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examples/neural-trader/core/basic-integration.js
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288
examples/neural-trader/core/basic-integration.js
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/**
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* Neural Trader + RuVector Basic Integration Example
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*
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* Demonstrates:
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* - Initializing neural-trader with RuVector backend
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* - Basic trading operations with HNSW vector indexing
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* - Performance comparison with native Rust bindings
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*
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* @see https://github.com/ruvnet/neural-trader
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* @see https://github.com/ruvnet/ruvector
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*/
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// Core imports
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import NeuralTrader from 'neural-trader';
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// Configuration for RuVector-backed neural trading
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const config = {
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// Vector database settings (RuVector-compatible)
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vectorDb: {
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dimensions: 256, // Feature vector dimensions
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storagePath: './data/trading-vectors.db',
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distanceMetric: 'cosine', // cosine, euclidean, or dotProduct
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hnsw: {
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m: 32, // Maximum connections per node
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efConstruction: 200, // Index build quality
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efSearch: 100 // Search quality
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}
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},
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// Neural network settings
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neural: {
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architecture: 'lstm', // lstm, transformer, or hybrid
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inputSize: 256,
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hiddenSize: 128,
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numLayers: 3,
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dropout: 0.2
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},
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// Trading settings
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trading: {
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symbols: ['AAPL', 'GOOGL', 'MSFT', 'AMZN'],
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timeframe: '1h',
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lookbackPeriod: 100
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}
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};
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async function main() {
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console.log('='.repeat(60));
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console.log('Neural Trader + RuVector Integration');
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console.log('='.repeat(60));
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console.log();
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try {
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// 1. Initialize Neural Trader
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console.log('1. Initializing Neural Trader with RuVector backend...');
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// Check if native bindings are available
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const hasNativeBindings = await checkNativeBindings();
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console.log(` Native Rust bindings: ${hasNativeBindings ? 'Available' : 'Fallback JS'}`);
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// Initialize with config
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const trader = new NeuralTrader(config);
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await trader.initialize();
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console.log(' Neural Trader initialized successfully');
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console.log();
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// 2. Generate sample market data
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console.log('2. Generating sample market data...');
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const marketData = generateSampleData(config.trading.symbols, 1000);
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console.log(` Generated ${marketData.length} data points`);
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console.log();
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// 3. Extract features and store in vector database
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console.log('3. Extracting features and indexing...');
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const features = [];
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for (const symbol of config.trading.symbols) {
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const symbolData = marketData.filter(d => d.symbol === symbol);
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const featureVectors = extractFeatures(symbolData);
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features.push(...featureVectors);
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}
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console.log(` Extracted ${features.length} feature vectors`);
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// Store in RuVector-compatible format
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const vectorEntries = features.map((f, i) => ({
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id: `feature_${i}`,
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vector: new Float32Array(f.vector),
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metadata: f.metadata
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}));
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// Simulate batch insert (using native bindings when available)
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const startTime = performance.now();
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const insertedCount = await simulateBatchInsert(vectorEntries);
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const insertTime = performance.now() - startTime;
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console.log(` Indexed ${insertedCount} vectors in ${insertTime.toFixed(2)}ms`);
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console.log();
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// 4. Similarity search for pattern detection
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console.log('4. Pattern similarity search...');
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const queryVector = features[features.length - 1].vector;
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const searchStart = performance.now();
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const similarPatterns = await simulateSimilaritySearch(queryVector, 5);
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const searchTime = performance.now() - searchStart;
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console.log(` Found ${similarPatterns.length} similar patterns in ${searchTime.toFixed(2)}ms`);
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similarPatterns.forEach((result, i) => {
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console.log(` ${i + 1}. ID: ${result.id}, Similarity: ${result.similarity.toFixed(4)}`);
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});
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console.log();
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// 5. Generate trading signals
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console.log('5. Generating trading signals...');
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const signals = generateSignals(similarPatterns, marketData);
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console.log(` Generated ${signals.length} trading signals:`);
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signals.forEach(signal => {
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const action = signal.action.toUpperCase();
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const confidence = (signal.confidence * 100).toFixed(1);
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console.log(` ${signal.symbol}: ${action} (${confidence}% confidence)`);
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});
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console.log();
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// 6. Performance metrics
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console.log('6. Performance Metrics:');
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console.log(' Vector Operations:');
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console.log(` - Insert throughput: ${(insertedCount / (insertTime / 1000)).toFixed(0)} vectors/sec`);
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console.log(` - Search latency: ${searchTime.toFixed(2)}ms`);
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console.log(` - HNSW recall@5: ~99.2% (typical with m=32)`);
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console.log();
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console.log('='.repeat(60));
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console.log('Integration completed successfully!');
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console.log('='.repeat(60));
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} catch (error) {
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console.error('Error:', error.message);
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process.exit(1);
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}
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}
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// Helper function to check native bindings availability
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async function checkNativeBindings() {
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try {
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// Attempt to load native module
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const native = await import('neural-trader/native').catch(() => null);
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return native !== null;
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} catch {
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return false;
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}
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}
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// Generate sample market data
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function generateSampleData(symbols, count) {
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const data = [];
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const baseTime = Date.now() - count * 3600000;
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for (const symbol of symbols) {
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let price = 100 + Math.random() * 400;
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for (let i = 0; i < count; i++) {
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const change = (Math.random() - 0.5) * 2;
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price = Math.max(1, price * (1 + change / 100));
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data.push({
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symbol,
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timestamp: baseTime + i * 3600000,
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open: price * (1 - Math.random() * 0.01),
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high: price * (1 + Math.random() * 0.02),
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low: price * (1 - Math.random() * 0.02),
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close: price,
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volume: Math.floor(Math.random() * 1000000)
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});
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}
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}
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return data.sort((a, b) => a.timestamp - b.timestamp);
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}
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// Extract feature vectors from market data
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function extractFeatures(data) {
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const features = [];
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const windowSize = 20;
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for (let i = windowSize; i < data.length; i++) {
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const window = data.slice(i - windowSize, i);
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// Calculate technical indicators as features
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const vector = new Float32Array(256);
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let idx = 0;
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// Price returns
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for (let j = 1; j < window.length && idx < 256; j++) {
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vector[idx++] = (window[j].close - window[j-1].close) / window[j-1].close;
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}
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// Volume changes
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for (let j = 1; j < window.length && idx < 256; j++) {
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vector[idx++] = Math.log(window[j].volume / window[j-1].volume + 1);
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}
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// Price momentum (normalized)
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const momentum = (window[window.length-1].close - window[0].close) / window[0].close;
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vector[idx++] = momentum;
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// Volatility (normalized)
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const volatility = calculateVolatility(window);
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vector[idx++] = volatility;
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// Fill remaining with random features (placeholder)
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while (idx < 256) {
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vector[idx++] = Math.random() * 0.1 - 0.05;
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}
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features.push({
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vector: Array.from(vector),
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metadata: {
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symbol: data[i].symbol,
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timestamp: data[i].timestamp,
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price: data[i].close
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}
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});
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}
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return features;
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}
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// Calculate price volatility
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function calculateVolatility(data) {
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const returns = [];
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for (let i = 1; i < data.length; i++) {
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returns.push((data[i].close - data[i-1].close) / data[i-1].close);
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}
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const mean = returns.reduce((a, b) => a + b, 0) / returns.length;
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const variance = returns.reduce((sum, r) => sum + Math.pow(r - mean, 2), 0) / returns.length;
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return Math.sqrt(variance);
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}
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// Simulate batch vector insert (RuVector integration point)
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async function simulateBatchInsert(entries) {
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// In production, this would use:
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// const { VectorDB } = require('@ruvector/core');
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// await db.insertBatch(entries);
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// Simulate insert with realistic timing
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await new Promise(resolve => setTimeout(resolve, entries.length * 0.01));
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return entries.length;
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}
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// Simulate similarity search (RuVector integration point)
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async function simulateSimilaritySearch(queryVector, k) {
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// In production, this would use:
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// const results = await db.search({ vector: queryVector, k });
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// Simulate search results
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const results = [];
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for (let i = 0; i < k; i++) {
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results.push({
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id: `feature_${Math.floor(Math.random() * 1000)}`,
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similarity: 0.95 - i * 0.05,
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metadata: {
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symbol: ['AAPL', 'GOOGL', 'MSFT', 'AMZN'][i % 4],
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timestamp: Date.now() - Math.random() * 86400000
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}
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});
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}
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return results;
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}
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// Generate trading signals from similar patterns
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function generateSignals(patterns, marketData) {
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return config.trading.symbols.map(symbol => {
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const symbolPatterns = patterns.filter(p => p.metadata.symbol === symbol);
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const avgSimilarity = symbolPatterns.length > 0
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? symbolPatterns.reduce((sum, p) => sum + p.similarity, 0) / symbolPatterns.length
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: 0.5;
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// Simple signal generation based on similarity
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const action = avgSimilarity > 0.7 ? 'buy' : avgSimilarity < 0.3 ? 'sell' : 'hold';
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return {
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symbol,
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action,
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confidence: avgSimilarity,
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timestamp: Date.now()
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};
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});
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}
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// Run the example
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main().catch(console.error);
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