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What classes for a career in trading?

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I am a second-year applied computer science undergrad student and I need to choose my syllabus for my third and last year of studies.

I want to work in financial services. This year I took Economic Theory and Finance modules and I already chose a Stochastic Financial Modelling module for next year.

As of this point I still need to choose 8 modules from the following list:

Machine Learning

Data Mining and Text Analytics

Parallel Scientific Computing

Web Services and Web Data

Data Science

Distributed Systems

Parallel Computation

Mobile Application Development


Programming Languages and Compilation

Intelligent Systems and Robotics

Information Visualization

User Adaptive Intelligent Systems

Computer Graphics

Combinatorial Optimisation

Secure Computing

Graph Algorithms and Complexity Theory

Functional Programming

Are there any modules in this list that would prepare me for a job in a trading firm or a hedge fund?

Thank you

** For non-UK people, modules = classes
Machine Learning

And look into decision sciences classes.

DS will teach you how to make the best decisions for any problem, ML will teach you how to automate the DS algorithms.

My meager quals: studied DS, Fin, Econ and self taught coding and ML.

If i could do it again i would study DS and ML with a dash of Fin theory
Honestly for trading, the name of your university and your grade will be more important than what you actually study as long as it is mathematics or physics intensive.

If you go the IT route make sure to make significant contributions to some famous open source projects.

From your list I would chose DS, ML, Combinatorial, Graph, PC, FP, DSystems.

I have no interest in pursuing a career in pure IT or SWE.

DS, ML and FP I can understand, but will PC, Dsitributed Systems and graph/combinatorial opt really come in handy in HFT/hedge funds?
understand statistics and supply vs demand

thats it.
Do you realize trading has been automated nearly everywhere and most graduate offers are either for developers or quants (which require a phd) ?

Anyway, they will come more handy than mobile application or computer graphics.
Machine learning
Data Science
Data Mining and Text Analytics
Combinatorial Optimisation
Graph Algorithms and Complexity Theory

would all be very useful - can you take any optional mathematics or statistics courses?

Basically the machine learning/data science stuff is useful but at undergrad it could be a bit weak. The combinatorics and graph stuff is good background material if you go on to study probabilistic graphical models - you'll find this can be a hot area for time series forecasting - particularly dynamic bayesian networks.

Programming modules probably depend on your prior experience - I don't know from the title whether say 'parallel scientific computing' is a better choice . than 'parallel computing'

More importantly though - what is your maths and stats knowledge like? Linear algebra is fundamental and needs to be learned well, a good grounding in stats is useful too. Look at say the prop firm taking over from banks in FX - why do you think they chose the name 'XTX'?

Machine learning is good and it is a hot area at the moment, systematic firms and HFT firms have been looking into applying it for a few years - more traditional hedge funds are starting to get keen to talk to people with ML skills. I'll tell you now though as a grad student studying this and interviewing with funds having a solid maths background is vital. As well as linear algebra you need to know convex optimisation well.

If you don't have these as options then spend your summer going over Gilbert Strang's linear algebra lectures on the MIT website and Stephen Boyd's optimisation lectures at Stanford.

Get yourself an MSc at least - in the UK you want to apply to one of Cambridge, Edinburgh or UCL for machine learning.
>Stochastic Financial Modelling module

this isn't going to be much use unless you want to work on the sell side in a pricing or risk role

if this is your one non-CS optional module then change it to something more useful from mathematics or statistics

in addition to linear algebra and optimisation - maybe a module on linear statistical models, bayesian stats, time series (Box Jenkins/ARMA type stuff)

Only up to 20 credits assuming I cover the pre-reqs but I already took Stochastic Financial Modelling so unless I change I won't be able to take any additional ones.

In terms of my math background, I covered discrete math, basic calculus, linear algebra and probability. I am currently taking an AI module which is very heavy on stats and probability.

An MSc is definitely an option since CS degrees are at a disadvantage when competing against math/physics/engineering applicants. I went to an optiver assesment center and I was the only CS undergrad student there. Needless to say I did not get an offer.

I can replace the SFM module with one in Statistics. I am currently taking bayesian stats as part of my AI module and the time series module is not offered to undergrad students outside of the math/econ department
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