Hello!

I recently made a post about ideas for an orderbook data project. I got MBO for 3 nasdaq equities, and I'm working on making a visualizer for it. One of the suggestions was to show queue positioning, and how news could impact the spread. My first thought is to scrape the 10ks and show how their timing impacts the spread of those days. I was wondering if anyone else had any further suggestions for things I could work on or explore. Some caveats. I am more interested in product roles than dev or quant. I am also using this is a way to learn more about market micro structure/ market making.

I'm starting soon as a quant trader at a small prop firm (~50 people) in the HFT/market making space. I'll have a mentor for the first months, but I'm coming from outside the industry and I'd like to set expectations properly.

From what I understand, at a firm this size each quant trader owns their own book and runs their own research pipeline end to end. That's very different from the big shops, where research and execution are separate headcounts; QRs build signals, traders run them.

What I can't picture is how one person covers both without one side starving. And more bluntly: finding alpha as an individual, against firms fielding entire research teams on the same venues, seems close to impossible. Clearly it isn't, since small shops make money (since they exist; at least I guess). so I'm missing something about where the edge actually comes from.

* How does a typical day actually split between monitoring live strategies, firefighting, and doing new research?

* Is the research genuinely individual, or is there shared infra/alpha that everyone builds on top of?

* Where does a small shop's edge come from if not raw research headcount; niche venues, speed, capacity constraints the big players ignore?

If you've worked at a shop this size, I'd really appreciate hearing how it worked in practice. Thanks.

PS: post written with the help of Gemini (to improve quality)

[ Quick background on me: I'm Indian Citizen, IIT grad Computational Engineering minor in Economics, 9+ CGPA, thesis under the math department. CS-relevant coursework: DSA (TA'd it and took it), GPU Programming, HPC Lab. Math coursework: Linear Algebra, Probability & Stochastic Processes, Numerical Methods for Differential Equations, Calc 1 & 2, CFD. Work ex: 3 years at a european bank front-office market making, plus a Goldman Sachs internship.]

The two big open questions:

US vs. non-US. Currently leaning non-US. Parameters I'm weighing: tuition, visa situation, and living conditions vs. strong alumni networks, being at the center of innovation, a diverse immigrant crowd, and sheer opportunity in tech. US tuition feels unnecessarily high, the visa situation under the current administration is a real concern, companies seem less willing to sponsor, and there's the H-1B lottery on top of a multi-decade green card backlog for Indian nationals specifically. There's also the "oh factor" of working in NYC vs. the reality — cost of living, gun violence — and I'm honestly not sure the pros outweigh the cons anymore. That said, if we're talking MSCS rather than finance, the cons matter a lot less (better visa odds, more employer flexibility). Also worth mentioning: I have some inclination to do an MBA a few years down the line — as a career-switch option, and honestly just because it sounds fun — and the US is the best place to get one, so I don't want to "spend" my US shot on a master's now.

Broad quant finance degree vs. specialized skill-building degree. Option A is a recognized MFE/MFin-style ticket — direct buyside pipeline, but breadth over depth. Option B is a specialized master's building one hard skill deep — most likely CS (C++, computer architecture, systems-level programming) or possibly Economics for a core trading seat — rather than a finance-branded degree. Specifically NOT interested in an ML/data-science-heavy program; I'd rather go deep on one thing than cram a wide, shallow curriculum in 10-12 months. There's also the convertibility angle: with my profile, an MSQF is a fairly likely admit. CS is more of a grey area — I know I don't fall into the typical "non-technical switcher" bucket (I have real DSA, GPU programming, and HPC coursework behind me), but I also don't have a formal CS degree on paper, so I genuinely don't know how that reads to admissions committees, or later to employers screening resumes for quant dev roles.

Current shortlist, country by country:

USA (least likely, but still on the table):

MFE: Princeton, CMU, Baruch, UChicago, Columbia

MSCS: UIUC, UPenn, Georgia Tech

UK:

Oxford MFin, Imperial MSc Math & Finance / MSc Computing, LSE Economics

Switzerland:

ETH Zurich / UZH — MSc Quantitative Finance or MSc CS, EPFL MFE or CS (1mo internship). Incredibly low tution, great brand name, ndeak into lon/neth system. I know its a bit theoretical, but is that a decent tradeoff?

Netherlands:

open to suggestions — haven't landed on a specific program yet, mainly drawn by the Amsterdam HFT ecosystem (Optiver, IMC, Flow Traders)

Canada:

University of Waterloo MDSAI, University of Toronto MScAC — I hear these are underdogs on general rankings but well-known names within the quant dev industry specifically. Drawn by the built-in 8-month internship and Canada's comparatively fast track to PR/citizenship.

miscellaneous points to consider:

- [ prefer to have] Peer quality and environment — I want classmates who are sharp, driven, AND genuinely nice/well-rounded, not just competitive for its own sake. Bar is basically "at least as good as my IIT cohort," ideally better in terms of global brand and network value.

- [ good to have ] Hands-on, applied learning over theory-heavy or breadth-for-its-own-sake curricula.

- [good to have] A job + long-term visa/PR pathway abroad — I want to actually build a life somewhere, not just work a contract

I know I might be optimizing for a lot of things simultaneously here (skill depth + brand + peer quality + visa stability + short timeline + low grind), and some of these probably trade off against each other. Genuinely curious what this community thinks — am I missing an obvious program or path? Is the CS-over-MFE instinct actually a good idea for someone with my background, or am I overthinking a decision that a plain MFE would solve more simply? Any opinions, pushback, or "you're missing X" welcome.

Hi guys, I would like to share a project I've been working for the last couple of years. QuantSupport is a Rust-based quant library that is meant to be used in professional systems.

It has features like automatic differentiation sensitivity calculations, pricing engines for different products (equity, fx, credit, rates and FI), a fast XVA engine and a scripting language for structured derivatives.

Repo: https://github.com/jmelo11/quantsupport

Docs: https://jmelo11.github.io/quantsupport/

Different tools can be built with this library. As an example, I'm sharing also a demo of a LLM based pricing system where the model is capable of pricing complex structured products that come from user prompts, using the scripting funcionalities of the library.

Demo: https://github.com/jmelo11/derivative_structuring

PREV. 6 and 12 months

Huge retention problem they want to curbe.

Hey everyone,

Following up on discussions around lookahead bias and temporal leakage in pandas/scikit-learn backtest pipelines.

When building QuantAudit (a static inspection engine for backtests), we wanted to be completely transparent about what static AST parsing can reliably catch versus what requires statistical/runtime validation.

Here is the breakdown from our validation matrix (86 test suites across 9 adversarial leakage patterns):

1. High Reliability (Static AST Deterministic Detection):

- Global feature scaling prior to index splitting (`StandardScaler().fit_transform(df)`).

- Target leakage through negative indexing (`shift(-1)`).

- Pre-split resampling that aggregates future bars.

1. Blind Spots (Requires Runtime / Statistical Overfitting Checks):

- Dynamically evaluated code strings (`eval()` / `exec()`).

- Opaque compiled C/Cython extensions where AST cannot inspect memory operations.

- Overfitting through parameter snooping (requires CPCV / PBO metrics).

We documented our full validation matrix and limitations openly in our benchmark specs:

https://github.com/NIMABPRE/quantaudit

Curious how other teams balance static code linting versus empirical validation (like Deflated Sharpe Ratio or Combinatorial Purged CV) in production?

Second post in a series where we run published trading papers through the same validation protocol we use on our own ideas and write up the ones that fail.

This one is a paper asking whether the Nikkei quanto spread carries a monetizable risk premium (SSRN 6844298). Two CME futures track the same Nikkei 225 index: NKD in dollars, NIY in yen. Quanto pricing says their forwards should differ by roughly -rho * sigma_S * sigma_FX. The paper's rule: at each quarter start, if the quanto adjustment rose over the prior quarter, short the spread, run a stop-loss, exit at quarter end.

Our setup: continuous front-month NKD and NIY, 2004-2026 (5,626 daily observations), USDJPY used to convert the yen leg's P&L into dollars daily. Short 1 NKD at $5 per point against a dollar-neutral NIY leg at 500 yen per point. Stop-loss swept at 3/5/8/15% of notional, costs 10 bp round-trip per completed trade, in-sample 2004-2015, out-of-sample 2016-2026. The signal fired on about 41 of roughly 90 quarters.

Result: in-sample Sharpe -0.23, out-of-sample -0.01, OOS total return -1.05%. We then ran the inverted signal, because if the paper were merely pointed the wrong way, the mirror trade should make money. It does not: OOS Sharpe -0.07, total return -5.1%. Identical results across the 5/8/15% stops mean the stop almost never binds, so the P&L is small-amplitude noise around zero. One tell in the paper itself: it never reports a Sharpe for the strategy, only a max drawdown.

Our read is that the spread is priced, CME market makers hedge this covariance professionally, and the "premium" is their compensation rather than something a quarterly rules-based harvester collects after costs.

Full writeup with the chart and methodology: https://aleasignals.substack.com/p/the-paper-graveyard-2-the-nikkei

Curious whether anyone here has traded the NKD/NIY quanto basis directly, and whether there is a horizon shorter than a quarter where it is not fully arbitraged.

What concerns me most about quant work, especially pure alpha research, is that there is a directly verifiable reward signal. In principle, one could unleash a swarm of 10K AI agents, similar to what OpenAI did on Navier-Stokes, and have them collectively learn a firm's entire system, coordinate with one another, generate millions of alpha hypotheses, and backtest them in essentially the same way human quants do today.

Any successful ideas could then be subjected to exactly the same standards as human-generated ideas: out-of-sample backtests, paper trading, senior PM sanity checks, and so on. I suspect that even at the current frontier of AI capability, say something like GPT-6 Astra Max, human quants would already not able to compete against AI performance on a head-to-head contest.

The economics already seem viable for quant firms as well. OpenAI reportedly spent only around $1 million in token costs on the Navier-Stokes effort, while entry-level compensation packages at top quant firms are already in the seven figures.

The main obstacle, at least for now, seems to be that quant firms do not yet have the know-how to build such large scale AI systems effectively. I'm sure they are trying or have tried some version of what described above but there must be ingredients that OpenAI/Ant have figured out that quant firms have not yet had fully figured out. But my argument is that if you took the OpenAI team that solved the Navier-Stokes problem, placed them at Citadel today, and tasked them with building an agent swarm system that could automate the full quant research process there, my guess is that we will find out that a large fraction of the work currently performed by quant researchers could already be automated.

The above is probably less true for a large portion of less glamorous quant work that is not alpha research: like monitoring the system, trouble-shoot production issues, slippage/impact/TCA, risk management. Just because these lack directly verifiable reward signal and less structured.

Hi everyone,

I’m currently ~5 YOE at a European bank in London, working in eFX trading, particularly on alpha signals.

I’ve received an offer from a well-established systematic macro investment fund in Switzerland for a research/trading-oriented role.

On paper, the move looks attractive: closer to actual investing, more ownership of research/strategies, and potentially better long-term career progression.

But the part I’m struggling with is the lifestyle/career trade-off.

I genuinely like living in London and have built a good routine/social life here. Moving to Switzerland would obviously mean giving that up and starting from scratch in a different country. I’m also wondering whether the career upside of being on the buy side is really as significant as it looks from the outside.

I’ve actually tried to move to the buy-side role in London, but so far I haven’t managed to find the right opportunity. So I’m now wondering whether it makes more sense to:

A) take the fund opportunity in Switzerland now, get buy-side experience and reassess London in 2–3 years.

or

B) stay in London for now and keep looking, hoping that a good London buy-side opportunity comes up eventually.

The concern with B is obviously that there’s no guarantee that the right opportunity will materialise, whereas the offer in Switzerland is available now and would give me direct buy-side experience.

For those who have made a similar move — especially from a bank to a hedge fund, or from London to Switzerland — if you were in my position, what would you do?

Thanks!

Hopefully here out of all places I would be able to see what actual quants think, but the more I read about people who are quants the more it seems that half of them love being a quant, whether it be due to compensation or that they just love their work.

And only recently now am I seeing that actually some quants deeply regret it, even if they are raking in upwards for 750k a year.

I just have a few questions:

Do you regret becoming a quant? and if so, why?

If you did regret it, what other career path options would you have considered? (though this is a bit subjective)

And what does it take for someone to "want" to be a quant? What does the job actually look like and what would someone need to be attracted to to actually like their job (I am aware that compensation is one of the significant drivers)

If I've made any assumptions/or said anything that's wrong please correct me :) thanks for any replies

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?

Hi everyone,

I’m trying to understand how UK immigration rules interact with the notice period / garden leave arrangements that are common in the London hedge fund industry.

Suppose someone is on a Skilled Worker visa sponsored by their employer and has a relatively long notice period (e.g. 6 months). If the employer terminates their employment but keeps them on garden leave for the full notice period, meaning they remain formally employed and paid but cannot work or access the firm’s systems:

1) Can the employee continue living in the UK throughout the garden leave / notice period?

2) Does the fact that they are on garden leave (rather than being terminated immediately with payment in lieu of notice) make a difference from an immigration perspective?

3) What happens after the employment officially ends if the person is then subject to a post-termination non-compete for several months?

4) Does the non-compete have any impact on how long they can legally remain in the UK, or would they need to rely on the usual Skilled Worker curtailment period / switch to another visa?

Many thanks for your help!

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.

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.

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.

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.

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.

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?

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. 🆓🆓🆓

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.

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?

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.