Secured a job as a Quant Researcher, but I have no math background, what should I learn?

I've secured a job as a Quant Researcher which starts in 2 months. due to my research background in the specific area they trade in. However none of my research has used any math, its mainly programming, data science and research. During the interview they shared some material which was math theory heavy. What math fundamentals should I learn so I can be ready when working there? I have no math background, only cs.


Bump. But I think the guy is underestimating himself. He seems to have a strong background in cs + research which should amount to enough math and surely more math than 99% of wso


Honestly I wish! Im sure there’s some imposter syndrome here. I’ve got a very strong research and CS background with very very good academic research, top of the field. But again none of it involved any math. It involves data science, reading code, reading spec sheets, obtaining all my own data, writing my own algorithms, failing countless times, rewriting and getting data until it worked. 

my math stopped in high school. I’m always open to learning and getting back up to speed. I really enjoy this field. 


no math background, only cs.

assuming that you know calc, basic probability/stats you should start building from the fundamentals up.
this is what my first real math class used: 
you prob wont use any of it day to day but it starts making you think like a mathematician.
Should also work through ross' Introduction to Probability Models. 

Most Helpful

Given your background is relevant to their trading I'm guessing it's not something options/derivatives pricing related in which case different things are useful but in my experience most quant firms tend to not be very mathematically rigorous but instead rely on rigorous research processes that can effectively work with extremely non-stationary real world data and all sorts of black box statistical/machine learning techniques. Being able to follow mathematical arguments is probably helpful although the level of rigor is a lot less than pure math and intuition for financial data is more important. A lot of firms like physics PhDs as they tend to have good mathematical/coding/statistical/experimental research backgrounds even if the physics content is irrelevant at most firms. Given your background you will probably be ok with knowing enough math to understand the relevant parts for your work of their fitting process and research methodologies. Intuition for numbers and basic mathematical concepts helps too although I don't think that is very easy to quickly pick up.

An example would be in some situations you want an estimate of the inverse of a high dimensional matrix computed from noisy real world data. Understanding math is definitely helpful for this but much more important than a pure mathematician's understanding of the problem is understanding how the inverse tends to be unstable (due to small eigenvalues if you want to get technical) and so you need to reformulate the problem/regularize in some way and most importantly how to assess if whatever you chose to do is effective in the context of your particular problem.


the maths and thing you’ve said means, literally, nothing to me. None of my previous research comprised of any math neither did my degree. It’s pure data science, writing algorithms, failing, writing new algorithms, getting more data, retrying until it worked. 

based on what you’ve said you’re are correct, which math topics would you consider to be essential? 


I wouldn't say it's truly essential as many people in data science presumably including yourself are successful purely via empirical experimentation but if you know some calculus learning basic linear algebra and stats will be helpful as honestly they are pretty foundational to data science even if everything is already implemented by existing packages (and much more efficiently than most quants could ever write).

Hopefully when you join your firm/manager will have some basic training for the most relevant parts for your work. A lot of the black box approaches can work as long as there is rigorous research practices to avoid overfitting to validation/evaluation data (finance datasets are finite so possible to burn all the data if not careful). Hopefully your firm already has good processes for that and potentially much more important than understanding all the mathematical details is understanding how you can run many experiments while still being able to trust out of sample results.


best to ask the team if there's any material that would be good for you to cover. heavy math theory can mean a lot of things. The kind math you need may a lot depending on if it's hedge fund or prop shop. Is it low, mid or high frequency? what products do they trade? is everything based on a linear model? Can easily start learning things that won't be best use of your time. 


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