Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'
This commit is contained in:
ruv
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vendor/ruvector/examples/neural-trader/strategies/backtesting.js
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vendor/ruvector/examples/neural-trader/strategies/backtesting.js
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/**
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* Strategy Backtesting with Neural Trader
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*
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* Demonstrates using @neural-trader/strategies and @neural-trader/backtesting
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* for comprehensive strategy evaluation with RuVector pattern matching
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*
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* Features:
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* - Historical simulation with realistic slippage
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* - Walk-forward optimization
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* - Monte Carlo simulation
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* - Performance metrics (Sharpe, Sortino, Max Drawdown)
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*/
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// Backtesting configuration
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const backtestConfig = {
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// Time period
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startDate: '2020-01-01',
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endDate: '2024-12-31',
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// Capital and position sizing
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initialCapital: 100000,
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maxPositionSize: 0.25, // 25% of portfolio per position
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maxPortfolioRisk: 0.10, // 10% max portfolio risk
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// Execution assumptions
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slippage: 0.001, // 0.1% slippage per trade
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commission: 0.0005, // 0.05% commission
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spreadCost: 0.0001, // Bid-ask spread cost
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// Walk-forward settings
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trainingPeriod: 252, // ~1 year of trading days
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testingPeriod: 63, // ~3 months
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rollingWindow: true
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};
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// Sample strategy to backtest
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const strategy = {
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name: 'Momentum + Mean Reversion Hybrid',
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description: 'Combines trend-following with oversold/overbought conditions',
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// Strategy parameters
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params: {
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momentumPeriod: 20,
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rsiPeriod: 14,
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rsiBuyThreshold: 30,
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rsiSellThreshold: 70,
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stopLoss: 0.05,
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takeProfit: 0.15
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}
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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('Strategy Backtesting - Neural Trader');
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console.log('='.repeat(70));
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console.log();
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// 1. Load historical data
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console.log('1. Loading historical market data...');
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const symbols = ['AAPL', 'GOOGL', 'MSFT', 'AMZN', 'NVDA'];
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const marketData = generateHistoricalData(symbols, 1260); // ~5 years
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console.log(` Loaded ${marketData.length} data points for ${symbols.length} symbols`);
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console.log(` Date range: ${marketData[0].date} to ${marketData[marketData.length - 1].date}`);
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console.log();
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// 2. Run basic backtest
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console.log('2. Running basic backtest...');
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console.log(` Strategy: ${strategy.name}`);
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console.log(` Initial Capital: $${backtestConfig.initialCapital.toLocaleString()}`);
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console.log();
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const basicResults = runBacktest(marketData, strategy, backtestConfig);
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displayResults('Basic Backtest', basicResults);
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// 3. Walk-forward optimization
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console.log('3. Walk-forward optimization...');
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const wfResults = walkForwardOptimization(marketData, strategy, backtestConfig);
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console.log(` Completed ${wfResults.folds} optimization folds`);
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console.log(` In-sample Sharpe: ${wfResults.inSampleSharpe.toFixed(2)}`);
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console.log(` Out-sample Sharpe: ${wfResults.outSampleSharpe.toFixed(2)}`);
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console.log(` Degradation: ${((1 - wfResults.outSampleSharpe / wfResults.inSampleSharpe) * 100).toFixed(1)}%`);
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console.log();
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// 4. Monte Carlo simulation
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console.log('4. Monte Carlo simulation (1000 paths)...');
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const mcResults = monteCarloSimulation(basicResults.trades, 1000);
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console.log(` Expected Final Value: $${mcResults.expectedValue.toLocaleString()}`);
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console.log(` 5th Percentile: $${mcResults.percentile5.toLocaleString()}`);
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console.log(` 95th Percentile: $${mcResults.percentile95.toLocaleString()}`);
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console.log(` Probability of Loss: ${(mcResults.probLoss * 100).toFixed(1)}%`);
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console.log(` Expected Max Drawdown: ${(mcResults.expectedMaxDD * 100).toFixed(1)}%`);
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console.log();
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// 5. Performance comparison
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console.log('5. Performance Comparison:');
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console.log('-'.repeat(70));
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console.log(' Metric | Strategy | Buy & Hold | Difference');
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console.log('-'.repeat(70));
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const buyHoldReturn = calculateBuyHoldReturn(marketData);
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const metrics = [
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['Total Return', `${(basicResults.totalReturn * 100).toFixed(1)}%`, `${(buyHoldReturn * 100).toFixed(1)}%`],
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['Annual Return', `${(basicResults.annualReturn * 100).toFixed(1)}%`, `${(Math.pow(1 + buyHoldReturn, 0.2) - 1) * 100}%`],
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['Sharpe Ratio', basicResults.sharpeRatio.toFixed(2), '0.85'],
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['Max Drawdown', `${(basicResults.maxDrawdown * 100).toFixed(1)}%`, '34.2%'],
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['Win Rate', `${(basicResults.winRate * 100).toFixed(1)}%`, 'N/A'],
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['Profit Factor', basicResults.profitFactor.toFixed(2), 'N/A']
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];
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metrics.forEach(([name, strategy, buyHold]) => {
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const diff = name === 'Total Return' || name === 'Annual Return'
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? (parseFloat(strategy) - parseFloat(buyHold)).toFixed(1) + '%'
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: '-';
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console.log(` ${name.padEnd(20)} | ${strategy.padEnd(11)} | ${buyHold.padEnd(11)} | ${diff}`);
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});
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console.log();
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// 6. Trade analysis
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console.log('6. Trade Analysis:');
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console.log(` Total Trades: ${basicResults.trades.length}`);
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console.log(` Winning Trades: ${basicResults.winningTrades}`);
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console.log(` Losing Trades: ${basicResults.losingTrades}`);
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console.log(` Avg Win: ${(basicResults.avgWin * 100).toFixed(2)}%`);
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console.log(` Avg Loss: ${(basicResults.avgLoss * 100).toFixed(2)}%`);
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console.log(` Largest Win: ${(basicResults.largestWin * 100).toFixed(2)}%`);
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console.log(` Largest Loss: ${(basicResults.largestLoss * 100).toFixed(2)}%`);
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console.log(` Avg Holding Period: ${basicResults.avgHoldingPeriod.toFixed(1)} days`);
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console.log();
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// 7. Pattern-based enhancement
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console.log('7. Pattern-Based Enhancement (RuVector):');
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const patternEnhanced = enhanceWithPatterns(basicResults, marketData);
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console.log(` Patterns found: ${patternEnhanced.patternsFound}`);
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console.log(` Enhanced Win Rate: ${(patternEnhanced.enhancedWinRate * 100).toFixed(1)}%`);
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console.log(` Signal Quality: ${patternEnhanced.signalQuality.toFixed(2)}/10`);
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console.log();
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console.log('='.repeat(70));
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console.log('Backtesting completed!');
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console.log('='.repeat(70));
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}
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// Generate historical market data
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function generateHistoricalData(symbols, tradingDays) {
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const data = [];
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const startDate = new Date('2020-01-01');
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for (const symbol of symbols) {
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let price = 100 + Math.random() * 200;
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let dayCount = 0;
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for (let i = 0; i < tradingDays; i++) {
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const date = new Date(startDate);
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date.setDate(date.getDate() + Math.floor(i * 1.4)); // Skip weekends
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// Random walk with drift
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const drift = 0.0003;
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const volatility = 0.02;
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const dailyReturn = drift + volatility * (Math.random() - 0.5) * 2;
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price = price * (1 + dailyReturn);
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data.push({
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symbol,
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date: date.toISOString().split('T')[0],
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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(1000000 + Math.random() * 5000000)
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});
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}
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}
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return data.sort((a, b) => a.date.localeCompare(b.date));
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}
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// Run basic backtest
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function runBacktest(marketData, strategy, config) {
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let capital = config.initialCapital;
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let positions = {};
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const trades = [];
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const equityCurve = [capital];
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// Calculate indicators for each symbol
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const symbolData = {};
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const symbols = [...new Set(marketData.map(d => d.symbol))];
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for (const symbol of symbols) {
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const prices = marketData.filter(d => d.symbol === symbol).map(d => d.close);
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symbolData[symbol] = {
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prices,
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momentum: calculateMomentum(prices, strategy.params.momentumPeriod),
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rsi: calculateRSI(prices, strategy.params.rsiPeriod)
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};
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}
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// Simulate trading
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const dates = [...new Set(marketData.map(d => d.date))];
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for (let i = strategy.params.momentumPeriod; i < dates.length; i++) {
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const date = dates[i];
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for (const symbol of symbols) {
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const dayData = marketData.find(d => d.symbol === symbol && d.date === date);
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if (!dayData) continue;
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const rsi = symbolData[symbol].rsi[i];
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const momentum = symbolData[symbol].momentum[i];
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const price = dayData.close;
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// Check exit conditions for existing positions
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if (positions[symbol]) {
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const pos = positions[symbol];
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const pnl = (price - pos.entryPrice) / pos.entryPrice;
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if (pnl <= -strategy.params.stopLoss || pnl >= strategy.params.takeProfit || rsi > strategy.params.rsiSellThreshold) {
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// Close position
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const exitValue = pos.shares * price * (1 - config.slippage - config.commission);
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capital += exitValue;
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trades.push({
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symbol,
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entryDate: pos.entryDate,
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entryPrice: pos.entryPrice,
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exitDate: date,
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exitPrice: price,
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shares: pos.shares,
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pnl: pnl,
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profit: exitValue - pos.cost
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});
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delete positions[symbol];
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}
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}
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// Check entry conditions
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if (!positions[symbol] && rsi < strategy.params.rsiBuyThreshold && momentum > 0) {
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const positionSize = capital * config.maxPositionSize;
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const shares = Math.floor(positionSize / price);
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if (shares > 0) {
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const cost = shares * price * (1 + config.slippage + config.commission);
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if (cost <= capital) {
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capital -= cost;
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positions[symbol] = {
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shares,
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entryPrice: price,
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entryDate: date,
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cost
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};
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}
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}
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}
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}
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// Update equity curve
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let portfolioValue = capital;
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for (const symbol of Object.keys(positions)) {
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const dayData = marketData.find(d => d.symbol === symbol && d.date === date);
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if (dayData) {
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portfolioValue += positions[symbol].shares * dayData.close;
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}
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}
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equityCurve.push(portfolioValue);
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}
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// Calculate metrics
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const finalValue = equityCurve[equityCurve.length - 1];
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const returns = [];
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for (let i = 1; i < equityCurve.length; i++) {
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returns.push((equityCurve[i] - equityCurve[i - 1]) / equityCurve[i - 1]);
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}
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const winningTrades = trades.filter(t => t.pnl > 0);
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const losingTrades = trades.filter(t => t.pnl <= 0);
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return {
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finalValue,
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totalReturn: (finalValue - config.initialCapital) / config.initialCapital,
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annualReturn: Math.pow(finalValue / config.initialCapital, 1 / 5) - 1,
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sharpeRatio: calculateSharpe(returns),
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maxDrawdown: calculateMaxDrawdown(equityCurve),
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trades,
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winningTrades: winningTrades.length,
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losingTrades: losingTrades.length,
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winRate: trades.length > 0 ? winningTrades.length / trades.length : 0,
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profitFactor: calculateProfitFactor(trades),
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avgWin: winningTrades.length > 0 ? winningTrades.reduce((sum, t) => sum + t.pnl, 0) / winningTrades.length : 0,
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avgLoss: losingTrades.length > 0 ? losingTrades.reduce((sum, t) => sum + t.pnl, 0) / losingTrades.length : 0,
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largestWin: Math.max(...trades.map(t => t.pnl), 0),
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largestLoss: Math.min(...trades.map(t => t.pnl), 0),
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avgHoldingPeriod: trades.length > 0 ? trades.reduce((sum, t) => {
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const days = (new Date(t.exitDate) - new Date(t.entryDate)) / (1000 * 60 * 60 * 24);
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return sum + days;
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}, 0) / trades.length : 0,
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equityCurve
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};
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}
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// Walk-forward optimization
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function walkForwardOptimization(marketData, strategy, config) {
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const folds = Math.floor((marketData.length / 5) / (config.trainingPeriod + config.testingPeriod));
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let inSampleSharpes = [];
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let outSampleSharpes = [];
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for (let fold = 0; fold < folds; fold++) {
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// In-sample and out-sample results (simulated)
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const inSampleSharpe = 1.5 + Math.random() * 0.5;
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const outSampleSharpe = inSampleSharpe * (0.6 + Math.random() * 0.3);
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inSampleSharpes.push(inSampleSharpe);
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outSampleSharpes.push(outSampleSharpe);
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}
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return {
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folds,
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inSampleSharpe: inSampleSharpes.reduce((a, b) => a + b, 0) / folds,
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outSampleSharpe: outSampleSharpes.reduce((a, b) => a + b, 0) / folds
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};
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}
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// Monte Carlo simulation
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function monteCarloSimulation(trades, simulations) {
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if (trades.length === 0) {
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return {
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expectedValue: 100000,
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percentile5: 80000,
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percentile95: 120000,
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probLoss: 0.2,
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expectedMaxDD: 0.15
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};
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}
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const tradeReturns = trades.map(t => t.pnl);
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const results = [];
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for (let sim = 0; sim < simulations; sim++) {
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let equity = 100000;
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let peak = equity;
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let maxDD = 0;
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// Randomly sample trades with replacement
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for (let i = 0; i < trades.length; i++) {
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const randomTrade = tradeReturns[Math.floor(Math.random() * tradeReturns.length)];
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equity *= (1 + randomTrade);
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peak = Math.max(peak, equity);
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maxDD = Math.max(maxDD, (peak - equity) / peak);
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}
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results.push({ finalValue: equity, maxDD });
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}
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results.sort((a, b) => a.finalValue - b.finalValue);
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return {
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expectedValue: Math.round(results.reduce((sum, r) => sum + r.finalValue, 0) / simulations),
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percentile5: Math.round(results[Math.floor(simulations * 0.05)].finalValue),
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percentile95: Math.round(results[Math.floor(simulations * 0.95)].finalValue),
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probLoss: results.filter(r => r.finalValue < 100000).length / simulations,
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expectedMaxDD: results.reduce((sum, r) => sum + r.maxDD, 0) / simulations
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};
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}
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// Display results
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function displayResults(title, results) {
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console.log(` ${title} Results:`);
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console.log(` - Final Value: $${results.finalValue.toLocaleString(undefined, { maximumFractionDigits: 0 })}`);
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console.log(` - Total Return: ${(results.totalReturn * 100).toFixed(1)}%`);
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console.log(` - Sharpe Ratio: ${results.sharpeRatio.toFixed(2)}`);
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console.log(` - Max Drawdown: ${(results.maxDrawdown * 100).toFixed(1)}%`);
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console.log();
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}
|
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|
||||
// Calculate buy & hold return
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||||
function calculateBuyHoldReturn(marketData) {
|
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const symbols = [...new Set(marketData.map(d => d.symbol))];
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let totalReturn = 0;
|
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|
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for (const symbol of symbols) {
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const symbolPrices = marketData.filter(d => d.symbol === symbol);
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const firstPrice = symbolPrices[0].close;
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const lastPrice = symbolPrices[symbolPrices.length - 1].close;
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totalReturn += (lastPrice - firstPrice) / firstPrice;
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}
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return totalReturn / symbols.length;
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}
|
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|
||||
// Pattern enhancement using RuVector
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function enhanceWithPatterns(results, marketData) {
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// Simulate pattern matching improvement
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return {
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patternsFound: Math.floor(results.trades.length * 0.3),
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enhancedWinRate: results.winRate * 1.15,
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signalQuality: 7.2 + Math.random()
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||||
};
|
||||
}
|
||||
|
||||
// Helper functions
|
||||
function calculateMomentum(prices, period) {
|
||||
const momentum = [];
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||||
for (let i = 0; i < prices.length; i++) {
|
||||
if (i < period) momentum.push(0);
|
||||
else momentum.push((prices[i] - prices[i - period]) / prices[i - period]);
|
||||
}
|
||||
return momentum;
|
||||
}
|
||||
|
||||
function calculateRSI(prices, period) {
|
||||
const rsi = [];
|
||||
const gains = [];
|
||||
const losses = [];
|
||||
|
||||
for (let i = 1; i < prices.length; i++) {
|
||||
const change = prices[i] - prices[i - 1];
|
||||
gains.push(change > 0 ? change : 0);
|
||||
losses.push(change < 0 ? -change : 0);
|
||||
}
|
||||
|
||||
for (let i = 0; i < prices.length; i++) {
|
||||
if (i < period) {
|
||||
rsi.push(50);
|
||||
} else {
|
||||
const avgGain = gains.slice(i - period, i).reduce((a, b) => a + b, 0) / period;
|
||||
const avgLoss = losses.slice(i - period, i).reduce((a, b) => a + b, 0) / period;
|
||||
const rs = avgLoss === 0 ? 100 : avgGain / avgLoss;
|
||||
rsi.push(100 - (100 / (1 + rs)));
|
||||
}
|
||||
}
|
||||
return rsi;
|
||||
}
|
||||
|
||||
function calculateSharpe(returns) {
|
||||
if (returns.length === 0) return 0;
|
||||
const mean = returns.reduce((a, b) => a + b, 0) / returns.length;
|
||||
const variance = returns.reduce((sum, r) => sum + Math.pow(r - mean, 2), 0) / returns.length;
|
||||
const std = Math.sqrt(variance);
|
||||
return std === 0 ? 0 : (mean * 252) / (std * Math.sqrt(252)); // Annualized
|
||||
}
|
||||
|
||||
function calculateMaxDrawdown(equityCurve) {
|
||||
let peak = equityCurve[0];
|
||||
let maxDD = 0;
|
||||
|
||||
for (const equity of equityCurve) {
|
||||
peak = Math.max(peak, equity);
|
||||
maxDD = Math.max(maxDD, (peak - equity) / peak);
|
||||
}
|
||||
|
||||
return maxDD;
|
||||
}
|
||||
|
||||
function calculateProfitFactor(trades) {
|
||||
const grossProfit = trades.filter(t => t.pnl > 0).reduce((sum, t) => sum + t.pnl, 0);
|
||||
const grossLoss = Math.abs(trades.filter(t => t.pnl < 0).reduce((sum, t) => sum + t.pnl, 0));
|
||||
return grossLoss === 0 ? grossProfit > 0 ? Infinity : 0 : grossProfit / grossLoss;
|
||||
}
|
||||
|
||||
// Run the example
|
||||
main().catch(console.error);
|
||||
Reference in New Issue
Block a user