Thank you for taking the time to look through the repository.
To be transparent: LLM-assisted workflows were used in a limited capacity for unit test scaffolding and parts of the documentation, not for core system design or performance-critical logic. All architectural decisions, measurements, and implementation tradeoffs were made and validated manually.
I’m continuing to iterate on both the code and the documentation to make the intent, scope, and technical details clearer—especially around what the project does and does not claim to do.
Thank you for taking the time to look through the repository. I’m continuing to iterate on both the code and the documentation to make the intent and technical details clearer. You can find my research paper(under peer review) here:
That’s a fair point, and I agree on wire-to-wire (SOF-in → SOF-out) hardware timestamps being the correct benchmark for HFT.
The current numbers are software-level TSC samples (full frame available → TX start) and were intended to isolate the software critical path, not to claim true market-to-market latency.
I’m actively working on mitigating the remaining sources of latency (ingress handling, batching boundaries, and NIC interaction), and feedback like this is genuinely helpful in prioritizing the next steps. Hardware timestamping is already on the roadmap so both internal and wire-level latencies can be reported side-by-side.
Appreciate you calling this out — guidance from people who’ve measured this properly is exactly what I’m looking for.
That number is for a non-trivial software path (parsing, state updates, decision logic), not a minimal hot loop. Sub-100 ns in pure software usually means extremely constrained logic or offloading parts elsewhere. I agree there’s room to improve, and I’m working on reducing structural overheads, but this wasn’t meant to represent the absolute lower bound of what’s possible.
It sounds like your typical LLM answering you. If you have been vibe-coding, the dude sounds vaguely familiar. It's like I've spent this afternoon with him (because I probably did?)
Thank you for bringing this to my attention, and my sincere apologies for the oversight. The Rust file was inadvertently missed in the previous commit.
I will update it promptly and ensure it is included correctly. Please give a star to repo, if you loved.
That’s a fair question — thanks for calling it out.
The Rust component is a small, standalone module (used for the latency-critical fast path) that was referenced in the write-up but was not included in the last public commit due to an oversight. Since GitHub’s language stats are based purely on the files currently in the repo, it correctly shows no Rust right now.
I’m updating the repository to include that Rust module so the implementation matches the description. Until then, the language breakdown you’re seeing is accurate for the current commit.
Appreciate the scrutiny — it helps keep things honest.
"The core-and most-critical component-was left-out." Jesus-h-cluster-fucking-catastra-christ. If one of these data centers ever catches fire I will show up and make smores.
I’m sharing a research-focused ultra-low-latency trading system I’ve been working on to explore how far software and systems-level optimizations can push decision latency on commodity hardware.
What this is
A research and learning framework, not a production or exchange-connected trading system
Designed to study nanosecond-scale decision pipelines, not profitability
Key technical points
~890ns end-to-end decision latency (packet → decision) in controlled benchmarks
Custom NIC driver work (kernel bypass / zero-copy paths)
Lock-free, cache-aligned data structures
CPU pinning, NUMA-aware memory layout, huge pages
Deterministic fast path with branch-minimized logic
Written with an emphasis on measurability and reproducibility
What it does not do
No live exchange connectivity
No order routing, risk checks, or compliance layers
Not intended for real trading or commercial use
Why open-source
The goal is educational: to document and share systems optimization techniques (networking, memory, scheduling) that are usually discussed abstractly but rarely shown end-to-end in a small, inspectable codebase.
Hardware
Runs on standard x86 servers
Specialized NICs improve results but are not strictly required for experimentation
I’m posting this primarily for technical feedback and discussion:
Benchmarking methodology
Where latency numbers can be misleading
What optimizations matter vs. don’t at sub-microsecond scales
That’s fair feedback — you’re right that the front-page wording overreaches given the current scope.
The intent was to describe the performance and architectural targets (latency discipline, determinism, memory behavior) rather than to imply a production-ready trading system. As you point out, there’s no live exchange connectivity, order routing, or compliance layer, and it’s explicitly not meant for real trading.
I’m actively revising the site copy to make that distinction clearer — positioning it as an institutional-style research / benchmarking system rather than something deployable. Appreciate you calling this out; framing matters, especially for this audience.
DHCS is a bio-inspired metaheuristic designed for high-dimensional and complex optimization problems, addressing limitations of conventional approaches like PSO or Genetic Algorithms.
Key features:
Dynamic clustering & adaptive roles: Each agent autonomously decides its behavior while maintaining swarm coherence.
Periodic synchronization: Ensures global coordination without sacrificing exploration.
Scalability: Tested on a 5000-dimensional Ackley function with superior convergence and robustness.
Efficiency: Reduces computational overhead while outperforming standard methods.
Versatility: Applicable to engineering design, supply chain optimization, ML hyperparameter tuning, and financial modeling.
This paper not only formalizes the DHCS framework but also presents a comprehensive experimental evaluation demonstrating its effectiveness in high-dimensional and dynamic environments.
I’d love feedback from the community, especially from those working in metaheuristics, swarm intelligence, and large-scale optimization problems.
The full C++ execution core is intentionally not published yet. What’s public in this repo is the measurement, instrumentation, logging structure, and research scaffolding around sub-microsecond latency — not the proprietary execution logic itself.
I should have stated that more explicitly up front.
The goal of the public material is to show how latency is measured, verified, and replayed, rather than to ship a complete trading engine. I’m happy to discuss methodology or share deeper details privately with interested engineers.
Thanks for checking it out! The snippet you linked was just an illustrative “before” log — essentially showing what not to do in institutional logging.
The actual framework uses multi-layered, auditable logs with:
Hardware timestamps (NIC, CPU, PTP-synced)
Cryptographic integrity manifests
Offline verification of latencies
PCAP captures for external validation
Everything in use follows the “after” model, designed for fully reproducible, evidence-based latency measurements. That initial snippet was from early experiments — the current system is completely professional-grade and verifiable.
For what it’s worth, I care more about whether the claims can be independently verified than how the explanation is phrased. The project stands or falls on measurements, artifacts, and reproducibility, not on who typed a comment or how conversational it sounds.
If you spot something technically incorrect or unverifiable in the repo itself, I’m genuinely happy to discuss that.
The full C++ execution core is intentionally not published yet. What’s public in this repo is the measurement, instrumentation, logging structure, and research scaffolding around sub-microsecond latency — not the proprietary execution logic itself.
I should have stated that more explicitly up front.
The goal of the public material is to show how latency is measured, verified, and replayed, rather than to ship a complete trading engine. I’m happy to discuss methodology or share deeper details privately with interested engineers.
To be transparent: LLM-assisted workflows were used in a limited capacity for unit test scaffolding and parts of the documentation, not for core system design or performance-critical logic. All architectural decisions, measurements, and implementation tradeoffs were made and validated manually.
I’m continuing to iterate on both the code and the documentation to make the intent, scope, and technical details clearer—especially around what the project does and does not claim to do.
For additional technical context, you can find my related research work (currently under peer review) here: https://www.preprints.org/manuscript/202512.2293
https://www.preprints.org/manuscript/202512.2270
Thanks again for your time.