A comprehensive, high-performance backtesting engine for algorithmic trading strategies built with Java Spring Boot, PostgreSQL, Redis, and Apache Kafka.

• Historical Data Analysis: Process and analyze large volumes of historical market data
• Strategy Backtesting: Test trading strategies against historical data with realistic simulation
• Risk Metrics Calculation: Comprehensive risk analysis including VaR, Sharpe ratio, max drawdown
• Portfolio Optimization: Advanced portfolio management with position sizing and risk controls
• Real-time Processing: High-performance data processing with async execution capabilities

• Multi-timeframe Support: Daily, weekly, monthly, and custom timeframes
• Risk Management: Configurable position limits, drawdown controls, and volatility checks
• Performance Analytics: Detailed performance metrics and risk statistics
• Scalable Architecture: Built for high-volume data processing and concurrent backtesting
• Caching Layer: Redis-based caching for improved performance
• Event Streaming: Kafka integration for real-time data processing

• Backend: Java 17, Spring Boot 3.2.0
• Database: PostgreSQL 15 with JPA/Hibernate
• Cache: Redis 7
• Message Queue: Apache Kafka 3.6
• Build Tool: Maven
• Container: Docker & Docker Compose

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Web API       │    │  Backtesting    │    │   Strategy      │
│   (REST)        │◄──►│    Engine       │◄──►│  Engine        │
└─────────────────┘    └─────────────────┘    └─────────────────┘
         │                       │                       │
         ▼                       ▼                       ▼
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   PostgreSQL    │    │      Redis      │    │     Kafka      │
│   Database      │    │     Cache       │    │   Streaming    │
└─────────────────┘    └─────────────────┘    └─────────────────┘

• Java 17 or higher
• Maven 3.6+
• Docker and Docker Compose
• 8GB+ RAM (for running all services)
• 10GB+ disk space

git clone <repository-url>
cd trading-backtesting-engine

docker-compose up -d

This will start:

• PostgreSQL on port 5432
• Redis on port 6379
• Kafka on port 9092
• Kafka UI on port 8080
• pgAdmin on port 8081

mvn clean install
mvn spring-boot:run

The application will start on port 8080.

# Health check
curl http://localhost:8080/api/v1/backtesting/health

# System info
curl http://localhost:8080/api/v1/backtesting/info

• POST /api/v1/backtesting/execute - Execute a backtest
• POST /api/v1/backtesting/execute/async - Execute backtest asynchronously

• GET /api/v1/backtesting/strategies - Get all strategies
• GET /api/v1/backtesting/strategies/{id} - Get strategy by ID
• POST /api/v1/backtesting/strategies - Create new strategy
• PUT /api/v1/backtesting/strategies/{id}/parameters - Update strategy parameters

• GET /api/v1/backtesting/symbols - Get available symbols
• GET /api/v1/backtesting/data/{symbol} - Get historical data

• GET /api/v1/backtesting/health - Health check
• GET /api/v1/backtesting/info - System information

Key configuration options in application.yml:

backtesting:
  data:
    historical-data-path: ./data/historical/
    max-lookback-period: 2520 # 10 years
    default-timeframe: DAILY

  risk:
    max-position-size: 0.1 # 10% of portfolio
    max-drawdown: 0.25 # 25% max drawdown
    risk-free-rate: 0.02 # 2% risk-free rate

  performance:
    benchmark-symbol: SPY
    transaction-costs: 0.001 # 0.1% per trade
    slippage: 0.0005 # 0.05% slippage

spring:
  datasource:
    url: jdbc:postgresql://localhost:5432/backtesting_db
    username: postgres
    password: password

curl -X POST http://localhost:8080/api/v1/backtesting/execute \
  -H "Content-Type: application/json" \
  -d '{
    "name": "SMA Crossover Test",
    "strategyId": 1,
    "symbols": ["AAPL", "GOOGL"],
    "startDate": "2023-01-01T00:00:00",
    "endDate": "2023-12-31T23:59:59",
    "initialCapital": 100000,
    "timeframe": "DAILY",
    "strategyParameters": {
      "shortPeriod": {"AAPL": 20, "GOOGL": 20},
      "longPeriod": {"AAPL": 50, "GOOGL": 50}
    }
  }'

curl http://localhost:8080/api/v1/backtesting/strategies

curl "http://localhost:8080/api/v1/backtesting/data/AAPL?startDate=2023-01-01T00:00:00&endDate=2023-01-31T23:59:59&timeframe=DAILY"

mvn test

mvn test -Dtest=*IntegrationTest

The application includes mock data generation for testing purposes. You can modify the MarketDataService to integrate with real data sources.

• API Security: Implement authentication and authorization for production use
• Data Validation: All inputs are validated and sanitized
• Rate Limiting: Consider implementing rate limiting for API endpoints
• Database Security: Use strong passwords and network isolation in production

• Redis caching for frequently accessed data
• JPA query result caching
• Strategy parameter caching

• Indexed queries for time-series data
• Batch processing for large datasets
• Connection pooling optimization

• Non-blocking backtest execution
• Parallel strategy evaluation
• Background data processing

1. Environment Setup

   export SPRING_PROFILES_ACTIVE=production
   export DB_PASSWORD=<secure-password>
   export REDIS_PASSWORD=<secure-password>

2. Database Migration

   # Run database migrations
   mvn flyway:migrate

3. Application Deployment

   # Build JAR
   mvn clean package -DskipTests
   
   # Run application
   java -jar target/trading-backtesting-engine-1.0.0.jar

# Build image
docker build -t backtesting-engine .

# Run container
docker run -p 8080:8080 backtesting-engine

• Application health endpoint: /api/v1/backtesting/health
• Database connectivity monitoring
• Service dependency health checks

• Spring Boot Actuator metrics
• Custom business metrics
• Performance monitoring

• Structured logging with JSON format
• Log levels configurable per package
• Audit logging for all operations

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Submit a pull request

# Install dependencies
mvn dependency:resolve

# Run in development mode
mvn spring-boot:run -Dspring-boot.run.profiles=dev

# Format code
mvn spotless:apply

• Spring Boot Reference
• PostgreSQL Documentation
• Redis Documentation
• Apache Kafka Documentation

• Backtrader - Python backtesting framework
• QuantConnect - Algorithmic trading platform
• Zipline - Python algorithmic trading library

This project is licensed under the MIT License - see the LICENSE file for details.

For support and questions:

• Create an issue in the GitHub repository
• Check the documentation and examples
• Review the API reference

• Machine Learning strategy support
• Real-time market data integration
• Advanced portfolio optimization algorithms
• Web-based strategy builder UI
• Multi-asset class support
• Cloud deployment templates

• GPU acceleration for calculations
• Distributed backtesting
• Advanced caching strategies
• Database query optimization