts in paper/dry-run mode by default. Switch to live trading only after reviewing config.\n\n## Risk Warning\n\nThis skill is for educational and research purposes only. Trading involves risk. Review all parameters carefully before enabling live mode.\n\n## License\n\nMIT\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":823,"content_sha256":"30805bace9f1f911a14bdde1a280a2bfe608c9be9f86ad613a1a4a14b483bdea"},{"filename":"requirements.txt","content":"websockets>=12.0\n","content_type":"text/plain; charset=utf-8","language":null,"size":17,"content_sha256":"09fd1c42119d9259fc7f4bf9206aaca1d7980618352bd6bc4aa3a69578cd17ac"},{"filename":"SUMMARY.md","content":"## Overview\n\nMacro Intelligence is a unified macro signal feed that reads 7 data sources, classifies macro events, scores market sentiment, and exposes real-time regime signals via a local HTTP API for other skills and agents to consume.\n\nCore operations:\n\n- Ingest macro events from 7 sources (Fed, CPI, gold, tariffs, whale flows, and more)\n- Classify events by type (rate decision, credit expansion, risk-on/off regime)\n- Score sentiment and generate AI insights per event\n- Expose live signals via a local HTTP API at `http://localhost:3260/signals`\n- Feed signals into downstream skills (e.g. rwa-alpha for trade confirmation)\n\nTags: `macro` `sentiment` `news` `signals` `fed` `cpi` `gold` `onchainos`\n\n## Prerequisites\n\n- No IP/region restrictions\n- No wallet or on-chain operations required (read-only signal feed)\n- Python 3.8+ (standard library only — no `pip install` required)\n- (Optional) Anthropic API key for AI-generated insights (`ANTHROPIC_API_KEY` env var)\n- Downstream skills (e.g. rwa-alpha) must be configured to point to `http://localhost:3260`\n\n## Quick Start\n\n1. **Install the skill**: `plugin-store install macro-intelligence`\n2. **Start the feed**: Run `python3 macro.py` — it begins ingesting sources immediately\n3. **Query signals**: `curl http://localhost:3260/signals` to see the latest classified macro events and sentiment scores\n4. **Connect downstream skills**: In `rwa-alpha/config.py`, set `MACRO_API = \"http://localhost:3260\"` to use this feed as a signal gate\n5. **Monitor the feed**: Check `http://localhost:3260` in your browser for a real-time view of macro events and regime scores\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":1628,"content_sha256":"195f6f07ed7dce7feef1c965d14abd1c4e4f0760c69a8d2768a1047c19e8a10b"}],"content_json":{"type":"doc","content":[{"type":"heading","attrs":{"level":1},"content":[{"text":"Macro Intelligence Skill v1.0 — Agent Instructions","type":"text"}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"Purpose","type":"text"}]},{"type":"paragraph","content":[{"text":"Unified macro intelligence feed. Reads news from 7 sources (NewsNow, Polymarket, Telegram, 6551.io OpenNews, Finnhub, FRED, Fear & Greed Index), classifies macro events, scores sentiment, generates AI insights, and exposes clean signals via HTTP API. ","type":"text"},{"text":"No trading logic","type":"text","marks":[{"type":"strong"}]},{"text":" — downstream skills consume signals.","type":"text"}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"Architecture","type":"text"}]},{"type":"code_block","attrs":{"wrap":false,"language":""},"content":[{"text":" NewsNow (HTTP, 120s) ──────┐\n Polymarket (HTTP, 120s) ────┤\n Finnhub (HTTP, 180s) ───────┤──→ process_signal() ──→ UnifiedSignal ──→ API :3252\n 6551.io OpenNews (WebSocket)─┤ │ noise filter │ classify │ sentiment\n Telegram (Telethon WS) ─────┘ │ dedup │ reputation │ AI insight\n │ │ token extract │ store\n FRED (HTTP, 3600s) ──────────→ context data ──→ /api/fred + significant change → process_signal()\n Fear & Greed (HTTP, 300s) ───→ context data ──→ /api/fng\n Price Tickers (HTTP, 60s) ───→ context data ──→ /api/prices (SPY, GLD, SLV, BTC, ETH)","type":"text"}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"Startup Protocol","type":"text"}]},{"type":"ordered_list","attrs":{"order":1,"listStyle":"number"},"content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"python3 macro_news.py","type":"text","marks":[{"type":"code_inline"}]},{"text":" — starts all collectors + HTTP server on ","type":"text"},{"text":":3252","type":"text","marks":[{"type":"code_inline"}]}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"python3 macro_news.py setup","type":"text","marks":[{"type":"code_inline"}]},{"text":" — interactive mode to list Telegram groups/channels","type":"text"}]}]}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Requirements","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Python 3.9+","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"pip install telethon","type":"text","marks":[{"type":"code_inline"}]},{"text":" (optional — 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Important: agents should read /llm.txt, /llms.txt, or /.well-known/skills.json to discover the public Skillopedia API.