Ask Me Anything - Buy Side Systematic Quant

Want to help those who have some questions about the industry.

Background: I have a PhD in (Pure) Math from a top 25 US school and went into the buy side right of out of finishing PhD.

Work: I started my career at a large hedge fund in a larger quant group working on creating systematic equity signals. After 2 years, I left and joined a PM team at a large multi manager fund. I work on systematic equity strategies.

Comments (186)

Oct 3, 2019 - 5:02pm

Have you ever rejected a strategy, with a t-stat at > 3, because you couldn't build an intuitive fundamental thesis? Everyone I've met says you need a strong fundamental thesis but I've never seen an idea get rejected over it.

Oct 4, 2019 - 6:22pm

A t-stat > 3 often doesn't really mean much. First, all a t-stat says is whether the effect size is different from zero. However, virtually nothing has a zero effect size in the real world, if only because it's correlated with another causal variable. Second, the more data points you have, the higher your t-stat. A very large t-stat could ultimately be meaningless in effect size space even if thre really is a relationship between two variables. Finally, there are all sorts of weird statistical issues which can arise from many forms of econometrics research, so the entire relationship could have been spurious to begin with. If after a dozen different variations of the same experiment, you still have to squint really hard to see the effect, it's probably not worth pursuing.

Oct 3, 2019 - 5:07pm

do you have any manual execution ability...or can you only trade algo strategies?
if so, how do you decide which on a daily (or hourly) basis?

just google're welcome
Oct 4, 2019 - 1:09am

My strategies are medium frequency, days to few weeks holding period, nothing intraday or high freq. At my old place we didn't do execution, we sent the trades we wanted to an execution team that filled our shares, it was a separate team than ran best execution algos.

Oct 10, 2019 - 7:25pm

3 questions
1) Are there things that you and other PhDs can do but smart Master students can't? If so what kind of tasks?

2) Has anyone made transition from sell side data science to quant research directly linked with portfolio performance ?

3) How well did you do on the Putnam? (Just curious)

Sorry I couldn't make an actual comment for some weird. HTML issue

Oct 4, 2019 - 1:21am

I did have a non compete; 12 months.

Went to a multi-manager b/c I felt working on a PM team was more upside. At a larger firm running central books, you are thought of as more of a cog in the machine despite the fact that you build strategies. The 'researcher' becomes an interchangeable part, i.e. if you hadn't researched a particular data set and found an alpha, someone else in your place probably would have and so your comp is not scaled to the outcome of your strategies as much, if at all. This is not so far from the truth, as a larger group is giving you the structure to succeed, i.e. the rsh framework, processes upstream and downstream that are advantageous to your strategies like efficient/good data processing and great execution for low tcosts. On the flip side, the comp is pretty stable year to year and unless you consistently suck you'll keep your job for a while. On a PM team you're more isolated and have to do more for yourself, but you also keep more of what you kill.

Given where I am at in my career, I felt comfortable taking the risk to potentially make more upside and learn more along the way even if it doesn't necessarily work out. I would say it's like taking on higher expected value and higher vol on my earnings, but def with more learning -- up to the individual on whether that is worth it to them depending on where they are in their life/career. I knew people at my old shop who were sr quants pulling in 1m a year steady w/ 2 kids that were 10-12 yrs old and they were happy with where they were at.

Oct 4, 2019 - 1:33pm

My area of research in math was in functional analysis and probability theory; there were no direct applications of my research into finance.

I knew I wanted to go into hedge fund space 2 years prior to finishing (unlike most PhDs), so I began preparing. For starters, I double majored in Math and Econ at an elite undergrad, and did an IB internship while an undergrad, and wrote a lower level undergrad Economics paper at that time. So, I'd say I've had some experience from the theory side of things, very high level in Math and a decent level in Econ. Applied finance, I had none. So, I spent those 2 years studying: math finance, statistics, machine learning/ai, and practical coding. I found the first three of these easy to pick up given my stronger background in theory, particularly in probability. For the math finance part, I focused much more on portfolio theory and mean variance optimization, then on derivatives (though, I did both) - but, this was because I knew I wanted to be on the equity buy side and not in derivatives market making at a prop shop or bank. The last was probably the most challenging, for me in particular, because I hadn't coded anything in 7-8 years; and never anything at any production quality level. I did do a Quant internship at another hedge fund the summer before which helped a lot, and got my toes wet. As for practical applied finance (balance sheets, kpis, etc) I learned once I got on the job, mostly by reading papers and being engaged in different data sets and talking to people. The biggest thing here is that I started the process early, learned everything I thought I needed while focusing on areas for what I really wanted to get into, I got an internship the year before I defended, and then it was easier to land a job directly into buy side. The buy side loves pure math phds when they find ones who are interested and pick up all the other stuff - imo more so than CS/Physics/Engineering phds; what they hate is that most pure math phds are usually arrogant about the fact that their subject is arguably the hardest, but then know almost nothing else.

As for signals, we create long/short factors, dollar neutral, that we take exposure on - while taking 0 exposure to all risk factors. If you have some portfolio theory experience, you will know what I mean. If not, its a good exercise. The job is basically to find long/short factors that are predictive and economically meaningful.

As for day one, no I didn't know what to look for. I was started on some baby projects adding features to backtesting/production framework to get my coding up to speed and so I could understand how we backtest. From there I went to working on some data sets/ideas with guidance from the team. After about a year, I was a normal contributing member, adding signals, and contributing to making our entire research/production process better.

Oct 10, 2019 - 5:50am

Hi flimby,

What else should I do to get the interview opportunity in the large hedge fund if I haven't got an intern experience? Billion of thanks for your help.


Most Helpful
Oct 4, 2019 - 1:47pm

ah, the comp question. My first year I was around 200 all in, my second 350. In my 3rd, I would have gotten to around 500-600k, or so I was told when they tried to convince me not to leave. From others I know with similar education experience, this is in line with a bigger shop for first two years - given my background/education. The increases in first few years, assuming you survive, are big - as you can see. My purported 3rd yr jump might have been b/c they didn't want me to leave, can't say for sure. But, def at around 4-5 years you should be around 500-600k. 200k first is prob standard across the space unless you're really bringing something that generates value from day 1.

Here's what I've found about comp, in general, that is probably more useful than numbers: it's all over the place and they will pay you the absolute minimum to keep you from leaving, not to keep you happy. remember that. This is an absolute cut throat business, 80% of the people around you are incompetent, half of them made it on politicking and taking out peoples legs from under them. They don't care if you're happy, they care that you don't leave. If you make a stink about it, they will almost always throw you something so you don't leave. Always ask for more if it's a good year, that's the easiest time to actually get more. And, always ask for WAY more than you think they'll accept - think 2-3x more.

  • Principal in HF - Event
Jan 20, 2020 - 7:47am


ah, the comp question. My first year I was around 200 all in, my second 350. In my 3rd, I would have gotten to around 500-600k, or so I was told when they tried to convince me not to leave. From others I know with similar education experience, this is in line with a bigger shop for first two years - given my background/education. The increases in first few years, assuming you survive, are big - as you can see. My purported 3rd yr jump might have been b/c they didn't want me to leave, can't say for sure. But, def at around 4-5 years you should be around 500-600k. 200k first is prob standard across the space unless you're really bringing something that generates value from day 1.

This seems about right, with my experience being slightly slower at the beginning and faster later on.

Sell side comp for quants is generally WAYYYYYYY lower. My friends who stayed there are pulling down half or less vs the people who jumped to buy side.

Oct 4, 2019 - 10:34am

is there opportunity for manual futures day traders at your shop? what would that look like?

just google're welcome
Oct 4, 2019 - 10:51am

Thanks for posting this. As you said, there is not much upside at the big single manager funds today. I'm not so sure about job security either, and know several people who got pushed out of such firms (often due to politics rather than their own or the fund performance). It seems like MM funds are more meritocratic today, and even if you get fired it will be for transparent reasons.

When did you start at the MM fund? Did you have your own end-to-end strategy to bring in or only some signals, and did you have to convince people they still worked after 12 months? When I tried to move a few years ago, I talked to 3-4 smaller SMs and groups at MMs, but found that they either explicitly wanted a full strategy or were just trying to get information on such strategies and didn't really intend to hire anyone, so I left the industry.

Oct 4, 2019 - 2:09pm

Politics is def huge, and was huge at my last firm. It's one of the biggest realizations I had when I joined out of academia. The positive for me was that I took note of it early, saw people who I felt were incompetent but still highly successful and observed their behavior/tendency to spot how they did things. 95% of the time its not amoral as such, but they def know how to maneuver the office and other people; I give them credit for having that skill. I try to avoid it, but I learned to stand up for myself and be confident; something most people don't have when coming out of PhDs where you can often get beaten down.

MMs def easier; smaller groups, way less political. No one really cares about you outside of your small team. The PM basically shields you from any external influences, so as long as you get along with your PM and your small team, you're good. You also eat what you kill and the only person who decides is the PM, so again - if you are good with them and can be candid, you can avoid all the politics revolving comp also. This was a big plus for me in decided to move over. It is hard to do right away though, most PMs run small/lean teams and are not interested in people without experience.

I will keep when exactly I started to myself. I brought ideas, signals, and know how of how someone else runs the end to end process. In general, PMs I interviewed with didn't care about the signals, they're happy if you say you have them and hope that when you start they'll be useful. Even if you have signals, you don't know how additive they are to what's already being run. But, you will never tell them what you have and they will never tell you what they have. A lot of it is just you saying you have them and them having to believe you. How confidently you state the performance metrics and can explain the characteristics will go a long way, but I wouldn't exaggerate them, as you can be let go after 2 months as easily as you can be hired in 1 month if you can't deliver. But, you would never talk in detail about any of them in an interview and they know that and wouldn't ask you questions. IF anyone ever asks you about a data set involving a signal, how it is constructed, or the ideas, you tell them to shove off...politely; if you tell them to shove off, that's a sign to them you know you have something of value. If they insist, that's a sign they're not worth speaking to anymore and you always hold your ground. They are most interested in how you evaluate ideas (so have at least one/two data sets prepared that you're comfortable talking about) and how you evaluate signals.

But, I know what you mean, I have spoken to PMs who will say they want someone who can run 500m in additional capital at live sharpe 3 -- to which I have responded, if I could do that...wouldn't I just go be a PM myself? What do I need you for then? A good PM is someone who is hiring you for the research you'll do, not just what you'll bring on. Ideally, you are an investment from their part in order for them to expand their business; not an acquisition.

Oct 4, 2019 - 4:59pm

This is good advice. For context, I worked in a SM systematic equity fund that had only a few people (all math/physics PhDs) but large AUM. Researchers wrote code for signals but had no visibility into the actual PM process and were denied access to parts of the codebase, although I managed to learn some key details anyway. I posted more thoughts in this thread. When I interviewed at other funds, I was often asked details about statistical models down to specific parameters, along with the usual leetcode/probability stuff. I either refused to answer or gave them false answers, and was rejected. One of those funds has been posting jobs and interviewing for years without apparently hiring anyone, so this seems to be the standard procedure for them.

My impression is that at one point in time, you got hired just for having a math PhD and had strong pay progression for contributing, but that doesn't really happen anymore. Even if you get in, quants are so common now that it's easy to replace anyone who leaves or gets fired. As you said, they pay just enough to keep you from leaving, which really applies to all jobs and industries. I don't see any math PhDs from my own program (top 3 school) going to these places today, and the ones I know who did in the last 5 years all got fired and left the industry like me.

Oct 4, 2019 - 2:46pm

I am not sure I am the best person to give advice on the matter or that I would want to say anything that would convince your son to do something other than what he thinks is best for himself. But, I will give my opinion nonetheless.

Really, I don't see any practical different between the two in the long term. As far as prestige, I don't see any difference between IB and S&T; not sure where that comes from. IB may be a bit more stable in the future, as M&A advisory is not going anywhere, while S&T (depending on where he is at) is slowly being automated more and more. 20 years ago you'd call someone to buy stocks, now it's all automated. 10 years ago you'd have to call someone to buy options on single stocks, now for the most part you dont. Most S&T is in exotic derivatives and illiquid instruments, as time goes on -- things become more liquid and more automated...that will never change. But, it is not clear at all that this will be his eventual career.

Chances are that in either route, he will end up in business school or doing a CFA in order to move up or laterally, one way or another. In either case, after further education he could end up back in a bank, or private equity, or at a big asset manager, or jumping into the tech sector on the business side, or even at a hedge fund as a fundamental analyst. There is nothing restrictive about being in S&T right out of undergrad. I graduated from an elite undergrad in math + econ. Of course this was many years ago, but I know many, many people who went from a range of things from IB to consulting to S&T -- and most of them are doing something different now. An IB guy who went to do an MBA and then went into tech. An IB guy who went into private equity. A S&T guy who went into a hedge fund into a a big investing role and made huge bucks. A S&T guy who flamed out but never went to school and now works at a small prop shop grinding it out. A tech guy who went and got an MBA and then went into M&A. I myself went to do a phd and ended up at a hedge fund.

I don't see anything wrong with the decision. In fact, in the short term, it's probably less hours at around the same pay.

  • Analyst 2 in Other
Oct 4, 2019 - 1:23pm

How should a non-PhD go about trying to get a shot at a similar role? Majored in physics at a top target and then a masters in quant finance but struggling to get looks for quant roles on both buy/sell side as my current role is only partially quant related. I've been playing around in my spare time with things like quantopian/tensorflow etc but it seems hard to know at what point you've made the transition between doing random hobby projects and having a profile that will be taken seriously

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Oct 4, 2019 - 4:23pm

Analyst 2 in Other:

How should a non-PhD go about trying to get a shot at a similar role? Majored in physics at a top target and then a masters in quant finance but struggling to get looks for quant roles on both buy/sell side as my current role is only partially quant related. I've been playing around in my spare time with things like quantopian/tensorflow etc but it seems hard to know at what point you've made the transition between doing random hobby projects and having a profile that will be taken seriously

Interested to hear about your perspective gehere also?

Oct 4, 2019 - 5:49pm

for the practicalities of interviewing, I will refer you to the numerous books written on it. As for the problem solving part of interviews (for first job), I can tell you I picked up every book on quant interviews and systematically went thru every problem over the course of 3-4 weeks. After that, it's mostly luck. Qnt interviews are like a fire fight, multiple rounds of exhausting math/cs/algorithm/etc questions. You just gotta be prepared, eat well in the morning, and try to crack out a 95%. If you interview at enough places, you can get thru it and you only need to do it once. Don't beat yourself up if you dont' get the job, just keep preparing and move on. One other advice is do not start applying until after you have prepared! some people try to early and burn interview opportunities. You prepare and prepare and prepare, then you go thru the gauntlet.

As for how to get people to look at your resume, the only thing I can suggest is networking. Go to conferences, build real long term relationships with people (not stupid 10 min convos at a networking event where you think that'll get you a job) and see if you can work on projects with them. Coming out of pure math phd, this is how I did it. No one came to head hunt me at a phd math program. Don't be modest, keep asking people until someone starts working with you. If you did a FE degree, you should have some firms that will come to your school. If your gpa is bad, not much I can say about that - it is what it is.

As for my particular role, honestly you will have a tough time. Most people hiring signal researchers look for people with experience, those that don't look for phds. Those who get in without a phd usually start in a different part of the business (risk, market making, etc) before moving into it.

And, just so you're prepared, know that down the road a masters will always be seen as lesser than a PhD. It's not your fault, and I don't mean to be biased, but most PhDs are just better than most MAs. Most Quant PMs have PhDs, not all, but most. That's not to say there aren't good MAs, but they're just usually not as successful on average.

Both have one up on the other when you enter. MAs more practical education and they usually get up and running in terms of coding and understanding day to day basics, but PhDs generally more mental horsepower from studying longer. But, it's easier to bridge the coding gap for PhDs then the education gap for MAs, and this is where it is harder for the MA. The first year you're likely to be doing better, after the first year the PhDs usually pull ahead. The MAs who succeed are ones who can build the skills a person with a PhD has coming in as they gain experience; you have to bridge the gap one way or another. I know many of them and it can def be done. But, the gap will exist, and you have to try to bridge it. A PhD just has more years of research experience, which means 3 big things: 1. they know their ideas are garbage 99% of the time, 2. that doesn't get them down and they push thru it, 3. their background makes them usually better/faster at idea generation. The first two are really important to learn for any researcher. The 3rd is just a function of spending a lot more time studying. If you give a typical math-laden paper to a phd and an ma, the phd will usually go thru it in 1/4 of the time and understand it more clearly; it doesn't mean they're innately smarter, but they have already gone thru 100s of other papers and so they're more efficient at it and not as slow. In the beginning there isn't much different, in the long run people will always prefer the PhDs - it just looks better to investors.

And, that is something I stress to MAs. Despite what people will tell you, look around at the top Quant PMs on buy side or sell side, the Quant Managing Directors, heads of quant rsh, all the top positions at varying firms - and count how many have MAs. You just don't see them at the top. It's partly the competition with PhDs, but also firms want people with good titles in visible rows; especially if they're visible to investors or the public. No one will hire a Head of Quant who has a masters, it wont look good. With that said, you can certainly have a good career; except to spend 4-7 years at entry to mid level to work your way up to the 2-3 yrs of a PhD.

Oct 9, 2019 - 7:53pm

I'm usually fairly bold in remarks and I think I may try again here . With what you said for idea generation and deciphering research, that tends and has been traditionally the innate trait that hedge fund managers (consistent ~7-8% and higher returns) have. Its finding truth through jargon and in this case productivity and numerical accuracy. While I can see the mathematical aptitude of a doctorate being more adept , it seems as though that's where a trait lies. As in your example, math Ph.d. know math and can pick up coding, but the differing disciplines leave room for question. A finance doctorate, economics doctorates, philosophical, legal doctorate; these few have not touched tensorflow, logistic, ridge, lasso, or other technologies (C++, bash, linux shell), a Ph.D. is superior, even outside of your discipline, and more so a socially trained researchers whose touched say SAS and Stata (no OO, no supervised models trained etc.)? How can those folk know what performs best if they have never done it, this can actually go for mathematics doctorates and perhaps physics as well !

Oct 25, 2019 - 3:14am

This is a really interesting point which I hadn't considered. I've got an offer from a relatively large buy side fund for systematic signal generation directly out of an MA (engineering) starting next summer. I have noticed that all the senior quant people are PHDs, both the actual systems PMs and the quant researchers.

Do you think it is wise/possible to grind for a couple years, then try and do a part-time PHD while still working? I think the opportunity now is far too good to turn down, but not sure how folks in the business view someone with ~4yrs xp breaking off employment for a PHD down the road.

Oct 29, 2019 - 3:34am

Could you give some advice on networking? I'm looking to go to a multi-manager, have a math background (I originally planned to do an algebraic geometry seeing you write Groethendieck as your favorite mathematician made me very happy :D), have been doing data science/ML engineering for a few years, currently at GS S&T.

I've met a number of people at multimanagers, both analysts and PMs, but the systematic people were either new grad hires or started as pure software engineers. I don't mind writing quality code at all, but I have trouble competing with people with actual CS degrees...

I've tried looking people up on LinkedIn and emailing them, but not every PM is hiring, so it feels kind of cumbersome. Asking friends who work at the big shops to send my resume to their internal recruiters hasn't worked at all.

Additional info: Most of my finance experience has been in equities and I know that space well enough to have my own trading ideas. I realized I have a much stronger passion for macro, but I have no idea about how anyone does systematic macro trading.

Oct 4, 2019 - 2:32pm

Thanks a lot for the AMA.

You mentioned 80% of the people around you are incompetent. Keen to hear your thoughts on what makes you successful in the equity quant field.

What is the best way to learn systematic equity trading/portfolio management? Can you please recommend any good books/study materials? I was looking at quantopian and the QEPM book written by Chincarini as an introduction. It would be great to hear from someone working in the industry.

What programming language do you use for your research projects/production framework?

Lastly, if you care to elaborate, which data provider (Reuters, S&P DJ, Factset etc), is considered as the industry standard for general security master database?

Oct 4, 2019 - 4:14pm

I think the biggest things are that I have always focused on 'doing the right job'' by creating good signals not just any signal that 'passes the test' (there is a difference) and producing good research not just 'deliverables', I have a fairly rigorous approach to research (I don't take short cuts), and I really really like my job, i.e. trying to conquer the market with math and critical thinking (most qnts I see are here because it's a lucrative field). The last one is the biggest reason.

Sorry, I don't have any books. I have never found a good one and its doubtful people will tell you anything beyond the basics. 50% of systematic trading is the process. How do you analyze signals? what do you look for? How do you test? How is your testing better than the other guy? etc etc. I consider these things as important as the signals themselves.

I use Python/C++. Python is the work horse language these days.

And, for SM, people use some or all of the ones you mentioned, I don't think it matters. If you are only using daily data, they're all basically fine. Anything more granular, people may even build their own systems if they can afford it. Established firms are def building and maintaining their own at tick level.

Oct 5, 2019 - 1:56am

Many thanks for your reply. Really appreciate it.

What would be your advice for someone who is trying to put on a systematic overlay to a manual strategy? I am trading equities based on some alpha signals from intuition. Other risk factors apart from market/sector are left unhedged..

What is the general way of testing and quantifying the strength of the signal? T-stat from hypothesis testing? Any recommended subjects to look up would be great.

Also for hedging risk factors, do you use factors created on your own or use those produced by 3rd party providers (Barra, Axioma etc)? Which method do you prefer?

For someone who doesn't have access to 3rd party risk factors, what is the suggested way to calculate factor exposures of an equity long/short book? I have been trying to calculate the beta to a factor index (i.e L/S momentum index published by sell-side) and look at the overall beta-adjusted delta to that factor.

Do you have a recommended way of learning python for quant equity? A lot of resources out there in the web. Which python libraries do you use often?

Oct 4, 2019 - 4:21pm

Run into finance/econ phds occasionally. On the purely quant side, they're usually in econometrics or optimization theory if they are finance/econ phds - since it's fairly applicable. In hedge funds in general, you can find more of the factor model or general finance guys on the fundamental side of the business, either directly on a PM team or within some group that provides Quant expertise to fundamental PMs.

I would say the perception is are you fundamental or quant inclined? You can tell pretty quickly once you see their research area, like I said above. But, beyond that, I've never had any other perception in terms of good/bad about them. They just have diff PhDs and a slightly diff focus that is applicable either more to quant or more to fundamental.

Oct 4, 2019 - 6:35pm

thanks again. I'm finishing my phd in finance from a top 5 (?) university but unfortunately they are very strict about internships (they know people don't come back to academia after). A couple large multistrat funds (think citadel, twosigma, aqr) contacted me for interviews, but I have not taken any because i feel out of practice with brainteasers and because I was thinking I'd have more leverage as an assistant professor. do you think being an AP is an advantage or just a waste of effort compared to preparing for interviews?

Oct 4, 2019 - 4:06pm

What are your thoughts on the general direction of the buy-side quant space? From what I've heard, it sounds like the best time to have joined was 4-5 years ago.

  • 1
Oct 4, 2019 - 4:36pm

This will probably be biased, but it is what it is. Personally, I think buy side quant space will over take fundamental in the next 2-3 decades, but never entirely. The trend is intact, decades ago it was 95% fundamental and 5% quant, now it's closer to 65/35, I think? There is nothing that a fundamental PM does that a quant cannot take over. First, quants are way better at managing the risk in their portfolios as they do it mathematically and optimize. Second, quants are not just 'top down' like people think. We build bottom up strategies all the time. In fact, the only limitations to doing so are either 1. the relevant data is not as understood by the quant b/c it's so idiosyncratic to the firm and 2. the relevant data is not readily available in an automated format.

2 moves towards quants every single day, as more and more data becomes automated. 25 years ago you had to read a 10K to get relevant numbers, now an NLP algorithm can read them right out and store it in a database for a quant, to produce a signal, and trade it before the fundamental PM opens his/her email and double clicks on the pdf. #1 is arguably way tougher, and that is def a skill a fundamental pm has, but it's nothing a quant can't learn. If anything, they can glean relationships and interactions that a fundamental PM has to come up with manually.

And, with the sheer amount of data out there, the quant landscape is becoming increasingly diversified so there is plenty of room to separate yourself from your peers; despite what you hear, people are running all sorts of strategies. When you hear quant strategies on bbg, they'll basically assume its all trend/momentum/reversion. Its wildly inaccurate. A lot of quant books are completely nuetral to those things and consider them risk.

Oct 4, 2019 - 11:40pm

Thanks for the response!

I noticed that your responses to other questions focus mainly on quants at the PhD level, reasonably so given that the space has been very PhD-heavy thus far. Recently, however, the large systematic shops have ramped up quant hiring from the undergraduate level. Do you think this is a trend that will continue (and if so do you think it would be because they can actually make significant contributions rather than fulfill a commoditized STEM skillset) or will the space remain dominated by PhDs?

  • 1
Oct 4, 2019 - 7:58pm

Don't know of any quants out of undergrad. Def developers and other middle/back office functions. Maybe there are analysts working with fundamental pms who are undergrads - i wouldn't know.

Oct 4, 2019 - 9:23pm

MM equity non-quant here. I've noticed an increase in the firm hiring systematic PMs that use signals derived from alternative data that a lot of non-systematic PMs have historically used with success (e.g. credit cards). I have little doubt that over time, the quants will be able to extract an increasing share of the alpha from this data, and quicker too.

As a fundamental guy one of the things i think about is whether and why a business deserves to earn an above-market return. So in that vein... do you ever think about whether and why the non-systematic PMs deserve to continue to make money from what they're doing?

Edit: Apologies i see you answered this somewhat above. Maybe a different angle - which component(s) of the non-systematic alpha generating skillset do you think quant will gobble up/overtake next?

Oct 4, 2019 - 10:02pm

I think the core skill set of a fundamental pm to break down and really understand, idiosyncratically, the core competencies of companies will stay intact.

First, new companies with new business models that have never existed come into being and until sufficient history is established, the fundamental pms are the only source for intuitive analysis. Second, we're very reliant on data and it doesn't always exist.

For an example, suppose a company announces its laying off it's 20% of its staff. How do I analyze the impact on the company? As a quant, perhaps I need to know other companies and their hiring/firing patterns, I need to look at other instances of companies that have laid of 20% of their staff and how it affected them, I probably need years of history on this data to do any meaningful testing. Is the history even relevant to today's time? Will firings in 08 crash distort my results? Maybe I need to slice it to an industry level. Will I even have enough data here? Where am I getting all this data from? I need enough data points to say anything with confidence. What if it's a strategic business move, how do I determine that? There are so many slices and the data isn't there to do the kind of quant analysis I'd need to do to even answer the question. Hell, is all of this work even worth the time for one stock? But a fundamental pm can sit down, look at the factors today, understanding that specific company, and do a better job if they have a good understanding of how that company works and use intuition to model the impact.

I think quants will cannabalize any relevant data set that becomes commodotized. Quants need data and they need history for that data to analyze its statistical significance. But, if any particular variable or input to your models that's particularly predictive becomes commodotized as data for a long enough period, it's inevitable that quants will seize on it. And once we do, we'll pounce on that alpha before a fundamental pm gets the chance. Of course, some times we find variables or relationships in data that everyone is already using, that they don't see - though this is still also reliant on the data existing in the first place.

Oct 5, 2019 - 12:37pm

Thanks for your thoughts. While I agree with your assessment of what non-systematic PMs can do, I think the effect of systematic cannibalizing data sets will push non-systematic PMs farther out the return horizon.

By that, I mean a lot of the timeliness of returns generated by non-systematic PMs can be attributed to alternative data which lets them know exactly which quarter a company's fundamentals are going to inflection up or down. Great public example is Melvin Capital shorting Nintendo (articles on Bloomberg are available). Now if quants start to cannibalize these data sets, I think a good chunk of non-systematic PMs will lose the ability to churn out alpha on a consistent quarterly basis. They can still rely on all the skills you mentioned to outperform quants over a longer investment horizon, but that return stream may no longer fit within the parameters of a MM. In essence I believe the quant cannibalization of alt data will make life at a MM increasingly untenable for non-systematic teams.

Would you generally agree with that assessment? Any areas where my logic breaks?

Oct 4, 2019 - 10:01pm

My background is quite similar to yours.

Background: PhD in Pure Math (functional analysis, particularly Banach Stone Theorem in Banach space theory) from a Singapore University (NTU) and would like to go to either side (buy/sell) after finishing PhD but incline to sell side.

I am in my second year this year, no finance, programming, financial maths background.
So started self-studying Math Finance, Stochastic calculus, econometrics, Machine Learning (neural network), Python and algorithms (sorting, graph search, dynamic programming) 4 months ago.I have 2 years to build up my background.

May I know what else should I do so that I can increase my chance of landing a job in sell side?

No internship experience so far as I previously planned to venture into academia but I find tha being a quant is quite challenging (steep learning curve).

Oct 4, 2019 - 10:08pm

Honestly, I'm not sure what the market is like in Signapore for Quants. I know a lot of hedge funds have offices there, but even so they're not largely staffed, mostly satellites for Asia region PMs.

Sell-side is mostly desk support for traders. You'll build tools for them to trade better and make markets. If you find that interesting, keep studying what you're studying. Really, they will ask you a bit about your thesis, they will ask you a few questions in stochastic calc if you say you know it, they will spend the other 80% of time asking grilling you on common quant questions. If you get thru this guantlet, you can get a job.

Prepare for interviews fully. If you can do that first, do some networking and land some interviews for an internship, then that's the way to go. An internship, whether they give you an offer at the end or not, will guarantee you more interviews for full time positions after. Prob of getting a full time job conditioned on you're landing an internship is very high.

Oct 4, 2019 - 10:13pm

Thanks for your suggestions. I agree that landing some internship's interviews plays a role in getting future full time interview.

I am interested to know more about common quant questions. Currently I am drilling Black-Scholes model using Mark Joshi's Concepts and Practices in Mathematical Finance as I find it quite mathematical.

Do you happen to know more about common quant questions? If yes, can share with me?

Oct 5, 2019 - 12:14am

do you think there is still alpha in traditional data sources (price, volume, etc). Put another way, how is your pnl roughly split between traditional and alternative data?

what's the highest frequency that you look at, and how importantly do you think having good execution strategy (at the tick level) is for improving alpha ( at various frequencies)?

Do you always use a risk model and hedge things out?

How many new alphas do you put into the book each year?

Oct 5, 2019 - 12:14pm

lol there is a lot I won't answer here. Sorry.

Yes, always a risk model to hedge. Is there alpha in prive/volume? Sure, I think a lot of people are still very successful at just doing that and everyone is doing it at some level, so it's just not easy. Execution is very important; the lower your tcosts the more you can trade into your signals. But, again, execution is very difficult to do well. if you run a large book with slow moving signals, you can get away with not worrying about it.

Oct 5, 2019 - 1:43am

Do you use Nuclear Phynance @ all?

Oct 5, 2019 - 3:42am

Hi! I have an stats degree in an elite undergrads but did banking/growth equity internships. Now I'm graduating and really didn't like the softer side of finance. There is nothing wrong about that, I just much prefer the quantitative stuff. However, most good prop trading shops and quant shops shy away when they see investment banking and growth equity in my resume (compared to my peers who are software engineers and academic research), so I wonder whether you have any advice on how I can pivot? I am decent in stats/math, can do high level programming languages, but no trader/software developing industry experience. Thank you!

Oct 5, 2019 - 12:18pm

Throw in some other stuff on your resume that highlights your quant skills. Most prop shops hiring traders out of elite undergrad will interview anyone with a quant discipline and put them thru the guantlet. are you not getting interviews? That would be surprising to me.

Oct 6, 2019 - 6:30am

Thanks for the AMA.
I would like to know your thoughts about possible career evolution.
I am actually in quant risk (model val/analytics) in a T3 bank (think about UniCredit, SocGen) and would like to move in quant trading/research.. do you think I should try to later from now or wait another bit of time maybe changing bank or going to a fund in the same role?

I have my algo trading strategy developed in Python and validated that results in a very good P&L, though I am not even an year into my actual position and think that probably funds or banks would not see positive to change so early


thx a lot

Oct 6, 2019 - 10:40am

Moving from Risk to developing strategies and trading can be difficult, you'll face hurdles, and my advice would be to try and do it as soon as possible. The longer you stay in risk, the harder it will be. Don't switch to the same role somewhere else if that isn't what you want, you will be even more pigeonholed into risk. With that said, I wouldn't be worried if you are less than a year in, I know plenty of quant PMs who started in risk in their first year or two.

After a year, I would def look to move. I would just start preparing for interviews, when you're confident start applying, and keep applying until you land what you want. I wouldn't want to spend more than 2. So that 2nd year is your applying year.

It might be easier to move to a strategy team that manages a book in asset management arm of the bank than directly into buy side; it's close enough. You could move into front office quant at the bank supporting traders, but this is also another stepping stone to moving into quant rsh developing trading strategies not entirely necessary, just depends on what you can land. The movement would be something like risk quant -> desk quant -> alpha qnt (what i am) -> qnt PM.

Oct 6, 2019 - 5:40pm


Thanks for the AMA.

I really, really would appreciate your advice if you happen to come across my comment, thank you so much in advance.
I am very curious, analytical and I have good problem solving skills. But I don't fit the mold of a "typical banker". I also have great communication skills and interpersonal skills and I think I am capable of outlining and presenting complex issues in a simple manner. I love to talk to people and i like to work both independently and within a team.

Is a career in Banking or at a (Quant-) HF right for me? What job at a HF or in Finance would suit me? Should I pursue an quant research career? Do you think Quant-Funds are still a good long-term-career?

Oct 7, 2019 - 9:40am

If you're not quantitatively minded, don't enjoy sitting by yourself working on individual problems for days, and consider your strongest skill communication - I would not go quant. Whilst the best quants (most high up on the food chain with big titles) have very good communications kills, that is certainly not their strongest skill - it's quantitative thinking.

As for every other position available in banking/finance/hedge funds - I don't think I'm qualified to speak too. And, with the little information, I probably cannot narrow it. Hope this helps a little, though. Good luck.

Oct 7, 2019 - 3:09pm


If you're not quantitatively minded, don't enjoy sitting by yourself working on individual problems for days, and consider your strongest skill communication - I would not go quant. Whilst the best quants (most high up on the food chain with big titles) have very good communications kills, that is certainly not their strongest skill - it's quantitative thinking.

As for every other position available in banking/finance/hedge funds - I don't think I'm qualified to speak too. And, with the little information, I probably cannot narrow it. Hope this helps a little, though. Good luck.

Hi, thanks for your answer.
I'm a math major, economics minor at a top school. So, I think I have strong analytical and quantitative skills. I love to investigate Datas like a Detective to get the answer of the question. But I also love to talk to people. I think I am nerdy with very good comunications skills. Hope this Informations helps

Would a career as a Quant Researcher suit me? Do you think Quant-Funds are still a good long-term-career?

Oct 6, 2019 - 7:26pm

what kind of roles at a quant fund could you see for a manual trader with a certain "market feel" ?

just google're welcome
Oct 7, 2019 - 9:42am

almost none. manual trading gone almost everywhere - unless you're really talking illiquid instruments, i.e. exotic derivs, or very large sums. Even in that case, no one at a HF submitting an order for those will want any 'market feel' exercises -- it would just be execution.

Oct 7, 2019 - 12:36pm

Do you have any opinion of the more factor oriented mostly long-only quant shops? These include PanAgora, AQR, Acadian, QMA, GSAM QIS and several others. While some of these firms have done poorly recently due to value factor underperformance they have done well over a longer time period.

Do you see these firms as entirely different from what MM quant PMs do or is there significant overlap? Do you see people from the type of firm I mentioned move over to the shorter term MM quant realm?

Oct 8, 2019 - 9:02am

Not sure about MMs but there is some overlap on the SM side. Many of the large SM funds offer both absolute return products that have low beta/factor exposure along with higher capacity products with significant exposure (sometimes long-only). They may use similar alpha signals but different portfolio constraints. It seems alpha signals that become standard over time and used by everyone are eventually treated as risk factors by the industry. I think value and size have not been working lately since they are now captured in the private markets instead of public ones.

Oct 8, 2019 - 10:57am

Mostly echo what cp5670 above. The larger managers run a lot of high capacity strategies w/ beta = 1 to factors like value, momentum, growth, etc -- most of which are considered risk by me. They run a wide array of hedge fund products: exposure to style factors, exotic options strategies, high vol strategies, etc - mostly managed as mutual funds or managed accounts, and pitched to investors (or in the case of GSAM, their high net worth clients) as 'strategies' they can investin. Other than that, I don't know too much about them.

My general impression has been 'they're ok'. I think, in general, they suffer from a brain drain as people leave GSAM and AQR for other funds. It's not a bad place to start out though. They're still 'buy side' and it's better to start there if you want to be on the quant investing side than to be a desk quant at a bank or prop shop.

Oct 8, 2019 - 11:26am

Thanks for the response. I work in this type of firm in a quant equity research capacity. I have been trying to move to an analyst role with a MM quant PM. I have been able to get some interviews but it seems like people take issue with the fact that most of my signal research experience has been at the monthly vs daily/weekly frequency. HR/headhunters say that the feedback is positive but they are just looking for someone with more relevant experience. Maybe that feedback is BS, I don't know. I've also been asked the sharpe ratios of the alpha signals that I have researched/implemented. Because the signal frequency is monthly, dollar neutral L/S spreads tend to be something like 0.4-0.6 but w/ low correlation to common risk factors. I think they hear a sharpe of 0.5 and then just stop listening. Do you have any advice for someone coming from this background trying to make the jump to higher sharpe/freq strategies?

Oct 9, 2019 - 4:57pm

Is there a significant contingent who have gone from Sell-Side to Buy-Side in the quant/algo space?

Oct 9, 2019 - 6:38pm

Generally, yes. But, it just depends. If your in sell side desk quant, you can move over as a buy side quant in model development (similar role, no running risk, and it may take years before you can actually develop strategies and run risk, if at all. If you're in sell side algo in high freq market making, you can move over directly to a HFT fund and trade your algos. If you're in asset management at a bank, then you can generally move over and if you come in with good strategies you can start running money quickly; just depends on how your strategies fit with the buy side objective.

Oct 10, 2019 - 12:21pm

in the interview process, how does a fund verify that your strategy has performed as you say it has? what stops the MM funds from hiring bullshit artists?

just google're welcome
Oct 11, 2019 - 1:06pm

So, I think an MA from Temple will get you into a risk quant position in buy side, at best (for the buy side). It can probably get you to a desk quant position at a bank if you interview well. For all quant positions, an MA from temple will put you in the bottom 10-15% of applicants worth looking at. You will have to prove yourself in interviews, but you will probably get a few.

If you're looking for a Quant role, a PhD will help -- but I never advise someone to go and get a PhD solely for a quant role. It's easier said than done, you may fail, and you may burn many years attempting it. In the end, you'll still have to learn all of the basic quant skills on your own, which is its own hurdle. In the end, you may have, individually, been better of with an MA and experience then attempting a PhD -- this is for you to decide.

Honestly, getting into signal research is the 2nd highest rung in the quant latter; the highest being quant pm. Succeeding is magnitudes harder. It is not an easy thing to get there, and even most that do will fail. Even now, if you asked me the probability I will be long term successful, I would say the odds are fairly strong against me. Take that for what it is.

Oct 10, 2019 - 1:53am

I'm a current sophomore undergrad double majoring in Econ and math and I'm interested in quant Finance. What do I need to work on to break into quant Finance and where can I learn it? What sort of internships should I seek?

  • 1
Oct 11, 2019 - 12:44pm

(1) What type of hours do you work
(2) Can you elaborate more on the coding part? What level of coding was required to break in. Would you say a lot of that was learned on the job or were you expected to have good algorithms before hand?
(3) Do you believe the Masters/PHD offered any value? I've seen some quant roles that dont require a grad degree (not the top shops obviously so lower pay) but there is the opportunity cost of your 20s being spent in college. So what do you think here?

Oct 11, 2019 - 1:33pm
  1. My hours are reasonable. 50 hrs or so a week. Mon-Thurs usually 10-12 hrs, and then 8 hrs on Fri (usually out after the close). There are two things: monitor production processes and research. Monitoring production processes are the only thing that restricts flexibility. At my old place, I had to occasionally trouble shoot processes that failed before the open. Now, I do have to monitor the book thru most of the early hours of trading - so this means I have to be at the comp at a set time each day. Other than that, I'm totally flexible for research, but one staggers their schedule to be as efficient as possible. With this, comes the occasional hour or two after dinner, or a few hours on Sunday. So, it means staggering backtests and running computations when you know you'll have down time. For ex, if I have to run a computation that is going to take four hours before running a 12 hr backtest on the output, then I'll prepare the code so I'll run computation at 5pm, head home and eat dinner + spend time with family, jump on comp at 9pm, check the output and kick off backtest by 10-11pm, should be done before lunch the next day for me to evaluate.

  2. Almost all of your real coding will be learned on the job. I don't care what courses you take, nothing prepares you adequately for real production level coding. With that said, you have to be able to come in and code a project that can get the job done. Now, I can almost garuntee your code will look like garbage, be almost completely unscaleable and also entirely inefficient - but get the job done is the entry benchmark. You will improve from there as you learn from others and see industry best practices.

  3. I can only speak for myself, not for any other individual. For me, it was immensely valuable. I took the time to learn an immense amount of material, became an expert in an area of math and highly proficient in a number of other fields (cs, stat, econ) because I had the time and inclination to do so. All of this has served me well, not only in my job, but in the way I approach the world and how I think about it and interact in it. It's one of the best decisions I've ever made, and I don't regret it one bit.

But, it's not always pretty. I will admit, at times I was very nervous and stressed as to whether I'd succeed. After undergrad, I turned down prop shop trading offers to study more, bc I personally felt strongly that if I was going to be make it to the top of the quant field (if I wanted too -wasnt 100% sure at that time), I'd need more studying - that was a gamble I took back then. Half way thru Phd, I decided I wanted to be a Quant PM, I worked hard to learn a lot of material I didn't know. When I applied to internships before my last year of PhD, the first few months I got no interviews; not a single bank ever interviewed me. You question yourself a lot when you make calls you think are right but there are low moments. Ultimately, I got interviews because of all the networking I did. At times, I wondered if I would land a job I wanted or would have to jump hoops to get where I wanted to go. I initially thought it would take me years to land a role even doing signal research, but I was willing to work my way up; decided early on I wouldn't' compromise for money or time - that was a personal choice. When you work that hard and put all your eggs in one basket, it brings anxiety and a feeling of risk with it. On the other hand, I went after want I wanted with gusto and it worked out. When I left my last job, I took another risk that I was good enough to land with a good quant PM, and that brought more anxiety. The moves have worked out. I have been fortunate to have a fairly successful post-PhD career thus far; I make good $$ and I love my job. Take that for what it is: everything is rosey when it's working out.

Oct 12, 2019 - 10:00am

I went through a lot of this too and put in a ton of effort into networking/interview prep to get a signal research role, but ultimately got very little for it. I concluded that I had just entered the whole field too late. I was always a strong programmer and statistician, which was a rare mix of skills at one time, but they are very common now. The quants who started 20+ years ago were successful because they joined a growing industry and brought in ideas and a mindset that was novel at the time, not by following a standard path. I definitely agree that doing the PhD was worth it for the same reasons you said, and wasn't even looking to get into this industry at first (ironically, that was the right time to get in) but got interested in it later on.

I think a lot of the signal research roles are not really great jobs anymore, despite being hard to get. It has to be the right group in the right firm, and it's hard to tell from the outside what you are getting into. The SM funds may pay well at the beginning but there is often no real measure of success or failure. You get exposed to the downside risk but not the upside, while the old timers who occupy senior roles have it the other way around.

Oct 12, 2019 - 8:27pm

Some people have mentioned quant on the sell side or prop shops, so I thought I'd give people some info on this.

Post-2008 crisis, banks had to spin off all their prop desks (where they actively take bets) due to regulations. Now, all banks run are market making desks and if they have an asset management arm, they are able to invest capital on behalf of clients (private wealth management or a hedge fund arm that's separate from the bank).

Within 'market making', you're either at a bank or a prop trading firm. Both make markets, and almost all quants in these firms are dealing with options market making, for some asset class: index options, options on fixed income/currencies, or exotics. It's either that or some new product the bank is pushing hard into and still needs to build out the quant aspect of; currently the new thing is 'data quants' which is basically just data science or etf quants. In general, the world of options for quants is fairly saturated. The boom for options quants was back pre-2008; now there are tons of quants with the skills to model various options, and there aren't a lot of new positions. Banks and prop trading firms still hire at the junior roles as it's a revolving door for the lowest rung of pay for them. Getting into exotics is tough, not a lot of roles. Generally, the roles are for whatever is new in options. Just 2-3 years ago it was options on indexes. For non-options roles, the bit thing today is ETF market making; with the boom of ETFs, banks are pitching new ETF products that follow everything from currencies, commodoties, stocks, bonds, and their mother. Basically, 'delta one' products.

In the asset management side of the bank, the roles are just building up. Banks like Goldman are making a big push these days to take on the 'low-middle net worth' individual, pitching products for them. They're on a hiring spree when it comes to quants to build out all sorts of things in this space.

In 3-5 years, the roles I mention above will have changed. If you're asking what quant roles are available on the sell side, look to the new products the banks are pitching and that's where you'll find the newer/fresher quant roles.

Oct 14, 2019 - 2:13pm

Signals on technical analysis to me are mostly a self fulfilling prophecy - there are so many people out there who think it's meaningful that it in some sense is. Or, maybe it is real? Who knows, hard to answer the question. But, in some sense, I'd say they can work. What does 'work' mean though?

The real question isn't whether technical analysis works, it's at what realized sharpe and at what scale? And, I think that's an easy answer. In general, at little to no sharpe on a large scale (100 million or more), and at a reasonable sharpe ( 2-3) at small scale (think a few million dollars). But remember, successful prop traders who trade a 2-10 million dollar book can consistently generate 10-30 sharpe at 60-100% returns. So relatively speaking, it's terrible at small capacity too.

Personally, it's not a part of anything we do at large scale, our books are much too large to be going on that kind of stuff; little return and high vol in those kinds of strategies. To me, it's mostly risk.

Oct 14, 2019 - 3:37pm

Another question from me. I'm a non-phd and also a signal quant at a long-only quant firm (think AQR/GSAM/etc.).

My basic question is to what extent you would advise spending time building your own IP separate from the firm you work for?

I have a lot of ideas that I at least think are fairly creative and good but they tend to get shot down in favor trying to fix value factors that haven't been working. So, I never really get the time to test a lot of the ideas that I think could potentially be pretty good.

As a result, I have been thinking investing in my own IP. This would involve building a fairly powerful computer, picking up some of the necessary datasets from quandl and start doing some alpha modeling in my spare time. I could maybe try trading on this model with actual money with monthly rebalancing while still adhering to trading restrictions. Or I could simply paper trade a market neutral portfolio.

Even if my ideas are good and work, would this be a waste of time given that I don't have the capital to really produce a track record that anyone would even care about?

Oct 14, 2019 - 3:47pm

Great for learning experience, very hard to build your own stuff and trade the same way as you would at a hf. I found this out when I was on my NC and built out as much as I could on my own day trading; most of the data sets you'd like to use are either unavailable to the individual investor or too expensive. What you'll get from Quandl is just too basic. With that said, you are likley running way less capital, can move in and out quickly, and if you know what you're doing, you can generate great returns. I made a killing the year I was out once my system got up and running, doing some fairly basic stuff.

If you're already at a long only type fund, you should be able to use that as a spring board to the buy side building more pure alpha strategies. If you pitch thats what you're looking for, it'll work out. You should come in saying you really want to do strategy research for pure alpha strategies i.e. more robust strategies that work at scale, and that you have some experirence with LO only strategies thats transferrable, but really what you can bring is extensive knowledge in optimization and tcost models. The last part will be what they will bring you on for, and the strategy research they will let you do once you come on. If you're not as strong on optmization/tcosts -- that's what I would spend my time learning. You might even find a PM at a MM fund who has great strategies but lacks expertise in opt/tcosts and will want someone to come on to build in more sophisticated stuff for them on this front; then you can angle into the strategy research. You have to sell them on some knowledge they find they need.

Oct 14, 2019 - 5:31pm

I've been doing this too. As flimby said, the strategies a big fund runs won't really make sense to run by yourself. You are limited to only the basic data (quandl) and have much higher execution costs, but you can still do simple things that take advantage of your small scale and don't require much tech infrastructure, especially with options or cryptocurrencies. You do end up paying a lot of tax on any gains though, and most funds and banks have trading restrictions for employees. I work at a tech firm now and can do whatever I like, but wouldn't have been able to do it at my old place.

Oct 14, 2019 - 6:24pm

to supplement this, in case you're day trading. I have found futures are great to trade if you have strategies on them. You get very cheap leverage and tcosts, relative to single name stocks. And, futures are taxed very differently - 60% is taxers at long term rate and 40% at short term. Take this into consideration when trading at home.

Oct 14, 2019 - 5:47pm

what about getting a shot running a book at a MM (millenium, blayasny, etc..)

if you have a strategy (not an algo, but a manual execution strategy), but have not been running it with real money yet (or you just started trading it with real money, but you only have that 25k is what you are trading)....what are the minimum requirement to convince a MM firm to say "sure, come over to us and run a 50mm book" ??

what sharpe, max drawdown, and for how long does a retail trader need to achieve to get a shot as a PM at a MM?

just google're welcome
Oct 14, 2019 - 6:32pm

25k doesn't even count as peanuts. IF someone showed me a strat on 25k, that didn't get 50% returns at sharpe 20, I wouldn't even bother. Moreover, even if they did, it would be more or less useless to a large shop, they wouldn't be able to monetize the same strategy on any amount of decent capital. A strategy at 25k is diff than at 50m and diff from 500m to a 1bn. You'd have to go to a small prop shop with that kind of strategy; they will let you run 1-10 million, u'd have to have 30-50% returns at sharpe 15-20, and then you'd keep around 50% of profits (notice the proportions here, in relation to a large hedge fund). You probably wouldn't be risk neutral here, b/c you'd be nimble enough to jump in and out; the investing is completely different. Plenty of people do this; look at small, no name prop firms in Chicago, if this is how you're coming in.

For a MM, you need to show you can run 50m to start, with potential to scale to 300-500m, at live sharpe 2-3 or backtest sharpe at 3-4, running 1-1.5% vol, max dd maybe 2-3%. This is probably atleast 80-90 alpha return, if not almost completely risk model neutral. At 5% dd most people get fired. Again, this depends on the strategy.

Oct 14, 2019 - 6:42pm

this is a good response...and i'd like to drill down a bit.

lets say that 25k account doubles in 1 year...and not scalping for 1-2 ticks...but taking 10-20 ticks (on futures)..the large liquid stuff (ES, ZB)

and lets not use sharpe, because that penalizes gains...lets talk about sortino ratio instead...what sortino ratio would the retail futures trader need to produce (while staying below the 5% max DD) before bothering to speak to a MM fund for an interview?

just google're welcome
Oct 17, 2019 - 8:04am

how has comp developed after moving from the single manager to multi manager? Obviously now you are paid from your group's profit, so bonus is probably more volatile, but has it increased significantly on average? Also, do you have a rough idea about profit sharing between your PM and the analysts?

Oct 17, 2019 - 12:09pm

First year I'm coming in and building out my signals, starting at smaller capacity. I expect to hit 500-600k for about 6 months of trading with most of the signals at initial capacity, If things continue as is for remainder of year. If so, I should be able to scale up more to capacity and then if we have another solid year next year I could be at 1.5 give or take a few hundred K. That'll prob platue there until I can build better/more signals to increase capacity. The goal is to be running around 500mil on my own sigs at sharpe 3, in 3-5 years. That should put me as a sub pm with 3-4 mil payout, less costs. A lot has to go right from now until then.

Oct 22, 2019 - 4:32pm

I think these numbers are quite a bit too optimistic, unless I misunderstood something. If you are working in a MM hedge fund your team will likely get something like 15-18% of its PnL after costs for comp. It sounds to me like you are a sub PM now, in the sense that you are running some signals and can attribute a PnL to this. But even then, your PM would not pay you the full amount that he or she earns from your PnL, but only a certain percentage. So, if you run say 10 Mio yearly risk on your own strategies and make 30 Mio PnL assuming you have Sharpe 3,your team would get maybe 5 Mio. But then you also have netting with the other strategies in your team and your PM will not consider these strategies "your own". So maybe you get 1.5 Mio if he really likes you and the team had a great year.

Also, keeping up a Sharpe above 3 over several years is extremely hard and you need to have a large amount of luck, unless you are doing something very specialised and capacity constrained.

Sorry, just wanted to give you my take on this. Maybe I'm wrong about your position, but I am also on the Quant buy side and have some decent insight into comp in different groups and your numbers seem a bit off to me.

Oct 18, 2019 - 1:00am

This part is really interesting:
For a MM, you need to show you can run 50m to start, with potential to scale to 300-500m, at live sharpe 2-3 or backtest sharpe at 3-4, running 1-1.5% vol, max dd maybe 2-3%. This is probably atleast 80-90 alpha return, if not almost completely risk model neutral. At 5% dd most people get fired. Again, this depends on the strategy.

How would one validate if the strategy is risk neutral or at least 80% alpha? If one didn't have access to Barra/Axioma risk models, what's the best way to accomplish this?

Snootchie Bootchies
Oct 21, 2019 - 12:38pm

Thanks a lot for sharing! This has been very helpful for all aspiring quants in this forum.

What skills you learned at school helped the most on your job? Among the three of time series econometrics, machine learning (and deep learning, reinforcement learning etc.), algorithms (which is a huge part of the recruiting gauntlet), which one would you say is the most important?

Also, how easy is it to switch firm/job as a signal quant? Incoming signal quant at a big shop but would like to become a pm one day.

Oct 22, 2019 - 7:44pm

- all of them or none of them. sorry, answer sounds stupid. But really, what matters is idea generation. Ideas come from knowing a lot of things and being creative. If you know many areas and can be creative on how to implement ideas for your particular data set, then that's good. If you only know econometrics, you are constrained to having your ideas mostly from this area, which may or may not be a good solution for the problem at hand. Learn as much as you can. Even so, none of it may be useful if you are not creative. So, I would just learn as much as you can and keep learning.

  • switching jobs:
    Depends on the person. your signals will matter the most. Then, it will be how much you learn on optimization and tcost modelling so you can put it all together on your own. If you have awesome signals right out of the gate and get super confident, you may jump straight to running your own book. If you cannot come up with anything useful, then in a few years you'll get let go and be working in risk/desk quant. A lot of luck involved here, like on which data sets you are asked to work on or who is mentoring you. I wouldn't think too far, just try to come up with good signals for now.
Oct 22, 2019 - 8:39pm

In some of the SM funds, the signals you work on are pretty specific to that investment process and not useful in another firm. This makes it harder to leave, and also means they won't increase your pay since you are stuck there. Even if you find a signal or anomaly, you have to think about how easily someone else can repeat your work. I think a lot of stuff people are doing with standard vendor data sets today is vulnerable to this. The best strategies of the past were not well known at one point, and the alpha eventually eroded after others started doing the same things.

Nov 3, 2019 - 2:23am

I want to develop a systematic macro trading strategy that will trade with a time horizons of a few months. It will consist of importing economic indicators, perform some mathematical studies, and have trade recommendations as output. For now, visualizing output through GUI is optional. What matters is the trade recommendations as outputs and the ability to backtest the strategy on past data going back few decades. Intraday fluctuations plays a quasi non-existent role and some economic data would be updated on a weekly basis and monthly basis, so speed is not a concern. Having said all that, I want to learn programming to execute this project and I do not know which program should I pickup, VBA or Python? People with experience are free to comment. Thanks

Nov 6, 2019 - 5:40pm

What are your favorite sources to learn new skills/ things?
What books have been among the most influential in your life?
Do you think Quant-Funds are still a good long-term-career?
I would like to know what are your thoughts on soft skills for Quants. Which are the most important?

Nov 16, 2019 - 2:53pm

not sure. new quant funds pop up all the time, I know several people who've started ones in the last 2 years. Hard to say they're rockstars until they start managing real capital (north of a few billion) and do it successfully for a few years.

Nov 16, 2019 - 7:44am

How did you choose the PM you worked for? How did you form the decision that this guy is a good fit for your ambitions?
I am kind of in a similar position. About 4yrs experience at a collaborative quant fund, working on various alphas, portfolio construction and t-costs. I talked to some MMs, but in the end I found that joining a team in a MM is always a bet on the PM. In the end, if the PM gets fired you will usually get fired with them. Given that you have to wait for one year before joining the new firm (my non compete sounds similar to yours) this is very risky. I know some people who waited 9-12months on non compete to join a MM, just to have this guy and the team getting fired right before or after they join. I am much more comfortable betting on myself than betting on some other person. So my plan is to stay at the collaborative fund, learn as much as possible and then change to MM once I am in a position to get a good offer.

Nov 16, 2019 - 3:06pm

Yea, it's difficult to choose and your concerns are def the right ones.

I tried to find someone who had run a book for several years and not a new pm. I turned down a few offers from first time pms b/c I wasn't willing to take that risk. On the other hand, it's much easier to get offers from a first time pm then an established one; established pms usually have their team and positions very rarely open up and when they do they're harder to get b/c you have to please the whole existing team rather than one guy. If you can find an experienced person who is moving firms but has to build out a new team b/c they can't take their old team with them, that can be easier. Or, if you can find an experienced PM trading one asset class who is looking to expand into your area, that can be easier as well. I interviewed with a bunch before I was able to actually land one, competition is tough for these positions, but they come with less risk than a first time PM. In the end, there is no guarantee on what you'll land.

the NC can be a snag, most PMs cannot project out 12 months. For me, I took the risk and left with confidence I would land something; either you have that confidence or you don't. It's unlikely that you can get an offer 12 months in advance at a MM from a PM and, like you said, even if you do you cannot be confident the person will still be there. Even if you do, you may not be better off b/c most PMs are projecting out 3 months, and if you restrict yourself to ones who can project 12 months, you'll be narrowing your pool of potential jobs; so in some sense, you're better off waiting when you're 3-6 months from starting so you have the largest pool of potential PMs to speak with and a better chance of landing a good one.

But, before I got this offer, I certainly had moments where I wondered if I would have been better off staying where I was, saving some more money, and then taking the risk. I used to think, If I had stayed and had a bunch of $$$ in the bank, I'd be less worried about failing with a first time PM. If you do have $$$ saved, then you can risk a first time PM and jump between a few before you land on a solid one; many people do this and it ends up find. It just means you have a few years of uncertainty, but it's the nature of the game. So, these are all valid concerns and will be as you look to make that jump. But, I don't think there is any way around them. You'll run plenty of what-if scenarios until you land the position you're comfortable with. Do what is most comfortable for you and know those thoughts will arise.

Nov 16, 2019 - 5:54pm

Wouldn't jumping around on a few different teams look bad for your resume? Or is it understood in the industry, that in general this happens and is not seen as a black mark? I mean, I've seen a couple of linkedin profiles where after leaving a large fund they've moved several places each less than a year.

What are the opportunities to get picked up by another team?

Is there any scope for networking at MMs?

Nov 16, 2019 - 2:32pm

Hi flimby,

Thank you for this AMA.

I am a European student interested in becoming a quant researcher.

In spite of having a heavy STEM background, I'm pretty decent with people and I'm a strong communicator. Hence, I really want to take on some type of leadership position later in my career. You're still at the start of your career but do you have any insight into how that could work out? Basically, what's the hierarchy like in the quant world?

Nov 16, 2019 - 3:24pm

on the quant investment side, there isn't really 'leadership'. You're either a PM running capital or a researcher helping build strategies to run that capital. If you're at a large single manager shop, there is some more management, but it's mostly title, seniority, and pay and not about directing others or business planning. In some sense, it's always you eat with you kill.

On the quant tech/risk/desk side, there is 'leadership'. It's more business-y and if you're thinking that is where your skill lies, then this is where you'd be. You could be in charge of a team that has a mandate to provide quant 'product's internally to investment side teams; usually for fundamentally oriented PMs or market making teams at a bank. This side has a lot more politics and is more hierarchical. You will almost certainly not be running a book of any kind. Pay can be very good at the top but a lot lower than a successful PM, a lot more steady, much more highly correlated w/ experience so the increases are slower as well. 80% of the people I see in these roles are more good politicians than good quants; but it's not easy to get there, b/c its less about how good of a quant you are and more a political game and it can be really really cut throat.

Personally, and this may sound bias, but quants I see who are really good would never go this route if they can avoid it. It's usually the ones who don't make it to the investment side that do it and if they're good 'communicators' they will flex their political skills to get to the top, taking out people who can threaten their job. The quants who are decent but not good enough to be on investment side and also not political end up working for these people b/c they can't move up and usually hate it; the ones who can't put up with incompetent/political bosses then move to tech.

Nov 19, 2019 - 8:23am

Assuming this is all on the investment side already working for a PM, I would say however long it takes you to develop strategies that can run a few hundred million at good sharpe and knowledge of everything else around quant trading (optimization, tcost modellling, etc). Practically speaking, it's however long until you grow the courage to go out on your own. Chances are if you are a researcher for a few years, someone will give you a shot as PM. Whether you succeed and last longer than 6 months as a PM is a different story.

Nov 16, 2019 - 11:08pm

Thanks so much for answering my previous question.
I have another one.

Besides stats, calculus, linear algebra, what kind of math do you think is useful for a quant on the buy side ?

I am thinking about watching some additional math classes on moocs, but can't decide on what to take.

Asked around a bit, get some answers such as pde, real analysis, and convex optimization.

But since my time is limited, obviously I can't take them all. What is your recommendation ?

Nov 18, 2019 - 4:24pm

flimby can speak for himself but I would definitely recommend taking real analysis. The content may not necessarily be directly applicable but it definitely helps with mathematical maturity. This will be helpful when reading research papers, which tend to skip a lot of steps or assume a lot of things. It will help you understand/communicate mathematical concepts more easily. Convex optimization is obviously hugely important for portfolio construction but it's definitely something that you can learn yourself if you have the mathematical background. Something like real analysis you can really only learn with the guidance of a professor for most people. I would say PDEs are probably less useful for quantitative finance but definitely a very interesting topic in general.

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