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 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.

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?

Paper is Epstein/Wang/Choi/Pelger, Neural Attention Factor Models for Statistical Arbitrage. They report net Sharpe 2.30 at K=30 on 24 years of US equities. No code released.

I've spent a few weeks on this and I keep getting a clean, reproducible negative. Posting because I'd rather find out I made a dumb mistake than conclude a published result doesn't hold.

Setup

• Universe: top 500 by market cap, Russell 1000 sourced, point-in-time membership
• 2016-06 to 2026-08, 2,542 trading days
• Survivorship-free: 835 names ever active, 336 departures retained for the periods they traded
• Train 2016–2023, evaluate 2024–2026, single split
• 38 characteristics (paper uses 39, I dropped one that was empty), all rank-normalized
• LongConv signal head, all training days, 100 epochs
• 5bps + 1bp short costs
• 3 seeds per config

Fundamentals built from SEC EDGAR rather than a vendor — filing-date lagged, restatements dropped, TTM values only visible once the last of their four quarters was filed. Measured lag: 10-Q median 38 days, 10-K median 58 days. Zero rows visible before period end.

Results

K=30 is both the least bad and the only one that reproduces tightly across seeds. Training converged smoothly (net_SR −9.9 → +0.10 over 100 epochs, monotonic, no oscillation) and exp_var rose properly with K (0.098 at K=1 → 0.282 at K=30), so the factor step is doing what it should. The model learns something stable in-sample that inverts out of sample.

Deterministic PCA residual mean-reversion on the same universe, as a sanity check: negative at every K from 1 to 50, every calendar year. At K=30 the decomposition is gross −0.59%, costs 5.86%, net −6.45% — annual turnover 9,190% on a 30-day signal.

Weekly 1/N on the same universe over the same window: +1.363.

Things I found and fixed along the way

• Look-ahead in most features in my first panel build — features at t used data from t, including the target itself as a feature. Rebuilt.
• Sign symmetry: output_proj had a bias term, so LongConv output was uniformly signed on init. The portfolio came out all-long or all-short depending on the seed, giving a ±1.35 coin flip. Fixed with bias=False and zero-meaning the output. Paper doesn't mention needing this, which makes me think their implementation differs somewhere.
• Factor neutralisation formula was wrong — w − ωᵀ(βᵀw) leaves residual exposure; correct projection is w − β(βᵀβ)⁻¹βᵀw.

Where I know I deviate

1. 8 years of training vs their 24. This is the one I suspect most. 38 features on 8 years is a lot of parameters per observation.
2. Single train/eval split, no rolling retrain.
3. The sign-symmetry fixes above.
4. My period is 2016–2026. Short-horizon reversal ran negative in my data (1-month reversal IC −0.027, t = −6) and book-to-market was negative in both sub-windows. Their span includes 2000-02 and 2008.

The question

Is 8 years just not enough for this architecture, or is there something structural I'm missing? Specifically:

• Has anyone reproduced this (or the Guijarro-Ordonez/Pelger/Zanotti predecessor) on post-2015 data? Everything I can find is either pre-2016 or reports numbers high enough to smell like overfitting.
• Does the sign-symmetry thing ring a bell? I can't tell if I'm patching around a bug of mine or around something the paper handles implicitly.
• Is a single train/eval split the problem? Would rolling retrain plausibly move a −0.70 to positive, or is that wishful?

Happy to share the panel construction details if useful. Mostly I want to know whether this is a real regime finding or whether I've spent three weeks carefully measuring my own mistake.

Just what the title said. I'm trying to do a project relating ESG scores to sharpes and I can't seem to find free data for historical ESG scores. Yfinance seems to not be able to return ESG scores for most companies and even then it's only current scores. Would be grateful if someone could provide a free dataset for these historical ESG scores

Why would somebody give away their alphas to them? And why would a successful quant become a consultant for them since their maximum pay for top performing quants is only 8000$/quarter? Is this a scam for talented people from poorer countries that don't have any other options?

I'm sorry if this is not where I should be asking.

As a Quant, how do you live? Do you have that typical 'billionaire's row', black suit, classy finance lifestyle, or are you closer to the CS side (same gray shirt with skinny jeans)

Thanks in advance.

update: I got flamed 😭

Just wondering how many buy-side quants are LGBTQIA+ and (relatively) out or open about their gender and sexuality to their colleagues.

Of course personal things of this sort are typically on a need-to-know basis, so perhaps I define being out as not hesitating to mention something that would out you if it came up in a conversation (say talking about your partner). Someone who is not out on the other hand would deliberately avoid saying anything that would hint at their gender, pronouns or sexuality. And then some people are out in this way only to select colleagues.

I am also curious how many straight quants have and know of their out queer colleagues to get information on both sides of the dynamic.

Also wondering how being queer interfaces with your professional life. I know that the modal answer is probably “not much,” but still curious to see what people say.

Genuinely curious. It feels like a really good close to top tier company but general in this subreddit as well as r/quant is gets shit on pretty often. What's the reason behind that?

Genuinely curious to understand how employees at funds use LLM safely given that any strategy logic/data shared with LLMs is potentially alpha leaking given that there is a probability that it ends up making better future models for others to use.

Do some funds have specific agreements to run the best LLMs off of private servers without any data sharing?
Do they use older and a bit less performing open models on private servers? (And accept less performance for more safety)
What is the real cost of sharing everything (knowing data/logic could be used) ? Do they even care?

Has anyone heard about them? or any things to know?

i've been slowly building out this database over time and it's gotten pretty big 279 million rows, about 17GB.

Daily prices on 8,000 stocks across US, China, India, and international markets, minute level FX and crypto data going back over a decade, futures, COT positioning data back to the 80s, and macro data for some countries going back to the 1850s (mostly useless that far back but it's there).

a small breakdown

I already went through and cleaned up the obvious stuff fixed split adjustments that were broken, checked for survivorship bias in the stock universe, killed some duplicate/junk data, that kind of thing.

Now I'm just sitting here with all this data and honestly running out of ideas on what to actually do with it. If you had this, what would you build first? Cross-market stuff, some kind of macro overlay, actually using the COT data for something real genuinely curious what people who do this seriously would go for.

Just looking at pod shop returns and so much money has gone into space in hope of allocating to next Citadel or Millennium but the returns are just not there. Balyasny has grown AUM like crazy to $38bn but are flat for 2026. They did 15% gross returns in 2023 and 2-3% net and are running a crazy 12% pass through. But Walleye, Verition and others also flat to barely up. Just wondering if this is peak pod shop? Even Ken Griffin is moving away from space with centralized commodities biz and that big portfolio trade vs Situational Awareness. Also so many pod shops are underperforming the guys who started in quant and going into discretionary (DE Shaw, QRT) these days https://rupakghose.substack.com/p/peak-pod-shop-and-balyasny-blues?r=1qelrn&utm_medium=ios

I mean I know what the role of a Quant Trader is but what does a Quant traders day-to-day on their trading desk look like? How does probability, expected value, pattern recognition being used by Quant traders?

Hi,

I am a quant at a well known hft, and it has been around 5-6 years now.

Though the past few years have been good, great learning, good bonuses, and we were able to scale up at quite a few places succesfully. But recently there has been stagnation in terms of learning and future growth seems bleak.

I am writing this post to ask about suggestions about changing firms with around this yoe, what to aim for, how to go about it. And if anyone has experienced this, please do share your experience as well. Thankyou

hear people say some firms like headlands specifically have good execution, what exactly does that mean?

This is a very interesting firm: there are nearly no public discussion about this firm, but somehow when I talked to friends (or just in general people in the industry) they are aware of it, which is surprising.

I understand that they are very small and probably very secretive but I expect more discussion about them: like how competitive are they in the market right now? They are big in crypto and they are planning to expand in equity, but how good are they exactly? Compared to other firms what are the main differences? At what level are they operating compared to their competitors?

Genuinely curious if others out there have experience working with extremely inept PMs?

A recent hire at my firm has strategies akin to that of a teenage youtube day trader. Their signals are entirely price based and their processes lack any statistical rigour.

Even worse, I know that they have been lying about backtest results to management. They are aware of bias and cherry picking yet still quote the results to higher ups. They supposedly have a decent track record but from what I gather, its pretty opaque.

I have heard quants work with non-technical PMs relatively frequently but this feels next level.

How common is this? Are PMs everywhere like this? Should I run?

Tldr;

Bad PMs - how common? How bad?

I am a quant on a fixed income desk. I primarily work with rates products, sometimes help with credit desks as well.

I have an opportunity to transition in the same firm to a fixed income trader role where the book mostly consists of structured products on bonds, cds. And some treasury options.

I have been working with these products for a while in my capacity as a quant.

I am not able to decide if I should take this up or not ? I find myself excited by the prospect of working as a trader, but I wager it comes with it's own downsides. And I don't know how it would be for long term career. I would be more like a quant who can trade as well.

Please share any suggestions you have. Thanks.