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I'm a junior buyside quant trader. AMA

Joined
1/13/23
Messages
82
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43
Hi all,

Recently we had a senior quantitative researcher provide an insightful Q&A (I'm a senior buy side quant researcher. AMA) and I thought it might also be useful to do one from the perspective of a junior quantitative trader. In the same format as Igna's post:

Who Am I: I’m a junior quantitative trader working in options. Recently graduated from a MFE program. Been on QuantNet for a few years and used it initially to inform my decision of what MFE program to join.

About My Company: One of the world's top prop trading firms in my opinion :)

Why Am I doing this: Andy initially asked and I thought it might be helpful to some people.

Ground rule: Similiar to Igna's original post, as long as there's no question that can be remotely perceived as sensitive information, I'm ok to answer. Everything I say is my personal opinion and I have very strong opinons.
 
Hi there! I have a few questions:

1. Can you tell us about your background? What credentials did you have to get you into your MFE?

2. When did you discover that you wanted to be a quant trader? What asset classes do you find intriguing and what do you specialise in?

3. Advice for people trying to get into top MFEs that don’t have the most prestigious background? (Non Ivy, Non G5)

4. What skill sets do you need in your job? (And what would you advise people to learn, E.g. Fin Maths knowledge, data science skills, Python packages etc.)

Thank you for the AMA!
 
1. Can you tell us about your background? What credentials did you have to get you into your MFE?

My undergrad was a generalist engineering program where I chose to branch into more financial engineering/ ML during the latter specialization years. For credentials, I had a somewhat decent GPA (second half of my undergrad made up for my pretty awful first half), some internships in quant risk (was fairly limited in my internship options due to me being dumb and lazy in my first years of undergrad), and a published paper at the intersection of quant finance/ ML. MFE opened the door for me to find a quant research internship role at a US hedge fund and let me 'reset' my GPA, and I leveraged these opportunities to eventually helped me land my current job at a prop trading firm.

2. When did you discover that you wanted to be a quant trader? What asset classes do you find intriguing and what do you specialise in?

I applied to both quant researcher and trader roles so I can't really say I ever 'discovered' I wanted to be a quant trader. However, I knew I wanted to be in quant because the culture aligned with what I wanted - I like working with the smartest people in the world, I like competing, and I like winning. As a new grad, our own preference to what asset class we want to work in is weighted quite little to what desk we get assigned to since we don't know enough to reaaally know what desk we want to work in (most of us don't even have any trading backgrounds). So I'm pretty indifferent across asset classes - I'll just do my best in whichever one I get assigned to and the 'core' of trading is pretty transferable across asset classes anyways. I don't really specialise in anything as of yet, don't think prop firms want new grads to be specialists because we just don't know enough - but I guess specialist in volatility if I had to say something concrete.

3. Advice for people trying to get into top MFEs that don’t have the most prestigious background? (Non Ivy, Non G5)

I'll answer this in terms of general advice for people trying to get into top jobs or any top program as this encompasses MFEs (and will be overkill to get into MFE). I didn't come from a prestigious background either but I would just aim to be one of the best in your class. You should try getting the best grades, the best internships, the best publications, etc. The 'best' is really important in the quant field because only like the top 3 players in each market can get any significant market share - if we don't think we can realistically become the best in a market we don't even enter it. Self-reflection is really important. If you're not getting the best grades you should be asking yourself "why am I not getting the best grades, am I not studying hard enough, am I studying the wrong thing? etc.". It also really helps to surround yourself with the best students. One of the biggest drivers of me going from crap grades to one of the top in the class was 1) I was mad at myself for not maximizing my potential and wanted to get my shit together 2) I became good friends with the top students in my program and really learned a lot from them in terms of mindset/work ethic. TLDR: focus on finding something that sets you apart from everyone else - general rule of thumb if its not challenging, it probably doesnt set you apart.

4. What skill sets do you need in your job? (And what would you advise people to learn, E.g. Fin Maths knowledge, data science skills, Python packages etc.)
The answer to this is actually pretty surprising in my opinion because I spent a lot of time learning quant math, coding etc. only to find it is not really transferrable at all to quant trading (but will definitely be important for quant research). I think the main skillsets needed in trading is mentality and problem solving ability (which is why many prop trading firms don't ask the trading candidates any coding questions but focus more on problem solving questions and trading games). The mentality is "I have to be the best, I have to win (with integrity)" and always thinking of how to get better. That's why we love IMO/IOI/Putnam winners, Poker and Chess champions, E-sport athletes (I was definitely nowhere near esport athlete level but I tell my parents playing League of Legends at a decently high MMR instead of studying in high school/start of uni probably helps me more in trading than all the studying I did afterwards). For improving general problem solving ability, I don't have the best answer - maybe just a lot of practice and reflection/deep thinking. TLDR: most important skillset imo is self awareness - the ability to admit to yourself "Im bad at X, I need to do Y and Z to get better"
 
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Hi quantcoach

One of the things that I wonder about is what is the quant part of a quant trader? Can I ask what kind of quant/coding projects do quant traders usually do and how long it takes on average to finish a project?

Thank you

"Quant" is typically just a job marketing buzzword that can be appended in front of anything: Quant Researcher = Researcher, Quant Trader = Trader, Quant Developer = Developer. I was surprised to find out that many QTs don't even code at all and are more macro oriented. QTs are generally not too project based - we're monitoring the markets during trading hours and then after market close we can think about some projects that might be useful. However, this changes person to person - if a QT really likes coding and project work and makes a meaningful case of how this project can contribute to the firm then by all means go for it. Also changes from desk to desk, D1 products are definitely more systematic and coding heavy compared to options. Don't think there's a hard rule for the time to finish a project - depends on the scope. Bottom line is there's quite a bit of freedom as long as you can prove you're capable of doing it and it benefits the firm.

I think this excerpt taken from Sheldon Natenberg Option Volatility and Pricing puts more color into what QTs do. No matter how good of a model the researcher creates, it will always have limitations. As a trader, it's important to understand these limitations and make rational decisions with the weaknesses of the model in mind. This is especially important in like the SVB situation for example, where many model assumptions are broken - we don't have time to go like 'let's do a project handling the SVB situation', it's more handling the situation as it's unfolding live.

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My undergrad was a generalist engineering program where I chose to branch into more financial engineering/ ML during the latter specialization years. For credentials, I had a somewhat decent GPA (second half of my undergrad made up for my pretty awful first half), some internships in quant risk (was fairly limited in my internship options due to me being dumb and lazy in my first years of undergrad), and a published paper at the intersection of quant finance/ ML. MFE opened the door for me to find a quant research internship role at a US hedge fund and let me 'reset' my GPA, and I leveraged these opportunities to eventually helped me land my current job at a prop trading firm.
What did you do to make yourself an attractive candidate for your quant risk internships, did you have prior relevant experience/projects besides your ML paper?
 
What did you do to make yourself an attractive candidate for your quant risk internships, did you have prior relevant experience/projects besides your ML paper?
I really don't think I was an attractive candidate back then - just a very average candidate. Quant risk is usually a middle office role with a much lower barrier to entry than front office roles where the top students ended up (and finance jobs in my city are in general less competitive than finance jobs in NY/Chicago). My undergrad program also had some reputation in my country so I could land the risk positions with subpar grades - didn't have any prior relevant experience (no one has relevent experience prior to their first internship) and ML paper was much later. Recruitors know this is not the most attractive profile and so I struggled very hard to land any FO interviews for internship during MFE (but was lucky enough to perform well in my very limited number of interviews to land a few decent internship offers).
 
Hi @QuantCoach,

If one has a coding/software programming background, would doing some interesting hobbyist projects in this space, be helpful in, say, creating interest amongst recruiters or be helpful in landing an interview call? If yes, what kind of tasks/toy projects/topics should he/she research about?

Sorry, if it's too broad a question.
 
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Hi @QuantCoach

1. As a follow up to your answer to the question on "what is the quant part of a quant trader", could you explain what's the difference between a traditional trader and quant trader (if they aren't the same), specifically in terms of the signals used, ways in which the data is consumed, etc.?

2. Also, you mentioned that a lot of quant math and coding courses you picked up were not very relevant to your current role. As someone joining a MFE program this Fall, could you list some courses/topic one could take within the curriculum which would be relevant to the quant trader roles?

3. Any other skillsets we can work on to be well prepared for the day to day tasks of a quant trader? You mentioned mentality and problem solving abilities are key, but any other technical components we can work on to be prepared for quant trading roles? Does having a knowledge of technical analysis, chart patterns, etc. help in the role?

Thank You!
 
Hi @QuantCoach,

If one has a coding/software programming background, would doing some interesting hobbyist projects in this space, be helpful in, say, creating interest amongst recruiters or be helpful in landing an interview call? If yes, what kind of tasks/toy projects/topics should he/she research about?

Sorry, if it's too broad a question.

I think in most cases hobbyist projects is a nice to have but not sufficient enough by itself to create interest amongst recruitors/helpful in landing an interview call. This is because anyone can just 'do' a fancy sounding project and so it's hard to accurately gauge how impressive it is. That being said, there are multiple programming oriented competitions hosted by top firms: Citadel has the datathons, Optiver has the ready trader go, IMC trading has Prosperity, etc. which may be something interesting to try out. Also projects with real consequences on the line are also more impressive - for example, I have friends that read sports betting papers to inspire their own model and made some real money from it, you can try your own algo strategy with a small bit of your money (will probably lose most of it but you can talk to your interviewers about why you thought it would be a good idea, why it didn't work, how you attempted to improve it). Projects are most interesting when you have skin in the game because trading fake money/backtesting improperly more often than not gives misleading confidence - when you trade your own money you get a slightly better idea of the real challenges in trading
 
Hi @QuantCoach

1. As a follow up to your answer to the question on "what is the quant part of a quant trader", could you explain what's the difference between a traditional trader and quant trader (if they aren't the same), specifically in terms of the signals used, ways in which the data is consumed, etc.?

Instead of thinking of traditional trader vs quant trader, I would suggest looking more into the style of trading for specific firms you're interested in. Some firms have more of a discretionary focus, some firms have more of a systematic focus. It's a bit tough to answer in terms of signals and data consumption but basically anything/ any method that can consistently give you an edge would probably be useful regardless of the semantics of the job title.

2. Also, you mentioned that a lot of quant math and coding courses you picked up were not very relevant to your current role. As someone joining a MFE program this Fall, could you list some courses/topic one could take within the curriculum which would be relevant to the quant trader roles?

To be honest, I just don't think any specific courses within the MFE curriculum is too relevant to quant trading roles - I think MFE is better in preparing for quant researcher positions instead. I feel FE courses is too rigid in the sense that too much emphasis is spent on "let's derive Black-Scholes, let's derive our greeks, let's approach this as a stochastic control problem and solve this BSDE". This is great and all but this doesn't give any sense of your dynamic risk exposures - in the real world your greeks can change very very fast and it's actually very easy to think you're long something while you're actually short. The real markets just has so much complexity that can't be captured too well in any school courses.

3. Any other skillsets we can work on to be well prepared for the day to day tasks of a quant trader? You mentioned mentality and problem solving abilities are key, but any other technical components we can work on to be prepared for quant trading roles? Does having a knowledge of technical analysis, chart patterns, etc. help in the role?

A good macro understanding is pretty useful. Just having a good idea of why the world works the way it works, why does x drive y. I don't think it's that useful to be too consumed by the very technical components - I think just learning how to learn is a good skill to have. For new grads, many firms don't require any background knowledge at all as they'll teach you everything you need to know. A blank slate is often better to work with than someone who's taught the wrong things.
 
@QuantCoach
Thank you for sharing your advice. I appreciate this a lot.
What are the classic mistakes/pitfalls that many MFE graduates make during their internship and job search?

Some of these are also mistakes that I made: They underestimate how tough the job search is way too much. They get too complacent/relaxed when they land the best programs but don't realize MFE is just the starting line. They apply way too late - should be applying to buyside internships before the program even starts, sellside internships usually open later. They procrasinate too much preparing for interviews. They aren't vocal enough during interviews. Aren't reflective enough of their relative strengths/weaknesses. Don't focus enough on the behavioral aspects - at the best firms where everyone is very smart so it's relatively easier to stand out on the softer aspects than trying to stand out with their quant skillset against IMO/IOI/Putnam winners. They don't understand that school is very different from work - the most transferable skills for top students is not necessarily the material they learned but their habits that got them to the top in the first place.
 
@QuantCoach
Thinking about the time when you joined QuantNet and researched your MFE programs, what would you like to see in student reviews of such programs that would make your mind on choosing a program.
 
@QuantCoach what kind of leetcode should MFE students do before the program starts? Any topics in particular that you recommend doing?
Dynamic programming, backtracking, probably start with blind's 100 list of questions. Typically won't be asked any coding questions for trading roles but would be useful prep for researcher roles (depending on the firm)
 
@QuantCoach
Thinking about the time when you joined QuantNet and researched your MFE programs, what would you like to see in student reviews of such programs that would make your mind on choosing a program.
To be completely honest, I weighted student reviews quite low when I researched MFE programs. To make my decision, I just read all the employment reports, used the QuantNet rankings, searched on Linkedin for where most of the alumni is now, and chose a city where many of my friends would be going. Didn't really care too much about curriculum or career services either.
 
Thanks for giving back to the community and paying it forward. It's rare to see that these days. It says more about one's character than anything.
Now that you have finished your MFE journey and started your career as a young finance professional, how can we make QuantNet as useful to you as your pre-MFE days?
 
Thanks for giving back to the community and paying it forward. It's rare to see that these days. It says more about one's character than anything.
Now that you have finished your MFE journey and started your career as a young finance professional, how can we make QuantNet as useful to you as your pre-MFE days?
I think the AMAs are quite useful. Maybe some kind of trackers of what the people that graduated from MFE programs 1, 3, 5, 10 years ago are up to now, some reflections from them about what they wish they knew earlier in their career or things they would have done differently if they could go back.
 
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