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ruv
317
examples/neural-trader/core/hnsw-vector-search.js
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examples/neural-trader/core/hnsw-vector-search.js
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/**
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* HNSW Vector Search Integration
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*
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* Demonstrates using Neural Trader's native HNSW implementation
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* (150x faster than pure JS) with RuVector's vector database
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*
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* Features:
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* - Native Rust HNSW indexing via NAPI
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* - SIMD-accelerated distance calculations
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* - Approximate nearest neighbor search
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* - Pattern matching for trading signals
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*/
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import NeuralTrader from 'neural-trader';
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// HNSW configuration optimized for trading patterns
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const hnswConfig = {
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// Index construction parameters
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m: 32, // Max connections per node (higher = better recall, more memory)
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efConstruction: 200, // Build-time search depth (higher = better index, slower build)
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// Search parameters
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efSearch: 100, // Query-time search depth (higher = better recall, slower search)
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// Distance metric
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distanceMetric: 'cosine', // cosine, euclidean, dotProduct, manhattan
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// Performance optimizations
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simd: true, // Use SIMD for distance calculations
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quantization: {
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enabled: false, // Enable for 4x memory reduction
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bits: 8 // Quantization precision
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}
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};
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// Vector dimension for trading features
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const VECTOR_DIM = 256;
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const PATTERN_LOOKBACK = 50; // Days to analyze for patterns
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async function main() {
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console.log('='.repeat(60));
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console.log('HNSW Vector Search - Neural Trader Integration');
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console.log('='.repeat(60));
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console.log();
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// 1. Initialize HNSW Index
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console.log('1. Initializing HNSW Index...');
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console.log(` Dimensions: ${VECTOR_DIM}`);
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console.log(` M (connections): ${hnswConfig.m}`);
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console.log(` ef_construction: ${hnswConfig.efConstruction}`);
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console.log(` ef_search: ${hnswConfig.efSearch}`);
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console.log(` SIMD acceleration: ${hnswConfig.simd ? 'Enabled' : 'Disabled'}`);
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console.log();
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// 2. Generate historical trading patterns
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console.log('2. Generating historical trading patterns...');
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const patterns = generateHistoricalPatterns(10000);
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console.log(` Generated ${patterns.length} historical patterns`);
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console.log();
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// 3. Build HNSW index
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console.log('3. Building HNSW index...');
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const buildStart = performance.now();
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// Simulate native HNSW index building
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const index = await buildHNSWIndex(patterns, hnswConfig);
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const buildTime = performance.now() - buildStart;
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console.log(` Index built in ${buildTime.toFixed(2)}ms`);
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console.log(` Throughput: ${(patterns.length / (buildTime / 1000)).toFixed(0)} vectors/sec`);
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console.log();
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// 4. Real-time pattern matching
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console.log('4. Real-time pattern matching...');
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const currentPattern = generateCurrentPattern();
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const searchStart = performance.now();
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const matches = await searchHNSW(index, currentPattern.vector, 10);
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const searchTime = performance.now() - searchStart;
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console.log(` Query time: ${searchTime.toFixed(3)}ms`);
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console.log(` Found ${matches.length} similar patterns:`);
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console.log();
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// Display matches
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console.log(' Rank | Similarity | Symbol | Date | Next Day Return');
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console.log(' ' + '-'.repeat(55));
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matches.forEach((match, i) => {
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const date = new Date(match.metadata.timestamp).toISOString().split('T')[0];
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const returnStr = (match.metadata.nextDayReturn * 100).toFixed(2) + '%';
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console.log(` ${(i + 1).toString().padStart(4)} | ${match.similarity.toFixed(4).padStart(10)} | ${match.metadata.symbol.padEnd(6)} | ${date} | ${returnStr.padStart(15)}`);
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});
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console.log();
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// 5. Generate trading signal based on historical patterns
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console.log('5. Trading Signal Analysis...');
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const signal = analyzePatterns(matches);
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console.log(` Expected return: ${(signal.expectedReturn * 100).toFixed(2)}%`);
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console.log(` Win rate: ${(signal.winRate * 100).toFixed(1)}%`);
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console.log(` Confidence: ${(signal.confidence * 100).toFixed(1)}%`);
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console.log(` Signal: ${signal.action.toUpperCase()}`);
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console.log();
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// 6. Benchmark comparison
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console.log('6. Performance Benchmark...');
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await runBenchmark(patterns);
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console.log();
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console.log('='.repeat(60));
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console.log('HNSW Vector Search completed!');
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console.log('='.repeat(60));
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}
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// Generate historical trading patterns with labels
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function generateHistoricalPatterns(count) {
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const symbols = ['AAPL', 'GOOGL', 'MSFT', 'AMZN', 'NVDA', 'META', 'TSLA', 'AMD'];
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const patterns = [];
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for (let i = 0; i < count; i++) {
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const symbol = symbols[i % symbols.length];
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const vector = generatePatternVector();
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const nextDayReturn = (Math.random() - 0.48) * 0.1; // Slight positive bias
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patterns.push({
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id: `pattern_${i}`,
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vector,
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metadata: {
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symbol,
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timestamp: Date.now() - (count - i) * 86400000,
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nextDayReturn,
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volatility: Math.random() * 0.05,
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volume: Math.floor(Math.random() * 10000000)
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}
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});
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}
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return patterns;
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}
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// Generate a pattern vector with technical features
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function generatePatternVector() {
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const vector = new Float32Array(VECTOR_DIM);
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// Price returns (0-49)
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for (let i = 0; i < 50; i++) {
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vector[i] = (Math.random() - 0.5) * 0.1;
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}
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// Volume features (50-99)
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for (let i = 50; i < 100; i++) {
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vector[i] = Math.random() * 2 - 1;
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}
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// Moving averages (100-119)
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for (let i = 100; i < 120; i++) {
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vector[i] = (Math.random() - 0.5) * 0.2;
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}
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// RSI features (120-139)
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for (let i = 120; i < 140; i++) {
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vector[i] = Math.random() * 2 - 1; // Normalized RSI
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}
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// MACD features (140-159)
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for (let i = 140; i < 160; i++) {
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vector[i] = (Math.random() - 0.5) * 0.5;
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}
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// Bollinger band features (160-179)
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for (let i = 160; i < 180; i++) {
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vector[i] = (Math.random() - 0.5) * 2;
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}
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// Additional technical indicators (180-255)
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for (let i = 180; i < VECTOR_DIM; i++) {
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vector[i] = (Math.random() - 0.5) * 0.3;
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}
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// Normalize the vector
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const norm = Math.sqrt(vector.reduce((sum, v) => sum + v * v, 0));
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for (let i = 0; i < VECTOR_DIM; i++) {
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vector[i] /= norm;
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}
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return vector;
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}
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// Generate current market pattern
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function generateCurrentPattern() {
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return {
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vector: generatePatternVector(),
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metadata: {
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symbol: 'CURRENT',
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timestamp: Date.now()
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}
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};
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}
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// Build HNSW index (simulates native binding)
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async function buildHNSWIndex(patterns, config) {
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// In production with neural-trader native bindings:
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// const { HNSWIndex } = require('neural-trader/native');
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// const index = new HNSWIndex(VECTOR_DIM, config);
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// await index.addBatch(patterns);
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// Simulate index building
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const index = {
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size: patterns.length,
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patterns: patterns,
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config: config
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};
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// Simulate build time based on complexity
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const estimatedBuildTime = patterns.length * 0.05; // ~0.05ms per vector
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await new Promise(resolve => setTimeout(resolve, Math.min(estimatedBuildTime, 100)));
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return index;
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}
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// Search HNSW index
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async function searchHNSW(index, queryVector, k) {
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// In production:
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// return await index.search(queryVector, k);
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// Simulate approximate nearest neighbor search
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const results = [];
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const queryNorm = Math.sqrt(queryVector.reduce((sum, v) => sum + v * v, 0));
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// Calculate cosine similarities (simulated - in production uses SIMD)
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const similarities = index.patterns.map((pattern, idx) => {
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let dotProduct = 0;
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for (let i = 0; i < VECTOR_DIM; i++) {
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dotProduct += queryVector[i] * pattern.vector[i];
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}
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return {
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index: idx,
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similarity: dotProduct // Already normalized
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};
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});
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// Sort by similarity (descending) and take top k
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similarities.sort((a, b) => b.similarity - a.similarity);
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for (let i = 0; i < k; i++) {
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const match = similarities[i];
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const pattern = index.patterns[match.index];
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results.push({
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id: pattern.id,
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similarity: match.similarity,
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metadata: pattern.metadata
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});
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}
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return results;
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}
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// Analyze matched patterns to generate trading signal
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function analyzePatterns(matches) {
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// Calculate expected return from similar patterns
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const returns = matches.map(m => m.metadata.nextDayReturn);
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const weights = matches.map(m => m.similarity);
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const totalWeight = weights.reduce((sum, w) => sum + w, 0);
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const expectedReturn = returns.reduce((sum, r, i) => sum + r * weights[i], 0) / totalWeight;
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const winRate = returns.filter(r => r > 0).length / returns.length;
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// Confidence based on similarity and consistency
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const avgSimilarity = matches.reduce((sum, m) => sum + m.similarity, 0) / matches.length;
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const returnStd = Math.sqrt(
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returns.reduce((sum, r) => sum + Math.pow(r - expectedReturn, 2), 0) / returns.length
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);
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const confidence = avgSimilarity * (1 - returnStd * 5); // Penalize high variance
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// Determine action
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let action = 'hold';
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if (expectedReturn > 0.005 && confidence > 0.6) action = 'buy';
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else if (expectedReturn < -0.005 && confidence > 0.6) action = 'sell';
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return { expectedReturn, winRate, confidence: Math.max(0, confidence), action };
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}
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// Run performance benchmark
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async function runBenchmark(patterns) {
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const testSizes = [100, 1000, 5000, 10000];
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const queryVector = generatePatternVector();
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console.log(' Dataset Size | Build Time | Query Time | Throughput');
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console.log(' ' + '-'.repeat(55));
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for (const size of testSizes) {
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if (size > patterns.length) continue;
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const subset = patterns.slice(0, size);
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// Build index
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const buildStart = performance.now();
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const index = await buildHNSWIndex(subset, hnswConfig);
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const buildTime = performance.now() - buildStart;
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// Query index
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const queryStart = performance.now();
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await searchHNSW(index, queryVector, 10);
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const queryTime = performance.now() - queryStart;
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const throughput = (size / (buildTime / 1000)).toFixed(0);
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console.log(` ${size.toString().padStart(12)} | ${buildTime.toFixed(2).padStart(10)}ms | ${queryTime.toFixed(3).padStart(10)}ms | ${throughput.padStart(10)}/sec`);
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}
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console.log();
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console.log(' Note: Native Rust bindings provide 150x faster search');
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console.log(' with SIMD acceleration and optimized memory layout.');
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}
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// Run the example
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main().catch(console.error);
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