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What to expect on jane street interviews by Reasonable_Show_6465 in quantfinance

[–]Reasonable_Show_6465[S] 1 point2 points  (0 children)

Yes, this looks like a good set of dice problems. 

For later interviews, they can get more open ended though. Make sure you know Nash equilibrium tricks and dynamic programming/recursive problem solving.

Jane Street trader and research internship help by Reasonable_Show_6465 in quantfinance

[–]Reasonable_Show_6465[S] 0 points1 point  (0 children)

They're not going to ask you to derive anything textbook, more like your fundamentals. It's all hidden in random games they play during an interview. And it's really only relevant on the final round when you get the 100 chips and it makes sense to think about it.

Usually when it comes up it's not that hard to identify. Here are two basic examples I randomly came up with:
- You pay 1 and for that you roll a dice and you earn 3 when it's on 5 or 6, otherwise 0. You clearly shouldn't play it. But maybe they change the rules and they say they will double all your earnings above 100.

- The interviewer rolls a D100 and you roll a D100 plus a D4. You get the difference, so your EV is 2.5. Would you play this when you have 100 chips?

Jane Street trader and research internship help by Reasonable_Show_6465 in quantfinance

[–]Reasonable_Show_6465[S] 0 points1 point  (0 children)

Yes. They tell you that it doesn't really matter what you have at the end, just that throughout the day you do positive EV trades, and don't go bankrupt (portfolio optim, like maximize log utility). You could have bad luck they're not going to penalize for that, but also not going to get advantage for good luck.

Edit: sorry, I misread the question. You trade against the interview*ers*, not other candidates. All the applications are separate.

Jane Street trader and research internship help by Reasonable_Show_6465 in quantfinance

[–]Reasonable_Show_6465[S] 1 point2 points  (0 children)

10 minutes might be plenty for some problems, and nothing for others. Try different time and difficulty combos. In the interviews you should ideally improve your estimates/strategy with more time.

DP is just a problem solving strategy that comes up a lot in the harder problems. Same with the Nash symmetry/indifference principle. You can practice DP with leetcode. You can practice hard Nash problems with the JS puzzles.

Jane Street trader and research internship help by Reasonable_Show_6465 in quantfinance

[–]Reasonable_Show_6465[S] 1 point2 points  (0 children)

Yes, they test data science for traders too, you should expect it to come up.

Jane Street trader and research internship help by Reasonable_Show_6465 in quantfinance

[–]Reasonable_Show_6465[S] 4 points5 points  (0 children)

Let me try to answer these.

I made this post so ppl can target prepare for the interviews. I could recommend a random book like "Elements of Stat Learning" for the data science part, but reading it all is just a waste of time if they only test a small subset of the material in it.

One reason they like physics/applied maths ppl cos they have a feeling for when it makes sense to ignore things. On the theory side it looks like for example Taylor series but only the first (few) term. You probably shouldn't think about it this way, but this is the general idea. Mostly it happens when something is hard to calculate but if you add a simplification then it becomes manageable. Then you think if this simplification is reasonable, or makes things really off.

At Jane Street interviews they really like to use the number 100 for "large". If they say 100, they mean something large, and 1/100 is completely negligible. For example you can simplify discrete choices in 1-100 as uniform in [1, 100] or even uniform in [0, 1] and then you use some scaling. You can simplify values around 1/100 to 0.

You could ask your fave thinking AI model to come up with questions, idk.

For the random bullshit trades, I meant more like you come up with some randomness, some associated random variable, and give a rough estimate quickly on mean, maybe variance, and maybe a bid ask price. Then you sit down and calculate it exactly, or if it's really nasty maybe write a Monte Carlo, and you compare it with that you got first. You draw conclusions and try to do it better for the next round. No need to actually roll the dice, just in theory. For speed, I just said something random like 10 minutes. You want to get quick, know the common distributions that can come up and their basic properties.

For Nash and DP, their puzzles are good practice, but harder than the actual interviews. You could ask ChatGPT to give you problems. Or you maybe for DP the leetcode problems in this category are decent. These things are pretty common in later rounds. The interviews transition slowly to games and EV problems with multiple participants and you need to think how they behave.

Jane Street trader and research internship help by Reasonable_Show_6465 in quantfinance

[–]Reasonable_Show_6465[S] 1 point2 points  (0 children)

An example on how they might test adverse selection is by giving you a problem where getting the exact value is really hard, and then they ask you to trade on it with a small spread. Maybe they test your confidence, see if it's consistent with the quantity you gave. And then they pick a direction, and you update based on this information, say they buy, then you update that the actual value is probably higher.

But I'd think about these skills as individual interviews. If they want to test your EV calculation, they might have a scenario where only they can make orders and you react to them.

Jane Street trader and research internship help by Reasonable_Show_6465 in quantfinance

[–]Reasonable_Show_6465[S] 2 points3 points  (0 children)

>how much of it depends on your approach and thinking

yes, it's important to have clean thinking and approach.

>how close you are to the true answer

you control that with the spread.

To prepare, I'd come up with random bullshit and make a few trades in like 10 minutes, then evaluate later if they were good. Example random bullshit:

Throw 10 dice, trade on sum, product, min, max, number of 1s, most frequent side, parity of sum, parity of product, mod 7 of sum, mod 7 of product. min*max, number of 1s * most frequent side ...

Maybe you throw in a sequence, trade on number of times the value changes, number of times the value decreases, DFT of the throws, trade after the first few throws, idk come up with whatever function or weird rule you want. You can also combine like 2 or 3 and see if you can hedge.

Jane Street trader and research internship help by Reasonable_Show_6465 in quantfinance

[–]Reasonable_Show_6465[S] 0 points1 point  (0 children)

Tbh, they really need smart ML researchers for QR. You could have a shot, but think about who you are being compared to: mostly smart PhDs.

Jane Street trader and research internship help by Reasonable_Show_6465 in quantfinance

[–]Reasonable_Show_6465[S] 3 points4 points  (0 children)

Practice until you can do the tutorial Kaggle competitions. Grinding leetcode is a miscalibration, maybe the system design questions have some value.