# 0burak/imperial_hft

Repository: https://github.com/0burak/imperial_hft
Canonical: https://ross.abutalabs.com/products/imperial_hft
Language: C++
License Family: other
Last push: 2026-05-16T20:20:57+00:00

## Health v2 (maintenance only)
Score: 65/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 82, release rhythm 35, longevity 78
- inputs: {"age_days": 1095, "days_push": 109, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1237, forks 200 (observed 2026-08-28T04:04:05.396507+00:00)

## What it is
A C++ repository of low-latency programming techniques for high-frequency trading, including cache warming, lock-free programming, loop unrolling, a Disruptor pattern implementation, and a statistical arbitrage pairs trading strategy. All techniques are accompanied by Google Benchmark-based latency benchmarks.

## Use cases
- learn low-latency C++ techniques for HFT
- benchmark lock-free vs lock-based code
- implement the Disruptor pattern in C++
- study a low-latency pairs trading strategy
- optimize cache efficiency in latency-critical systems

## When to choose
- you are learning or teaching low-latency C++ for trading systems
- you want benchmarked examples of latency-reduction techniques
- you need a reference Disruptor pattern implementation in C++

## When to avoid
- you need a production-ready trading engine or exchange connectivity
- you require a maintained library with a license and stable API
- you work outside C++ or low-latency domains

## Facets
- artifact type: learning-resource
- maturity: active
- function: benchmarking, concurrency, developer-tools
- domain: fintech, performance, developer-tools
- platform: cpp, cross-platform
- tags: hft, low-latency, disruptor-pattern, lock-free, statistical-arbitrage, pairs-trading, cache-optimization, algorithms, linux

## Member repositories
- 0burak/imperial_hft (main) score 65

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:05.396507+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T08:21:49.506203+00:00, confidence not recorded.
  - readme: https://github.com/0burak/imperial_hft (fetched 2026-08-28T04:04:05.396507+00:00, sha 65581d2139ce)
- Data as of 2026-08-30T08:39:29.467469+00:00.