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High-Frequency Trading Infrastructure Platform

Building the engine for a high-frequency trading firm, solo. I architected the low-latency infrastructure and MLOps pipeline from the ground up to handle the extreme demands of real-time algorithmic trading.

Production October 2022 - October 2023 ML Infrastructure Engineer

The Challenge

Extreme Performance Requirements

High-frequency trading demands sub-10ms latency for predictions. Every millisecond of delay directly translates to lost opportunities and reduced profitability in competitive markets.

Solo Development Under Pressure

Tasked with building the entire MLOps pipeline with minimal support, requiring full-stack ownership from infrastructure to API development that would typically require a full team.

Zero Tolerance for Failure

In algorithmic trading, system failures mean immediate financial losses. The infrastructure needed to be fault-tolerant with automatic failover and recovery mechanisms.

The Process & My Contribution

This was a trial-by-fire experience that forced rapid development of full-stack capabilities under extreme performance constraints.

1

Infrastructure Architecture

Designed and implemented low-latency C++ infrastructure optimized for high-frequency trading. Built custom memory pools, lock-free data structures, and zero-copy message passing to minimize latency.

2

MLOps Pipeline Development

Created end-to-end pipeline for model training, validation, and deployment. Implemented A/B testing framework for safe model rollouts with automatic rollback on performance degradation.

3

Real-time Prediction System

Built distributed prediction system handling 1M+ daily predictions with sub-10ms latency. Implemented circuit breakers and rate limiting to prevent cascade failures during market volatility.

4

API Development & Testing

Developed and tested REST and WebSocket APIs for real-time data streaming and model inference. Created comprehensive test suites simulating various market conditions and edge cases.

Architecture & Technical Deep Dive

System Architecture

The trading infrastructure was built as a distributed, event-driven system optimized for minimal latency and maximum throughput.

Market Data Ingestion

Custom C++ components directly interfacing with exchange feeds, processing millions of market events per second with nanosecond-precision timestamps.

ML Inference Engine

Optimized Python/C++ hybrid system using ONNX Runtime for model inference, with custom kernels for performance-critical operations.

Time-Series Database

High-performance time-series storage using InfluxDB for historical data and Redis for real-time caching with sub-millisecond access times.

Risk Management

Real-time position tracking and risk calculation with automated circuit breakers to prevent excessive exposure during abnormal market conditions.

Performance Optimizations

Memory Management: Custom allocators and memory pools to eliminate allocation overhead during trading hours
CPU Affinity: Pinned critical threads to specific CPU cores to minimize context switching and cache misses
Network Optimization: Kernel bypass networking using DPDK for ultra-low latency packet processing
Model Optimization: Quantization and pruning techniques reducing model size by 75% without accuracy loss

Tech Stack

Core Infrastructure

C++17 Python DPDK ZeroMQ

ML/AI

PyTorch ONNX Runtime XGBoost NumPy

Data & Storage

Redis InfluxDB Apache Kafka PostgreSQL

DevOps & Monitoring

Docker Kubernetes Prometheus Grafana

Outcomes & Impact

<10ms
Prediction Latency
1M+
Daily Predictions
40%
Cost Reduction
3x
Throughput Increase

Extreme Performance: Achieved consistent sub-10ms latency for model inference in production trading environment

Scale Achievement: System successfully processed over 1 million predictions daily without degradation

Cost Optimization: Reduced infrastructure costs by 40% through efficient resource utilization and optimization

Reliability: Maintained 99.95% uptime during market hours with automatic failover mechanisms

Full-Stack Ownership: Successfully delivered complete system as solo developer, demonstrating end-to-end capabilities

Key Learnings

This trial-by-fire experience provided accelerated learning in end-to-end system ownership under extreme constraints.

  • Systems Thinking Under Pressure: Learned to design self-contained systems where feedback loops are immediate and unforgiving
  • Performance Engineering: Developed deep expertise in low-level optimization techniques critical for HFT environments
  • Full-Stack Development: Gained experience across entire stack from kernel-level networking to API design
  • Risk Management: Understood importance of defensive programming and fail-safe mechanisms in financial systems
  • Solo Ownership: Demonstrated ability to deliver complex systems independently under tight deadlines

Future Vision

While the project achieved its technical objectives during the contract period, there remains significant untapped potential. The underlying technology and architecture built during this engagement forms a solid foundation for next-generation trading systems. Active discussions are underway regarding future opportunities to fully realize the vision of this platform.