Event-Driven Backtesting Engine

A comprehensive C++ implementation of an event-driven backtesting system for algorithmic trading strategies. This engine provides a realistic simulation environment that closely mimics the behavior of live trading systems through proper event sequencing and modular component architecture.

Table of Contents

• Features
• Architecture
• Quick Start
• Core Components
• Creating Custom Strategies
• Configuration
• Examples
• Performance Considerations
• Contributing

Features

• Event-Driven Architecture: Proper event sequencing that mirrors real trading systems
• Modular Design: Easy integration of custom strategies, execution handlers, and data sources
• Realistic Simulation: Includes slippage, commissions, and market impact modeling
• Position Management: Comprehensive position tracking with average cost basis
• Multiple Strategy Support: Run multiple strategies simultaneously
• Extensible Framework: Clean interfaces for adding new components
• Performance Tracking: Portfolio value and position monitoring

Architecture

The system follows a clean event-driven architecture with four main event types:

Market Data → Strategy → Portfolio → Execution → Fill
     ↓           ↓          ↓           ↓        ↓
MarketEvent → SignalEvent → OrderEvent → ExecutionHandler → FillEvent

Event Flow

1. MarketEvent: New market data arrives (price, volume, timestamp)
2. SignalEvent: Strategy generates buy/sell signals
3. OrderEvent: Portfolio converts signals to orders
4. FillEvent: Execution handler simulates order fills

Quick Start

Compilation

g++ -std=c++14 -O2 -o backtester backtesting_engine.cpp

Basic Usage

#include "backtesting_engine.hpp"

int main() {
    // Create engine
    BacktestingEngine engine;
    
    // Add strategy
    auto strategy = std::make_unique<SMAStrategy>(20);
    engine.addStrategy(std::move(strategy));
    
    // Add market data
    engine.addMarketData("AAPL", 150.0, timestamp, 1000);
    
    // Run backtest
    engine.run();
    
    return 0;
}

Core Components

1. Event System

All events inherit from the base Event class:

• MarketEvent: Market data updates
• SignalEvent: Trading signals from strategies
• OrderEvent: Orders to be executed
• FillEvent: Completed order executions

2. Strategy Interface

class Strategy {
public:
    virtual void calculateSignals(const MarketEvent& market_event) = 0;
    virtual std::string getName() const = 0;
};

3. Portfolio Management

• Position tracking with average cost basis
• Capital allocation and risk management
• Signal-to-order conversion
• Real-time portfolio valuation

4. Execution Handler

• Order execution simulation
• Slippage modeling (configurable)
• Commission calculation
• Market impact simulation

5. Backtesting Engine

• Event queue management
• Component coordination
• Event processing and routing
• Performance tracking

Creating Custom Strategies

Step 1: Inherit from Strategy

class MyCustomStrategy : public Strategy {
public:
    void calculateSignals(const MarketEvent& market_event) override {
        // Your strategy logic here
        std::string symbol = market_event.getSymbol();
        double price = market_event.getPrice();
        
        // Generate signals based on your logic
        if (shouldBuy(symbol, price)) {
            auto signal = std::make_shared<SignalEvent>(
                symbol, OrderDirection::BUY, 1.0, "MyStrategy"
            );
            if (signal_callback_) signal_callback_(signal);
        }
    }
    
    std::string getName() const override { 
        return "MyCustomStrategy"; 
    }
    
private:
    bool shouldBuy(const std::string& symbol, double price) {
        // Your buy logic
        return false;
    }
};

Step 2: Register with Engine

auto my_strategy = std::make_unique<MyCustomStrategy>();
engine.addStrategy(std::move(my_strategy));

Configuration

Portfolio Settings

Portfolio portfolio(initial_capital);  // Default: $100,000

Execution Parameters

The execution handler can be configured for:

• Slippage: Default 0.1% for market orders
• Commission: $1 base + 0.1% of trade value
• Market Impact: Configurable based on order size

Strategy Parameters

Each strategy can be customized with its own parameters:

SMAStrategy sma_fast(10);   // 10-period moving average
SMAStrategy sma_slow(50);   // 50-period moving average

Examples

Simple Moving Average Strategy

BacktestingEngine engine;

// Add SMA crossover strategy
auto sma_strategy = std::make_unique<SMAStrategy>(20);
engine.addStrategy(std::move(sma_strategy));

// Load historical data
std::vector<double> prices = {100, 101, 99, 102, 105, 103};
int64_t timestamp = 1640995200;

for (size_t i = 0; i < prices.size(); ++i) {
    engine.addMarketData("AAPL", prices[i], timestamp + i * 86400);
}

engine.run();

Multiple Strategies

BacktestingEngine engine;

// Add multiple strategies
engine.addStrategy(std::make_unique<SMAStrategy>(10));
engine.addStrategy(std::make_unique<SMAStrategy>(30));
engine.addStrategy(std::make_unique<MyCustomStrategy>());

// Strategies will run simultaneously
engine.run();

Custom Data Source

// Read from CSV, database, or API
void loadMarketData(BacktestingEngine& engine, const std::string& filename) {
    // Your data loading logic
    // Call engine.addMarketData() for each data point
}

Performance Considerations

Memory Management

• Uses smart pointers for automatic memory management
• Event objects are efficiently managed through shared_ptr
• Minimal memory footprint for large datasets

Processing Speed

• Event queue provides O(1) insertion and removal
• Hash maps for O(1) position and price lookups
• Optimized for high-frequency event processing

Scalability

• Supports multiple symbols simultaneously
• Can handle large historical datasets
• Modular design allows for distributed processing

Extending the Engine

Adding New Event Types

1. Create new event class inheriting from Event
2. Add corresponding EventType enum value
3. Update event processing in BacktestingEngine

Custom Execution Models

class MyExecutionHandler : public ExecutionHandler {
    // Override execution logic
    void executeOrder(const OrderEvent& order) override {
        // Custom execution implementation
    }
};

Risk Management

class RiskManager {
public:
    bool validateOrder(const OrderEvent& order) {
        // Position limits, drawdown checks, etc.
        return true;
    }
};

Output Format

The engine provides detailed logging of all events:

MarketEvent: AAPL @ 150.00 Vol: 1000
  -> SignalEvent: BUY AAPL Strength: 1.0
    -> OrderEvent: MARKET BUY 100 AAPL @ 0.0
      -> FillEvent: BUY 100 AAPL @ 150.15
Fill processed: BUY 100 AAPL @ 150.15 | Capital: $84985

Backtest completed!
Final Portfolio Value: $101500.75

Requirements

• C++ Standard: C++14 or later
• Compiler: GCC 5.0+, Clang 3.8+, or MSVC 2015+
• Dependencies: Standard library only (no external dependencies)

License

This project is released under the MIT License. See LICENSE file for details.

Contributing

Contributions are welcome! Please:

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

Areas for Contribution

• Additional strategy implementations
• Enhanced risk management
• Performance analytics and reporting
• Data source integrations
• Optimization algorithms
• Documentation improvements

Support

For questions, issues, or feature requests, please open an issue on GitHub.

Roadmap

• Multi-threaded event processing
• Real-time data integration
• Advanced order types (stop-loss, take-profit)
• Portfolio optimization tools
• Risk analytics dashboard
• Database integration
• REST API interface
• Python bindings