running prettier for cleanup
This commit is contained in:
parent
24b7ed15e4
commit
8955544593
151 changed files with 29158 additions and 27966 deletions
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@ -1,239 +1,236 @@
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import { getLogger } from '@stock-bot/logger';
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import { EventBus } from '@stock-bot/event-bus';
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import { VectorEngine, VectorizedBacktestResult } from '@stock-bot/vector-engine';
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import { DataFrame } from '@stock-bot/data-frame';
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import { ExecutionMode, BacktestContext, BacktestResult } from '../framework/execution-mode';
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export interface VectorizedModeConfig {
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batchSize?: number;
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enableOptimization?: boolean;
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parallelProcessing?: boolean;
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}
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export class VectorizedMode extends ExecutionMode {
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private vectorEngine: VectorEngine;
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private config: VectorizedModeConfig;
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private logger = getLogger('vectorized-mode');
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constructor(
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context: BacktestContext,
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eventBus: EventBus,
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config: VectorizedModeConfig = {}
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) {
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super(context, eventBus);
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this.vectorEngine = new VectorEngine();
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this.config = {
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batchSize: 10000,
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enableOptimization: true,
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parallelProcessing: true,
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...config
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};
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}
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async initialize(): Promise<void> {
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await super.initialize();
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this.logger.info('Vectorized mode initialized', {
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backtestId: this.context.backtestId,
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config: this.config
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});
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}
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async execute(): Promise<BacktestResult> {
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const startTime = Date.now();
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this.logger.info('Starting vectorized backtest execution');
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try {
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// Load all data at once for vectorized processing
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const data = await this.loadHistoricalData();
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// Convert to DataFrame format
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const dataFrame = this.createDataFrame(data);
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// Execute vectorized strategy
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const strategyCode = this.generateStrategyCode();
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const vectorResult = await this.vectorEngine.executeVectorizedStrategy(
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dataFrame,
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strategyCode
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);
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// Convert to standard backtest result format
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const result = this.convertVectorizedResult(vectorResult, startTime);
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// Emit completion event
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await this.eventBus.publishBacktestUpdate(
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this.context.backtestId,
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100,
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{ status: 'completed', result }
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);
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this.logger.info('Vectorized backtest completed', {
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backtestId: this.context.backtestId,
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duration: Date.now() - startTime,
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totalTrades: result.trades.length
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});
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return result;
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} catch (error) {
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this.logger.error('Vectorized backtest failed', {
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error,
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backtestId: this.context.backtestId
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});
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await this.eventBus.publishBacktestUpdate(
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this.context.backtestId,
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0,
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{ status: 'failed', error: error.message }
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);
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throw error;
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}
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}
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private async loadHistoricalData(): Promise<any[]> {
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// Load all historical data at once
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// This is much more efficient than loading tick by tick
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const data = [];
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// Simulate loading data (in production, this would be a bulk database query)
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const startTime = new Date(this.context.startDate).getTime();
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const endTime = new Date(this.context.endDate).getTime();
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const interval = 60000; // 1 minute intervals
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for (let timestamp = startTime; timestamp <= endTime; timestamp += interval) {
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// Simulate OHLCV data
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const basePrice = 100 + Math.sin(timestamp / 1000000) * 10;
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const volatility = 0.02;
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const open = basePrice + (Math.random() - 0.5) * volatility * basePrice;
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const close = open + (Math.random() - 0.5) * volatility * basePrice;
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const high = Math.max(open, close) + Math.random() * volatility * basePrice;
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const low = Math.min(open, close) - Math.random() * volatility * basePrice;
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const volume = Math.floor(Math.random() * 10000) + 1000;
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data.push({
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timestamp,
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symbol: this.context.symbol,
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open,
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high,
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low,
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close,
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volume
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});
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}
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return data;
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}
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private createDataFrame(data: any[]): DataFrame {
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return new DataFrame(data, {
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columns: ['timestamp', 'symbol', 'open', 'high', 'low', 'close', 'volume'],
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dtypes: {
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timestamp: 'number',
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symbol: 'string',
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open: 'number',
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high: 'number',
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low: 'number',
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close: 'number',
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volume: 'number'
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}
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});
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}
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private generateStrategyCode(): string {
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// Convert strategy configuration to vectorized strategy code
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// This is a simplified example - in production you'd have a more sophisticated compiler
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const strategy = this.context.strategy;
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if (strategy.type === 'sma_crossover') {
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return 'sma_crossover';
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}
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// Add more strategy types as needed
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return strategy.code || 'sma_crossover';
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}
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private convertVectorizedResult(
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vectorResult: VectorizedBacktestResult,
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startTime: number
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): BacktestResult {
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return {
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backtestId: this.context.backtestId,
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strategy: this.context.strategy,
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symbol: this.context.symbol,
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startDate: this.context.startDate,
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endDate: this.context.endDate,
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mode: 'vectorized',
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duration: Date.now() - startTime,
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trades: vectorResult.trades.map(trade => ({
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id: `trade_${trade.entryIndex}_${trade.exitIndex}`,
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symbol: this.context.symbol,
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side: trade.side,
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entryTime: vectorResult.timestamps[trade.entryIndex],
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exitTime: vectorResult.timestamps[trade.exitIndex],
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entryPrice: trade.entryPrice,
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exitPrice: trade.exitPrice,
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quantity: trade.quantity,
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pnl: trade.pnl,
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commission: 0, // Simplified
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slippage: 0
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})),
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performance: {
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totalReturn: vectorResult.metrics.totalReturns,
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sharpeRatio: vectorResult.metrics.sharpeRatio,
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maxDrawdown: vectorResult.metrics.maxDrawdown,
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winRate: vectorResult.metrics.winRate,
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profitFactor: vectorResult.metrics.profitFactor,
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totalTrades: vectorResult.metrics.totalTrades,
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winningTrades: vectorResult.trades.filter(t => t.pnl > 0).length,
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losingTrades: vectorResult.trades.filter(t => t.pnl <= 0).length,
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avgTrade: vectorResult.metrics.avgTrade,
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avgWin: vectorResult.trades.filter(t => t.pnl > 0)
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.reduce((sum, t) => sum + t.pnl, 0) / vectorResult.trades.filter(t => t.pnl > 0).length || 0,
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avgLoss: vectorResult.trades.filter(t => t.pnl <= 0)
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.reduce((sum, t) => sum + t.pnl, 0) / vectorResult.trades.filter(t => t.pnl <= 0).length || 0,
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largestWin: Math.max(...vectorResult.trades.map(t => t.pnl), 0),
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largestLoss: Math.min(...vectorResult.trades.map(t => t.pnl), 0)
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},
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equity: vectorResult.equity,
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drawdown: vectorResult.metrics.drawdown,
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metadata: {
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mode: 'vectorized',
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dataPoints: vectorResult.timestamps.length,
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signals: Object.keys(vectorResult.signals),
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optimizations: this.config.enableOptimization ? ['vectorized_computation'] : []
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}
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};
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}
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async cleanup(): Promise<void> {
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await super.cleanup();
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this.logger.info('Vectorized mode cleanup completed');
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}
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// Batch processing capabilities
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async batchBacktest(strategies: Array<{ id: string; config: any }>): Promise<Record<string, BacktestResult>> {
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this.logger.info('Starting batch vectorized backtest', {
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strategiesCount: strategies.length
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});
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const data = await this.loadHistoricalData();
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const dataFrame = this.createDataFrame(data);
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const strategyConfigs = strategies.map(s => ({
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id: s.id,
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code: this.generateStrategyCode()
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}));
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const batchResults = await this.vectorEngine.batchBacktest(dataFrame, strategyConfigs);
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const results: Record<string, BacktestResult> = {};
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for (const [strategyId, vectorResult] of Object.entries(batchResults)) {
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results[strategyId] = this.convertVectorizedResult(vectorResult, Date.now());
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}
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return results;
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}
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}
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export default VectorizedMode;
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import { DataFrame } from '@stock-bot/data-frame';
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import { EventBus } from '@stock-bot/event-bus';
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import { getLogger } from '@stock-bot/logger';
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import { VectorEngine, VectorizedBacktestResult } from '@stock-bot/vector-engine';
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import { BacktestContext, BacktestResult, ExecutionMode } from '../framework/execution-mode';
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export interface VectorizedModeConfig {
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batchSize?: number;
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enableOptimization?: boolean;
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parallelProcessing?: boolean;
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}
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export class VectorizedMode extends ExecutionMode {
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private vectorEngine: VectorEngine;
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private config: VectorizedModeConfig;
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private logger = getLogger('vectorized-mode');
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constructor(context: BacktestContext, eventBus: EventBus, config: VectorizedModeConfig = {}) {
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super(context, eventBus);
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this.vectorEngine = new VectorEngine();
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this.config = {
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batchSize: 10000,
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enableOptimization: true,
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parallelProcessing: true,
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...config,
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};
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}
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async initialize(): Promise<void> {
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await super.initialize();
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this.logger.info('Vectorized mode initialized', {
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backtestId: this.context.backtestId,
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config: this.config,
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});
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}
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async execute(): Promise<BacktestResult> {
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const startTime = Date.now();
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this.logger.info('Starting vectorized backtest execution');
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try {
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// Load all data at once for vectorized processing
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const data = await this.loadHistoricalData();
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// Convert to DataFrame format
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const dataFrame = this.createDataFrame(data);
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// Execute vectorized strategy
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const strategyCode = this.generateStrategyCode();
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const vectorResult = await this.vectorEngine.executeVectorizedStrategy(
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dataFrame,
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strategyCode
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);
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// Convert to standard backtest result format
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const result = this.convertVectorizedResult(vectorResult, startTime);
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// Emit completion event
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await this.eventBus.publishBacktestUpdate(this.context.backtestId, 100, {
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status: 'completed',
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result,
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});
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this.logger.info('Vectorized backtest completed', {
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backtestId: this.context.backtestId,
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duration: Date.now() - startTime,
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totalTrades: result.trades.length,
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});
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return result;
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} catch (error) {
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this.logger.error('Vectorized backtest failed', {
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error,
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backtestId: this.context.backtestId,
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});
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await this.eventBus.publishBacktestUpdate(this.context.backtestId, 0, {
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status: 'failed',
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error: error.message,
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});
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throw error;
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}
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}
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private async loadHistoricalData(): Promise<any[]> {
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// Load all historical data at once
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// This is much more efficient than loading tick by tick
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const data = [];
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// Simulate loading data (in production, this would be a bulk database query)
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const startTime = new Date(this.context.startDate).getTime();
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const endTime = new Date(this.context.endDate).getTime();
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const interval = 60000; // 1 minute intervals
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for (let timestamp = startTime; timestamp <= endTime; timestamp += interval) {
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// Simulate OHLCV data
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const basePrice = 100 + Math.sin(timestamp / 1000000) * 10;
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const volatility = 0.02;
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const open = basePrice + (Math.random() - 0.5) * volatility * basePrice;
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const close = open + (Math.random() - 0.5) * volatility * basePrice;
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const high = Math.max(open, close) + Math.random() * volatility * basePrice;
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const low = Math.min(open, close) - Math.random() * volatility * basePrice;
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const volume = Math.floor(Math.random() * 10000) + 1000;
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data.push({
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timestamp,
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symbol: this.context.symbol,
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open,
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high,
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low,
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close,
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volume,
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});
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}
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return data;
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}
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private createDataFrame(data: any[]): DataFrame {
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return new DataFrame(data, {
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columns: ['timestamp', 'symbol', 'open', 'high', 'low', 'close', 'volume'],
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dtypes: {
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timestamp: 'number',
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symbol: 'string',
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open: 'number',
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high: 'number',
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low: 'number',
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close: 'number',
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volume: 'number',
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},
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});
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}
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private generateStrategyCode(): string {
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// Convert strategy configuration to vectorized strategy code
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// This is a simplified example - in production you'd have a more sophisticated compiler
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const strategy = this.context.strategy;
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if (strategy.type === 'sma_crossover') {
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return 'sma_crossover';
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}
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// Add more strategy types as needed
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return strategy.code || 'sma_crossover';
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}
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private convertVectorizedResult(
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vectorResult: VectorizedBacktestResult,
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startTime: number
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): BacktestResult {
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return {
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backtestId: this.context.backtestId,
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strategy: this.context.strategy,
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symbol: this.context.symbol,
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startDate: this.context.startDate,
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endDate: this.context.endDate,
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mode: 'vectorized',
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duration: Date.now() - startTime,
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trades: vectorResult.trades.map(trade => ({
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id: `trade_${trade.entryIndex}_${trade.exitIndex}`,
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symbol: this.context.symbol,
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side: trade.side,
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entryTime: vectorResult.timestamps[trade.entryIndex],
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exitTime: vectorResult.timestamps[trade.exitIndex],
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entryPrice: trade.entryPrice,
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exitPrice: trade.exitPrice,
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quantity: trade.quantity,
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pnl: trade.pnl,
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commission: 0, // Simplified
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slippage: 0,
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})),
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performance: {
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totalReturn: vectorResult.metrics.totalReturns,
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sharpeRatio: vectorResult.metrics.sharpeRatio,
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maxDrawdown: vectorResult.metrics.maxDrawdown,
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winRate: vectorResult.metrics.winRate,
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profitFactor: vectorResult.metrics.profitFactor,
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totalTrades: vectorResult.metrics.totalTrades,
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winningTrades: vectorResult.trades.filter(t => t.pnl > 0).length,
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losingTrades: vectorResult.trades.filter(t => t.pnl <= 0).length,
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avgTrade: vectorResult.metrics.avgTrade,
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avgWin:
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vectorResult.trades.filter(t => t.pnl > 0).reduce((sum, t) => sum + t.pnl, 0) /
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vectorResult.trades.filter(t => t.pnl > 0).length || 0,
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avgLoss:
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vectorResult.trades.filter(t => t.pnl <= 0).reduce((sum, t) => sum + t.pnl, 0) /
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vectorResult.trades.filter(t => t.pnl <= 0).length || 0,
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largestWin: Math.max(...vectorResult.trades.map(t => t.pnl), 0),
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largestLoss: Math.min(...vectorResult.trades.map(t => t.pnl), 0),
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},
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equity: vectorResult.equity,
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drawdown: vectorResult.metrics.drawdown,
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metadata: {
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mode: 'vectorized',
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dataPoints: vectorResult.timestamps.length,
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signals: Object.keys(vectorResult.signals),
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optimizations: this.config.enableOptimization ? ['vectorized_computation'] : [],
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},
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};
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}
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async cleanup(): Promise<void> {
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await super.cleanup();
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this.logger.info('Vectorized mode cleanup completed');
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}
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// Batch processing capabilities
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async batchBacktest(
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strategies: Array<{ id: string; config: any }>
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): Promise<Record<string, BacktestResult>> {
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this.logger.info('Starting batch vectorized backtest', {
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strategiesCount: strategies.length,
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});
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const data = await this.loadHistoricalData();
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const dataFrame = this.createDataFrame(data);
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const strategyConfigs = strategies.map(s => ({
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id: s.id,
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code: this.generateStrategyCode(),
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}));
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const batchResults = await this.vectorEngine.batchBacktest(dataFrame, strategyConfigs);
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const results: Record<string, BacktestResult> = {};
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for (const [strategyId, vectorResult] of Object.entries(batchResults)) {
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results[strategyId] = this.convertVectorizedResult(vectorResult, Date.now());
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}
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return results;
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}
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}
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export default VectorizedMode;
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