Quant PM in the MM model and long time beneficiary of this forum. Recently switched platforms and wrapping up the last few months of a garden leave, thought I would do one of these before I get busy.
Open to broadly providing career advice where I can, but will not answer any questions fishing for alpha.
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| +2 | Walleye SA 2027 | 4 | 3d |
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Book size and performance 2025?
Sat out for 2025
For numbers will give range to protect anonymity (sorry if they’re wide). Run between 50-100mm in annual vol on a few yards of GMV
Thanks.
That's a lot of vol for your bonus range 5 - 15 assuming a 5% drawdown limit.
What was your turnover, return, universe size in 2024?
Do you build your own risk model?
What are some interesting types of data you've used?
what type of products do you trade? eg systematic equities, macro, vol etc.
what is ur background/career trajectory?
biggest bonus u made? (a range is good ig if u can’t share the exact no.)
My background is in mid frequency quant equities though I have managed people that trade other asset classes and horizons
Non-PhD but strong undergrad, built an early career as a QR at large collaborative firms and climbed the ranks, before moving to pods where I have been the last 5-10 years
First part of guarantee will be the largest bonus I have collected to date, but prior to that will give range of 5-15mm for max bonus
Thanks. Any tips for a new grad?
Will be joining one of the top prop shops (CitSec, Five Rings etc.) on a FICC/ Vol desk. Appreciate that it’s different to mid freq equities but any general advice would still be great.
Thank you for doing this as this forum doesn't have much quant talk.
I have been working as a data scientist for a few years at PE firms and prior to that did a few years of fundamental investing. I have always been interested in transitioning to quant and I thought a quant equity seat would leverage my background best.
Thanks!
Can I ask what the "right" skills are to double down on? My programming expertise is in Python and SQL along with familiarity of SWE best practices (CI/CD etc). On the theory side I have a good understanding of classical ML algorithms such as clustering, regression, boosted trees etc and less so on current generative AI. I wondered if there is anything you would say is very much investing time learning?
Disagree on the MFE part. If you’re at a top program you can further develop needed skills and break into quant. Unlikely that will be a quant equity buy side seat at a hedge fund, but I’ve seen plenty of people transition from adjacent roles developing the right skills.
did you run factor neutral? somewhat inspired by the ft article yesterday - do you have any thoughts on alphas/idio vol "decaying" to become factors in a sense? if so, have you had to deal with that + how so.
Book has factor limits and equally likely to be long or short a factor at any point in time.
What Barra or an internal risk model calls "idio" is almost never true idio. Almost every signal you find will have correlation > 0 for the "idio" returns of its bets, because other people are trading the same thing or the data is linking the bets through some real world concept, each of which your risk model doesn't know about.
Arguably every signal is a factor, just on a spectrum. The more crowded a factor gets, the lower the return premium, and the higher the volatility (from increased bet correlation) and the more it feels like risk vs. alpha
What do you do about it? New technology is constantly being created, which creates opportunities to find new signals. You need to always be re-inventing the book, cycling out of risk and replacing it with new alpha.
I’m a QR in a collab shop but I don’t work on the alpha gen side. I work on the monetization/portfolio construction side instead. It’s a vol book so monetization isn’t trivial and the projects can be interesting and have impact on PnL/sharpe. Is moving into a PM seat impossible given I don’t have direct experience working on alpha?
Would it not make sense to join a pod as senior QR type role so u get exposure to the whole research process from data acquisition to trade execution?
Then when you are ready to become a PM ull have a much better top down view. Although if u have say 10+ yrs of experience then the downside to this might not be worth it.
It will be hard to jump directly without a seat in between first. Join a startup pod where you can wear many different hats and get a good look at every part of the process. You will also learn what it is like to build an infra from scratch, which may be a lot harder than you assume. You don't want to be doing this for the first time when you are a PM.
Outside of that, make sure to pick a PM who is open to giving you attribution and build a track with your cluster of ideas. Then use the track to move to a PM seat.
Would you say it is a natural/possible move to go from an equities construction researcher seat to alpha research? They seem like different roles
Thanks for doing this. Keeping it vague, I am currently a quantitative analyst focused on research in a niche sector for a medium sized SM hedge fund, where I work on quantitative research for a low frequency sector specific quantamental strategy. I would like to move to a role where I can focus on a full quant strategy instead of quantamental (still low frequency or MfT) but come from a non traditional background for quants (Econ major at top school). Do you think this move to another quant research role that is a fully quant driven strategy is possible with my current work experience and background, or would I need more higher education? I have been unsure the best next step to take since it’s rare to meet anyone in this field without a stem background, but I also have actual buyside research experience now. Thoughts?
Aim for pods, it will be harder to get into a large SM quant fund with that profile. That experience isn't really transferrable (low frequency sector specific) to most fully quant strategies, so if you want to change would recommend doing it sooner rather than later.
Make sure you really want to move though, there is a career for you in fundamental L/S that you would be throwing away and basically starting from scratch.
Thank you for the honest advice. Do you mean quant pods or L/S pods? I would be worried that I don’t have the fundamental background for L/S pods since other analysts are focused on the fundamental side of the strategy while I am only focused on the quantitative side. Do you think my background would give me a shot at lower frequency or MFT pod? Also, would a technical masters like an MFE or masters in stats be a helpful in making this type of transition, or a waste of time?
Thank you for doing this, Sir. As my system is scheduled to go live next week, I would be interested in your professional opinion of a system, which is designed to analyze (on the tick level) how the order flow interacts with the top-of-the-book and also what kind of "ripple effects" those interactions cause to the higher/lower levels of the Order Book. Initially I developed this system out of professional curiosity, but in the process I got interested in the subject. Given my background in theoretical physics, I would very much appreciate a professional opinion regarding the approach I chose for my system, given that the goal is to have a system, which could be easily adjusted to work with any instrument as long as the instrument is liquid and data flow is constant (ES, SPY, etc.). Cheers!
https://www.wallstreetoasis.com/forum/hedge-fund/index-equity-futures-or-index-equity-etfs
I remember reading this thread earlier in the year... sounds like you have been "rolling out your system" for 6+ months? How has it performed live, if you can post a chart of your results here I would be more interested in discussing that.
You are correct. The first version of my system was live when I posted that comment. It performed quite well during April, May and couple weeks in June, when the volatility was high, as that version was designed to predict the turning points and take a position in advance. The results were good, but when the hi-vol periods become less frequent (July and August) the system started losing money. At that time I learned enough to realize that: 1) even during the high-vol periods, when the system performs well, the capacity of this strategy is not high enough. I also learned about the role the 0DTE-related strategies play in reducing volatility. Other pieces of info (JS in India, for instance) helped me to realize why the same algos that worked correctly during the first half of the day, quite often were losing money during the second half of the day. Therefore, in Sept I stopped that version, and started working on the next iteration, the one I am planning to launch next week. The main difference with the previous version, is a more detailed risk management process. The first version operated mostly in a "fire-and-forget" mode when the order was tracked in a very rudimentary way. The new version is different as after placing order, the system constantly tracks it by recalculating the expected trajectory of the instrument, based on dynamics of the Order flow-Order book interaction. Given that I work on my own, and I am not a part of the industry, it took a bit longer, as getting the right knowledge is not quite easy, thus I am grateful for the advices and info I get here. Cheers!
You mentioned your background was in a strong UG, no PHD. What do you class as a “strong UG” for the UK (assuming someone is interested in quant trading as opposed to research)?
Top ~10% at Oxbridge
How does lateral scene in the states look like?
I'm in a small shop in APAC so keen to know. We still have people at decent firms being moved over to US (e.g. Akuna, Optiver, etc.), not sure about smaller firms without presence in the states, particularly with the goal of making the jump to US-based MM.
Visa probably won't be an issue as us Aussies can have the prioritised route that doesn't depend on H1B lotto.
So far my experience has been if PM wants, platform more than willing to support, they share documented processes for hiring and transferring candidates in each region. BD often pushes these profiles for cost saving benefits or when trying to expand strategies globally. I suspect may be harder now but will get a better look soon.
Thanks for doing this ama! I'm a junior trader/researcher at a top collaborative firm staffed on the us equities desk. My firm is very new to equities, though, which means that
I was wondering what you'd advise someone in my situation to do? I've mostly been reading through papers and trying to implement/improve on their findings or come up with related but different hypothesis and have a few good leads, but wondering what you'd recommend?
This can be a really good learning opportunity, not many people get to build from scratch in a job that sounds like it has more seat stability than a pod. Lack of senior staff also likely means more ownership and flexibility for you.
It is hard to give you advice in a way that is non competitive. The one pointer I will provide is generally stay away from papers (most of them are really bad and the good ones are priced out) and focus on datasets. Look through the materials each data vendor provides and focus on coming up with original ideas from the data. Trial new ones (most will offer free trials) if your shop is not currently subscribed to good ones.
As a junior QR, would you say it's better to be in a platform setting where learning is maximized + EV of comp is higher, than in a pod setting where mean of comp might be more but fatter tails and lesser median comp. Asking with a quant equity stat arb profile in mind at both places
Early in your career you want to optimize for learning, later in your career the right tail outcomes.
If I was starting out today, Jane Street is where I would want to go, it hits pretty much everything you want early on:
There are a lot of places that will hit many of these, but they tend to be the larger collaborative groups not pods. You want to move to a pod when you know what you are doing and all you want is to maximize $$ OR you keep getting siloed and it is the only opportunity you have to learn the full stack.
Thank you for doing this. Thoughts on moving from a large platform to a prop shop (SIG, Jump, Optiver)? I have been running semi-systematic strategies and don’t have a good sense of how risk taking approach/appetite may differ.
Historically props have preferred higher sharpe strategies but that is quickly changing and I have seen notable exceptions (entertaining ~1 sharpe but lot of scale).
In many ways the experience is not that different from a large hedge fund platform today, so I would ask why the reason for the move. Switching can be quite expensive so unless your next employer is paying up for it, I wouldn't move until you are forced to or want something materially different.
Expensive as in time value of sitting out?
It’s a breadth versus depth trade-off for me. I’ve been successful over the past 5-8 years because I’m a generalist and know how products within my asset class interact with one another. I am now being told I need to specialize at my platform as a PM and become an expert within a niche.
The prop shop is offering me meaningful leash around what/how I can trade.
I see pros and cons. Goodwill and internal reputation have value, but there is some (I think understandable) worries about getting pigeonholed.
Why don't quants look to take over the RV and curve trades that the rates guys are into. Seems like there'd be a great intersection in the necessary skills.
Is mathematical ability as a quant trader a game of diminishing returns. In other words, do quant traders only need to clear a certain bar and additional knowledge doesn't yield much return?
What do you think was a driving factor in your success as a quant? What would it be for other people?
Bit immature, but is there any crazy payday lore in the industry? If not, around what do your peers or other quants earn?
Thanks!
Human overlay adds a lot of value when bet count is low. Many CTAs harvest this sort of thing systematically, but performance is mediocre.
Absolutely
Not sharing mine for anonymity. But areas I've seen others build an advantage in:
Read the Rentech book. They've been splitting $5-10B/yr across a few hundred people for a long time now.
.
This is some really interesting discussion here. Thank you very much for doing this. A couple of follow up questions based on your brilliant previous answer please:
1. When monetizing new datasets with little history, what single failure mode do you see most often?
2.what kind of crowding signal shows stress before price moves?
3. Where does HFT-style infrastructure add the most value for non-HFT strategies?
4. Which type of liquidity surge tends to be most overestimated by quants?
A partial answer works too. Thank you again for the kindness and wish you best luck in the new journey.
RV and curve trades? thats literally how LTCM died
Thanks for doing this! Wanted to ask, as someone who has been in M&A for the last >2 years and wanted to pivot into a quant research role - what would you suggest I look into, especially if I would eventually like to go to into a buyside role?
For context, have been in MM M&A in London having graduated with a high First from a top university (not Oxbridge) in Mathematics, looking to perhaps try an MFE in the US to get exposure to that market?
Many thanks again!
Not going to sugarcoat it, that is a difficult transition to make. The traditional path to becoming a QR is getting a PhD first.
Avoid the MFE as it generally supplies talent to middle office quant or sell side roles, many good buy side groups will use it as a signal to filter out a candidate. Your best bet may be a CS or statistics masters at a top school, which will open some optionality in tech as well.
A lot of QRs are good at signal research but consistently monetizing their research is not everyone’s cup of tea.
What are some of the biggest lessons that made you into a profitable PM ,high level of course ? What advice would you give to QRs who aspire to be PMs down the road ?
Hire a team not a person. Think Moneyball here, "stars" are expensive, difficult to work with, and usually leave for better opportunities. You win by gathering overlooked talents with some weaknesses that are solved for by the team.
Return on effort is highly nonlinear. Don’t give up (or launch) too early.
Know when to buy vs. build. Outsource everything that isn’t your edge.
Have a process for making decisions when there isn’t statistically significant data.
Analyze periods where you are making a lot of money just as much as periods where you are losing a lot of money. Both have important lessons in risk management.
Humble your expectations a lot, then some more.
Thanks for doing this, OP. I wanted to ask about my chances of breaking into a HF in a Quant role. I have a STEM degree (Chemistry) from the equivalent of an Ivy League in the UK.
I went into investment banking after graduating which I did for 4 years, which included ~3 years at a small boutique shop and 1 year at a BB. I got tired of banking and exited to CorpDev at a large corporation, where I have spent the last 2 years.
I no longer work terrible hours and have decent comp relative to the hours worked, however I feel I am not using my brain and recently started researching the Quant field. I had always assumed this was reserved for Math / Physics PhDs but does not seem to be the case.
If I were to work on my programming (e.g. Python) skills and general knowledge of the space including perhaps a personal project as I do like to trade, would I have a chance to break in? Are my motivations even valid?
I would refer to this post above, you're in a similar bucket:
The additional experience in IB / CorpDev makes it a bit more challenging and might be a bit "late" to use a masters as a pivot. If you were open to sell side roles you might have better chances.
Been working as a QR in analytics covering both discretionary and systematic strategies for a 1.5 years now (straight from undergrad). Feels like the work can get a little too intellectual / not commercial and although I see / back into a lot of strategies being ran, the most I can do is analyze / make recommendations to the people actually running the books. I was wondering if you have seen people pivot out of those kinds of roles and eventually end up in alpha research or something closer to the pnl? Any advice on how to execute the transition best?
That can be a lot of roles... are you analyzing transaction costs? Measuring risk? Forecasting KPIs? Part of a central book?
Moving into newer pods is usually an option for this profile, but depends on what exactly you are doing.
Thank you for the follow-up. The role has been a combination of analyzing book-level performances and overall fund exposures + construction, but there is also a separate risk team that I do not work closely with. Haven't spent much time thinking about transaction costs either. My team has input into fund-level allocations to each book.
Hope that adds enough clarity!
For quant newbies like me, could you explain if you ever take fundamental processes and try to make them systematic? How much if ever do you think as a fundamental PM?
All the time. Many signals have a fundamental basis to them, rooted in financial accounting, filings, interpreting management, demand models, etc.
The difference is breadth vs. depth, we try to look for ideas we can systematically apply to every company (ideally around the world) so we can diversify out any risk and make our small edge look bigger. This makes qualitative analysis or understanding the narrative behind each name more difficult because it is hard to scale. Fundamental PMs will go a few steps deeper on every name and hold a more concentrated portfolio, but fewer bets also means they have a higher return bar to make the additional analysis worthwhile.
Thank you for your response. How much of your skillset is quant vs fundamental? Is it a hard transition for a fundamental analyst to move to quant?
Who was the best guy still at your former employer, and can I hire him?
Nice try
Thank you for sharing your experiences. I'm a junior trader who fills the "desk quant" profile on my current, fundamental LO credit team (top 5 AUM under product class). I work with our systematic PM team and QR team in many capacities especially related to quantamental, quant/factor, and pure passive funds. Because we are not a HF, I'm curious what types of career path/progression or product development trajectory you see for LO/AM quants in the next few years?
Do you have any suggestions on moving from a quantitative fixed income strategy to equities/options? I started my career as a quantitative analyst for an illiquid fixed income derivatives desk on the sell side and am now working at a HF focused on a quantitative fixed income strategy. I would like to switch to equities, ideally a pod since I am am likely not a fit for a HFT shop with my experience, but am not sure how to switch since my experience is in fixed income? How would you suggest making the move, and do you think it’s a good idea?
Asset class switches are challenging. It is rather unfortunate but most quants I know were dealt a strategy / asset class to start in and for the most part stuck to it for their careers or left the industry (and a lot of variance in their outcomes is explained by whether or not they were placed in the right strategy).
It depends on YOE, but most examples of changes I have seen are in more collaborative and "generalist" organizations or multi-asset class teams (not pods). For seniors, usually the switch look like a person who is already established as "credible" within an organization wants to try something different and switches teams, as opposed to hops firms to make the transition (though both happen). If you're changing jobs the hiring manager is already taking a risk hiring someone new, adding the risk of hiring someone changing asset classes makes it an even higher bar to clear. Usually when making a switch you need to be doing it from a position of strength and are cashing the reputation in ("we wouldn't be able to get X to work for us at Y price unless we let them change strategy")
I am a quantitative headhunter, may I have the opportunity to cooperate?
which statistical concepts do you use frequently? Do you think statistical methods that are used in the industry can be learnt by self-study?
Anything can be self-studied but there are certain foundations that are helpful to have (the theory behind probability distributions and optimization goes a long away for QR) along with general mathematical literacy and the ability to read technical papers. Most of the methods actually used on the job fall either under (classical) ML, hypothesis testing, or designing an opt, all of which can be picked up pretty easily if you have the right background.
For ML, I would recommend closely reading through the documentation of your package of choice, and make sure you understand the theory behind every model and when to apply each one. It is helpful to go through each input parameter and make sure you understand how it impacts fit, and what assumptions it makes. It can also be helpful to implement models on your own and compare with the package's implementation, and understand where / why they are doing it differently. If through this process you find yourself hitting many roadblocks or recursing down long paths to understand concepts you probably have foundational gaps and should be able to identify pretty quickly what coursework is missing.
The best approaches for hypothesis testing usually comes from papers (often from outside of finance) so once you understand the basics keeping up on reading is most helpful here. For opts I find the best approaches are in keeping it simple and finding dimensions where you can reduce your problem, the amount you need to learn for ML is more than enough.
what a great insight thank you so much!!!!
.
Going to keep these fairly generic:
i disagree on the opt comment. many mid/low freq quant equity firms/teams end up with correlated alphas. opt makes a big difference esp. the modeling of market impact across assets and horizons. if your opt allows you to trade faster without overtrading that's a big competitive advantage. you'd be surprised that many places still rely on primitive optimization procedures.
thank you for doing this!
all job post for quants i see are for fresh grads (or phd). how does someone pivot to quant if they have 3+ years of experience in non-trading related field (lets say valuations)?
Thanks!
Sorry no longer going to answer questions along the line of "how do I pivot or transition into quant". Did a few above but I am not well qualified to answer this, almost everyone I've worked with has taken a fairly traditional path and the exceptions were each unique and didn't fit a template. Your best bet is look on LinkedIn for people that made a similar move and reach out.
No specific question, but just wanted to say if I knew you in person I'd be nudging for many coffees so I can pick your brain. Appreciate the AMA!
Thanks for doing this! Current penultimate-yr undergrad pursuing STEM degree @ an Ivy debating b/t two summer internship offers. One is an analytics / financial engineering role for a top asset manager; the role consists of product-centric model development + analytics platform development work for portfolio + risk strategies. The other is a quant trading role within a mid-tier bank which is somewhat suboptimal; desk placement is currently uncertain. Base is higher for the former but not 100% sure how bonus structure works in both contexts. My goal is to pivot to a QR / quant-strategy role at a HF / HFT long-term — any advice? Ty!
I’m going to a mm platform to start a pod. Currently deciding offers from the following firms, would you share your view of these platforms in terms of culture, infra and data?
Balyasny, cubist, exodus point, quantbot, Jain Global, engineers gate.
cubist - one of the more attractive ones right now in the MM space, long time quant player with more established infra and think Geoffrey was a great hire for them so interested to see where they go. Any of the big 4 have what you need to do the basics well today but platform resources were originally designed for L/S equity so pale in comparison to the prop trading firms or e.g. Two Sigma.
Balyasny - restarted their quant ~5 years ago and is doing a lot better now. Think they have crossed 20 systematic PMs with half in equities and lot of variance in quality. Also new management in the last ~ year from cubist so ? as to how that plays out.
engineers gate - odd setup gives you a lot of "prebuilt" alphas to work with but also plays a more active role in moving you away from datasets that will lead to correlation to main book. small number of the early teams own most of the risk and make up most of the pnl. Not the best place to go if you have working alpha and trying to monetize vs. could be good if trying to find something new.
Jain Global - avoid
Exodus / Quantbot - haven't had much interaction
Why avoid Jain? (I can guess, but maybe you can spell it out for me.)
Thanks a lot for your opinion. Do you happen to have any opinion on Polymer Capital?
Hi Sir, thank you so much for doing this. I held a Bachelor degree of math outside of US and graduated from a top MFE in 2025, after which I worked as a rates Quant in a BB bank. I really want to break into the quant trading / quant research role, I wonder do you have any suggestions?
I have been regretting not pursuing a PhD right after my bachelor (but probably even if I do so, I would not go to a very good PhD program). Now with my background, if I get the chance to publish several papers at top conference (first author), will this help?
Also, as now AI is advancing so fast, building codes to implement a technical model is not as difficult as before, plus many academic papers are not very practical. Under this condition, do you think the market sense / ability to productionize things / understanding of trading (risk mgm, execution, portfolio implementation etc.) will be more and more important? WHat should a Quant do when coding can be easily realized?
Thank you so much!
Can you realistically get into quant research - specifically in portfolio construction - at one of the quant funds from a Booth MBA? How is this type of profile viewed? I'd have some side projects and experience working in investment management in fundamental research
Thanks for all the information so far.
Certain firms (and pretty much only these firms) really like to hire from their PhD program but MBA is uncommon
Just curious: since you already have the fundamental research experience, what’s pulling you toward the quant/systematic side? Any particular reason you aren't looking to stick with a L/S equity role instead?
Also, on the career switcher front: do you think it’s realistic to pivot into HF (fundamental L/S) or ER via a Booth MBA? Many talk about the Value Investing Program at CBS being helpful for switchers, but does Booth have a similar track record or specific resources for people trying to make that jump into the fundamental space?
Thanks!
Booth is home of Fama and efficient market hypothesis. Also lot of behavioral economics leaders, so it is well rounded. But you will learn about how difficult it is to beat the market and the empirical results of mutual funds and hedge funds. It’s incredibly hard and only getting harder for many discretionary/ fundamental segments. I love the markets and probably will continue in fundamental L/S but it is worth thinking about other ways to participate.
For investment management, MBAs are mainly recruited into long only mutual funds where there is a very established pipeline. You can do L/S but there’s not many formal pipelines. Now, due to performance, mutual funds have been shrinking for years and are in secular decline. This means there’s a tiny amount of seats that are being competed for (10-20 seats per year across all schools). Most mutual fund seats go to top 5 schools (Booth is strong). Very competitive.
For hedge funds, CBS value investing program might place better due to proximity and alumni. Worth looking into on LinkedIn if you want hedge fund instead of mutual fund.
It depends what you mean by career switcher? If you did former investing then you have a good shot. I did PE and had success. But in my experience those without some adjacent or direct investing experience have really struggled in this current hiring environment for long onlys. I would think it’s even harder for hedge funds to recruit someone without experience in current environment. Hope this helps.
Really appreciate you doing this. I was recently promoted to Quant PM and would appreciate some advice on the managerial side of things. My situation is a bit unique in that my team is running multiple internal strategies (not at a MM) and we are tasked with building out everything basically from scratch, including datasets, infrastructure, etc.:
Thanks so much for doing this. And of course not expecting you to divulge anything you're not comfortable with, just want to hit the ground running with my new team. Thanks again!
1. I give a lot of discretion to researchers, highly talented people usually want a lot of autonomy. I let the team play offense and view my job as playing defense. Risk management should never leave your hands, it is your job to monitor exposures of the book, correlation between people's work, leveraging and deleveraging flows in the market, and anticipate operational problems or technical debt buildup before they happen. Some of this is also anticipating people problems before they happen and stepping in when necessary.
2. Sometimes rabbit holes are good to explore. I always have a portfolio of R&D bets, some that should be very quick to test, and others that may require more time.
3. Not often.
4. Given the length of my garden haven't had a lot of time with using LLMs in a production setting, they were just starting to get useful at coding when I left my last job. My view so far is they are heavily biased towards Python and will take over most programming there (in part because that language choice implies certain use cases which AI is good for) but currently there is more risk in using with other languages in other settings.
Very interesting thread. Thank you for making this AMA.
I have heard a lot about very strict drawdown limits at pods. Would it please be possible for you to tell me what your soft drawdown limit was? And if you ever went through it? I have heard that this is the most stressful part of working in a pod. How “normal” is it for quant pods to breach their soft limits please?
Any color would be highly appreciated. Thank you once again.
Most pod shops have roughly the same limits which are soft draw at 1x your annual volatility, and hard draw at 1.5x
Not "normal" to breach your soft limits you have a pretty good chance of getting cut if you do. But everyone knows the rules of the game so you don't manage the book as if the limit doesn't exist, you cut risk as you approach the limit. Usually that means sacrificing EV and increasing time to recovery but survival is the name of the game.
Thank you very much for a very helpful reply. I hope you won’t mind me bothering you again.
Is 5-7% annual volatility normal for equity stat-arb please?
I must apologize once again for such naive questions, but it is very rare to come across someone like you from the stat-arb world. Thank you once again, Sir.
Posted twice by accident. Sorry!
Incoming at UPenn for AI/CS. Penn engineering offers both as a major, and I think AI would be better for quant… advice for breaking in without a USACO / AIME or similar prodigy type background?
If not going for a PhD, your best option is the Quant Trading job at a prop trading firm. Try to avoid the market making side and aim to get placed on a slower team if you eventually want to move to a hedge fund.
For the tier 1 firms (JS / Jump / HRT / etc) you will need to stand out on the resume screen. High GPA, demonstrated experience via projects, a referral, etc. Once you land the interview its all your own performance.
For tier 2, a good GPA is enough to get interviews.
Hi, Thanks for doing this.
I am a relatively new PM(starting out 1 or 2 years), and have the following questions:
1. I currently run fixed income/futures stat arb books(price volume signals mostly). But we have the autonomy to actually touch different assets. (US equities, Asian equities, convertibles, etc). Do you suggest broadening first or diving deeper first?
2. Do you have any anxiety for alpha decays , alpha not working anymore? Sometimes, pnl drawdown can continue a long time, even if it is a supposedly sharpe 2 book.
3. If one signal stops perform for the past year, but was good historically, do you move it out or just keep it?
4. How important do you think is price/volume/level2/level3 ob data relatively to alternative data in your space?
Thanks
Very very interesting read. Thank you. I was wondering if you could tell me what kind of volatility does a quant pod target ideally? You are the first person who has explained the rationale behind really tight drawdown limits (1x annual volatility). So basically your volatility is being dictated by your drawdown limit. I have read that certain multistrats halve the size at 2.5%. Isn’t this kind of low volatility too restrictive in high-vol environments?
Again it is more important to think about this as annual $ vol vs. $ drawdown limit. For example managing $50mm in annual vol usually means you can afford to drawdown $50mm before being cut in half.
Your capital allocation is specific to strategy and negotiated with risk when you sign. Some quant strategies will use 5 yards to hit $50mm in annual vol (so soft draw is 1% of GMV). Others do it with only 1 yard (soft draw is 5% of GMV). Two main levers that determine this in portfolio math are concentration of positions and correlation risk. A factor neutral book with evenly sized positions will be many times less volatile than a concentrated portfolio that is also taking modest factor bets.
Risk usually understands this well and sets the correct capital allocation to meet the vol target and they have pretty good defaults in place for various strategies. But a number like "2.5%" being "low" is totally meaningless without context on the strategy. For the 5 yard strategy that could be a very generous drawdown limit and for the 1 yard strategy it could be punitive.
Thank you for explaining it. I was wondering about these risk limits in a different context. I have an offer from a multistrat quant pod and an another from a collaborative quant fund. One of the points raised by the collaborative quant fund was that their risk limits were much looser than that of multistrats (equity market neutral). Thus they can deploy capital where multistrat pods would be shut down. July 2025 was given as an example. I was made to believe that excessive risk throttling was making quant pods at multistrats suffer, as PMs were ensuring their own survival (drawdown limit). Of course, this came from a collaborative quant fund so I suppose I need to take it with a pinch of salt. What is your opinion about it?
Thanks so much for doing this. Very insightful and goes a long way for people who are really interested in this stuff. A few questions:
Thank you!
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