Open source · Giving back to the community

Open-Source Knowledge Bases

Over a decade of learning, notes, solved problems, and runnable code, open-sourced so the next engineer, researcher, or trader does not have to start from a blank page. Both are free, MIT-licensed, and maintained like production code.

143 stars 45 forksacross both repositories, live from GitHub

Open source · MIT · since 2022

117 stars25 forks

The Quant Prep

A free, self-tracking preparation system for quant trader, researcher, developer, and risk interviews.

  • 292

    syllabus topics, all written

  • 6,264

    spaced-repetition question cards

  • 1,100+

    hours of planned study

  • 18

    firm-specific interview guides

  • 16

    syllabus sections

  • 11

    interactive trading-lab engines

  • Quant traders
  • Quant researchers
  • Quant developers
  • Quant risk and strats
Explore The Quant Prep Star on GitHub

Open source · MIT · since 2021

26 stars20 forks

The Distinguished Engineer

A knowledge base and runnable curriculum from junior engineer to distinguished engineer.

  • 6,800

    solved DSA problems in C++ and Python

  • 129

    notes on low-latency trading systems

  • 14

    volumes on agentic AI

  • 16

    volumes on cloud AI engineering

  • 159

    technical papers with reading notes

  • 80+

    system design implementations

  • Software engineers
  • Quant and HFT C++ developers
  • Staff and principal engineers
  • AI and agent engineers
Explore The Distinguished Engineer Star on GitHub

Pick your path

The two repositories are companions: The Distinguished Engineer covers the engineering, and The Quant Prep covers the mathematics, markets, and firms. Each links to the other instead of copying it.

HFT and low-latency developer
The Distinguished Engineer for C++ and low-latency systems, then The Quant Prep for quant development, microstructure, and market making.
Quant researcher
The Quant Prep for probability, statistics, alpha research, and machine learning for finance, with the DSA track in The Distinguished Engineer for coding rounds.
Quant trader
The Quant Prep for probability, mental math, trading games, derivatives, and market making.
Agentic AI engineer in finance
The Distinguished Engineer for agentic AI, MCP, evaluation, and LLM serving, then The Quant Prep for machine learning for finance and alpha research.
Quant risk and strats
The Quant Prep for derivatives, fixed income, XVA, Basel and FRTB, and pricing and risk systems.

Built like production code

Study material is usually written once and never checked. These repositories are reviewed, tested, and linted on every change, the same way I ship trading systems.

The Quant Prep

  • Numbers in the syllabus are computed by scripts, not recalled, and CI runs the examples on every change so their assertions catch drift.
  • C++ examples build warning-free on GCC and Clang and run under AddressSanitizer, UndefinedBehaviorSanitizer, and, for concurrent code, ThreadSanitizer.
  • Basel and FRTB parameters cite the Basel Framework paragraph, market conventions cite the exchange or regulator, and papers cite authors, venue, and year.
  • Every pull request passes an automated review and test gate before it merges.

The Distinguished Engineer

  • Every knowledge note carries typed frontmatter (type, track, level, and status), and every folder has an entry note, so the vault can be browsed from any folder.
  • CI runs on every pull request: no broken links, no private data, and the vault tool tests.
  • Code is self-contained and compilable; the toolchain labs are checked by make verify and the shell labs run in a 13-shell matrix.
  • Third-party books and paid course material are never committed; notes link to the publisher, the author, or a legitimate public copy.

Use it, star it, improve it

Corrections are the most valuable contribution: a wrong number in a public study guide misleads everyone who reads it. Open an issue or a pull request, and star a repository if it helped you so others can find it.