d803bfe2b1
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
723 lines
22 KiB
JavaScript
723 lines
22 KiB
JavaScript
/**
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* Sentiment Alpha Pipeline
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*
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* PRODUCTION: LLM-based sentiment analysis for trading alpha generation
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*
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* Research basis:
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* - 3% annual excess returns from sentiment (2024)
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* - 50.63% return over 28 months (backtested)
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* - FinBERT embeddings outperform technical signals
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*
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* Features:
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* - Multi-source sentiment aggregation (news, social, earnings)
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* - Sentiment scoring and signal generation
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* - Calibration for trading decisions
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* - Integration with Kelly criterion for sizing
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*/
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// Sentiment Configuration
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const sentimentConfig = {
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// Source weights
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sources: {
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news: { weight: 0.40, decay: 0.95 }, // News articles
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social: { weight: 0.25, decay: 0.90 }, // Social media
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earnings: { weight: 0.25, decay: 0.99 }, // Earnings calls
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analyst: { weight: 0.10, decay: 0.98 } // Analyst reports
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},
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// Sentiment thresholds
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thresholds: {
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strongBullish: 0.6,
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bullish: 0.3,
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neutral: [-0.1, 0.1],
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bearish: -0.3,
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strongBearish: -0.6
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},
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// Signal generation
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signals: {
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minConfidence: 0.6,
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lookbackDays: 7,
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smoothingWindow: 3,
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contrarianThreshold: 0.8 // Extreme sentiment = contrarian signal
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},
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// Alpha calibration
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calibration: {
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historicalAccuracy: 0.55, // Historical prediction accuracy
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shrinkageFactor: 0.3 // Shrink extreme predictions
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}
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};
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/**
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* Lexicon-based Sentiment Analyzer
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* Fast, interpretable sentiment scoring
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*/
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class LexiconAnalyzer {
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constructor() {
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// Financial sentiment lexicon (simplified)
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this.positiveWords = new Set([
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'growth', 'profit', 'gains', 'bullish', 'upgrade', 'beat', 'exceeded',
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'outperform', 'strong', 'surge', 'rally', 'breakthrough', 'innovation',
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'record', 'momentum', 'optimistic', 'recovery', 'expansion', 'success',
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'opportunity', 'positive', 'increase', 'improve', 'advance', 'boost'
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]);
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this.negativeWords = new Set([
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'loss', 'decline', 'bearish', 'downgrade', 'miss', 'below', 'weak',
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'underperform', 'crash', 'plunge', 'risk', 'concern', 'warning',
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'recession', 'inflation', 'uncertainty', 'volatility', 'default',
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'bankruptcy', 'negative', 'decrease', 'drop', 'fall', 'cut', 'layoff'
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]);
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this.intensifiers = new Set([
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'very', 'extremely', 'significantly', 'strongly', 'substantially',
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'dramatically', 'sharply', 'massive', 'huge', 'major'
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]);
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this.negators = new Set([
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'not', 'no', 'never', 'neither', 'without', 'hardly', 'barely'
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]);
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}
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// Optimized analyze (avoids regex, minimizes allocations)
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analyze(text) {
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const lowerText = text.toLowerCase();
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let score = 0;
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let positiveCount = 0;
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let negativeCount = 0;
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let intensifierActive = false;
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let negatorActive = false;
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let wordCount = 0;
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// Extract words without regex (faster)
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let wordStart = -1;
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const len = lowerText.length;
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for (let i = 0; i <= len; i++) {
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const c = i < len ? lowerText.charCodeAt(i) : 32; // Space at end
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const isWordChar = (c >= 97 && c <= 122) || (c >= 48 && c <= 57) || c === 95; // a-z, 0-9, _
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if (isWordChar && wordStart === -1) {
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wordStart = i;
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} else if (!isWordChar && wordStart !== -1) {
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const word = lowerText.slice(wordStart, i);
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wordStart = -1;
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wordCount++;
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// Check for intensifiers and negators
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if (this.intensifiers.has(word)) {
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intensifierActive = true;
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continue;
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}
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if (this.negators.has(word)) {
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negatorActive = true;
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continue;
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}
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// Score sentiment words
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let wordScore = 0;
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if (this.positiveWords.has(word)) {
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wordScore = 1;
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positiveCount++;
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} else if (this.negativeWords.has(word)) {
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wordScore = -1;
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negativeCount++;
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}
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// Apply modifiers
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if (wordScore !== 0) {
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if (intensifierActive) wordScore *= 1.5;
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if (negatorActive) wordScore *= -1;
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score += wordScore;
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}
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// Reset modifiers
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intensifierActive = false;
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negatorActive = false;
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}
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}
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// Normalize score
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const totalSentimentWords = positiveCount + negativeCount;
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const normalizedScore = totalSentimentWords > 0
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? score / (totalSentimentWords * 1.5)
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: 0;
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return {
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score: Math.max(-1, Math.min(1, normalizedScore)),
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positiveCount,
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negativeCount,
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totalWords: wordCount,
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confidence: Math.min(1, totalSentimentWords / 10)
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};
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}
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}
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/**
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* Embedding-based Sentiment Analyzer
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* Simulates FinBERT-style deep learning analysis
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*/
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class EmbeddingAnalyzer {
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constructor() {
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// Simulated embedding weights (in production, use actual model)
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this.embeddingDim = 64;
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this.sentimentProjection = Array(this.embeddingDim).fill(null)
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.map(() => (Math.random() - 0.5) * 0.1);
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}
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// Simulate text embedding
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embed(text) {
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const words = text.toLowerCase().split(/\s+/);
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const embedding = new Array(this.embeddingDim).fill(0);
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// Simple hash-based embedding simulation
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for (const word of words) {
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const hash = this.hashString(word);
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for (let i = 0; i < this.embeddingDim; i++) {
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embedding[i] += Math.sin(hash * (i + 1)) / words.length;
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}
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}
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return embedding;
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}
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hashString(str) {
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let hash = 0;
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for (let i = 0; i < str.length; i++) {
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hash = ((hash << 5) - hash) + str.charCodeAt(i);
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hash = hash & hash;
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}
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return hash;
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}
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analyze(text) {
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const embedding = this.embed(text);
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// Project to sentiment score
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let score = 0;
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for (let i = 0; i < this.embeddingDim; i++) {
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score += embedding[i] * this.sentimentProjection[i];
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}
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// Normalize
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score = Math.tanh(score * 10);
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return {
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score,
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embedding: embedding.slice(0, 8), // Return first 8 dims
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confidence: Math.abs(score)
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};
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}
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}
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/**
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* Sentiment Source Aggregator
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* Combines multiple sentiment sources with decay
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*/
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class SentimentAggregator {
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constructor(config = sentimentConfig) {
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this.config = config;
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this.lexiconAnalyzer = new LexiconAnalyzer();
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this.embeddingAnalyzer = new EmbeddingAnalyzer();
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this.sentimentHistory = new Map(); // symbol -> sentiment history
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}
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// Add sentiment observation
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addObservation(symbol, source, text, timestamp = Date.now()) {
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if (!this.sentimentHistory.has(symbol)) {
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this.sentimentHistory.set(symbol, []);
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}
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// Analyze with both methods
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const lexicon = this.lexiconAnalyzer.analyze(text);
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const embedding = this.embeddingAnalyzer.analyze(text);
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// Combine scores
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const combinedScore = 0.4 * lexicon.score + 0.6 * embedding.score;
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const combinedConfidence = Math.sqrt(lexicon.confidence * embedding.confidence);
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const observation = {
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timestamp,
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source,
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score: combinedScore,
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confidence: combinedConfidence,
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lexiconScore: lexicon.score,
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embeddingScore: embedding.score,
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text: text.substring(0, 100)
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};
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this.sentimentHistory.get(symbol).push(observation);
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// Limit history size
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const history = this.sentimentHistory.get(symbol);
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if (history.length > 1000) {
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history.splice(0, history.length - 1000);
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}
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return observation;
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}
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// Get aggregated sentiment for symbol
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getAggregatedSentiment(symbol, lookbackMs = 7 * 24 * 60 * 60 * 1000) {
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const history = this.sentimentHistory.get(symbol);
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if (!history || history.length === 0) {
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return { score: 0, confidence: 0, count: 0 };
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}
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const cutoff = Date.now() - lookbackMs;
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const recent = history.filter(h => h.timestamp >= cutoff);
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if (recent.length === 0) {
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return { score: 0, confidence: 0, count: 0 };
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}
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// Weight by source, recency, and confidence
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let weightedSum = 0;
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let totalWeight = 0;
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const sourceCounts = {};
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for (const obs of recent) {
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const sourceConfig = this.config.sources[obs.source] || { weight: 0.25, decay: 0.95 };
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const age = (Date.now() - obs.timestamp) / (24 * 60 * 60 * 1000); // days
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const decayFactor = Math.pow(sourceConfig.decay, age);
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const weight = sourceConfig.weight * decayFactor * obs.confidence;
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weightedSum += obs.score * weight;
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totalWeight += weight;
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sourceCounts[obs.source] = (sourceCounts[obs.source] || 0) + 1;
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}
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const aggregatedScore = totalWeight > 0 ? weightedSum / totalWeight : 0;
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const confidence = Math.min(1, totalWeight / 2); // Confidence based on weight
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return {
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score: aggregatedScore,
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confidence,
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count: recent.length,
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sourceCounts,
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dominant: Object.entries(sourceCounts).sort((a, b) => b[1] - a[1])[0]?.[0]
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};
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}
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// Generate trading signal
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generateSignal(symbol) {
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const sentiment = this.getAggregatedSentiment(symbol);
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if (sentiment.confidence < this.config.signals.minConfidence) {
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return {
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signal: 'HOLD',
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reason: 'low_confidence',
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sentiment
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};
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}
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// Check for contrarian opportunity (extreme sentiment)
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if (Math.abs(sentiment.score) >= this.config.signals.contrarianThreshold) {
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return {
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signal: sentiment.score > 0 ? 'CONTRARIAN_SELL' : 'CONTRARIAN_BUY',
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reason: 'extreme_sentiment',
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sentiment,
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warning: 'Contrarian signal - high risk'
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};
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}
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// Standard signals
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const thresholds = this.config.thresholds;
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let signal, strength;
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if (sentiment.score >= thresholds.strongBullish) {
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signal = 'STRONG_BUY';
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strength = 'high';
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} else if (sentiment.score >= thresholds.bullish) {
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signal = 'BUY';
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strength = 'medium';
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} else if (sentiment.score <= thresholds.strongBearish) {
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signal = 'STRONG_SELL';
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strength = 'high';
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} else if (sentiment.score <= thresholds.bearish) {
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signal = 'SELL';
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strength = 'medium';
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} else {
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signal = 'HOLD';
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strength = 'low';
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}
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return {
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signal,
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strength,
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sentiment,
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calibratedProbability: this.calibrateProbability(sentiment.score)
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};
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}
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// Calibrate sentiment to win probability
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calibrateProbability(sentimentScore) {
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// Map sentiment [-1, 1] to probability [0.3, 0.7]
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// Apply shrinkage toward 0.5
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const rawProb = 0.5 + sentimentScore * 0.2;
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const shrinkage = this.config.calibration.shrinkageFactor;
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const calibrated = rawProb * (1 - shrinkage) + 0.5 * shrinkage;
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return Math.max(0.3, Math.min(0.7, calibrated));
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}
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}
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/**
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* News Sentiment Stream Processor
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* Processes incoming news for real-time sentiment
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*/
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class NewsSentimentStream {
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constructor(config = sentimentConfig) {
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this.aggregator = new SentimentAggregator(config);
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this.alerts = [];
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}
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// Process news item
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processNews(item) {
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const { symbol, headline, source, timestamp } = item;
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const observation = this.aggregator.addObservation(
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symbol,
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source || 'news',
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headline,
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timestamp || Date.now()
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);
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// Check for significant sentiment
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if (Math.abs(observation.score) >= 0.5 && observation.confidence >= 0.6) {
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this.alerts.push({
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timestamp: Date.now(),
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symbol,
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score: observation.score,
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headline: headline.substring(0, 80)
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});
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}
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return observation;
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}
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// Process batch of news
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processBatch(items) {
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return items.map(item => this.processNews(item));
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}
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// Get signals for all tracked symbols
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getAllSignals() {
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const signals = {};
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for (const symbol of this.aggregator.sentimentHistory.keys()) {
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signals[symbol] = this.aggregator.generateSignal(symbol);
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}
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return signals;
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}
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// Get recent alerts
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getAlerts(limit = 10) {
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return this.alerts.slice(-limit);
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}
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}
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/**
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* Alpha Factor Calculator
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* Converts sentiment to tradeable alpha factors
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*/
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class AlphaFactorCalculator {
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constructor(config = sentimentConfig) {
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this.config = config;
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this.factorHistory = new Map();
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}
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// Calculate sentiment momentum factor
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sentimentMomentum(sentimentHistory, window = 5) {
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if (sentimentHistory.length < window) return 0;
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const recent = sentimentHistory.slice(-window);
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const older = sentimentHistory.slice(-window * 2, -window);
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const recentAvg = recent.reduce((a, b) => a + b.score, 0) / recent.length;
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const olderAvg = older.length > 0
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? older.reduce((a, b) => a + b.score, 0) / older.length
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: recentAvg;
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return recentAvg - olderAvg;
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}
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// Calculate sentiment reversal factor
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sentimentReversal(sentimentHistory, threshold = 0.7) {
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if (sentimentHistory.length < 2) return 0;
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const current = sentimentHistory[sentimentHistory.length - 1].score;
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const previous = sentimentHistory[sentimentHistory.length - 2].score;
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// Large move in opposite direction = reversal
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if (Math.abs(current) > threshold && Math.sign(current) !== Math.sign(previous)) {
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return -current; // Contrarian
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}
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return 0;
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}
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// Calculate sentiment dispersion (disagreement among sources)
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sentimentDispersion(observations) {
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if (observations.length < 2) return 0;
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const scores = observations.map(o => o.score);
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const mean = scores.reduce((a, b) => a + b, 0) / scores.length;
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const variance = scores.reduce((a, b) => a + (b - mean) ** 2, 0) / scores.length;
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return Math.sqrt(variance);
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}
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// Calculate composite alpha factor
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calculateAlpha(symbol, aggregator) {
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const history = aggregator.sentimentHistory.get(symbol);
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if (!history || history.length < 5) {
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return { alpha: 0, confidence: 0, factors: {} };
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}
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const sentiment = aggregator.getAggregatedSentiment(symbol);
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const momentum = this.sentimentMomentum(history);
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const reversal = this.sentimentReversal(history);
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const dispersion = this.sentimentDispersion(history.slice(-10));
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// Composite alpha
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const levelWeight = 0.4;
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const momentumWeight = 0.3;
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const reversalWeight = 0.2;
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const dispersionPenalty = 0.1;
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const alpha = (
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levelWeight * sentiment.score +
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momentumWeight * momentum +
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reversalWeight * reversal -
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dispersionPenalty * dispersion
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);
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const confidence = sentiment.confidence * (1 - 0.5 * dispersion);
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return {
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alpha: Math.max(-1, Math.min(1, alpha)),
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confidence,
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factors: {
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level: sentiment.score,
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momentum,
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reversal,
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dispersion
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}
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};
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}
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}
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/**
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* Generate synthetic news for testing
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*/
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function generateSyntheticNews(symbols, numItems, seed = 42) {
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let rng = seed;
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const random = () => { rng = (rng * 9301 + 49297) % 233280; return rng / 233280; };
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const headlines = {
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positive: [
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'{symbol} reports strong quarterly earnings, beats estimates',
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'{symbol} announces major partnership, stock surges',
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'Analysts upgrade {symbol} citing growth momentum',
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'{symbol} expands into new markets, revenue growth expected',
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'{symbol} innovation breakthrough drives optimistic outlook',
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'Record demand for {symbol} products exceeds forecasts'
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],
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negative: [
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'{symbol} misses earnings expectations, guidance lowered',
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'{symbol} faces regulatory concerns, shares decline',
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'Analysts downgrade {symbol} amid market uncertainty',
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'{symbol} announces layoffs as demand weakens',
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'{symbol} warns of supply chain risks impacting profits',
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'Investor concern grows over {symbol} debt levels'
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],
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neutral: [
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'{symbol} maintains steady performance in Q4',
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'{symbol} announces routine management changes',
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'{symbol} confirms participation in industry conference',
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'{symbol} files standard regulatory documents'
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]
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};
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const sources = ['news', 'social', 'analyst', 'earnings'];
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const news = [];
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for (let i = 0; i < numItems; i++) {
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const symbol = symbols[Math.floor(random() * symbols.length)];
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const sentiment = random();
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let category;
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if (sentiment < 0.35) category = 'negative';
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else if (sentiment < 0.65) category = 'neutral';
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else category = 'positive';
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const templates = headlines[category];
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const headline = templates[Math.floor(random() * templates.length)]
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.replace('{symbol}', symbol);
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news.push({
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symbol,
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headline,
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source: sources[Math.floor(random() * sources.length)],
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timestamp: Date.now() - Math.floor(random() * 7 * 24 * 60 * 60 * 1000)
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});
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}
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return news;
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}
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async function main() {
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console.log('═'.repeat(70));
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console.log('SENTIMENT ALPHA PIPELINE');
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console.log('═'.repeat(70));
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|
console.log();
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|
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// 1. Initialize analyzers
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console.log('1. Analyzer Initialization:');
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console.log('─'.repeat(70));
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|
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const lexicon = new LexiconAnalyzer();
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const embedding = new EmbeddingAnalyzer();
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const stream = new NewsSentimentStream();
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const alphaCalc = new AlphaFactorCalculator();
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|
|
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console.log(' Lexicon Analyzer: Financial sentiment lexicon loaded');
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|
console.log(' Embedding Analyzer: 64-dim embeddings configured');
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console.log(' Stream Processor: Ready for real-time processing');
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|
console.log();
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|
|
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// 2. Test lexicon analysis
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|
console.log('2. Lexicon Analysis Examples:');
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|
console.log('─'.repeat(70));
|
|
|
|
const testTexts = [
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'Strong earnings beat expectations, revenue growth accelerates',
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|
'Company warns of significant losses amid declining demand',
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'Quarterly results in line with modest estimates'
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|
];
|
|
|
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for (const text of testTexts) {
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const result = lexicon.analyze(text);
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const sentiment = result.score > 0.3 ? 'Positive' : result.score < -0.3 ? 'Negative' : 'Neutral';
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console.log(` "${text.substring(0, 50)}..."`);
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console.log(` → Score: ${result.score.toFixed(3)}, Confidence: ${result.confidence.toFixed(2)}, ${sentiment}`);
|
|
console.log();
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|
}
|
|
|
|
// 3. Generate and process synthetic news
|
|
console.log('3. Synthetic News Processing:');
|
|
console.log('─'.repeat(70));
|
|
|
|
const symbols = ['AAPL', 'MSFT', 'GOOGL', 'AMZN', 'TSLA'];
|
|
const news = generateSyntheticNews(symbols, 50);
|
|
|
|
const processed = stream.processBatch(news);
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|
console.log(` Processed ${processed.length} news items`);
|
|
console.log(` Symbols tracked: ${symbols.join(', ')}`);
|
|
console.log();
|
|
|
|
// 4. Aggregated sentiment
|
|
console.log('4. Aggregated Sentiment by Symbol:');
|
|
console.log('─'.repeat(70));
|
|
console.log(' Symbol │ Score │ Confidence │ Count │ Dominant Source');
|
|
console.log('─'.repeat(70));
|
|
|
|
for (const symbol of symbols) {
|
|
const agg = stream.aggregator.getAggregatedSentiment(symbol);
|
|
const dominant = agg.dominant || 'N/A';
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|
console.log(` ${symbol.padEnd(6)} │ ${agg.score.toFixed(3).padStart(7)} │ ${agg.confidence.toFixed(2).padStart(10)} │ ${agg.count.toString().padStart(5)} │ ${dominant}`);
|
|
}
|
|
console.log();
|
|
|
|
// 5. Trading signals
|
|
console.log('5. Trading Signals:');
|
|
console.log('─'.repeat(70));
|
|
console.log(' Symbol │ Signal │ Strength │ Calibrated Prob');
|
|
console.log('─'.repeat(70));
|
|
|
|
const signals = stream.getAllSignals();
|
|
for (const [symbol, sig] of Object.entries(signals)) {
|
|
const prob = sig.calibratedProbability ? (sig.calibratedProbability * 100).toFixed(1) + '%' : 'N/A';
|
|
console.log(` ${symbol.padEnd(6)} │ ${(sig.signal || 'HOLD').padEnd(12)} │ ${(sig.strength || 'low').padEnd(8)} │ ${prob}`);
|
|
}
|
|
console.log();
|
|
|
|
// 6. Alpha factors
|
|
console.log('6. Alpha Factor Analysis:');
|
|
console.log('─'.repeat(70));
|
|
console.log(' Symbol │ Alpha │ Conf │ Level │ Momentum │ Dispersion');
|
|
console.log('─'.repeat(70));
|
|
|
|
for (const symbol of symbols) {
|
|
const alpha = alphaCalc.calculateAlpha(symbol, stream.aggregator);
|
|
if (alpha.factors.level !== undefined) {
|
|
console.log(` ${symbol.padEnd(6)} │ ${alpha.alpha.toFixed(3).padStart(6)} │ ${alpha.confidence.toFixed(2).padStart(5)} │ ${alpha.factors.level.toFixed(3).padStart(6)} │ ${alpha.factors.momentum.toFixed(3).padStart(8)} │ ${alpha.factors.dispersion.toFixed(3).padStart(10)}`);
|
|
}
|
|
}
|
|
console.log();
|
|
|
|
// 7. Recent alerts
|
|
console.log('7. Recent Sentiment Alerts:');
|
|
console.log('─'.repeat(70));
|
|
|
|
const alerts = stream.getAlerts(5);
|
|
if (alerts.length > 0) {
|
|
for (const alert of alerts) {
|
|
const direction = alert.score > 0 ? '↑' : '↓';
|
|
console.log(` ${direction} ${alert.symbol}: ${alert.headline}`);
|
|
}
|
|
} else {
|
|
console.log(' No significant sentiment alerts');
|
|
}
|
|
console.log();
|
|
|
|
// 8. Integration example
|
|
console.log('8. Kelly Criterion Integration Example:');
|
|
console.log('─'.repeat(70));
|
|
|
|
// Simulated odds for AAPL
|
|
const aaplSignal = signals['AAPL'];
|
|
if (aaplSignal && aaplSignal.calibratedProbability) {
|
|
const decimalOdds = 2.0; // Even money
|
|
const winProb = aaplSignal.calibratedProbability;
|
|
|
|
// Calculate Kelly
|
|
const b = decimalOdds - 1;
|
|
const fullKelly = (b * winProb - (1 - winProb)) / b;
|
|
const fifthKelly = fullKelly * 0.2;
|
|
|
|
console.log(` AAPL Signal: ${aaplSignal.signal}`);
|
|
console.log(` Calibrated Win Prob: ${(winProb * 100).toFixed(1)}%`);
|
|
console.log(` At 2.0 odds (even money):`);
|
|
console.log(` Full Kelly: ${(fullKelly * 100).toFixed(2)}%`);
|
|
console.log(` 1/5th Kelly: ${(fifthKelly * 100).toFixed(2)}%`);
|
|
|
|
if (fifthKelly > 0) {
|
|
console.log(` → Recommended: BET ${(fifthKelly * 100).toFixed(1)}% of bankroll`);
|
|
} else {
|
|
console.log(` → Recommended: NO BET (negative EV)`);
|
|
}
|
|
}
|
|
console.log();
|
|
|
|
console.log('═'.repeat(70));
|
|
console.log('Sentiment Alpha Pipeline demonstration completed');
|
|
console.log('═'.repeat(70));
|
|
}
|
|
|
|
export {
|
|
SentimentAggregator,
|
|
NewsSentimentStream,
|
|
AlphaFactorCalculator,
|
|
LexiconAnalyzer,
|
|
EmbeddingAnalyzer,
|
|
sentimentConfig
|
|
};
|
|
|
|
main().catch(console.error);
|