Building Real-Time Trading Systems

The Challenge

In modern financial markets, speed is everything. The difference between profit and loss can be measured in microseconds. Building a real-time trading system requires careful consideration of every component in the architecture, from data ingestion to order execution.

This article explores the key architectural patterns and technical decisions that enable low-latency, high-throughput trading systems capable of processing millions of market events per second.

Core Architecture Components

1. Market Data Ingestion

The foundation of any trading system is reliable, low-latency market data. Key considerations include:

• Direct market data feeds (FIX protocol, binary protocols)
• Data normalization and validation pipelines
• Multi-feed redundancy for failover protection
• Clock synchronization using PTP (Precision Time Protocol)

2. Event Processing Engine

The heart of the system processes incoming market events and generates trading signals:

• Lock-free data structures for minimal latency
• Single-threaded event loops to avoid context switching
• Memory-mapped files for ultra-fast data access
• Custom memory allocators to prevent garbage collection pauses

3. Risk Management Layer

Critical safeguards that operate in real-time:

• Pre-trade risk checks (position limits, capital limits)
• Real-time P&L monitoring and circuit breakers
• Market risk calculations (Greeks, VaR)
• Automated position flattening mechanisms

Technical Implementation

Language and Framework Choices

For ultra-low latency requirements, the technology stack becomes critical:

• C++: For core trading logic requiring sub-microsecond performance
• Python: For strategy development and backtesting frameworks
• Go: For microservices and API gateways
• Rust: Emerging choice for memory safety without performance penalty

Infrastructure Considerations

• Co-location in exchange data centers
• FPGA-based hardware acceleration for critical paths
• Kernel bypass networking (DPDK, Solarflare)
• CPU isolation and real-time scheduling

Monitoring and Observability

Real-time systems require specialized monitoring approaches:

• Latency histograms with percentile tracking (P50, P99, P99.9)
• End-to-end latency measurements from market data to order
• Real-time alerting for system anomalies
• Performance regression detection in CI/CD pipelines