stock-bot/apps/strategy-service/src/backtesting/modes/vectorized-mode.ts

import { DataFrame } from '@stock-bot/data-frame';
import { EventBus } from '@stock-bot/event-bus';
import { getLogger } from '@stock-bot/logger';
import { VectorEngine, VectorizedBacktestResult } from '@stock-bot/vector-engine';
import { BacktestContext, BacktestResult, ExecutionMode } from '../framework/execution-mode';

export interface VectorizedModeConfig {
  batchSize?: number;
  enableOptimization?: boolean;
  parallelProcessing?: boolean;
}

export class VectorizedMode extends ExecutionMode {
  private vectorEngine: VectorEngine;
  private config: VectorizedModeConfig;
  private logger = getLogger('vectorized-mode');

  constructor(context: BacktestContext, eventBus: EventBus, config: VectorizedModeConfig = {}) {
    super(context, eventBus);
    this.vectorEngine = new VectorEngine();
    this.config = {
      batchSize: 10000,
      enableOptimization: true,
      parallelProcessing: true,
      ...config,
    };
  }

  async initialize(): Promise<void> {
    await super.initialize();
    this.logger.info('Vectorized mode initialized', {
      backtestId: this.context.backtestId,
      config: this.config,
    });
  }

  async execute(): Promise<BacktestResult> {
    const startTime = Date.now();
    this.logger.info('Starting vectorized backtest execution');

    try {
      // Load all data at once for vectorized processing
      const data = await this.loadHistoricalData();

      // Convert to DataFrame format
      const dataFrame = this.createDataFrame(data);

      // Execute vectorized strategy
      const strategyCode = this.generateStrategyCode();
      const vectorResult = await this.vectorEngine.executeVectorizedStrategy(
        dataFrame,
        strategyCode
      );

      // Convert to standard backtest result format
      const result = this.convertVectorizedResult(vectorResult, startTime);

      // Emit completion event
      await this.eventBus.publishBacktestUpdate(this.context.backtestId, 100, {
        status: 'completed',
        result,
      });

      this.logger.info('Vectorized backtest completed', {
        backtestId: this.context.backtestId,
        duration: Date.now() - startTime,
        totalTrades: result.trades.length,
      });

      return result;
    } catch (error) {
      this.logger.error('Vectorized backtest failed', {
        error,
        backtestId: this.context.backtestId,
      });

      await this.eventBus.publishBacktestUpdate(this.context.backtestId, 0, {
        status: 'failed',
        error: error.message,
      });

      throw error;
    }
  }

  private async loadHistoricalData(): Promise<any[]> {
    // Load all historical data at once
    // This is much more efficient than loading tick by tick
    const data = [];

    // Simulate loading data (in production, this would be a bulk database query)
    const startTime = new Date(this.context.startDate).getTime();
    const endTime = new Date(this.context.endDate).getTime();
    const interval = 60000; // 1 minute intervals

    for (let timestamp = startTime; timestamp <= endTime; timestamp += interval) {
      // Simulate OHLCV data
      const basePrice = 100 + Math.sin(timestamp / 1000000) * 10;
      const volatility = 0.02;

      const open = basePrice + (Math.random() - 0.5) * volatility * basePrice;
      const close = open + (Math.random() - 0.5) * volatility * basePrice;
      const high = Math.max(open, close) + Math.random() * volatility * basePrice;
      const low = Math.min(open, close) - Math.random() * volatility * basePrice;
      const volume = Math.floor(Math.random() * 10000) + 1000;

      data.push({
        timestamp,
        symbol: this.context.symbol,
        open,
        high,
        low,
        close,
        volume,
      });
    }

    return data;
  }

  private createDataFrame(data: any[]): DataFrame {
    return new DataFrame(data, {
      columns: ['timestamp', 'symbol', 'open', 'high', 'low', 'close', 'volume'],
      dtypes: {
        timestamp: 'number',
        symbol: 'string',
        open: 'number',
        high: 'number',
        low: 'number',
        close: 'number',
        volume: 'number',
      },
    });
  }

  private generateStrategyCode(): string {
    // Convert strategy configuration to vectorized strategy code
    // This is a simplified example - in production you'd have a more sophisticated compiler
    const strategy = this.context.strategy;

    if (strategy.type === 'sma_crossover') {
      return 'sma_crossover';
    }

    // Add more strategy types as needed
    return strategy.code || 'sma_crossover';
  }

  private convertVectorizedResult(
    vectorResult: VectorizedBacktestResult,
    startTime: number
  ): BacktestResult {
    return {
      backtestId: this.context.backtestId,
      strategy: this.context.strategy,
      symbol: this.context.symbol,
      startDate: this.context.startDate,
      endDate: this.context.endDate,
      mode: 'vectorized',
      duration: Date.now() - startTime,
      trades: vectorResult.trades.map(trade => ({
        id: `trade_${trade.entryIndex}_${trade.exitIndex}`,
        symbol: this.context.symbol,
        side: trade.side,
        entryTime: vectorResult.timestamps[trade.entryIndex],
        exitTime: vectorResult.timestamps[trade.exitIndex],
        entryPrice: trade.entryPrice,
        exitPrice: trade.exitPrice,
        quantity: trade.quantity,
        pnl: trade.pnl,
        commission: 0, // Simplified
        slippage: 0,
      })),
      performance: {
        totalReturn: vectorResult.metrics.totalReturns,
        sharpeRatio: vectorResult.metrics.sharpeRatio,
        maxDrawdown: vectorResult.metrics.maxDrawdown,
        winRate: vectorResult.metrics.winRate,
        profitFactor: vectorResult.metrics.profitFactor,
        totalTrades: vectorResult.metrics.totalTrades,
        winningTrades: vectorResult.trades.filter(t => t.pnl > 0).length,
        losingTrades: vectorResult.trades.filter(t => t.pnl <= 0).length,
        avgTrade: vectorResult.metrics.avgTrade,
        avgWin:
          vectorResult.trades.filter(t => t.pnl > 0).reduce((sum, t) => sum + t.pnl, 0) /
            vectorResult.trades.filter(t => t.pnl > 0).length || 0,
        avgLoss:
          vectorResult.trades.filter(t => t.pnl <= 0).reduce((sum, t) => sum + t.pnl, 0) /
            vectorResult.trades.filter(t => t.pnl <= 0).length || 0,
        largestWin: Math.max(...vectorResult.trades.map(t => t.pnl), 0),
        largestLoss: Math.min(...vectorResult.trades.map(t => t.pnl), 0),
      },
      equity: vectorResult.equity,
      drawdown: vectorResult.metrics.drawdown,
      metadata: {
        mode: 'vectorized',
        dataPoints: vectorResult.timestamps.length,
        signals: Object.keys(vectorResult.signals),
        optimizations: this.config.enableOptimization ? ['vectorized_computation'] : [],
      },
    };
  }

  async cleanup(): Promise<void> {
    await super.cleanup();
    this.logger.info('Vectorized mode cleanup completed');
  }

  // Batch processing capabilities
  async batchBacktest(
    strategies: Array<{ id: string; config: any }>
  ): Promise<Record<string, BacktestResult>> {
    this.logger.info('Starting batch vectorized backtest', {
      strategiesCount: strategies.length,
    });

    const data = await this.loadHistoricalData();
    const dataFrame = this.createDataFrame(data);

    const strategyConfigs = strategies.map(s => ({
      id: s.id,
      code: this.generateStrategyCode(),
    }));

    const batchResults = await this.vectorEngine.batchBacktest(dataFrame, strategyConfigs);
    const results: Record<string, BacktestResult> = {};

    for (const [strategyId, vectorResult] of Object.entries(batchResults)) {
      results[strategyId] = this.convertVectorizedResult(vectorResult, Date.now());
    }

    return results;
  }
}

export default VectorizedMode;