Technical Blog

A hands-on guide to profiling and optimizing latency in C++ trading code: hardware-aware design, kernel-bypass networking, lock-free queues, memory layout, and measurement best-practices.

Derivation and practical guide to extracting a local volatility surface from market implied volatilities (Derman–Kani / Dupire), with implementation notes and a Python example.

How CRTP works, when to use it, policy/mixin patterns, C++20 improvements, pitfalls, and practical examples you can compile and run.

When the set of types is known ahead of time, prefer std::variant and visitors to eliminate virtual calls and improve performance and ownership semantics.

Building high-performance systems by combining languages—practical patterns for FFI, safety, and zero-cost abstractions

How type hints, mypy, and modern type checkers transform Python from a dynamically typed language into a safer, more maintainable development experience

Building robust ML-based trading systems that survive real market conditions

A deep dive into zero-copy techniques in Rust for building ultra-low-latency network services, with practical examples from real-world trading systems.

Exploring how functional programming principles reduce bugs and improve reliability in financial systems

Using neural networks to build adaptive portfolio optimization systems that handle non-linear dependencies and regime changes

Techniques for migrating production databases without service interruption in mission-critical financial applications

Advanced TypeScript patterns for building type-safe financial systems, including branded types, discriminated unions, and compile-time validation for monetary calculations.

Building profitable market making strategies using deep reinforcement learning in real-time trading environments

Advanced PostgreSQL optimization techniques for high-throughput financial applications, covering indexing strategies, query optimization, and configuration tuning.

Practical insights on when to use microservices and when a well-structured monolith is the better choice

Building intelligent risk management systems that combine ML with traditional risk models for real-time portfolio protection

Modern approaches to financial time series forecasting, combining classical methods with deep learning for robust predictions

How proper monitoring, logging, and tracing prevented millions in losses and reduced MTTR from hours to minutes

Designing and implementing ultra-low latency market data feeds that process millions of messages per second with microsecond precision

Comparing type systems in C++, Rust, OCaml, Python, and TypeScript, and how static typing prevents bugs in mission-critical financial applications.

Comprehensive testing approaches for mission-critical financial applications where bugs cost millions

Implementing option pricing models and risk calculations for derivatives trading systems—practical approaches for production environments

Designing and implementing a high-performance risk management system using event sourcing patterns, CQRS, and stream processing for real-time trade monitoring.

How we reduced trading system latency from 500μs to 50μs—and why every microsecond matters in high-frequency trading

Architectural patterns and implementation techniques for building sub-microsecond trading systems in C++ and Rust, with performance comparisons and trade-offs.

How event sourcing provides auditability, temporal queries, and debugging superpowers in financial applications

Leveraging OCaml's type system and formal verification tools to mathematically prove correctness in trading algorithms and risk calculations.

Comparing two languages that take type safety seriously, and why it matters for mission-critical financial applications