How to utilize a one-year gap to prepare for Quant roles before MSc in Mathematical Finance?

[Abhimanyu singh]

Hi everyone,


I have received a conditional offer from QMUL for MSc in Mathematical Finance. I have a one-year gap before joining, and I want to use this time effectively to prepare for Quant roles (Quant Developer / Analyst / Trader) in hedge funds or big banks.


A little about my background:

• B.Tech in CSE
• Basic knowledge of Python, Math, and Finance concepts
• Completed some Python and Math courses

My current plan for the gap year is:

1. Python for Finance – Improve skills in numpy, pandas, matplotlib, scipy, and libraries like backtrader or zipline. Explore financial APIs like yfinance or Quandl.
2. Mathematics for Quant/ML – Strengthen probability, statistics, linear algebra, calculus, and stochastic calculus.
3. Quant Projects – Build projects like Monte Carlo simulations, portfolio optimization, and trading bots.
4. Machine Learning – Apply ML techniques for finance, like predicting volatility, returns, or trends.

My doubts / questions:

1. Is this approach optimal to maximize my skills for Quant roles before joining MSc?
2. How can I effectively align Python, Math, and Finance while working on projects?
3. Are there additional things I should focus on during this gap year to improve my chances in hedge funds or big banks?

I would really appreciate any advice, suggestions, or resources. Thanks in advance for your help!

 

[Daniel Duffy]

C++
Numerical maths

 

[Abhimanyu singh]

Thank you so much for your advice! I understand the importance of C++ and numerical methods, and I’ll definitely add them to my gap year plan.

Since I already have some Python background, would you recommend learning numerical methods directly in C++, or first practicing them in Python and then translating to C++? Also, are there any must-know numerical math topics you think I should prioritize before starting my MSc?

 

[Daniel Duffy]

Thank you so much for your advice! I understand the importance of C++ and numerical methods, and I’ll definitely add them to my gap year plan.

Since I already have some Python background, would you recommend learning numerical methods directly in C++, or first practicing them in Python and then translating to C++? Also, are there any must-know numerical math topics you think I should prioritize before starting my MSc?

Python won't help much as a stepping stone to C++.
I would learn C++ and not couple it with numerics. Single Responsibility Principle (SRP).
Numerics is independent of the language; learn the maths first.

Online Courses

www.datasim.nl

 

[Abhimanyu singh]

Thank you very much for the advice and the course link. I understand now that I should focus on C++ as a core skill and learn numerical mathematics separately. I’ll incorporate that into my gap year plan, alongside Python for finance, maths for ML, and projects like Monte Carlo simulations, portfolio optimization, and a trading bot. I really appreciate your guidance!

 

[fabiantum]

Thank you very much for the advice and the course link. I understand now that I should focus on C++ as a core skill and learn numerical mathematics separately. I’ll incorporate that into my gap year plan, alongside Python for finance, maths for ML, and projects like Monte Carlo simulations, portfolio optimization, and a trading bot. I really appreciate your guidance!

Tbh, I think a trading bot seems unrealistic, you should rather learn the fundamentals.
I would do the following:
1. start with maths. Maybe also write some latex files with examples to show that you understand the concepts and it also helps later if you need them.
2. learn C++
3. learn python (not just for finance, but in general. It will be easy mode after learning C++)
4. learn financial mathematics (stochastic calculus, PDEs) and choose an area of interest
4. Combine your maths and python skills into projects, which are relevant to quant finance.

Maybe also consider taking part in some competitions.

 

[Abhimanyu singh]

Tbh, I think a trading bot seems unrealistic, you should rather learn the fundamentals.
I would do the following:
1. start with maths. Maybe also write some latex files with examples to show that you understand the concepts and it also helps later if you need them.
2. learn C++
3. learn python (not just for finance, but in general. It will be easy mode after learning C++)
4. learn financial mathematics (stochastic calculus, PDEs) and choose an area of interest
4. Combine your maths and python skills into projects, which are relevant to quant finance.

Maybe also consider taking part in some competitions.

Thank you so much for your detailed advice! I completely agree that focusing on fundamentals first makes sense. I’ll start with strengthening my maths foundation, possibly using LaTeX to document and practice the concepts. Then I’ll move on to C++, followed by Python, and integrate financial mathematics like stochastic calculus and PDEs into my learning.

I plan to combine these skills into projects like Monte Carlo simulations, portfolio optimization, and related quant finance applications. I’ll also consider participating in competitions to get practical exposure.

I really appreciate your guidance and time it helps me refine my gap year plan and focus on what’s truly important for a career in quant finance.

 

[Abhimanyu singh]

Tbh, I think a trading bot seems unrealistic, you should rather learn the fundamentals.
I would do the following:
1. start with maths. Maybe also write some latex files with examples to show that you understand the concepts and it also helps later if you need them.
2. learn C++
3. learn python (not just for finance, but in general. It will be easy mode after learning C++)
4. learn financial mathematics (stochastic calculus, PDEs) and choose an area of interest
4. Combine your maths and python skills into projects, which are relevant to quant finance.

Maybe also consider taking part in some competitions.

 

[Daniel Duffy]

Get John Hull's book and program the models therein in Python and C++.
A clear project.

A prominent "learn by doing" quote comes from Aristotle, who said, "For the things we have to learn before we can do them, we learn by doing them". Other well-known versions include Richard Branson's "You don't learn to walk by following rules. You learn by doing, and by falling over", and an ancient proverb stating, "The best way to learn is by doing".

 

[Abhimanyu singh]

Get John Hull's book and program the models therein in Python and C++.
A clear project.

A prominent "learn by doing" quote comes from Aristotle, who said, "For the things we have to learn before we can do them, we learn by doing them". Other well-known versions include Richard Branson's "You don't learn to walk by following rules. You learn by doing, and by falling over", and an ancient proverb stating, "The best way to learn is by doing".

Thanks Daniel, I’ll begin working on Hull’s models in Python/C++. Do you suggest I keep my focus entirely there first, or is it fine to experiment with a small trading bot alongside for practice?

 

[Daniel Duffy]

Thanks Daniel, I’ll begin working on Hull’s models in Python/C++. Do you suggest I keep my focus entirely there first, or is it fine to experiment with a small trading bot alongside for practice?

'entirely' ?
A good idea could be to learn stuff by taking Hull's book as the 'critical path'. Of course, there are others.

//
Those are my principles, and if you don't like them... well, I have others.

Groucho Marx

 

[Abhimanyu singh]

'entirely' ?
A good idea could be to learn stuff by taking Hull's book as the 'critical path'. Of course, there are others.

//
Those are my principles, and if you don't like them... well, I have others.

Groucho Marx

Got it, thank you. I’ll keep Hull as the critical path and build my maths + coding around it. That seems like a solid backbone, and I can supplement with other resources if needed. Appreciate the guidance