Initial release — Crucix Intelligence Engine v2.0.0
26-source OSINT intelligence engine with live Jarvis dashboard, auto-refresh via SSE, optional LLM layer (4 providers), delta/memory system, and Telegram breaking news alerts. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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calesthio
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lib/llm/ideas.mjs
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189
lib/llm/ideas.mjs
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// LLM-Powered Trade Ideas — generates actionable ideas from sweep data + delta context
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/**
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* Generate LLM-enhanced trade ideas from sweep data.
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* @param {LLMProvider} provider - configured LLM provider
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* @param {object} sweepData - synthesized dashboard data
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* @param {object|null} delta - delta from last sweep
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* @param {Array} previousIdeas - ideas from previous runs (for dedup)
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* @returns {Promise<Array>} - array of idea objects
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*/
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export async function generateLLMIdeas(provider, sweepData, delta, previousIdeas = []) {
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if (!provider?.isConfigured) return null;
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let context;
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try {
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context = compactSweepForLLM(sweepData, delta, previousIdeas);
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} catch (err) {
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console.error('[LLM Ideas] Failed to compact sweep data:', err.message);
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return null;
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}
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const systemPrompt = `You are a quantitative analyst at a macro intelligence firm. You receive structured OSINT + economic data from 25 sources and produce 5-8 actionable trade ideas.
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Rules:
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- Each idea must cite specific data points from the input
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- Include entry rationale, risk factors, and time horizon
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- Blend geopolitical, economic, and market signals — cross-correlate across domains
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- Be specific: name instruments (tickers, futures, ETFs), not vague sectors
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- If delta shows significant changes, lead with those
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- Do NOT repeat ideas from the "previous ideas" list unless conditions have materially changed
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- Rate confidence: HIGH (multiple confirming signals), MEDIUM (thesis supported), LOW (speculative)
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Output ONLY valid JSON array. Each object:
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{
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"title": "Short title (max 10 words)",
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"type": "LONG|SHORT|HEDGE|WATCH|AVOID",
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"ticker": "Primary instrument",
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"confidence": "HIGH|MEDIUM|LOW",
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"rationale": "2-3 sentence explanation citing specific data",
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"risk": "Key risk factor",
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"horizon": "Intraday|Days|Weeks|Months",
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"signals": ["signal1", "signal2"]
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}`;
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try {
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const result = await provider.complete(systemPrompt, context, { maxTokens: 4096, timeout: 90000 });
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const ideas = parseIdeasResponse(result.text);
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if (ideas && ideas.length > 0) {
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return ideas;
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}
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console.warn('[LLM Ideas] No valid ideas parsed from response');
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return null;
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} catch (err) {
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console.error('[LLM Ideas] Generation failed:', err.message);
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return null;
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}
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}
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/**
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* Compact sweep data to ~8KB for token efficiency.
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*/
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function compactSweepForLLM(data, delta, previousIdeas) {
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const sections = [];
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// Economic indicators
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if (data.fred?.length) {
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const key = data.fred.filter(f => ['VIXCLS', 'DFF', 'DGS10', 'DGS2', 'T10Y2Y', 'BAMLH0A0HYM2', 'DTWEXBGS', 'MORTGAGE30US'].includes(f.id));
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sections.push(`ECONOMIC: ${key.map(f => `${f.id}=${f.value}${f.momChange ? ` (${f.momChange > 0 ? '+' : ''}${f.momChange})` : ''}`).join(', ')}`);
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}
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// Energy
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if (data.energy) {
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sections.push(`ENERGY: WTI=$${data.energy.wti}, Brent=$${data.energy.brent}, NatGas=$${data.energy.natgas}, CrudeStocks=${data.energy.crudeStocks}bbl`);
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}
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// BLS
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if (data.bls?.length) {
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sections.push(`LABOR: ${data.bls.map(b => `${b.id}=${b.value}`).join(', ')}`);
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}
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// Treasury
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if (data.treasury) {
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sections.push(`TREASURY: totalDebt=$${data.treasury}T`);
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}
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// Supply chain
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if (data.gscpi) {
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sections.push(`SUPPLY_CHAIN: GSCPI=${data.gscpi.value} (${data.gscpi.interpretation})`);
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}
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// Geopolitical signals
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const urgentPosts = (data.tg?.urgent || []).slice(0, 5);
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if (urgentPosts.length) {
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sections.push(`URGENT_OSINT:\n${urgentPosts.map(p => `- ${(p.text || '').substring(0, 120)}`).join('\n')}`);
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}
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// Thermal / fire detections
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if (data.thermal?.length) {
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const hotRegions = data.thermal.filter(t => t.det > 10).map(t => `${t.region}: ${t.det} detections (${t.hc} high-conf)`);
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if (hotRegions.length) sections.push(`THERMAL: ${hotRegions.join(', ')}`);
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}
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// Air activity
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if (data.air?.length) {
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const airSum = data.air.map(a => `${a.region}: ${a.total} aircraft`);
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sections.push(`AIR_ACTIVITY: ${airSum.join(', ')}`);
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}
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// Nuclear
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if (data.nuke?.length) {
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const anomalies = data.nuke.filter(n => n.anom);
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if (anomalies.length) sections.push(`NUCLEAR_ANOMALY: ${anomalies.map(n => `${n.site}: ${n.cpm}cpm`).join(', ')}`);
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}
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// WHO alerts
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if (data.who?.length) {
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sections.push(`WHO_ALERTS: ${data.who.slice(0, 3).map(w => w.title).join('; ')}`);
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}
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// Defense spending
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if (data.defense?.length) {
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const topContracts = data.defense.slice(0, 3).map(d => `$${((d.amount || 0) / 1e6).toFixed(0)}M to ${d.recipient}`);
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sections.push(`DEFENSE_CONTRACTS: ${topContracts.join(', ')}`);
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}
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// Delta context
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if (delta?.summary) {
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sections.push(`\nDELTA_SINCE_LAST_SWEEP: direction=${delta.summary.direction}, changes=${delta.summary.totalChanges}, critical=${delta.summary.criticalChanges}`);
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if (delta.signals?.escalated?.length) {
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sections.push(`ESCALATED: ${delta.signals.escalated.map(s => `${s.label}: ${s.previous}→${s.current} (${(s.changePct||0) > 0 ? '+' : ''}${(s.changePct||0).toFixed(1)}%)`).join(', ')}`);
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}
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if (delta.signals?.new?.length) {
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sections.push(`NEW_SIGNALS: ${delta.signals.new.map(s => s.label || s.text?.substring(0, 60)).join('; ')}`);
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}
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}
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// Previous ideas (for dedup)
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if (previousIdeas.length) {
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sections.push(`\nPREVIOUS_IDEAS (avoid repeating):\n${previousIdeas.map(i => `- ${i.title} [${i.type}]`).join('\n')}`);
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}
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return sections.join('\n');
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}
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/**
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* Parse LLM response into ideas array. Handles markdown code blocks.
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*/
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function parseIdeasResponse(text) {
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if (!text) return null;
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// Strip markdown code block wrappers
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let cleaned = text.trim();
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if (cleaned.startsWith('```')) {
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cleaned = cleaned.replace(/^```(?:json)?\n?/, '').replace(/\n?```$/, '');
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}
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try {
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const parsed = JSON.parse(cleaned);
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if (!Array.isArray(parsed)) return null;
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// Validate each idea has required fields
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return parsed.filter(idea =>
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idea.title && idea.type && idea.confidence
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).map(idea => ({
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title: idea.title,
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type: idea.type,
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ticker: idea.ticker || '',
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confidence: idea.confidence,
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rationale: idea.rationale || '',
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risk: idea.risk || '',
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horizon: idea.horizon || '',
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signals: idea.signals || [],
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source: 'llm',
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}));
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} catch {
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// Try to extract JSON array from mixed text
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const match = cleaned.match(/\[[\s\S]*\]/);
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if (match) {
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try {
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const arr = JSON.parse(match[0]);
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return arr.filter(i => i.title && i.type).map(idea => ({
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...idea,
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source: 'llm',
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}));
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} catch { /* give up */ }
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}
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return null;
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}
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}
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