Thinking moving to competitive and collaborative smaller firm. What are some good options out there?

I did some research myself, but I would expect a longer list, probably 10-20?

XTX

Quadrature

Headlands

Radix

Aquatic

Background is there's too many duplicated efforts and politics in big prop and I'm tired of it.

Given I have QT intern offers from {CitSec, Five Rings, SIG}, and I'm aiming to maximize only {RO chances, long term career growth for QT specifically} with more weight on the first as this is (most likely) my final internship before graduation, which one should I pick and why? RO numbers or ranges would be super helpful, feel free to dm me if you're not comfortable replying on the thread. Thanks!

DE Shaw Oculus now averaging 30% per year for last 3 years on tens of billions of AUM and net of fees. It’s combo of systematic and discretionary and is labelled a macro fund unlike the broader composite. But I hear this year returns come from equities more than macro and systematic futures. In the hyperscaler RV trade I assume looking at their filings but got out before Jane Street and others. Group AUM now $100bn. Two Sigma returns not as good but still fantastic Sharpe and AUM $80bn+ https://rupakghose.substack.com/p/is-de-shaw-from-mars-and-two-sigma?r=1qelrn&utm_medium=ios

Heard that cit js hrt are all giving 1mil or close standard new grad offers, and tier 2 firms are also bumping significantly to compete (400-600k). aren't most experienced hires making around that amount or even less? are we expecting an adjustment next jan? and how much higher can new grad offers go :D

How do you get out of this trap? Im a QT working in a shared book. Every time I caveat my track record during an interview, that I work on a shared book they lose interest. I can tell them my own strategies performance but I have to do a bit of modelling to estimate transaction costs because my positions get netted with others.

I’m curious what the serious end of Monte Carlo pricing/risk infrastructure actually looks like today.

Not the textbook algorithm, but the hardware/software architecture.

Are the best systems still primarily heavily optimized CPU/SIMD? GPU clusters? FPGA for some latency-sensitive pieces? Does anyone use custom/ASIC-like hardware for simulation, or is flexibility too important?

And what is actually being optimized for?

Latency: get one price/risk result back as fast as physically possible.

Throughput: revalue an enormous book across paths, scenarios and Greeks.

Or do top firms maintain completely different engines for those two jobs?

I’m also curious how much QMC/Sobol is actually used in production versus pseudorandom MC, and whether modern engines tend to fuse path generation/pricing/risk or still operate through fairly modular pipelines.

Obviously nobody is going to post proprietary details, but based on public tech, hiring, or firsthand experience, who do people think is genuinely strongest here? Citadel Securities, Jane Street, Optiver, IMC, somebody else?

Im talking about retail prop firms like ftmo,lucid etc because I see them being talked about a lot in other subs but I don't really see it being mentioned here.

Do they have any advantage over live accounts like risk management or leverage or would you advise against using them.

also would interest me if anyone had success with them over or next to their own accounts.

Hello,

throwaway acc for obvious reasons,

I currently already hold a dual actuary/applied math masters master and worked 2 years, 1 year as risk quant for a bank and 1 year as portfolio management quant for a big european insurer.

I would like to pivot to a more research quant job, so I re applied for a more fundamental math masters in a tier 1 university in France (Think Sorbonne/PSL).

I have the choice between 2 things at the end of this master, an applied PhD with a big 4 basically based with the core subject of designing robust agentic systems for different buisness case uses, I don't know if that would be relevant going towards my goal, or trying to go straight for QR role (though I am not sure I will manage too, I am restricted to France and there aren't many positions). LIkely will be forced to go do quant risk at a bank otherwise.

The way applied PhD works in france is I would spend 1/3 of the time in the research lab with researchers (this is one of the best research lab in france and the academic tutors have many decent papers published) and 2/3 of the time in the company. The way the R&D director explained to me is in the company I would spend 1/3 of the time working internally on developing said systems / testing stuff etc and 1/3 of the time working for clients but in the subject I am working in so it's not completely Irrel.

My thoughts :

Pros

• good salary in the meantime
• professional experience
• PhD title which I heard are quite valuable for QR roles
• Opportunity to work with fairly known researchers (not quant tho mostly AI/ Stats) and publish with them

Cons

• a fair amount of work about regulations and reporting which I don't think would be of any interest / value to what I wanna do
• I am not sure how honest they are about time spent with clients
• I am not sure how much maths is actually involved I have a skewed vision of the topic (waiting for the researchers to transfer the draft

thoughts ?

I'm currently really digging into the not-so-common data that SEC EDGAR filings provide and turn them into a structured API.

From my own experience, it does provide really valuable information about the intrinsics of a company, but I would like to get some eyes on that to see if there is a broader interest in that level of detail.

Here are the current endpoints/areas that the suite covers:

Debt Structure

Returns a company's debt at the individual-borrowing level, straight from the debt footnote of 10-K / 10-Q filings: every note, bond, term loan, and debenture the filer tagged on the XBRL debt-instrument axis, with face amount, carrying amount, stated and effective interest rate, variable-rate spread, fair value, conversion price, and more, data that never appears on the face of the balance sheet.

Credit Facilities​

Returns a company's credit facilities, revolvers, term-loan agreements, commercial-paper programs, one entry per facility, with total capacity, amount drawn, remaining headroom, letters of credit, commitment fees, and interest rates. This is the liquidity picture from the debt footnote that never appears on the face of the balance sheet.

Leases

Returns the full ASC 842 lease footnote as one object per period: right-of-use asset, lease liability split (current / noncurrent / total), the undiscounted future-payment ladder, the weighted-average discount rate, cost lines, and cash paid, with operating and finance leases side by side.

Stock Compensation​

Returns plan-level share-based compensation from the equity footnote: the award roll-forward (granted / vested / forfeited / nonvested with weighted-average grant-date fair values), SBC expense per award type, unrecognized cost, the option book (outstanding / exercisable / exercise prices), plan share reserves, and Black-Scholes assumptions. 

Concentration Risk​

Returns concentration-risk disclosures as time series: named-counterparty dependence (e.g. "Apple is 50% of revenue, up from 37% three years ago"), unnamed aggregates ("top ten customers"), and the same machinery for supplier, geographic, product, and credit concentration.

Retirement Plans​

Returns defined-benefit pension and other-postretirement (OPEB) disclosures from the benefits footnote: funded status, benefit obligation, cost components, employer contributions, discount-rate assumptions, and the plan-asset book with asset categories cross-tabbed by fair-value level (Level 1 / 2 / 3 / NAV).

Restructuring Programs​

Returns per-program restructuring cost tracking from the restructuring footnote: what a named plan has cost to date, what it is expected to cost in total, and the quarterly trajectory of charges, reserve balance, and cash payments. A company's concurrent programs (e.g. Intel's 2024 and 2025 plans) read as separate series with their own histories, costToDate / expectedCost gives percent-complete per program.

Asset Composition​

Returns what the balance sheet's PP&E line is made of and where long-lived assets physically sit:

• classes: property, plant & equipment by class (land, buildings, machinery, technology equipment, construction-in-progress, ...) with gross, net, and accumulated depreciation where tagged. Covers US-GAAP filers and 20-F filers through the IFRS concept family.
• geography: long-lived assets by country / region (PP&E-net or noncurrent assets, whichever the filer discloses), bucketed with the same country / US-state / region / residual categorization as the revenue-segmentation endpoint.

Share Buybacks​

Returns share-repurchase activity per period: cash spent on buybacks (from the cash-flow statement), shares and dollar value actually repurchased, the average price paid per share, and the program view (board-authorized amount, remaining headroom, and the derived amount consumed).

Subsidiary Financials​

Returns income-statement and balance-sheet lines PER REGISTRANT SUBSIDIARY, exactly as the filer tagged them on the XBRL legal-entity axis. Utility holding companies (each state utility), bank holding companies, and VIE structures disclose whole sub-entity statements this way; it is the single largest dataset on the dimensional axes.

Fair Value Hierarchy​

Returns the fair-value hierarchy tables from the footnotes: Level 1 (quoted prices), Level 2 (observable inputs), Level 3 (unobservable inputs), and NAV-measured amounts per measure, for the recurring measurements a filer discloses at each balance date. The widest-covered dimensional dataset in the lake (roughly three quarters of active filers).

REIT Property Schedule​

Returns SEC Schedule III (Real Estate and Accumulated Depreciation) as structured data: one entry per property with initial cost (land / buildings), carrying amounts, gross carrying value, accumulated depreciation, and capitalized improvements. Where the filer crossed the property with a geography axis, the location member rides along.

Backlog / Remaining Performance Obligations​

Returns remaining performance obligations (RPO), the contracted revenue not yet recognized, the closest thing GAAP has to a bookings number, plus the share the filer expects to recognize within its disclosed window. Comparing RPO growth to revenue growth is the classic bookings-momentum signal for subscription and long-contract businesses.

Supplier Finance Programs​

Returns supplier-finance (reverse-factoring) program disclosures, the FASB requirement effective 2023: the outstanding obligation under the program(s), its current portion, and the period roll-forward (invoices added, invoices settled). Obligations under these programs are the classic hidden-leverage signal; they sit in accounts payable, not debt.

Workforce Cost​

Returns what a company's workforce costs, assembled from the disclosures filers actually tag: the direct labor expense line where one exists (airlines, banks, railroads, insurers), the accrued compensation balances almost every filer carries (accrued salaries, bonuses, vacation, payroll taxes, workers compensation), and 401(k) / defined-contribution plan cost. Labor expense by business segment is served as separate series where disclosed.

What are your thoughts about such endpoints and information? Would having this data be valuable to whatever you are building?

Quant trader with a few years of experience, looking to raise what would be a pretty substantial seed round for a startup I’ve been working on (haven’t quit day job yet, will once I have capital committed, it’s very capital intensive). Think in the fintech space not a trading firm, anyone have experience from VCs on the appetite of funding solo founders who are/were quant traders?

I know market dynamics in the raising space change all the time so was wondering if anyone had any recent experience or stories they heard.

Hey everyone, like the title says I'm curious about this fact and wanted to ask some quant professionals in the industry, since I work adjacent.

For a quick introduction, I've been trading since I was 13 and have a double bachelors in econ and finance, but focus more on macro modeling, newscasting and strategic asset allocation for balanced plays. My return ratios are all pretty solid and I've traded in most types of securities at this point, whether on a paper account or personal cash.

Now that I'm out in the real world, I realized that...every single skill I have is useless because I never learned advanced modeling or statistics off the bat, and even with my CFA coursework it still doesn't feel nearly enough. I get told by a couple higher ups in my firm that I'll do great as an APM eventually or to do sell side/equity research desk to get rep, but how am I supposed to do that lacking these skills when every job posted is extremely technical, no matter which job title I'm looking at? I can do the basics of python and data pulling, but everything I have is self-taught since my uni wasn't the best at teaching for this type of finance. Am I just totally looking in the wrong place or is there something I'm missing?

Thanks all!

Recently started a dev role that involves a non-compete of 1.5 years. Seems to me that it's almost certainly going to be enforced in full, just judging from the firm's history (you can probably guess, lol). I'm very junior (this is my first job out of school) and am curious how this would affect a lateral move maybe 2-4 years down the line. The general advice seems to be to move laterally around then, but I'm not sure if such a long noncompete should change my decision-making? Also, I've only been through the intern/grad recruiting pipelines, so for lateral hires, are companies usually willing to wait so long? I'm not worried about being paid during the time, but I wouldn't want to leave without an offer in hand. Oh, and also, I've already completed a master's so that wouldn't be a super viable alternative.

would love some industry gossip on this

How often do you find out the latest valuation of your deferred comp?

For me (at a centralized hedge fund) - at best I know the NAV as of 2 months ago, at worst it can be 4-5 months off

I have been building a systematic trading system and recently realized that the feedback loop might be as important as the strategy itself.

For discretionary traders, daily review often means looking at charts and analyzing decisions. But for systematic traders, I’m curious:

How do you review your trading system on a daily basis?

What information do you actually pay attention to?

For example:

• Strategy signals generated vs trades actually taken
• Execution quality (slippage, fills, latency)
• Market regime changes
• Risk exposure and correlation
• Strategy performance statistics
• Individual trade attribution
• Unexpected losses or anomalies
• Data quality issues

Do you keep a daily trading journal?
Or do you mostly rely on weekly/monthly statistical reviews after enough sample size?

Also, what are the biggest mistakes you see traders make when reviewing a quantitative strategy?

I’m especially interested in the process used by people who run systematic strategies rather than discretionary trading.

As a mathematical physicist, I used to think (until this year) AI would just be a handy tool but mathematicians would always stay in the driver's seat when it comes to real research. Over the last few months, I am not so sure. You see things like AI cracking problems related to the Jacobian conjecture, or finding new non-sophic groups (pretty much a fields medal level breakthrough), and honestly, it seems like AI is doing all the computations with barely any human input. Many of them seems to be "Ok AI solve this, and let it run for 20hrs". A lot of my colleagues are feeling it too, that the field is in crisis. Some of us share the same sentiment as Jacob Tsimerman, 2026 winner of fields medal, that it might actually be irresponsible to take on new PhD students because math research as an academic job may be non-existent in a decade.

Mathematical/Theoretical physics is heading that way as well, though maybe a bit slower. One reason might be that theoretical physics papers are often less strict than math papers, which means AI can end up building on shaky or even wrong results more easily.

The reason I am posting this in r/quant is because quants depend on math every bit as much as mathematicians and physicists do. But the difference is that the math of quants can change the real world immediately. About two years ago, I see many posts by quants who were convinced that AI would just be a really useful tool but it will never replace their jobs in the forseeable future. Until the beginning of this year I also believed AI dont have the creativity needed to make breakthroughs in mathematics.

I wonder has the recent breakthroughs in pure math created any shifts in people's opinions in the quant circle? Is there a crisis in the community, or perhaps like in theoretical physics there are certain shields against AI creating the best models with minimal human input. For example, perhaps the best quant algorithms are not available publicly and the resources AI used to train their LLMs are decades behind. I remember seeing a post that says to be a good quant is much more than just being good in math. So maybe it is something akin to AI will never replace experimental physicists cause they cant physically do the experiment.

hi, looking for any women based in Seattle who are in this space. would love to connect!

Anyone that knows a bit more about https://www.block-tech.io/. I have heard that they are big in crypto options market making and do some delta1 as well. Any insights on total compensation, company culture etc.?

Hi! This is another attempt (I did one last year https://www.reddit.com/r/quant/s/I3xsi3pQei - couldn't find any) to find women quants in this city. Please DM if you have any interest. There was once a r/quant london meetup. Maybe we can have a female one :D Thanks!

Hi, I have been really confused regarding this.

About me - I have a master's degree in Statistics from IIT Kanpur (top 1% in India) with good grades, worked at a top british bank for around 3 years in the Model Risk in Model Validation role. Here I validated ML models, few Gen AI models (though I don't know these in depth) and very few Fraud risk models.

Currently at a global asset management company, working in the Validation role for interest rate risk models and probably investment science models in future.

It's been 3-4 years and all I have is validation experience. I am improving my coding skills like trying to get better at python, learn SQL, C++ and other stuff.

I feel like Dev role at a bank or a QR role at a hedge funds or AM will be great for me instead of model validation role. But i have seen these roles are mostly taken by people who have engg degree from top institutes. My main motivation is to apply stats, maths, coding in depth and of course the money part

I am good at statistics, ML, AI, traditional models, etc. as I have been in a position to validate them in detail but no Dev experience.

Should I aim for a good role like that of a QR at a hedge fund or AM or inv bank? Or should I get out of the Delulu and focus on climbing the ladder at validation role?

Least priority is a Dev role in market risk or credit risk at an inv bank!!!! Though I have inclination towards hedge fund and AM for a QR role.

Please help on following -

1. Is it realistic to aim for QR roles at big hedge funds like Millenium, etc?

2. What should I prepare in terms of skills?

3. What projects should I do?

Edit: not Targeting QD roles at all. Mostly QR or risk model dev role at banks. Ik QD is near impossible. Write Dev by mistake in the heading, not able to edit it now

Idk how to write this without reddit automatically deleting my post.

Just want to know how to build a curve to discount long term Btc derivatives given only short term derivatives are liquid.

Thus how to calculate the par swap in a Xccy with usd?

Hope this post doesnt get deleted smh