Building Quant Programming Skills with QuantLib

Summary

The document asks how a physics undergraduate with some Python, machine-learning coursework, and numerical computing experience can prepare for quant roles. The answer proposes using QuantLib as a practical test of programming and financial modeling ability, with interest-rate modeling as an example. It highlights working through C++ templates, day-count conventions, business calendars, forward curves, market indices, and model calibration. Together these tasks connect software skills with the financial conventions and instruments used in quantitative finance.

The advice is a compact project direction rather than a complete learning plan. It does not compare Python and C++, prescribe a sequence of study, or provide evidence that completing these tasks predicts hiring outcomes. QuantLib work can still expose gaps in both implementation and product knowledge, especially when a task requires linking curves and conventions consistently. The cited exercises are most useful when approached as real modeling and debugging work, with assumptions and results checked carefully rather than as a checklist for professional readiness.

Key ideas

• QuantLib can provide hands-on practice with financial modeling and quantitative software.
• Interest-rate modeling exercises can combine coding with market conventions and instrument details.
• Useful topics include day counts, business calendars, forward curves, market indices, and calibration.
• Debugging complex C++ templates can build experience with the software infrastructure used in quantitative libraries.
• The answer suggests a project direction but does not establish that these tasks alone prepare someone for a quant role.

Tags

# How should I develop my coding ability in order to set myself up for a quant role?


# How should I develop my coding ability in order to set myself up for a quant role?












First of all, I apologise if similar questions have already been asked; I've googled around but most similar questions aren't focused on developing specifically quant-friendly programming skills.

I'm a final year undergrad (physics) in the UK. As I enjoy programming, I've decided to apply to Master's courses in CS or Machine Learning/Data Science and I'm thinking about quant-like roles after that.

However, while I basically know how to program, I'm not sure what sort of things I should try or projects I should get involved with to help prepare me for this line of work, or any job which involves a mixture of math and coding.

I've used Python to solve the first 50 Project Euler problems, and I recently completed Andrew Ng's online Machine Learning course. A few years ago I took a course which culminated in using Java to simulating the major bodies of the Solar System. This semester I'm taking a course in numerical computing that involves things like DFT's, numerical integration and Monte Carlo simulations. I'm able to use Python if I want for this, although I'm tempted to try using C++ (even if it means I get a lower grade).

What do you guys suggest I do beyond this?

## Answer by SmallChess (score 4)

https://quant.stackexchange.com/a/31928

To test your programming skills, try QuantLib. Can you do interest-rate modelling with QuantLib? Can you debug the 10-level C++ template? Do you know how to use day count? Do you know how to use business calendar? Do you know how to link a forward curve with a LIBOR market index? Do you know how to calibrate a model?

If you could, you have proven yourself a real professional.

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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.