Capture Microsecond Advantages with AI-Optimized Systems

The core trading engine: tick-to-trade pipelines in the sub-10-microsecond range, kernel bypass networking via DPDK or RDMA, FPGA-based market data handling, is specialized work that sits outside our core offering. That work is best kept with dedicated latency engineers who live in that domain. Where we add value for HFT firms is everything around the engine: the risk, compliance, research, and operational infrastructure that your latency team shouldn't be spending their time on. If you're unsure whether your requirement falls in our zone or theirs, a 30-minute scoping call will clarify it quickly.

A real-time risk dashboard that consumes execution feeds, aggregates position and P&L by strategy and desk, surfaces limit-breach alerts, and provides intraday drill-down typically requires a two to three engineer team and is a Production Application engagement: $60K to $200K over 3 to 5 months. Scope variables include the number of execution venues you're connecting, whether we're reading from an existing normalized feed or building normalization from raw FIX logs, and the complexity of your limit hierarchy.

Our engineers have worked with FIX protocol (4.2, 4.4, 5.0SP2), ITCH and OUCH for exchange direct feeds, Kafka and Kafka Streams for high-throughput event pipelines, ClickHouse and KDB+/Q for time-series storage and analytics, and Apache Arrow for in-memory columnar processing of large historical datasets. We also have experience normalizing data from Bloomberg, Refinitiv, and ICE Data Services for research workflows. We're comfortable adopting your existing tech stack or recommending a stack based on your throughput and latency requirements.

We've built surveillance and reporting systems against FINRA Rule 3110, SEC Rule 15c3-5 (Market Access Rule), Reg SHO locate and close-out reporting, and MiFID II transaction reporting. Our approach is to start with your compliance team's specific regulatory obligations and the evidence formats required by your self-regulatory organization, then build the detection logic, audit trail, and report generation to satisfy those requirements precisely. We work with your legal and compliance counsel throughout. We implement what they specify, and we document the implementation in a way that supports regulatory examination.

A quant research platform we typically deliver includes: a clean historical data store with corporate action adjustments and vendor data normalization, a vectorized backtesting framework (Python/Pandas or Rust-based depending on performance requirements), strategy versioning and parameter management, a paper trading bridge to your execution infrastructure, and a web-based interface for running and comparing strategy results. A full initial platform is a Production Application build, typically 3 to 5 months with a three-engineer team. Firms that need tighter integration with existing execution systems or custom data vendors add four to six weeks for those connectors.