Bit of a shock since starting and also quite annoying. I speak about my rent and get shamed in front of people for spending 15%! of my salary on rent. People also get sensitive when it comes to things flights for a weekend trip, or getting dinner outside of work events.

Live a little, it’s so annoying. Maybe my circle is small and biased but curious if this is industry wide?

It’s one thing to be frugal but another to speak down on others people’s spending - and yet they don’t stop asking. “What are you paying in rent”, “oh I don’t want to spend $200 on flights”, …

Edit: Preface: shamed for it being too high. This has less to do with asking why I don’t see penthouses and Rolex’s and more to do with why it’s a cost minimising function and not much life utility consideration

Let us know your worst/funniest interview experience

Was wondering if anyone has read this latest book by Stefan Jansen - "Machine Learning for Trading: A disciplined workflow from research to live execution, with nine case studies and AI agents". It seemed to have great reviews online, so I started to work through it but I find it impossible to read - the entire thing feels like AI slop. The way the sentences are structured are so obviously AI generated - it's difficult to explain but easy to spot when you read it. The accompanying notebooks are also very obviously AI generated. Pretty disappointing for a book in this price range...

Can anyone recommend similar books that aren't AI generated? Or if anyone has read this entire book, is it worth sticking with?

Some friends' funds are making a killing while others are getting killed. It is obvious that we are looking at a market condition drastically different from first half of the year. what are your thoughts on this

Hoping to hear experiences from people working under the broader XVA desks, is it still as big considering a lot of transactions are centrally cleared? Are the charges tricky to calculate? Is the work stimulating and comp competitive?

I am a NG (started in Jan.) at a pod shop doing factor-neutral MFT stat arb in equities, ie. the worst performing asset this year by a WIDE margin. Basically flat this year and my PM really likes me and trusts me (I am like a quarter of his US alpha on a >1.5bn GMV book), but obviously vibes do not pay the bills.

Not really sure what advice I'm asking for, but am curious how other juniors deal with subpar performance early in their career. I can't say that it's my fault (literally no alpha these days) and we are faring much better than many other pods in the industry. But I do have the feeling of not doing enough, kind of like this will stunt my growth because I don't have strong PnL to my name yet (plus my bonus this year will be trash).

Thoughts?

Let go after 1.5 years. First job out of uni, QR at a hedge fund in London, still have a few months of garden leave.

This was partially for performance; I had some negative feedback, worked hard to get back on track and from follow-ups it sounded like I had addressed it.
My feedback when I was let go was essentially "improved but not quite enough, and not the best fit communication-wise" which is fair enough.

What is the best way to handle this in calls with recruiters/interviews? Half the posts look like they're saying to be completely honest from the start, which seems like reducing my optionality for no reason.

My quant friends at other firms have pretty unanimously said "just say as little as possible", but none of us are anywhere near being hiring managers so I was hoping to get some more experienced input.

Honestly pretty happy to be moving on from that job, but don't want to shoot myself in the foot for no reason. Old/throw away account for obvious reasons.

“Seem” is important, it’s a sample of me talking with three commodities traders at major banks.

But on general they don’t seem to manage more flow or bigger books compared to traders in utilities / shippers but tend to work longer hours, from 08:00 to 19:00 .

It’s quite a lot, the wlb for FO roles seems better in gas / power shops. And it’s not like politics doesn’t play any role at BP or Shell.

I’m a QR with ~5 YoE with an asset manager, working on low frequency quant equity strategies (think systematic equity factors, quarterly rebalance). I want to get into mid frequency equity strategies with holding periods of days to weeks and would appreciate some advice from people who are already in this space.

I’ve been experimenting with some basic reversal strategies for interest. One thing I wonder is when mid freq QR test new signals whether they strip out equity factors like momentum from the signal? If you had a daily or weekly signal, this would mean having to orthogonalise the signal daily would be quite intensive. Or is this done on some industry optimiser like Barra or Axioma. Any thoughts here?

I am a C++ developer and just started my 1 year non-compete.

Curious what others here have done during this time. I am thinking of exploring ML inference optimization or some other technically challenging work, preferably involving C++.

Would love to hear what you guys worked on or explored during your non-competes (I don't think I can take break for more than a couple of months).

Optiver borrowing a lot of money, interesting that they decide to go with external debt financing vs reusing some of their revenue/profit? I'm not sure how prop trading firm typically raise money

Curious to hear how different quant shops, prop trading firms, and funds approach return targets and risk expectations right now.
Given that many HFT firms and smaller-scale systematic funds regularly push triple-digit annual returns on capital, combined with the fact that passive benchmarks like SPY have also delivered strong performance, the opportunity cost and return landscape feel quite competitive.
For those working in the industry:
What is your firm's internal target return (or Sharpe/Sortino ratio target)?
For those managing external capital or dealing with LPs, what are their realistic return requirements and drawdown tolerances given today's market alternatives?
Would love to hear how different strategies (HFT vs. statistical arbitrage vs. directional quant) approach this.

I’m trying to build a picture of the systematic commodities landscape, coming from a different asset class. I know the big multi-strats and prop shops (Citadel is well known for power, for example), but I’d like to understand the structure of the space.

- Which firms or desks are the leaders, and in which markets (power/gas, crude and products, metals, ags, etc.)?

- Roughly how many teams are running highly systematic strategies focused on commodities?

- What do they typically trade (outrights, spreads/curve, cross-commodity, options) and how do the approaches differ between firm types?

- Are there smaller or lesser-known shops that punch above their weight?

Even a partial answer on one sector or one type of firm would be really useful. Thanks!

I have over 10y experience in quant research/trading, and have been considering switching to AI (ideally something that combines AI and finance).
To people who made a similar move, how hard was it to find the right opportunity (market seems a bit slow atm)? what about the learning curve? are you leveraging your quant skills in your new job?

New grad pay this year for 2027 is pretty inflated. So I just did a one-shot pull from LinkedIn/company job postings for all the 2027 ng comp as posted. Might have mistakes so feel free to correct.

QT / QR base:

Five Rings: 300 / 300
HRT: 300 / 300
Jane Street: 300 / 300
Jump: 300 / 300
SIG: 300 / 300
Two Sigma: N/A / 300
DE Shaw: N/A / 275-300
DRW: N/A / 250-300
Citadel: 235-300 / 235-300
CitSec: 235-300 / 235-300
IMC: 250 / 250
Tower: 150-250 / 150-250
CTC: 200 / 200
Old Mission: 200 / N/A
Optiver: 200 / 200
Flow Traders: 175 / 175
Squarepoint: N/A / 150 minimum
Akuna: 145 minimum / N/A
Belvedere: 140 / N/A

Already heard people saying HRT/CitSec/Jump etc are giving at or near 1m first-year packages. Also heard some of that is a 400k sign-on you have to pay back if you quit within 4 years lol. But still pretty impressive even with that catch.

Guessing competition with AI labs has something to do with it. Also seems like a lot of quant shops have had a good couple of years, probably helped by all the Trump vol. Not every firm here raised base this year obviously, but seeing 300k at this many places is pretty interesting.

I personally know quite a few people at these places with a couple years of experience whose base is now lower than the incoming new grads. Curious what happens at year end. Do they match the new grad base? Just make it up in bonus? Or leave it as is?

Anyone heard how their firm is handling this?

Hi. I'm a QR with a pretty small shop. We mostly do ETF spread capture / liquidity provision strategies. I used to run liquidity provision / MM strategies in cash equities a looong time ago (a few years either side of 2012) and considering getting back into it. I'm curious how the game has changed since then, we were pretty much just trading the local book imbalances back then. For equities, what would you say is the relative importance these days between book-local signals (OFI), cross-venue, cross-instrument signals (covariance matrices/PCA) and signals taken from Futures and other derivatives?

Hi I’m an Ex QT , current QR on serving notice before a role switch.
Now I hate leetcode , I hate actually preparing for coding interviews , luckily I’ve never had to code for them.
But I don’t seem to understand how the fuck can I run a profitable market making strategy on my own but I can’t solve a leetcode easy hahah.

If one day someone asks me this I’m going to fumble so hard.
But I am quite good on the market side of things , thesis development , trade idea generation , understanding the regime , creating factors etc etc..

If not for Ai I wouldn’t be milking with this MM stuff on the side.
I constantly question my intelligence on this.
I still think I am quite dumb when it comes to coding.

Is this normal to feel?

Hey! Wanted to understand how I can accelerate my coding + problemspolving/planning set up more with AI. just been using some skills and claude code. But what is actually working for you?

Hey! I’ve heard a decent bit about Man Group as a whole, but not as much about Numeric. What’s their performance compared to the rest of Man Group/other firms like AQR, 2S? How do they treat their QR’s and what’s the culture? I understand it be a collaborative fund as opposed to more siloed shops, but curious how comp and career progressions is like as well.

Thanks so much!

What is the community's insight on Engineers Gate as a firm? Is it just another run of the mill pod shop? What are they good at historically? And more importantly, with the current drawdown they're on, is it in a very unstable environment/are teams getting let go?

Any insight would be greatly appreciated!

Voloridge seems like one of those names people in the industry have heard of but never paid much attention to. The more I look into them the more fascinated I am.

First, they look nothing like a typical quant fund. The whole place has the vibe of an ordinary American business. The people come across as regular guys you might run into at a hardware store or local dealership, like middle aged white dudes with a lot of muscles. Their website looks very unpolished with 90s color combination and keeps saying that the office is five minutes from the beach. Could easily be a website of a local restaurant or used-car dealership.

The researcher backgrounds also seem very low-key compared to leading quant firms. The founder did go to MIT in the ’90s, but his subsequent career looks fairly low-key, did a master at nyu and then was swe/consultants/manager in some random businesses before starting voloridge out of nowhere. It seems like he just self-studied data science and went around doing those kaggle type competition and ran into some guy from "Quantitative Investment Management" who was willing to fund him to start voloridge... Other than him, other people just come from Florida State, UCF, and UF graduates, with basically none of the HYPSM. I can't a single guy who could've made past resume check at a tier one fund.

another weird thing is this whole Voloridge Health business in parallel to the fund, which seems quite goofy to me.

Yet they reportedly manage close to $40 billion with roughly 150 employees. That’s around Balyasny’s AUM, but Balyasny has something like 3,000 people. 2S has double their AUM but 10x employees. also haven’t found much negative commentary about their performance. Almost everything I come across describes their returns as consistently strong.

I also couldn’t find much info on comp. The DOL H1B filings I looked at seemed to put researcher base salaries around $150K. Seems quite low given AUM per employee. I suspect that because they don't need to compete for the same pool of super-elite talents, they don't need to match those crazy packages.

This company makes no sense to me. Does anyone know more about them?

Hi Everyone,

I'm looking for historical market data, ideally the whole list below.

1. US stocks: every trade on the consolidated tape, including all exchanges, FINRA off-exchange trades and delisted tickers, 2010 to now. Each trade: microsecond time, price, size, exchange and sale condition codes (sweep, odd lot, opening print).
2. NBBO quotes for US stocks (bid, ask, sizes, exchange), 2015 to now, or 1 second snapshots.
3. US options: all OPRA trades and quotes for about the 500 largest underlyings, plus SPX, SPY and QQQ, from 2013. Each trade with the bid and ask at that moment and condition codes.
4. Options end of day, one row per contract per day: open interest, implied volatility and greeks, from 2004.
5. Options opening and closing volume by customer type (bought to open, sold to open, and so on), per series per day, from 2010.
6. CME futures: ES, NQ, RTY and VIX, every trade and quote or 1 minute data, from 2010.
7. Volatility indexes: VIX, VIX9D, VVIX and SKEW, daily and 1 minute data, from 2004.
8. Event history: earnings dates marked before or after the open, with the surprise; CPI, jobs and Fed releases with release time, actual and consensus values; S&P 500 and Nasdaq 100 additions with announcement and effective dates. From 2005.
9. Short data: short interest, daily short sale volume and weekly off-exchange volume per stock, from 2010.
10. Daily bars for all US stocks including delisted ones, adjusted for splits and dividends, from 1993.
11. News headlines with timestamps and tickers, from 2010.

Format: Parquet or CSV, microsecond timestamps, sale condition codes. It could come to roughly 40 to 60 TB.

Please let me know if you know any sources where this complete or partial data can be acquired, or point me in a right direction or connect with someone who can help to acquire this data.

Thank you!

The average pod shop excluding Citadel which gained from Sit Awareness was up only 4% in first 9 months. Baly flat, Verition +3%, Walleye flat, ExodusPoint +4%, Schonfeld +6%.

And pass throughs keep going up. Lot of hiring of even teams that are well below high watermarks starting at zero at new firm.

Many big pod shops are 10-15% gross returns and almost zero net.

And this week will add to this pain. Crowded unwind.

When will allocators start to push back? https://rupakghose.substack.com/p/peak-pod-shop-part-2?utm_source=app-post-stats-page&r=1qelrn&utm_medium=ios

I've been developing an evolutionary optimizer specifically for ETF rotation strategies in WealthLab. It starts by evaluating oscillator-based ranking methods, then evolves combinations of indicators, weights, and conditions to find promising strategies. I recently added the ability to normalize non-oscillator indicators, which expands the range of indicators the optimizer can experiment with.

The obvious concern is overfitting. Searching through thousands of combinations makes it easier to find attractive historical results, but that doesn't necessarily mean they'll hold up. I've been using rolling-window backtests to compare the evolved strategies across different historical periods.

I'm curious how others approach this. Do you use evolutionary algorithms or genetic programming for strategy discovery? What validation methods have you found most useful for avoiding overfitting?