Backtest Engine

Overview

The Backtest Engine service provides comprehensive historical simulation capabilities for trading strategies within the stock-bot platform. It enables strategy developers to evaluate performance, risk, and robustness of trading algorithms using historical market data before deploying them to production.

Key Features

Simulation Framework

Event-based Processing: True event-driven simulation of market activities
Vectorized Processing: High-performance batch processing for speed
Multi-asset Support: Simultaneous testing across multiple instruments
Historical Market Data: Access to comprehensive price and volume history

Performance Analytics

Return Metrics: CAGR, absolute return, risk-adjusted metrics
Risk Metrics: Drawdown, volatility, VaR, expected shortfall
Transaction Analysis: Slippage modeling, fee impact, market impact
Statistical Analysis: Win rate, profit factor, Sharpe/Sortino ratios

Realistic Simulation

Order Book Simulation: Realistic market depth modeling
Latency Modeling: Simulates execution and market data delays
Fill Probability Models: Realistic order execution simulation
Market Impact Models: Adjusts prices based on order sizes

Development Tools

Parameter Optimization: Grid search and genetic algorithm optimization
Walk-forward Testing: Time-based validation with parameter stability
Monte Carlo Analysis: Probability distribution of outcomes
Sensitivity Analysis: Impact of parameter changes on performance

Integration Points

Upstream Connections

Market Data Gateway (for historical data)
Feature Store (for historical feature values)
Strategy Repository (for strategy definitions)

Downstream Consumers

Strategy Orchestrator (for optimized parameters)
Risk Guardian (for risk model validation)
Trading Dashboard (for backtest visualization)
Strategy Development Environment

Technical Implementation

Technology Stack

Runtime: Node.js with TypeScript
Computation Engine: Optimized numerical libraries
Storage: Time-series database for results
Visualization: Interactive performance charts
Distribution: Parallel processing for large backtests

Architecture Pattern

Pipeline architecture for data flow
Plugin system for custom components
Separation of strategy logic from simulation engine
Reproducible random state management

Development Guidelines

Strategy Development

Strategy interface definition
Testing harness documentation
Performance optimization guidelines
Validation requirements

Simulation Configuration

Parameter specification format
Simulation control options
Market assumption configuration
Execution model settings

Results Analysis

Standard metrics calculation
Custom metric development
Visualization best practices
Comparative analysis techniques

Future Enhancements

Agent-based simulation for market microstructure
Cloud-based distributed backtesting
Real market data replay with tick data
Machine learning for parameter optimization
Strategy combination and portfolio optimization
Enhanced visualization and reporting capabilities

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