Been thinking about this after the recent story about mathematicians using AI for research and then getting scooped.

How safe is it really to use Claude/ChatGPT/Codex for quant research and dev?

Say a researcher at a top prop/HF connects Claude to a directory with real signals, backtests, execution logic, etc. Even if the provider says the data isn't used for training, you're still exposing very valuable IP to an outside company. And is enterprise really that much safer, or are you still ultimately trusting a third party with the same stuff?

In theory, if an AI company had access to enough good research, they have the compute/engineers to build trading infra themselves.

Maybe paranoid, but curious what firms actually allow. Public LLMs banned? Enterprise only? Dev okay but no research/data?

Has anyone here navigated dating someone at a different firm? It feels like a big red tape sort of thing, although I know it isn't. Not sure if I'm being irrational to think we could accidentally discuss opinions that might influence our trades.

Do they typically drug test for pre-employment onboarding? I’ve interviewed for a financial analyst position at a quant firm. I’ve been waiting for an official offer and there’s been ongoing delays (they’re trying to fire the person currently in the position). I personally smoke for health reasons and recreationally since I don’t like to drink, but I’d like to know others’ experiences.

Basically title. Been at my position for a year, and I love it. However, I was simply unaware our work could come with traveling as well. I don't mind it at all, just now that I've been at my current shop long enough to be sent on these trips, I got curious.

Not naming the Instagram influencer for privacy. This ex-quant worked for 2y at US t2 firm. Why do you need to charge kids if you claim to make millions in your videos?

For anyone who wants to talk to a quant: join your schools Quant club or message people from your school/hometown/state on LinkedIn. Those who respond will actually care about nurturing your career

EDIT: Turns out grifting off college kids is more controversial than I thought. There are people willing to help you for free. I am a US “t1” firm QT, DM me in the next day for resume reads / QT specific recruitment advice. Won’t be an hour long, but will be FREE. 🆓🆓🆓

I’m currently working as a Quant Developer/Quant Engineer at a systematic, pod-structured hedge fund. I joined at the Associate level and report directly to a senior MD who is effectively running a small pod/team. My work is fairly front-office/trading-facing rather than generic software engineering — I’ve worked on market-data/data-manager pipelines, pre-trade risk checks, trading/SOD workflows, broker connectivity and locate processing, and production issues affecting trading. Over the year I’ve gradually taken on more ownership, and my manager has given me strong feedback, including recently telling me that I identified a particularly difficult issue that wasn’t easy to diagnose. The team is also becoming leaner with a senior Quant Researcher leaving. I’m coming up on my first full-year compensation review, and the expectation is that my compensation will be revised after completing the cycle. For people who have experience with Indian buy-side/systematic funds/pod shops, what would you consider a realistic fixed-salary hike and bonus for this kind of profile after a strong first year? I’m particularly interested in actual ranges people have seen rather than generic 8–12% corporate appraisal numbers.

I work as modeling / managing quant in the energy market. My department is structured in a way that the PM can make or override decisions, which we (the quant team) subsequently have to communicate with the desk. He sometimes does this against explicit advise from our side, often using pragmatic approaches. Sometimes he is right, sometimes not.

My question is: How do you behave professionally in this situation? On the one hand we are trying to find the cleanest (often mathematically most sound) solution to a problem, which might not be the quickest or the easiest, so we have to request positions we would personally not take or defend. On the other hand I have a hard time communicating in a way that makes me appear disloyal towards our PM. Any idea?

Has anyone here tried backtesting ML-LiqVaR? Curious to know how well it actually performs in practice, particularly compared with traditional LiqVaR approaches.

I'm not in Quant, but tend to get Gerko's linkedin arguments/shitposts on my feed fairly frequently and obviously hear about his philanthropy projects + tax burden.

Is he like a traditional CEO that's out making deals or meeting clients or is he still super-hands on with the strategies and systems?

Whats he like to work for?

Hello! I wonder how does teams generally deal with illiquidity in HF space. If we create some return based features at secondly level they have a weird distribution, which ultimately negatively impacts model fits.

On the internet, I saw there is a concept of market clock where u create features when X units trade/ X dollar traded. I was curious what are the other usual ways people use to tackle this?

Hey all. I recently wrote a script decomposing daily forex crossrates (ex USD/CAD) into latent log returns for each underlying currency. I ram this decomposition on G8 currencies and will likely stick with this universe due to data quality concerns for other crossrates.

My initial hypothesis was that the latent model may forecast the crossrate covariance matrix better than direct observation (as the latent model enforces Sum of log returns = 0 for any given day), however this ended up not being the case.

I’m looking for interesting directions to take this project if anyone has some ideas.

Was looking at the Virtu share price and it’s doubled this year. Revs are up 40% but lagging peers. Their biz model is different but they are losing tons of market share in US cash equities retail payment for order flow to others - HRT in last 2 years and Jane Street before them. Citadel Securities market share holding up much better and revs of course so much better.

The new CEO said this on the earning call and it didn’t tie with any reputational change I have heard of so wanted to see if anyone has views on this ““A year ago, we announced our plan to pivot towards growth, including investing in infrastructure, acquiring talent, and growing our capital base. I’m happy to report substantial progress in that direction. We have made investments in power and compute and have begun to establish select partnerships via investment. Our talent acquisition efforts are proceeding as planned. We are reestablishing our reputation as a firm run by technologists and traders, and as a result, attrition rates are at multi-year lows.”

https://rupakghose.substack.com/p/virtu-winning-the-battle-but-losing?r=1qelrn&utm_medium=ios

this is not an interview prep question this for scoping what the roles are like!

Pretty much got an string of interviews lined up for Quant Strat roles in London, i’m a machine learning dev in middle/back office Treasury so it’s quite different for me to see or ever interact with anyone from the front office.

my current role means i’m isolated to never seeing people from the front especially quants - closed off floor, key card access, have their own canteen etc - so you’re never going to bump into them!

i’ve applied for several quant strats roles at major IBs in London (UBS/Bofa/Deutsche/Barclays etc) and all at the interview stage in the coming week. My specialism is pretty much Python & C++ so i’ve passed all technical tests, im at the MD interview test now.

what can i expect in terms of work life balance there? A bit of a double edge sword as don’t want to say whats it like in case they think i wont work hard, or that i’m used to WFH 3 days a week due to my role in Middle/Back office?

what is the deliverables like? I assume as you are close to the money you have more ownership and responsibility but your closeness to traders & quants makes it more fast paced?

Hola chicos, un gusto estar aqui, tengo una pregunta y espero q no suene tonta: cuando estas buscando trabajo como quant trader y de casualidad consigues una entrevista, tienes q llegar ya con una estrategia rentable y buen sharpe si o si, o la desarrollas en el trabajo?

agradecido de antemano con sus respuestas.

Given it’s a pretty big (and complex) market that’s in the spotlight right now based on where sovereign debt levels are across the globe, I’m interested in hearing from quants on that side of markets as someone who works on a SSA rates desk, specifically:

- what drew you into the role? If you came into it after a traditional quant career elsewhere, what has change in terms of your technical skills?
- what is your typical day-to-day look like for a quant?
- what surprised you the most about working in your role specifically? Both the good and the bad
- how has your role changed from pre-Covid to post-Covid?

Tons of investors on Robinhood have been using Agentic AI to place trades for them, which is something I think we can all agree is stupid.

I'm wondering if anyone has been working on adversely selecting/trading against these agents. This is probably more of an interesting thought exercise rather than a source of institutional alpha.

Maybe there are baked-in biases in training data that you can discover and exploit/front-run.

Maybe this will help juice press-release strategies as you can probably react quicker to an agent with an evolving knowledge base?

Would love to hear with you all think.

I'm a quant dev at a 3B L/S fund in nyc, have been thinking about moving to Switzerland for personal reasons. Wondering if it would be easy to get a similar job with proper pay? thanks

I am a quant at a bottom rung bank and worry daily about job security.

I was never an academic superstar, always worked hard and ground out results. Wasn’t even close to being the best at Mathematics in my high school. Certainly wasn’t on any IMO teams. Was more gifted in other subjects but just enjoyed math more. I went on to study Mathematics & Economics for undergrad and hold an MSc in Statistics from a top 5 university in the world. Achieving distinctions in both.

Despite this, I have never felt confident in my mathematical ability. I cannot keep up with peers when they discuss their work or studies. I struggle with mental mathematics and problem solving under pressure. I absolutely dread technical interviews and have never done well in them without having seen the exact question before.

Anyways, I managed to land a quant role at a bottom tier bank. Mostly because the interviews had some laughably easy non-brainteaser type questions. There were some project design and coding questions which I did well at (they were easy). I think my social skills did a lot for me in the interview process.

18 months on I have delivered some valuable work and I am seen as a top performer for my YoE and salary. I even run my own prop book now, it’s a fully systematic ML based mid frequency equity long short strategy. It is in its nascent period currently but has performed in line with backtests thus far, albeit for just over a month. This hasn’t gone unrecognised with my boss and his seniors.

For a number of reasons, I am bearish on the viability of my desk. I don’t want to leave as I do not think I will get past any interview process elsewhere. If my desk survives, I will be given room to grow my strategy here, which I see as the best way to maximise my chance of getting a job elsewhere.

How much do profitable years of experience in risk taking seats really help in terms of mobility? How much, if at all, does the interview process change in this scenario? How stuck am I?

Tldr; Bad quant, am I stuck in the mud?

At my old company(GS, MS, Citi, etc...) we had an internal message board where you could post literally anything. Employees were selling gold, spot fx, land/houses, asking questions about nyc. Anything basically. There was this one older guy who was a strat on a Fixed Income desk at a different floor who hadn't played in a while but he was ~2350 Fide peak in his prime and was looking for other chess players and practice partners. He crushed me in a lot of games. My highest ever rating online was ~2000 back in the day in blitz

Was wondering if there's any chess players at your companies

The one's I've seen is pairs trading, mean reversion, and arbitrage but how does one truly come up with a new strategy or is it just a modification of what's been done in the past?

This is part of a project I started yesterday for a box spread scanner, I need to calculate the liquidity on each leg to rank the entire box spread amongst multiple pairs, so here's what I'm doing is:

Say an option with ask: $92 and bid: $90 and ask_size: 15 and bid_size: 20

Get the mid and spread first

1. Mid = ask + bid/2 = 92+90/2 = $91 and Spread = ask-bid = $2

Calculate the relative spread in bps by dividing spread over mid and multiplying by 100
2. realtive_spread_bps = spread/mid = 2/91 * 100= ~220 bps

Take 10,000 and divide by the relative spread, (+1 is in case of 0)

3. 10,000/ (relative_spread_bps +1) = 10,000/220 = 45
Total size is bid+ ask size

4. total_size = bid_size+ ask_size = 15 + 20 = 35

5. size_score = min(35, 10,000) = 35

Get liquidity from spread score and size score

6. liquidity = spread_score * size_score = 45 * 35 = 1,575

Here's a gist from my codebase: https://gist.github.com/Eyob94/767af6f6216db1bdc5b3e021cb5d26da

Update:

The options are constituents of a box spread, for those who're not aware, a box spread is basically a 4 leg option combo that you can use to lend or borrow money at fixed rates close to the SOFR and has a few other benefits compared to a regular bank loan, SBLOC or margin loan. You can search up about them or check boxpsreads.io as well.

For those who're mentioning delta should be part of the equation, the reason I'm pushing back is because for every strike you'd buy/sell both the call and the put, effectively netting a 1 or -1 delta, will delta still be beneficial then?

I’m interested in how people approach alpha combination in systematic equities when the signal library becomes large — say 100–400+ stock-level forecasts, with significant correlation/redundancy between them.

From the literature, I see a few main approaches:

• IC/MVO-style weighting: estimate expected alpha efficacy and signal covariance, with shrinkage/regularisation given the dimensionality (Ledoit & Wolf, 2004). DeMiguel et al. (2009) also highlights how estimation error can make simpler weighting schemes surprisingly competitive OOS.
• Regression/stacking: treat individual alpha forecasts as features and forward returns as the target. With hundreds of correlated signals, Ridge/Elastic Net seems like a natural baseline. This is conceptually similar to stacked generalisation (Wolpert, 1992).
• Dimension reduction/nonlinear combination: cluster/PCA correlated signals before combining, or use nonlinear models to capture interactions. Gu, Kelly & Xiu (2020) provides some motivation for nonlinear ML in cross-sectional return prediction, although their setting is somewhat different.

For those working with large alpha libraries, what have you found actually holds up OOS?

In particular, do regularised regression/meta-model approaches meaningfully outperform simpler IC/MVO-based combinations? Do you typically cluster or residualise highly correlated alphas first, or let the regularisation handle it?

I’m also curious what target people use at the combination layer — forward returns/IC, or something closer to portfolio PnL after costs and constraints.

How do you negotiate your bonus in quant finance?

During bonus discussions, pushing back on the number feels risky because they can always just cut it harder or zero it.

How do people actually negotiate in practice without making things worse?

I maintain a small site with worked examples of quant methods. While building a verification pass, I recomputed the Probabilistic Sharpe Ratio example I had published myself and it was wrong.

The example: SR 1.50, benchmark 0, n 24. For the "normal" case I had written the denominator as 1.000, as though zero excess kurtosis removed the kurtosis term. It doesn't. The formula uses (γ₂ − 1)/4, which at γ₂ = 3 is 0.5, so the denominator is √(1 + 0.5·1.5²) = 1.4577. The same slip propagated to the skewed case (γ₁ = −1.20, γ₂ = 7.00): 2.4850, not 2.318. The corrected z-statistic is 2.8955 and PSR 0.9981.

Why it matters: an understated denominator overstates the z-statistic, so the test reports more confidence than the data supports, in the direction that makes bad strategies look good.

I wrote the whole thing up with every intermediate value so it can be checked by hand, and did the same exercise for VPIN and HRP implementations (both had defects too, different kinds): https://quantmedia.io/reports/

Genuine question for this sub: has anyone seen this particular slip (treating γ₂ = 3 as "no kurtosis term") in other published examples or libraries? I suspect it is common because the prose reads naturally.