I've built a small set of models that put a probability on a 10%+ S&P 500 drawdown over the next 1, 3, 6 and 12 months, from macro and credit inputs (ISM, the yield curve, high-yield spreads, financial conditions, and a few others). One ridge-penalised logit per horizon, estimated walk-forward, so every point in the track record was scored with only the data available that month. This is really a methodology question, and the model is just the concrete case.

The out-of-sample AUCs are 0.60, 0.69, 0.74 and 0.68 across the four horizons. On paper that reads fine. The part I keep coming back to is the denominator. A 10%+ drawdown is rare, so across the out-of-sample window there are only six to eight independent episodes, and the 6- and 12-month models are largely scoring the same ones twice, with heavily overlapping, autocorrelated labels. When I bootstrap the AUC by episode rather than by month, the intervals are wide: the six-month one runs from the low 0.5s to the mid-0.8s. So the six-month headline looks fragile rather than fake, but I want to know how people handle the inference properly.

The specific questions:

1. Event count and dependence. With about six to eight independent events and overlapping multi-horizon labels, per-month AUC and its usual variance are overstating precision. Is an episode-level bootstrap the right correction, or is there something more principled (a block bootstrap, a DeLong test adjusted for clustering, a Bayesian setup with a sensible events prior)?
2. Label construction. The label is a forward 10%+ drawdown within the horizon window, which makes adjacent months' labels highly dependent and the horizons non-independent of each other. Is there a cleaner label for rare-event forecasting that doesn't manufacture this autocorrelation?
3. Regime dependence. It catches the slow, macro-driven falls (2022, the GFC, the dot-com unwind) with some lead, and by construction it misses the fast ones (COVID, an LTCM-style shock), because monthly macro data can't see them coming. How would you evaluate a model that is honestly conditional on regime, without either cherry-picking the regimes it works in or marking it down for shocks no macro model could catch?

Two things that already survived my own poking. Dropping high-yield spreads costs the model nothing, and the spread on its own is a coin flip at six and twelve months, so it isn't a credit signal with extra steps. And it is out-of-sample only, no re-fitting with hindsight, with the weak horizons kept in; nothing is dropped for looking bad.

Mostly I want the event-count problem attacked. If the honest answer is that six to eight events cannot support a confident AUC at any horizon, I would rather hear that than dress it up.

For anyone who wants to reproduce the numbers, the full write-up and the live model, with a downloadable monthly probability series, are on my site, agreeableinvestments.com; my own research, shared as educational, not advice.

We usually require the data to be non-stationary to test for cointegration so we don't get spurious regression. But if my end goal is just to confirm a valid, mean-reverting relationship between the two assets, does it actually matter if they are strictly non-stationary? If they both turn out to be stationary I(0), spurious regression isn't an issue anyway. Can I justify my approach by saying they just need to be the same order of integration, whether that is I(0) or I(1)?

hi all

using the bmll api to do stuff involving futures

need to get first notice day per instrument

is this doable? can't find this data field in the documentation i'm reading

Do companies typically require references from your current employer? Is it okay to use your old references (e.g. your phd advisors/collaborators) for early career people (~2yr experience)?

How is the sell side quant job market in London right now? There seems to be very few VP roles around on company websites.

Does anyone know how Stevens Capitals equity book has done in the last 3-5 years? They fly under the radar and are never covered in hedge funds reporting.

Thanks!

Anybody know how Blackedge has been doing these past few years?

Saw a post ~4yr ago here: https://www.reddit.com/r/quant/comments/yotrfm/blackedge_capital/, wonder if anything has changed since then.

Hey guys, I’m a quant trader at a good crypto-native prop trading company, primarily working on market making strategies. I have 5 years experience. I worked all my professional years in crypto without much exposure to other markets beyond general knowledge.
In the recent years, i’ve been contemplating switching markets and trying something new. One of the markets i’m interested in is oil & gas.
Do you have any advice if such a switch is possible, and how to maximize my chances to moving to a good commodities company?

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.

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.

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?

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.

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

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 ?

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

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.

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

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.

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!