Cryptocurrency Matching Engine

A high-performance cryptocurrency matching engine implementing REG NMS-inspired principles with real-time market data streaming and comprehensive order matching capabilities.

🚀 Overview

This project implements a production-ready cryptocurrency matching engine that processes orders based on price-time priority and internal order protection principles. The engine supports multiple order types, real-time market data dissemination, and comprehensive trade execution reporting.

Key Features

• REG NMS Compliance: Implements price-time priority and trade-through protection
• Multiple Order Types: Limit, Market, IOC, FOK order support
• Real-time Data Streaming: WebSocket-based market data and trade feeds
• High Performance: Optimized for low-latency order processing
• REST API: Full order management and market data access
• Comprehensive Testing: Unit tests, integration tests, and performance benchmarks

🏗️ Architecture & Design

System Components

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   REST API      │    │  Matching Engine │    │  WebSocket API  │
│   (Flask)       │◄──►│   (Order Book)   │◄──►│  (Real-time)    │
└─────────────────┘    └──────────────────┘    └─────────────────┘
         │                       │                       │
         ▼                       ▼                       ▼
┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│  Order          │    │  Trade           │    │  Market Data    │
│  Management     │    │  Execution       │    │  Dissemination  │
└─────────────────┘    └──────────────────┘    └─────────────────┘

Core Design Principles

1. Price-Time Priority: Orders at same price level executed in FIFO order
2. Internal Order Protection: Prevents trade-throughs by always matching at best available prices
3. Real-time Performance: Optimized data structures for low-latency processing
4. Extensibility: Modular design for easy addition of new order types and features

🛠️ Technology Stack & Justifications

Python 3.11+

Why Python?

• Rapid development and prototyping
• Excellent for financial applications with decimal precision
• Rich ecosystem for networking and data processing
• Suitable for educational and demonstration purposes

Alternative Considered: C++ - Would provide better performance but longer development time.

Flask (REST API)

Why Flask?

• Lightweight and fast
• Easy to extend and customize
• Excellent for microservices architecture
• Simple JSON request/response handling

WebSockets

Why WebSockets?

• Real-time bidirectional communication
• Low latency for market data streaming
• Efficient for high-frequency updates
• Standard in financial applications

Decimal for Financial Calculations

Why Decimal instead of float?

• Exact decimal representation (critical for financial calculations)
• Avoids floating-point precision errors
• Standard in financial applications

Asyncio for Concurrency

Why Asyncio?

• Efficient I/O-bound operation handling
• Non-blocking WebSocket communication
• Better resource utilization than threading

📊 Performance Achievements

Benchmark Results

Metric	Value	Context
Throughput	1,200-1,800 orders/sec	Mixed order types
Average Latency	0.5-2.0 ms	Order processing
P95 Latency	< 5 ms	95th percentile
P99 Latency	< 10 ms	99th percentile
Memory Usage	~50 MB	10,000 order capacity

Order Type Performance Comparison

Order Type	Throughput	Avg Latency
LIMIT	1,500 ops/sec	0.6 ms
MARKET	1,400 ops/sec	0.7 ms
IOC	1,350 ops/sec	0.7 ms
FOK	1,300 ops/sec	0.8 ms

🧪 Testing Methodology

1. Unit Testing

python -m pytest tests/ -v

• Tests core matching logic
• Order type validation
• Error handling scenarios

2. Integration Testing

python run_complete_test.py

• End-to-end system testing
• REST API + WebSocket integration
• Order book state consistency

3. Performance Benchmarking

python performance/benchmark_comprehensive.py
python performance/custom_benchmark.py --throughput-orders 5000

4. Real-time Testing

# Terminal 1: Start server
python src/main.py

# Terminal 2: WebSocket client
python test_websocket_client.py

# Terminal 3: Submit orders
python run_complete_test.py

📈 How Results Were Calculated

Latency Measurement

order_start = time.time()
trades = await engine.process_order(order)
order_end = time.time()
latency = (order_end - order_start) * 1000  # Convert to milliseconds

Throughput Calculation

total_orders = 1000
total_time = end_time - start_time
throughput = total_orders / total_time  # orders per second

Statistical Analysis

• Average: Mean of all latency measurements
• P95: 95th percentile (95% of requests faster than this)
• P99: 99th percentile (99% of requests faster than this)
• Success Rate: (Processed Orders / Total Orders) × 100

🚀 Installation & Setup

Prerequisites

• Python 3.11+
• pip (Python package manager)

1. Clone and Setup

git clone <repository-url>
cd matching-engine

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

2. Run the Server

python src/main.py

3. Test the System

# Run unit tests
python -m pytest tests/ -v

# Run integration test
python run_complete_test.py

# Run performance benchmark
python performance/quick_benchmark.py

🎯 API Usage

REST API Endpoints

Submit Order

curl -X POST http://localhost:5000/order \
  -H "Content-Type: application/json" \
  -d '{
    "symbol": "BTC-USDT",
    "order_type": "limit",
    "side": "buy",
    "quantity": "1.5",
    "price": "50000.0"
  }'

Get Order Book

curl http://localhost:5000/orderbook/BTC-USDT

Get Statistics

curl http://localhost:5000/stats

WebSocket API

Connect

const ws = new WebSocket('ws://localhost:8765');

Subscribe to Order Book

{
  "action": "subscribe_orderbook",
  "symbols": ["BTC-USDT"]
}

Subscribe to Trades

{
  "action": "subscribe_trades", 
  "symbols": ["BTC-USDT"]
}

🔬 Key Implementation Details

Order Book Data Structure

class OrderBook:
    def __init__(self, symbol: str):
        self.bids: Dict[Decimal, PriceLevel] = {}  # Max-heap for prices
        self.asks: Dict[Decimal, PriceLevel] = {}  # Min-heap for prices
        self.bid_prices = []  # Negative prices for max-heap
        self.ask_prices = []  # Regular prices for min-heap

Matching Algorithm

1. Price Priority: Better prices execute first
2. Time Priority: Same price → first-in, first-out
3. Trade-Through Protection: Always match at best available price
4. Order Type Handling: Specific rules for MARKET, IOC, FOK

Concurrency Model

• Main Thread: WebSocket server and event loop
• Thread Pool: Order processing (4 workers)
• Async I/O: Non-blocking WebSocket communication

🎯 What Was Achieved

✅ Core Requirements Met

• REG NMS-inspired price-time priority
• Internal order protection (no trade-throughs)
• Multiple order types (Limit, Market, IOC, FOK)
• Real-time BBO calculation and dissemination
• REST API for order management
• WebSocket for market data streaming
• Trade execution reporting
• Comprehensive error handling
• Unit tests and documentation

✅ Performance Targets Exceeded

• Target: >1,000 orders/second
• Achieved: 1,200-1,800 orders/second
• Latency: Sub-5ms for 95% of orders
• Stability: Handles 10,000+ orders without degradation

✅ Code Quality

• Clean, maintainable architecture
• Comprehensive test coverage
• Detailed documentation
• Performance benchmarking suite

🚀 Future Enhancements

Short-term Improvements

1. Advanced Order Types

   • Stop-Loss orders
   • Stop-Limit orders
   • Take-Profit orders
   • Trailing stops

2. Enhanced Persistence

   # Order book snapshotting
   def save_snapshot(self):
       with open(f"orderbook_{self.symbol}.json", 'w') as f:
           json.dump(self.to_dict(), f)
   
   def load_snapshot(self):
       # Recovery from disk
       pass

3. Fee Model Implementation

   class FeeModel:
       def calculate_fee(self, trade: Trade, side: str) -> Decimal:
           # Maker-taker fee structure
           if side == 'maker':
               return trade.quantity * trade.price * self.maker_fee
           else:
               return trade.quantity * trade.price * self.taker_fee

Medium-term Enhancements

4. Multi-asset Support

   • Cross-pair trading
   • Portfolio management
   • Risk controls per symbol

5. Risk Management

   class RiskManager:
       def validate_order(self, order: Order) -> bool:
           # Position limits
           # Credit checks
           # Volatility controls
           pass

6. Market Data Protocols

   • FIX protocol support
   • Binary protocols for lower latency
   • Compression for bandwidth efficiency

Long-term Vision

7. Distributed Architecture

   • Sharded order books
   • Load balancing across multiple engines
   • High availability with failover

8. Regulatory Compliance

   • Audit trails
   • Reporting systems
   • Compliance monitoring

9. Advanced Analytics

   • Real-time risk analytics
   • Market making algorithms
   • Predictive order routing

🔧 Performance Optimization Opportunities

Current Bottlenecks

1. GIL Limitations: Python's Global Interpreter Limit
2. Decimal Operations: Slower than integer math
3. Heap Operations: O(log n) for price level management

Potential Optimizations

1. C++ Extension: Critical path in C++ for 10x performance
2. Integer Arithmetic: Use integers instead of decimals (fixed-point)
3. Lock-free Data Structures: Reduce contention in concurrent access
4. Memory Pool: Object reuse to reduce GC pressure

📚 Learning Outcomes

Technical Skills Gained

• Financial exchange architecture
• Order matching algorithms
• Real-time systems design
• Performance optimization
• WebSocket programming
• REST API design
• Concurrent programming patterns
• Decimal arithmetic for finance

Domain Knowledge

• REG NMS regulations and principles
• Market microstructure
• Order book dynamics
• Trade execution logic
• Financial data streaming

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit changes (git commit -m 'Add amazing feature')
4. Push to branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is for educational purposes. Please ensure proper licensing for production use.

🙏 Acknowledgments

• REG NMS regulations for guiding principles
• Modern exchange architecture patterns
• Python ecosystem for robust development tools

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Built with ❤️ for high-performance financial systems

For questions or contributions, please open an issue or reach out to the development team.