Building a Quant Developer Reference Library
Not as a pure math textbook. Not as a collection of interview flashcards. Not as a random pile of disconnected notes.
I wanted something practical. Something that connects pricing theory to implementation. Something that helps you move from "I know the formula" to "I can build this correctly, test it, and trust it in production."
So I started building the Quantitative Developer Reference Library. JordiCorbilla/Quantitative-Developer-Reference-Library: Quantitative-Developer-Reference-Library
Why This Exists
A lot of great quantitative finance material already exists. The problem is not the lack of information. The problem is fragmentation.
If you are a quant developer, the knowledge you need is usually scattered across:
- textbooks that go deep on theory but say little about implementation,
- code samples that skip market conventions and edge cases,
- desk knowledge that lives in someone's head,
- internal wikis that are useful but never portable,
- and years of trial and error that are expensive to repeat.
That leaves a gap between understanding the model and building the system.
In practice, that gap is where most of the pain lives:
- curves built with the wrong conventions,
- volatility surfaces that look smooth but produce nonsense risk,
- pricing engines that work for the happy path but fail on real trades,
- PnL explain that does not reconcile,
- market data that is technically available but not analytically usable,
- and analytics platforms that become hard to trust as they grow.
This repository is meant to close that gap.
What The Library Tries To Do
The goal is simple:
Build a practitioner-focused reference for quant developers that connects products, pricing, risk, market data, numerical methods, and production architecture.
This is not meant to be read cover to cover like a course.
It is designed to work as:
- a reference you can return to when building something real,
- a bridge between theory and implementation,
- a map of how the major pieces fit together,
- and a foundation that can keep growing over time.
What Is In The Repo
The library is organized to cover both the instrument side and the engineering side.
Core product chapters
- options,
- futures and forwards,
- equities,
- FX,
- fixed income,
- interest rates,
- credit,
- commodities,
- and cross-asset topics.
Quant engineering chapters
- market data,
- pricing architecture,
- risk and PnL,
- testing and validation,
- performance and production.
There is also a numerical methods chapter because, in practice, almost every serious pricing problem ends up there eventually.
The idea is that a quant developer should not need to mentally separate:
- "the finance part,"
- "the code part,"
- and "the production part."
Those are usually the same problem, just seen from different angles.
The Point Of View
This repo is opinionated.
It assumes that quantitative correctness is not just about deriving the right formula. It is also about:
- using the right conventions,
- consuming the right market data,
- choosing the right numerical method,
- exposing the right risk,
- and building the system so the result is reproducible and explainable.
That is why the library focuses on questions like:
- What does the desk actually quote?
- What data do you really need?
- What assumptions are hidden in this model?
- What tends to break in production?
- How should this be tested?
- How do you know the output is sane?
Those questions matter just as much as the equation itself.
Who It Is For
I built this primarily for:
- quant developers,
- engineering-minded quants,
- developers moving into front-office or analytics roles,
- and practitioners who want a more implementation-grounded way to study the field.
If you are preparing for interviews, building pricing libraries, working on market data or risk systems, or trying to connect multiple years of learning into one coherent framework, this should be useful.
It should also be useful to people already in the field who want a clean reference instead of searching across old notes and half-remembered documents.
What Makes It Different
There are plenty of resources that explain option pricing. There are plenty of resources that explain fixed income. There are plenty of resources that explain numerical methods.
What is rarer is a resource that keeps all of the following in the same frame:
- product intuition,
- pricing formulas,
- market conventions,
- implementation shape,
- validation logic,
- and production pitfalls.
That is the standard I want this repository to meet.
For example, an options chapter should not stop at Black-Scholes. It should also talk about:
- implied volatility surfaces,
- no-arbitrage checks,
- surface risk conventions,
- American exercise intuition,
- implementation pitfalls,
- and the difference between a formula that works on paper and an analytics workflow that works on a desk.
The same standard applies across rates, fixed income, credit, market data, risk, and architecture.
Why I Am Sharing It Early
I do not want this to become a private note system that only becomes visible once it is "finished."
A library like this gets better in the open.
It improves when people use it, challenge it, extend it, and point out where the real complexity lives. Quant development is too broad, too practical, and too experience-driven for one person to pretend they can write the final word on it alone.
So I am sharing it as a growing public reference.
The current version already has the foundation:
- a consistent structure,
- broad chapter coverage,
- deeper treatment in options, fixed income, rates, and numerical methods,
- and dedicated chapters for the engineering side of quant work.
From here, the plan is to keep making it more useful.
Where I Want To Take It
Over time, I want this repository to become a serious long-term resource for the quant developer community.
That means:
- deeper product coverage,
- better worked examples,
- more implementation patterns,
- more sanity-check frameworks,
- stronger cross-links between topics,
- and eventually a body of material that is genuinely useful both for study and for real-world system design.
Some of the areas I especially want to deepen:
- volatility modelling,
- multi-curve rates,
- fixed-income relative value and spread analytics,
- calibration workflows,
- pricing architecture patterns,
- risk explain,
- and the operational side of production quant systems.
If You Work In This Space
If you are a quant developer, quant, trader, or engineer working near pricing and risk systems, I would love for you to take a look.
Use it. Challenge it. Steal from it. Improve it.
If a section is missing something important, I want to know. If a convention should be clarified, I want to know. If there is a subtle implementation trap that every real system hits, that is exactly the kind of knowledge worth capturing here.
The best technical references are not just correct. They are useful.
That is the bar.
Repository
If this sounds useful, you can explore the repo here:
Quantitative Developer Reference Library
Start with:
README.md00-overview.md01-options.md05-fixed-income.md06-interest-rates.md10-numerical-methods.md
Closing Thought
Quant finance has no shortage of complexity. What it often lacks is structure.
The point of this library is to create more structure:
- structure around the products,
- structure around the implementation,
- structure around the failure modes,
- and structure around the knowledge itself.
If it saves someone time, helps someone build something better, or gives the community a shared reference that is more practical than what already exists, then it is worth doing.
That is the project.
Short Version For Social Posts
I built a Quantitative Developer Reference Library: a practical, growing repo for quant developers that connects products, pricing, risk, market data, numerical methods, and production architecture.
It is designed to bridge the gap between "I know the formula" and "I can build this properly."
Current coverage includes options, futures, equities, FX, fixed income, rates, credit, commodities, cross-asset topics, plus market data, pricing architecture, risk/PnL, testing, and production engineering.
If you work in quant dev, pricing, risk, or analytics engineering, I would love feedback.
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