Quant interview preparation

Questions

100

Traced to a firm

53

Firms

15

Updated

September 2026

Asked at

All firmsOld Mission Capital12Tower Research Capital10Jump Trading7Akuna Capital5Citadel4DED.E. Shaw3Jane Street3ACAQR Capital Management2DRW2Millennium Management2Schonfeld2SCSquarepoint Capital2Susquehanna International Group2Belvedere Trading1Optiver1

Topic

All topicsProbability10Coins, cards and games6Expected value8Statistics11Market making15Estimation and mental maths4Stochastic processes4Regression5Machine learning6Time series6Programming10Options and derivatives8Fit and motivation7

Level

AnyCoreIntermediateHard

Type

AnyBrainteaserTechnicalCaseMarket viewFit

1. 070Something goes badly wrong on your book during the session. How do you react?Market makingIntermediatetechnicalOld Mission CapitalProp Trading · Chicago · 2025

   Say this

   Reduce risk first, diagnose second, and tell someone immediately. In that order. The instinct to understand the problem before acting on it is the wrong instinct when the position is still live and the loss is still growing.

   Then walk it

   1. Step one, stop the bleeding. Pull quotes, flatten or hedge the exposure I did not intend to have, and cap any automated system that might still be adding to it. Getting smaller is almost never the wrong move under uncertainty.
   2. Step two, escalate. Tell the senior trader on the desk and the risk desk straight away, before I know the cause. Every trading floor's disaster stories are about someone who tried to fix it quietly first.
   3. Step three, establish the facts. What is my actual position, what is the realised and unrealised loss, is the pricing wrong or is the position wrong, and is anything still running that I have not stopped.
   4. Step four, only then diagnose and fix. A bad parameter, a stale feed, a hedge that did not go through, a fat finger, a genuine adverse move.
   5. And afterwards, write it up. A one-page post-mortem with a concrete control change is what stops the same failure twice. What a desk actually wants to hear from a junior candidate is that you act to reduce risk without needing permission, and escalate without needing to look competent first. Composure plus disclosure, in that order.

   Where candidates lose it

   Answering that you would investigate the cause first. On a live book that is exactly backwards, and a prop trading interviewer is listening for the reduce-then-escalate-then-diagnose sequence. Also do not claim you would stay completely calm. Say you would act on a checklist precisely because you would not be calm.

   Expect next

   • Who do you tell, and how quickly?
   • Tell me about a time you made a real mistake and what you did.
   • What would you put in the post-mortem?

   Reported by candidates at Old Mission Capital (Prop Trading, Chicago, 2025). Source: Wall Street Oasis.

2. 071Here is a scenario. Walk me through how you would analyse the trade.Market makingHardcase studySchonfeldQuantitative Research · New York · 2021

   Say this

   I would structure it as five questions: what is the thesis and what would make it wrong, what is the expected value, how do I size it, how do I hedge what I am not trying to be exposed to, and what is my exit. Then say the number, because a trade analysis without a number is an opinion.

   Then walk it

   1. Thesis first, stated as a falsifiable claim with a horizon. Not this looks cheap, but I think this spread compresses from 80 to 50 basis points over three months because of a specific mechanism, and if it is still at 80 in three months I am wrong.
   2. Expected value: probability times payoff on each branch. If there is a 60 percent chance of making 3 and a 40 percent chance of losing 2, that is 1.8 minus 0.8, so plus 1 with a 5-point range of outcomes. The range matters as much as the mean.
   3. Sizing: from the loss branch, not the win branch. I size so that the bad case is a loss I can carry, which in practice means a fraction of my risk budget, and I say what that fraction is.
   4. Hedging: separate the exposure I want from the ones that come attached. If the view is idiosyncratic, hedge out the market beta, the sector, and the rate duration, then check what basis risk remains after hedging, because that is the risk I did not choose.
   5. Exit and monitoring: the level or the date at which I am out, plus the two or three observables that would tell me the thesis is breaking before the P&L does. And I would name the thing I cannot hedge, because every trade has one and being explicit about it is what makes the analysis credible rather than promotional.

   Where candidates lose it

   Describing the thesis at length and never getting to sizing, hedging or the exit. Anyone can have a view. What a multi-manager platform is hiring for is the risk framework around it, so spend at least half your answer on size, hedge and exit, and name the unhedgeable residual yourself.

   Expect next

   • What is your stop, and why there?
   • What would make you double the position?
   • What risk are you left with after hedging?

   Reported by candidates at Schonfeld (Quantitative Research, New York, 2021). Source: Wall Street Oasis.

3. 072Where does the money come from in market making versus a systematic hedge fund strategy?Market makingIntermediatetechnicalProp trading firmsQuant trading

   Say this

   A market maker gets paid a fee for providing immediacy and aims to be flat at the end of the day. A systematic fund takes a position because it forecasts a return and holds risk overnight. One sells a service, the other takes a view.

   Then walk it

   1. Market making: high turnover, tiny edge per trade, thousands of trades a day, Sharpe ratios that can be very high because the law of large numbers works for you, and capacity limited by volume rather than capital. Risk is inventory and adverse selection, measured in seconds to minutes.
   2. Systematic trading: lower turnover, larger edge per position, Sharpe typically 0.5 to 2, capacity limited by market impact, and risk measured in days to months. You are exposed to being simply wrong about the forecast.
   3. The counterparty differs, which is the deepest version of the answer. A market maker's profit comes from other participants' demand for immediate execution. A systematic fund's profit comes from other participants' mispricing, behavioural bias, or need to shed risk.
   4. Which tells you what kills each one. Market makers die from a fast informed move against a large inventory, or from technology failure. Systematic funds die from crowding, regime change, and leverage in a deleveraging.
   5. And it explains the career difference, which is usually the real reason the question is asked. Market making gives you feedback in minutes and rewards fast reaction under pressure. Research gives you feedback in months and rewards patience and statistical honesty. Saying which one suits you, with a reason, is what they are listening for.

   Where candidates lose it

   Treating them as the same job with different time horizons. The economic source of the profit is different, and saying it plainly, a fee for liquidity versus a return for taking a view, is what demonstrates real understanding. Then connect it to which seat you want, because that is where the question is going.

   Expect next

   • Which of those do you want to do and why?
   • Why can market makers run much higher Sharpe ratios?
   • What kills each business?
4. 074How would you model market impact and slippage for a strategy you are sizing?Market makingHardtechnicalQuant researchQuant trading

   Say this

   Split the cost into spread, temporary impact and permanent impact. The empirical regularity worth knowing is the square-root law: impact scales roughly with the square root of the order size as a fraction of daily volume, times the volatility.

   Then walk it

   1. The square-root law: impact in volatility units is approximately a constant times the square root of order size over average daily volume, with the constant usually estimated around 0.5 to 1. So trading 1 percent of ADV in a 2 percent daily vol name costs roughly 0.1 times 2 percent, about 20 basis points.
   2. That non-linearity is what caps capacity. Doubling your size only increases impact by 41 percent per share, but total cost grows as size to the power 1.5, so cost eats your edge faster than your edge grows.
   3. Separate temporary from permanent. Temporary impact reverts after you stop trading and is a function of how fast you trade. Permanent impact is the information your trading revealed, and it does not come back. Almgren-Chriss style frameworks trade off the two against the risk of trading slowly.
   4. Estimating it honestly: use your own fills against arrival price, not a vendor model, and regress realised shortfall on participation rate, volatility and spread. You need a lot of trades, and you must control for the fact that you traded more aggressively when you had more signal, which biases the estimate.
   5. And the modelling discipline: be conservative, because impact is the parameter most likely to turn a profitable backtest into a losing strategy. I would rather assume twice the cost and discover I was pessimistic than the reverse. That preference is the answer they want to hear.

   Where candidates lose it

   Assuming linear impact or using the quoted spread as the whole cost. For any size that matters the spread is the small part. Know the square-root law and know that cost scaling as size to the power 1.5 is what determines capacity, because that is the link between a research result and a business decision.

   Expect next

   • Why does cost scale as size to the power one and a half?
   • How do you separate permanent from temporary impact empirically?
   • How does impact determine the capacity of a strategy?
5. 075How has electronic market making changed over the last decade, and where do you think the edge is now?Market makingIntermediatetechnicalProp trading firmsQuant trading

   Say this

   Spreads have compressed to a tick or less in liquid products, the pure speed race has largely been won and commoditised by a handful of firms, and the remaining edge has moved to breadth of product, quality of the pricing model, and access to less-contested flow.

   Then walk it

   1. What changed: colocation and microwave or hollow-core fibre links turned latency into a fixed capital cost rather than an edge, exchange data got faster and cheaper, and the number of firms who can compete at the top tier is small.
   2. Where it went. First, breadth: applying the same infrastructure across equities, options, futures, crypto, ETFs and fixed income, since each new product is incremental revenue on a paid-for stack. Second, modelling: in options and ETFs the hard part is pricing thousands of related instruments consistently, which is a research problem, not a wire problem.
   3. Third, flow quality. Internalising or purchasing retail flow is valuable precisely because it is less informed. That is the economics behind payment for order flow, and it is the reason the regulatory debate about it matters commercially.
   4. The structural trend in fixed income and credit is worth naming: electronic market making has moved into products that were voice-traded a decade ago, and ETF creation and redemption is the mechanism that makes bond market making hedgeable at all.
   5. My honest view, offered as a view and not a fact: the marginal edge now sits in products where pricing is genuinely hard rather than where speed is hard, because speed has a ceiling that has been reached and modelling does not. And I would caveat that I am reading this from the outside, which is part of why I want to work somewhere that sees it from the inside.

   Where candidates lose it

   Reciting high-frequency trading is about speed as if it were still 2010. The interviewer works at one of these firms and will know instantly. Have a specific, current view, name the shift from latency to breadth and modelling, and flag that it is your view rather than asserting inside knowledge you do not have.

   Expect next

   • Is payment for order flow good or bad for the end investor?
   • Why is options market making harder than equities?
   • What do you think our firm's edge is?

Firm tags come from public, anonymous candidate reports on Wall Street Oasis: strong signal, not sworn testimony. Firms are named as the places a question was reported, not as partners of Fin Maverick. Answers are written for this page to show how to think out loud; they are not scripts to recite.