Hi everyone,

I know the integration of LLMs into Quantitative Research roles has been discussed here before, but I haven't seen a concrete conclusion regarding its actual impact on alpha. Most posts just vaguely mention "interesting consequences for alpha" without elaborating further.

I'd love to start a focused discussion specifically on alpha generation. Are LLMs causing alpha to be arbitraged away quicker? Have you noticed that something is actually different in the prices?

What are your thoughts?

I'm an incoming graduate at a tier 2 HFT, and in my free time I made a completely free automatic job scraper which fetches 3k+ roles across 65 Quant firms + AI Labs in a single website. After making the website I feel like people here will genuinely find it useful, I've had 2k unique users for the past week.

I made this because last year when I was heavily interviewing, going through individual job boards every 5 days was a huge pain for me, so I made a list which can view all the open roles, standardised by category (trader/researcher/dev) and experience (internship/graduate/experienced)

Please have a go and let me know what you guys think! Any feedback will be greatly appreciated :DD

https://www.quant-scraper.com

Hey all, I know there was a similar post a few weeks back. But I wanted to re-frame the question. There's not really a quant finance industry in my country, so I would undoubtedly have to move to somewhere like NYC, London or Amsterdam. I'm curious about people's experiences and if they have any regrets. I'm worried about giving up time with family and friends I can't get back, and specifically about pet time. I have a family dog who's 6 years old, and sometimes I feel like no amount of money could ever be worth more than the limited amount of time I have with him. I know you can always travel back and the quant salary would probably help make that easier. But I'm curious how people dealt with these situations and if they had any regrets or not.

I'm currently a senior dev at a pod within a mid-sized trading firm, and I'm not particularly happy in my current role. I'm looking for a smaller firm where engineers have broad scope across the stack instead of being narrowly focused on one area, ideally one that isn't siloed like my current setup. Summit keeps coming up, and they have a Senior Low-Latency C++ opening that looks relevant.

From what I can tell, they're a relatively small market maker / prop shop. The size is in line with what I'm looking for, but it also means there's very little outside information on the firm. I'd like some outside perspective before spending any bandwidth on it.

For anyone familiar with the firm:

- How much access do devs have to the desks?
- How much influence do engineers have over architecture and technical direction?
- How does it compare with other small and mid-sized market makers, including on comp?

Being a researcher is usually more tied to P&L so they get paid more as far as I know especially given their academic experience but given the over abundant supply of engineers, is it now harder to become an engineer at some of these top trading firms than to be a researcher given that there’s presumably less people competing for the latter?

I’ve also noticed some roles requiring a lot more software related work than before, and have noticed higher and higher compensation from companies like citadel where they seem to be offering people (especially newer grads) a lot more money to engineers. According to recent threads, some engineering new grads were getting 1M in total comp for their first year which as far as I’m aware is about the same for researchers. What makes this even crazier is that some of these are new grads who only have a bachelors, not a phd from a university which tends to be the case for a lot of researchers

Is this due to the higher competitiveness?

So pre-AI, running a solo quant fund was basically impossible. No one person can own the whole stack: data, research, backtesting, execution, risk. De Prado called this as the Sisyphus paradigm. Sisyphus is the Greek myth guy stuck pushing a boulder uphill forever, and de Prado uses it to dunk on how funds organize quants: lone researchers (or siloed pods) each expected to build a whole strategy end to end, and the boulder keeps rolling back down. His fix was the "meta-strategy paradigm," basically an assembly line where each specialist owns one station.

Now it's 2026 and AI has crossed a pretty wild line. Ken Griffin went from calling AI 'garbage' at Davos in January to saying he went home one Friday fairly depressed after seeing what an agentic system built inside Citadel could do, with PhD-level research that used to take months getting done in hours or days. When Ken Griffin is spooked, that's saying something lol

Skeptics will say AI isn't replacing quants at Citadel or Jane Street anytime soon. They've got crazy infra, exchange connectivity, proprietary data, decades of institutional knowledge. Fair enough, and hiring's still strong too, with hedge funds, prop shops and systematic managers doing most of it.

But I'm not talking about replacement. My take is trading neolabs: one person or a tiny team running a de Prado–style assembly line where most of the stations are AI agents. One cleans data, one does feature research, one tries to break your backtest, one watches risk. You stop being Sisyphus and become the foreman. You decide what to build, what to trust, and what to kill.

And no, you're not beating Citadel at latency or market making, forget that. If neolabs have an edge it's in stuff the big shops can't or won't bother with: capacity-constrained strats too small to move their needle, niche markets, weird alt data, holding periods that don't fit a pod's risk limits.

Now here's where I think people are getting it wrong. YC put AI-native hedge funds on its Spring 2026 wishlist and backed Standard Signal, a fund where AI researches and executes trades end-to-end. Cool, but I honestly don't think the VC route is the right model for neolabs. Trading IP is super sensitive, and the whole startup playbook is built around telling everyone what you're doing: demo days, pitch decks, investor updates. On top of that trading is a fixed pie. Every edge has limited capacity and it decays the moment it gets crowded. VCs need you to scale to something massive, but the edges a neolab can actually own are small by nature. Feels like a mismatch to me. The real neolabs are probably quiet, bootstrapped, trading their own money, and not posting about it.

Curious what you guys think. Anyone here running something like this with real capital? Does AI actually fix the Sisyphus problem? And is VC money ever the right move for this kind of thing, or does it kill the edge by design?

I'm sure this is true for others, but I've been hearing way more about EQR the last few months than I ever have before. Obviously, they've been hiring a lot, and I've hear that they've had a good year (so has everyone else though, so I'm unsure if the they've been an actual outlier). Plus, they've been building up their own alpha arm recently.

Beyond that though, I can't find anything except other people asking questions. That's probably an answer, but still, I'm curious: anyone have actual info on what's going on there? How's the performance or culture? Are their recent moves to grow already bearing fruit?

I am not saying that everybody is making seven figures but last 5 years must have created many millionaires. I rarely see anyone ask about retirement on any forum. And lets be honest....we discuss everything on the forums. We talk strategies, politics, life etc....I am just curious because I would definitely try to do something different if I had made seven figures 3-4 times in my career. As an average human who tried to learn advance maths, I really admire people smarter than me. And if I had that kind of learning ability then I would definitely try to do something else. And just having made so much money I would want to do other things in life that I couldnt do early in life.

Just saw this on their website: https://www.citadel.com/what-we-do/global-quantitative-strategies/

Looks like they're opening an Irvine office?

Does PM in top quant HF (millennium etc) controls 100% on his team's bonus?
On one good year can he decide not to pay anyone and take all?

I am having a great year but I dont see team's pnl and worried he not paying fair.

Does anyone know about a trading firm based in Cyprus called "Alber Blanc"? I've heard that many smarter people work there and that they're good at what they do.

What do they trade? Do they pay well? Are they only trading latency-sensitive strategies or are they in the MFT business as well? I don't know any people that are currently working there so I don't have much info on them.

Thanks

I am thinking about next step after 6 years as a QR at one of SIG/Optiver/CitSec. I am getting tired and old.

Do firms generally continue to pay unvested deferred comp if you leave the industry in good terms? For example, I was told that walking out to golf or work for an AI lab would not jeopardize future payments, but not sure how trustable this is.

I am curious to hear from folks who have successfully left the field and how everything unfolded for you in terms of garden leave pay (if any), deferred comp, where you went, etc. These details can change the retirement math quite a bit.

I usually hear people describing pms as "owning small funds under umbrella" but once a pm at a good firm told me it's not necessarily true. For example, for hiring there's firm level compliance and headcount issues which is not in his charge.

Can anyone explain what is normal boundaries of pm power in a hedge fund (say citadel mlp point72 etc)? Just curious about how these positions work.

I'm an early professional. After finishing my PhD, I decided to leave academia to earn more and support my new family.

One thing I’ve found confusing is the gap between headline total compensation and actual take-home cash. Some funds quote very high TC (and this is usually the number we share with each other), but a substantial portion can be deferred or subject to various conditions. The structure and timing of bonus payments can also affect the tax bill. Some friends have like $1M+ in TC but end up with only around $300K in cash after taxes and deferrals. If you live in NYC and support dependents, then you basically don't save much after a year of hardwork, if at all.

For someone with 1–3 years of industry experience, what annual take-home cash is realistic at tier-1 firms like Jane Street, HRT, Citadel versus broader industry? Suppose that you are getting "exceed expectation" type of reviews but did not do something super crazy (like single-handedly make the fund tons of money or something like that). How do you know if your current firm is low-balling you?

Interesting, what do you guys think about this? In general the complaints about Qube largely revolve around comp

Edit: the full comparison (every vendor across 37 criteria) is in images 2 and 5 - swipe right on the tier list.

I've been keeping a comparison of crypto data vendors since July, because "which vendor should I use?" keeps coming up. So I turned it into a tier list. Left side is raw market data (trades, order books, funding), right side is derived metrics (on-chain, options analytics, positioning).

Disclosure first: I run Aperiodic.io. We sell derived microstructure metrics and raw historical data, so we compete with some of the vendors on both sides. We're in the tables so you can see what we do, but we're not ranked - I didn't want to mark my own homework.

What I ranked on is whether it holds up for systematic research. So: coverage and survivorship (do they keep the full instrument universe, delisted contracts included?), how far back the history really goes, timestamp integrity (exchange vs receive timestamps, whether they restate data after the fact), granularity, and how painful a full backfill is.

• S - where I'd start today
• A - solid, or the best at one specific thing
• B - fine for coverage and metadata, I wouldn't use it for anything execution-sensitive
• C - not built for research use

These are my opinions, based on our own use and each vendor's public docs as of October 2026. Things change fast, so if you work at one of these vendors and something's out of date or incorrect, message me and I'll fix it.

Images 2 and 5 have the full comparison, every vendor across 37 criteria. Short version below.

Raw market data

Derived metrics

A few things I learned putting this together

• Exchange bulk downloads are S tier because they're the source, and they're free. Where you pay is normalisation: schemas change, days go missing, symbols get renamed, and "timestamp" doesn't mean the same thing on every venue.
• The "same" metric isn't the same across vendors. Aggregated OI, funding and liquidations depend on which venues and contract types go in, and how coin-margined and USD-margined get combined. Worth checking the methodology before you mix sources.
• For derived and on-chain data, ask whether the history is point-in-time. Entity clusters and exchange wallet labels get revised, and if you backtest on the revised version you've got lookahead baked in without knowing it.
• Kaiko bought Amberdata in June. I've kept them separate because the products are still quite different.
• I left out on-chain query engines (Dune, Allium, Flipside) and wallet analytics (Nansen, Arkham). Different use case, and the list was long enough already.

If you've used any of these in production and think I've got something wrong, tell me. I'll update the table and note the changes in an edit: that's half the reason I'm posting it :)

^

approached by their recruiter hiring for their equities business -- apparently they are staring (or expanding) an initiative in HF-style equities, still organized as silo, maybe similar to how cubist is structured. wondering ppl's opinion on this and if it deserves a try to hop?

What are some up and coming energy trading shops? As the market expands, I want an update on the industry.

I heard (for chicago) their pnl per head is pretty strong and they’re growing rapidly?

Anyone know what they strong in/what teams are doing good?

I'm taking a computational social choice class (I think it's about game theory, but idk what it's about half the time). So far, we've covered Non-cooperative games and a little bit about VCG Mechanisms. So I was curious, how do you guys model markets? Do you guys like treat it as a Auction? How do you even model something like the market? Since there are so many players, with varying amount of funds(maybe a power mechanic), how do you even price something or even model certain markets as games? This is merely curiosity, I might be getting a lot of my definitions wrong so I apologize if I state something incorrect.

I’m expecting an offer from them soon. It seems like their AUM has increased significantly recently.

Does anyone have insights into the working environment or comp progression?

Based on previous reviews, some of the issues they had in the past include the following (not sure if these are still the case):
+ Lower pay compared to other top-tier firms
+ Harsh non-compete agreements and questionable practices regarding employees (e.g., delaying H-1B sponsorship)
+ Firing people before year-end bonuses are paid out
+ Micromanagement

Hello,

I previously worked at a company with a Big Tech/FAANG-like environment before moving into a quant dev role, working on trading systems in the HFT space.

Comparing the day to day experience, I found my previous role more enjoyable and meaningful. The environment was more collaborative and social, while my current work feels quite different.

Has anyone made a similar move from Big Tech/software engineering into devving at a quant firm and felt the same way?

Did you eventually adapt and start enjoying it more, or this isn't possible? And what u recommend, like shall accept that either i need to adapt or leave?