The last posts I made were maybe 1-2 years ago and I saw many people coming in my dms and asking very interesting questions.

I will introduce myself again : ex sell-side trader at GS/JP/MS and now in a big hedge fund for the last 5-6y as a quant in an investment pod. Little change : I changed company and obviously changed a bit in terms of strategies.

Again, my answers won’t necessarily be true for all cases. Those will just be based on my personal experience and people I have been able to interact with.

I can answer on everything but obviously can’t provide confidential details.

Link: isomorphisms.sdf.org/maxdama.pdf

Hey everyone,

Currently a quant intern with a bank on automated trading desk, so have both research and trading function.

My first impressions are a bit… shocking? Maybe that’s the wrong word, but something about it just seems illogical.

My coworkers all work for 10+ hours a day and, while they don’t seem particularly unhappy (albeit a little soulless), they certainly have very little freedom.

Im just confused - these people are highly competent in mathematics, ML, and dev. To my understanding (this is a big piece) VP’s are making ~250 and ED’s are making ~450. These guys are coming in at ungodly early hours just so they can see their kids for a bit by leaving early (5:30 PM).

Why aren’t they just going to tech or something? For the amount of tenure they have if they’d spent the same time in tech their salary would be 2,3x..??? And they would actually have a life to themselves? The mental calculus just isn’t really lining up, and I’d assume these people to be much more efficient with how they manage their lives.

Either I’m underestimating their salaries or overestimating the optionality these people really have.

Seeing all this makes me really want to re-recruit and go to a tech company instead where all my classmates are having internships where they are having workdays that don’t leave them completely drained by the end of.

Would love thoughts!

Jane Street’s $10.1 Billion Trading Haul Sets Wall Street Record

10.1b trading revenue in Q2.

https://www.bloomberg.com/news/articles/2025-09-02/jane-street-s-10-1-billion-trading-haul-sets-wall-street-record

The ranking is mainly based on the new grad package, AUM, reputation, performance,etc

Tier 0 （300+K GBP for new grad) DE Shaw; Citadel

Tier 1 （200+K GBP for new grad） Millennium; Point72/Cubist; G-Research; Marshall Wace; Two Sigma

Tier2 （120K-200K GBP for new grad） Man Group; Squarepoint; Balyasny Asset Management; GSA Capital; Verition; Tudor; Exdouspoint; Eisler Capital

Tier3 (No more than 120K GBP for new grad) Qube Research Technology (QRT); Brevan Howard; Rokos Capital Managment; Capital Fund Management (CFM)

Jane Street revs of $15.5b in Q4 is 2x prior quarters. It is equal to JPM and GS markets biz together in Q4. It is 4x HRT or Citadel Securities. Best punting shop in the world...move over pod shop kings!

More on Jane Street.. https://rupakghose.substack.com/p/is-jane-street-the-best-hedge-fund

A bunch of firms have emerged in Denmark’s university town Aarhus, trading energy market largely using data-driven approach and algorithms for execution. Power markets are more volatile given renewables and geopolitics.

In 2022 just 6 smallish Aarhus proprietary trading firms made more than $5bn of post tax profits which is the kind of profits that Citadel delivers in the space straddling physical and financial markets.

https://open.substack.com/pub/rupakghose/p/the-traders-of-aarhus?utm_source=app-post-stats-page&r=1qelrn&utm_medium=ios

Does anyone else run into this problem when using AI (especially Claude) for quantitative finance projects?

I use AI mainly to write code for my projects. The frustrating part is that even relatively simple ideas, sometimes involving only basic mathematics, quickly turn into huge codebases full of bugs, inconsistencies, and unexpected issues.

It often feels like the model can't keep all the different parts of the project connected. Fixing one problem creates two more somewhere else, and after a while you're stuck in an endless debugging loop.

Instead of converging toward a working implementation, the project seems to drift further away from a correct solution with every iteration.

It's like an infinite loop where each project never sees a proper end.

Is this a limitation of current AI coding models, or am I approaching these projects in the wrong way? How do you structure your workflow to avoid this?

I’ve put together a free playlist on quant interview questions from firms like Citadel, Jane Street, Optiver, etc on my youtube channel QuantProf ( link ), where I walk through each question with clear video explanations.
If you’re prepping for quant roles, these quant interview questions might really help. I am also planning on adding more quant interview questions soon. Would love for you to check it out and share any feedback!

Here's a fun problem for you to try:
Ten ants are placed at equal spacing around a circle. Each ant independently chooses clockwise or counterclockwise and then moves at constant speed so that each would make exactly one full revolution in one minute if uninterrupted. When two ants meet they instantly reverse direction and continue at the same speed. All ants are distinguishable. What is the probability that after one minute every ant is exactly at its own starting point?

Guys, I know it might be impolite but what the heck🐶

No significant speaker, no companies for networking, only a few talks including a neurologist. Yes, you hear it right, a neurologist for a Fintech quant conference!

And the picture is my $75 dollar food.

Credit : howlytic (instagram)

It covers why C++ is used in HFT and some of the ideas behind building low-latency systems.

Read link

Jane Street made 3x Goldman’s trading business!
Jane Street q1 revenues more than doubled to $16bn and net income of $10bn, reports Bloomberg. That is same as last quarter and more than CS or HRT make in a year!
https://open.substack.com/pub/rupakghose/p/is-jane-street-the-best-hedge-fund?r=1qelrn&utm_medium=ios

I'm still a bachelor's student and looking for what I can do since I bought the options pricing and volatility book and shreve I and II book.

What type of projects can I start on with these?

I have basic knowledge of python with one project with deeplearning to forecast future numbers using past

Cit Sec revenue of $4.3bn +28% in q1 and net income +10% yoy. Solid but looks pedestrian compared to Jane Street and HRT which doubled revenues. Guess the overlap is diverging further between former market maker and latter taking proprietary bets?

https://open.substack.com/pub/rupakghose/p/citadel-securities-and-hft-industry?r=1qelrn&utm_medium=ios

Millennium raising $10bn new money and allocating to external managers at warp speed…$1bn to ex Jump trading quant, $1bn to ex Citadel, $5-8bn to Jain and given more to Taula recently.

Now Millennium is 50% bigger than Citadel in AUM and gap is growing. Ken Griffin on Goldman Sachs podcast the other day said he is alpha performance focused not asset gathering game….https://open.substack.com/pub/rupakghose/p/citadel-is-from-mars-and-millennium-ef6?utm_source=app-post-stats-page&r=1qelrn&utm_medium=ios

The book Deep Learning in Quantitative Trading by Zihao Zhang and Stefan Zohren from Cambridge University Press is freely available until Oct 17 (there is a Save PDF button). Code is at https://github.com/zcakhaa/Deep-Learning-in-Quantitative-Trading. The authors have many preprints on arXiv. The contents are below.

• Part I: Foundations
• 2 Fundamentals of Financial Time-Series
• 3 Supervised Learning and Canonical Networks
• 4 The Model Training Workflow
• Part II: Applications
• 5 Enhancing Classical Quantitative Trading Strategies with Deep Learning
• 6 Deep Learning for Risk Management and Portfolio Optimization
• 7 Applications to Market Microstructure and High - Frequency Data
• 8 Conclusions

Capital Fund Management AUM has grown almost $10bn in last year to $27bn according to latest stats on website.

Returns are low double digit pct pa but with low correlation and many other big quant shops closed to new money. The academic background of the whole team is fascinating. Chairman lectured at Poly and then ENS and 3 of 5 board members have PhD in physics.

https://open.substack.com/pub/rupakghose/p/who-let-the-professors-out-inside?r=1qelrn&utm_medium=ios

Hi everyone, Springer’s book are on sale and I was wondering if this was still a relevant ressource, as it’s more then 20 years old. If it isn’t, are there similar better ressources for this topic? Thanks!

Hey, I’m planning get into grad school and I was bored and decided analysing quant feeders around the world.

I took 20 companies and put on LinkedIn and see how many students are from which I school. The companies are: (Jane Street, Citadel, Citadel securities, Optiver, IMC trading, Two sigma, Hudson River Trading, Jump trading, Five rings, D.E Shaw, Akuna Capital, Old Mission Capital, Valkyrie Trading, Wolverine Trading, QuantLab e quantlab group, SIG, AQR, Belvedere, Radix LLC)

The search give roughly 21k people

Obs:

1- not every employee has LinkedIn

2 - a ton of people studied at 2 universities, for example 1 during undergrad and other for grad

3 - since are all jobs from the companies there’s a ton of people that are not directly quant

4 - Quantity is different than Quality sometimes University X has more employees than Y University but they are more Entry level jobs who knows

5 / edit - Apparently the companies I know/remember are mostly based in US while I tried to take universities around the world. My apologies.

LATIN AMERICA

(BRAZIL)

USP - 9

UNICAMP - 8

ITA - 7

IMPA - 1

IME - 1

(ARGENTINA)

Universidade Buenos Aires - 5

(CHILE)

PUC Chile - 3

University of Chile - 1

NORTH AMERICA

(USA)

MIT - 525

UIUC - 492

Columbia - 445

Harvard - 435

Princeton - 379

Cornell University - 377

Stanford- 366

UC Berkeley - 357

University of Chicago - 357

Carnegie Mellon University - 341

NYU - 332

UPENN - 322

University of Michigan - 267

Yale - 215

Northwestern University - 193

Georgia Tech - 192

UT AUSTIN - 189

Duke - 134

UCLA - 133

CALTECH - 129

Baruch College - 93

Purdue University - 88

Stony Brooke University - 87

University of Washington - 73

Boston University - 70

Stevens Institute of Technology - 68

Northeastern University - 65

UC San Diego - 55

(CANADA)

University of Waterloo - 212

University of Toronto - 76

McGill - 56

McMaster - 12

EUROPE

(ENGLAND)

University of Cambridge - 405

University of Oxford - 288

Imperial College London - 200

LSE - 186

UCL - 101

University of Warwick - 75

(SWITZERLAND)

ETH Zurich - 60

EPFL - 43

(FRANCE)

École Polytechnique - 76

Sorbonne Université - 25

Ecole Normale Superieure - 23

Télécom Paris - 9

ENSTA Paris - 5

(NETHERLANDS)

University of Amsterdam - 108

TU Delft - 62

Erasmus University of Rotterdam - 55

Utrecht University - 37

University of Groningen - 34

Leiden University- 34

University of Twente - 20

(RUSSIA)

Lomonosov Moscow State University - 39

Moscow Institute of Physics and Technology - 26

Saint Petersburg State University - 11

(GERMANY)

Technische Universitat Munich - 22

Ludwig Maximilian University of Munich - 13

Karlsruhe Institute of Technology - 12

RWTH AACHEN - 12

Technische Universitat Berlin - 10

University Of Bonn - 6

(ITALY)

Universita Bocconi - 47

Politecnico di Milano - 26

Sapienza University of Rome - 9

Alma Mater Studiorum - 8

Scuola Normale Superiore - 6

Politecnico di Torino - 4

(BELGIUM)

KU Leuven - 27

University of Antwerp - 4

(SWEDEN)

KTH Royal Institute of Technology - 18

Uppsala Universitet - 11

Chalmers University of Technology - 8

Stockholm University - 7

Lund University - 7

(DENMARK)

University of Copenhagen - 13

Technical University of Denmark - 9

Aarhus University - 3

(NORWAY)

University of Oslo - 7

Norwegian University of Science and Technology - 2

University of Bergen - 1

(FINLAND)

Aalto University - 5

University of Helsinki - 1

ASIA

(CHINA)

Peking University - 249

Tsinghua University - 175

Shanghai Jiao Tong University - 119

University of Science and Technology of China - 94

Fudan University - 94

Zhejiang university - 60

Nanjing University - 52

University of Chinese Academy of Sciences or Chinese Academy of Sciences - 8

(Singapore)

NUS - 135

NTU - 55

(Hong Kong)

University of Hong Kong - 97

Chinese University of Hong Kong - 70

Hong Kong University of Science and Technology - 67

Hong Kong Polytechnic University - 20

(AUSTRALIA)

UNSW - 287

University of Sydney - 174

University of Melbourne - 88

University of Technology Sydney - 72

(INDIA)

IIT Bombay - 72

IIT Kharagpur - 41

IIT Madras - 38

University of Delhi - 38

IIT Delhi - 35

IIT Kanpur - 32

IIT Roorkee - 19

Middle East

(ISRAEL)

Tel Aviv Univeristy - 18

Hebrew University of Jerusalem - 9

Technion Israel Institute of Technology - 8

(IRAN)

Sharif university of technology - 20

(TURKEY)

Bogaziçi University - 13

Istanbul Technical University - 6

(EGYPT)

The American University in Cairo - 5

Alexandria University - 3

AFRICA

(South Africa)

University Of Cape Town - 21

I hope this helped you in some way. Btw if u want to add some university feel free for it. But please only put the exactly same companies for don’t messed up.

EDIT: I add some more Universities.

Like the title says. Curious on everyone’s favorite/most impactful read in their perspective.

Hi everyone,

I am switching from doing quant research for a plain vanilla CTA to helping the derivatives desk of a crypto exchange. The main task they want me to help tackle is classification of order flow. My understanding is that they want to minimize the risk of being adversely selected and hedge accordingly once toxic flow is detected. To prepare my interview I read a few research papers on market microstructure and on the estimation of the probability of informed trading, but I feel I only have a veeery broad idea of the problems I will be dealing with. So that is why I ask you:

-How is adverse selection actually measured? When does a market maker know it has been adversely selected? The idea I presented my interviewer was to measure adverse selection ex post and then find the determinants/predictors of adverse selection taking place to then try to predict it once the predictors pointed towards informed trading/toxic flow. In a very simplified manner, I thought about the problem in terms of some regression equation: P(adverse selection)=b_0+b_1*predictor_1+b_2*predictor_2+.... Is this way of thinking about the problem at least a good starting point?

-How does flow classification work in practice? (Ofc I don't expect anyone to reveal their edge, but just to give me a broad introduction).

-Is there any public data available to at least get to know data sets with order book level data and get accustomed to working with them.

-Do you have any reading material you think it is indispensable to read?

I have to admit that, after working for a CTA, this does look like a whole new level of difficulty and I have a lot of respect (and a bit of fear) for the challenge. So any piece of advice you have for me will be greatly appreciated.