A leading global investment bank approached NordVarg to modernize their aging high-frequency trading (HFT) platform. The existing system, built over 15 years ago, was experiencing latency issues that put the firm at a competitive disadvantage in the ultra-competitive HFT market where microseconds matter.
The legacy platform was written in C++03 and had accumulated significant technical debt:
- 10ms average latency (10,000 microseconds)
- Limited scalability beyond 50,000 orders/second
- Memory fragmentation causing unpredictable performance
- Inability to leverage modern hardware features
- Complex codebase with minimal documentation
- Missing profitable trading opportunities
- Losing market share to competitors with faster systems
- Inability to expand into new markets
- High maintenance costs
- Difficulty attracting top engineering talent
- Must maintain full audit trail
- Required 99.99% uptime SLA
- Real-time risk management integration
- Compliance with MiFID II regulations
We conducted comprehensive profiling to identify bottlenecks:
- Network Stack: 40% of latency from inefficient TCP handling
- Memory Allocation: 25% from heap fragmentation
- Order Matching: 20% from suboptimal data structures
- Risk Checks: 15% from synchronous database calls
Complete system redesign focusing on ultra-low latency:
// Lock-free memory pool for order objects
template<typename T, size_t PoolSize>
class LockFreePool {
alignas(64) std::atomic<size_t> head{0};
alignas(64) std::array<T, PoolSize> pool;
public:
T* allocate() noexcept {
size_t index = head.fetch_add(1, std::memory_order_relaxed);
return &pool[index % PoolSize];
}
// Zero-copy allocation, no locks, cache-aligned
};
- Implemented using DPDK (Data Plane Development Kit)
- Direct hardware access eliminating kernel overhead
- Custom UDP protocol for market data
- InfiniBand for inter-datacenter communication
- Lock-free order book using atomic operations
- Cache-optimized data structures
- SIMD instructions for price level matching
- Predictive branch optimization
- Gradual migration strategy to minimize disruption
- Parallel running of old and new systems
- Comprehensive testing including chaos engineering
- Performance benchmarking against industry standards
- Phased rollout across trading desks
- Real-time monitoring and alerting
- Load testing at 5x peak capacity
- Failover and disaster recovery procedures
struct MarketDataUpdate {
uint64_t timestamp;
uint32_t symbol_id;
double price;
uint64_t volume;
} __attribute__((packed, aligned(64)));
// Process updates directly from NIC ring buffer
inline void process_update(const MarketDataUpdate* update) {
// Cache-aligned, no memory copies
order_book[update->symbol_id].update(
update->price,
update->volume
);
}
- No mutexes or spin locks in critical path
- Atomic operations for thread synchronization
- Per-core order books to eliminate contention
- RCU (Read-Copy-Update) for data structure updates
- Critical path offloaded to FPGA
- Sub-microsecond order validation
- Hardware-based risk checks
- Deterministic latency regardless of load
// Predict next likely price levels and prefetch
__builtin_prefetch(&price_levels[predicted_level]);
__builtin_prefetch(&price_levels[predicted_level + 1]);
| Metric | Before | After | Improvement |
|---|
| Median Latency | 10ms | 800μs | 92% faster |
| 99th Percentile | 25ms | 1.2ms | 95% faster |
| Orders/Second | 50K | 300K | 500% increase |
| Memory Usage | 32GB | 16GB | 50% reduction |
| CPU Utilization | 85% | 45% | 47% reduction |
- $50M additional annual revenue from improved execution prices
- Expanded into 15 new markets previously inaccessible
- Reduced infrastructure costs by 40% through efficiency gains
- Attracted top-tier talent with modern technology stack
- Won "Best Trading Platform" industry award
- 99.999% uptime (5 nines) - exceeding SLA
- Zero data loss incidents
- Recovery time under 5 seconds for any component failure
- Automated failover tested monthly
- Handles 500K messages/second from 20+ exchanges
- Protocol normalization (FIX, binary protocols)
- Multicast UDP for low-latency distribution
- Automatic reconnection and replay
- Sub-millisecond order routing
- Real-time position tracking
- Integrated pre-trade risk checks
- Full audit trail for regulatory compliance
- Smart order routing across venues
- VWAP, TWAP, and custom algo strategies
- Dynamic slicing based on market conditions
- Post-trade analytics and TCA
- Real-time P&L calculation
- Position limits by desk, trader, strategy
- VaR and stress testing
- Automated position flattening on breach
- C++20 - Core trading engine
- Python - Analytics and configuration
- Rust - Market data parsers
- Go - Monitoring and tooling
- Linux Kernel 5.15 - Custom RT patches
- DPDK 21.11 - Kernel bypass networking
- InfiniBand - Low-latency networking
- Redis - Real-time caching
- TimescaleDB - Time-series data
- Intel Xeon Scalable - 3.8GHz turbo
- Mellanox ConnectX-6 - 200Gbps NICs
- Intel Optane - Persistent memory
- Xilinx Alveo U280 - FPGA acceleration
- CMake - Build system
- Conan - Dependency management
- Google Test - Unit testing
- Prometheus - Monitoring
- Grafana - Visualization
- Problem: Standard allocator causing 25% of latency
- Solution: Custom lock-free memory pools with cache-line alignment
- Result: Allocation time reduced from 200ns to 15ns
- Problem: Inconsistent latency spikes from kernel networking
- Solution: Kernel bypass with DPDK, CPU pinning, IRQ affinity
- Result: Jitter reduced from ±5ms to ±50μs
- Problem: Poor data locality causing CPU stalls
- Solution: Custom data structures, prefetching, cache-aligned allocations
- Result: L3 cache miss rate reduced by 60%
- Problem: Difficult to test microsecond-level performance
- Solution: Hardware timestamping, custom profiling tools, statistical analysis
- Result: Deterministic performance validation
- ✅ Incremental migration minimized risk and allowed continuous trading
- ✅ Comprehensive profiling identified true bottlenecks, not assumptions
- ✅ Hardware co-design (FPGA) provided deterministic latency
- ✅ Modern C++20 features improved code quality without performance cost
- ✅ Automated testing caught regressions before production
- ⚠️ Initial estimates were too aggressive; actual timeline was 125% of plan
- ⚠️ Team size should have been 20% larger for parallel workstreams
- ⚠️ Documentation lagged development; should have been concurrent
- ⚠️ Training for operations team needed more hands-on sessions
"NordVarg delivered beyond our expectations. The new platform not only met our latency requirements but exceeded them by a significant margin. The team's deep expertise in both financial markets and low-latency systems was evident throughout the project. We've seen a measurable impact on our bottom line and competitive position."
— CTO, Global Investment Bank
- Machine learning integration for predictive order routing
- Quantum-resistant encryption for secure communications
- Multi-region active-active deployment for disaster recovery
- Enhanced analytics with real-time strategy optimization
- Support for 1M orders/second
- Expansion to crypto and commodities markets
- Integration with DeFi protocols
- Cloud-hybrid deployment option
- Latency matters: In HFT, even microseconds translate to millions in revenue
- Profile first: Don't optimize based on assumptions; measure everything
- Hardware awareness: Modern systems require co-design with hardware
- Lock-free algorithms: Essential for true low-latency performance
- Incremental migration: De-risks large system replacements
- Team expertise: Deep domain knowledge is critical for success
Interested in modernizing your trading systems? Get in touch to discuss how we can help you achieve similar results.
Project Duration: 6 months
Team Size: 8 engineers
Technologies: C++20, DPDK, FPGA, InfiniBand
Industry: Financial Services
Location: New York, London