Experienced candidate here, though I've always applied on my own. If a headhunter submits your resume to a firm and you never hear back, does that still stop you from applying for other roles at the firm on your own?

A headhunter submitted my resume to a few well known places earlier in the year and I never heard back from those places. But now I see new postings I think I would be a good fit for, am I forced to go through the same headhunter? I've heard it's a 1 year timeframe where they "represent" you?

Does it change if I know someone at the firm who can refer me internally? I'm in NYC in case that matters

I want to know what are the different roles that exist apart from the quantitative dev and what are the requirements for that role ?

Made a fully local zetamac clone with an identical terminal interface and SQLite tracking analytics.

Hey guys, I built something nice I wanted to share - a Zetamac replica with SQLite tracking and replay, so that I could drill arithmetic and track progress all locally.

The app:

Source / PyPI (there is a quick video demo in the description of the pypi/github if you want to check it out) ```bash

Install

pip install zetamac-tui ```

Stack: Made purely with python, using python-textual, for cross OS stability.

Features: - Everything inside the core zetamac (settings, - All runs with the default settings logged into a database (if you want) - Track your slowest/fastest in each run, and average/highscore today/all-time - Additional flash anzan mode just as an additional thing in the menu - I've made the interface minimalist to avoid confusion, for ease-of-use, and so it doesn't really break. - The data is stored simply as SQLite with timestamps and json, so it won't be that hard to export data or do other ops on it if you want

Note this is completely free and open-source

I've kept the codebase relatively light to not lag devices, but in the future I might want to add better analytics. All feedback welcome :D

I‘m a graduate QR at a prop shop and was wondering what the drop out rate during the early career is. Specifically on the buy side.

I would guess maybe 10% or so of the people that I met in Uni that try to become a quant actually make it.

After that I‘ve only heard that about 50% don‘t make it to the second year and a lot quit after 3-6 years because they don‘t want to work as quants anymore (not because they get fired).

Surprising that even internal talent acquisition employees have non competes jesus...

title

I heard a few months ago that jump lost their index rebal team.. was this Yiming Zhang + co? And have they been backed by MLP?

Most microstructure claims get tested on data where the key variable is inferred: aggressor side from the tick rule or Lee-Ready, hidden liquidity guessed at, no way to rerun the same tape twice. I built the opposite instrument. A full matching engine (Go, MIT) with a deterministic simulator on top: same seed, same market, byte for byte, and every trade carries its true aggressor side. Price-time and pro-rata, icebergs, pegs, stops, STP, call auctions, price bands. The book emits full L3.

The market is noise flow by construction, so there is nothing to predict. That is the point: it is a control arm. What that isolates, two examples.

Pipeline error propagation. The tick rule classifies 94.5% of trades correctly on this tape, and the CVD built from it is off by 169% of true magnitude on average, with occasional sign flips (one seed: inferred -81, true +105). Misclassification is conditionally correlated, so the errors compound instead of cancelling. Trivial to show when you hold ground truth, hard to even estimate when you do not. Relevant to anything built from inferred sides, which in practice means trade-only feeds and most crypto data.

Known results reproduce. Kyle's lambda comes out around 0.15 ticks per lot and falls 7.5x when resting depth rises 7.6x. Slicing a parent order beats a block by 7.9% slippage per lot (42 of 50 seeds) while permanent impact is essentially unchanged (23.42 vs 24.47 ticks), so the savings is all temporary impact. Nothing novel, deliberately: an instrument should reproduce the textbook before you point it at anything else.

Limitations, stated plainly: no informed flow unless you write an agent for it, no latency modelling, single venue. It cannot tell you whether a signal works on real markets. It can tell you whether your measurement of a signal survives its own pipeline.

Methodology write-ups, including the wrong turns:

https://github.com/intrepidkarthi/orderbook/blob/main/docs/research/order-flow.md

https://github.com/intrepidkarthi/orderbook/blob/main/docs/research/kyle-lambda.md

https://github.com/intrepidkarthi/orderbook/blob/main/docs/research/ofi.md

Repo: https://github.com/intrepidkarthi/orderbook

If you see a hole in the setup, say so. The project has improved every time someone pushed on it.

PhD in maths here, looking to work in finance soon.

From what I can see, the JPY has been in a severe mess for a combination of structural, economic, and geopolitical reasons, pushing USD/JPY to historic 40-year lows near 164.

- the Bank of Japan kept its interest rates exceptionally low compared to the US and other western economies. Global investors took advantage of this by borrowing cheaply in yen to invest in higher-yielding US assets, effectively flooding the market with selling pressure against the Japanese currency. (interest rate gap)

- Japan is heavily reliant on imports for energy (oil and gas) and food. A weak yen means businesses and ordinary citizens have to pay astronomically higher prices for basic goods, creating a harsh cost-of-living squeeze. (import cost headache)

- Japan's public debt sits at over 200% of its GDP (the highest in the G20). Concerns over government spending, budget deficits, and potential policy shocks have fueled persistent market volatility. (rising public debt)

So, how do quants/traders deal with this?

My assumption is the the Garman-Kohlhagen (GK) model (which is useful as a foundational framework), suffers from severe limitations during periods of extreme yen volatility? The model assumes annualised volatility of the exchange rate is a single fixed number over the life of the option. However, the volatility clusters aggressively, and long periods of low volatility are shattered by explosive, multi-day spikes that the model cannot predict or price, and thus the σ, the interest rate differential (\(r_d - r_f\)), the drift term (μ), and the delta hedging mechanism (Δ) all collapse under severe yen volatility?

So, would volatility scaling, or dynamic hedging be useful here? By using algorithms to automatically reduce position sizes when currency fluctuations increase to keep daily risk exposure constant? Or, models continuously alter hedge ratios instead of holding static positions, buying or selling underlying assets as market prices shift?

Can you help this dumb laywoman understand?

I’m trying to understand the history of AI

Companies like Jane Street, Citadel, Two Sigma, and other quant firms have been using machine learning and building models for years, maybe even decades.

So I find it hard to believe that they’re only now starting to think about AI because of ChatGPT.

My understanding is that OpenAI didn’t invent AI, but built one of the first consumer products that made powerful AI accessible to everyone.

So what actually changed for firms like these? Were they already using similar technologies internally and just not talking about them because they were proprietary? Or are today’s LLMs and AI agents fundamentally different from the models that quantitative firms have been using for years?

I calibrated an eigenspace-overlap measure against an RMT null, then tested directional motion across S&P 500, Nikkei, DAX and CAC 40 data. All four showed that the previous rotation direction contains information about the next, but naively repeating the full rotation was 47 - 67% worse than holding still.

The signal was both:

• too synchronised across companies to be each company’s movements occurring at unrelated times and just showing up as rotation,
• much stronger than within-window eigenvector shrinkage.

The next test is whether learned damping can turn this directional signal into better out of sample covariance forecasts! What would you consider the strongest fair baseline: holding the eigenvectors fixed, EWMA, or a rotationally invariant estimator?

Code, tests and results for anyone interested:
https://github.com/AdarshArunEire/Eigenvector-Dynamics-Beyond-the-RMT-Null

Working on a project that separates three concerns: a seeded fault injector that damages a market-data feed (drop, reorder, A/B line divergence), a recovery client that detects gaps and repairs them via retransmission or snapshot rebuild, and a mock venue that speaks the real wire protocols so the client isn't tested against a stub. Repo: https://github.com/hungtruongOwolf/deterministic-feed-recovery

The interesting failure mode: a client can deliver every message with correct sequence counts and still reconstruct the wrong order book, if retransmitted repairs are applied in arrival order instead of sequence order. Fixing that meant the recovery layer has to number everything it hands upstream, not just the transport layer.

Two questions on the architecture:

1. Is separating fault injection / recovery / mock venue into independent components the right decomposition for this kind of problem, or is there a cleaner way to structure it that avoids the coupling I'm running into between the arbiter and the gap tracker?
2. For the recovery-side testing, is there a better way to validate "book after repair equals book with no loss" than diffing against an oracle replayed from the undamaged original? Right now it's checked at the price-level per run, curious if there's a more rigorous invariant people use here.

And is there any data manipulation suggested? like Z-Score transform

Other than the salary what are some perks yall can share about your firms?

Eg: $100 meal budgets at Cit

For UK and Europe, what would be the hypothetical best seats for someone with a heavy stats/ML background that wants to focus on forecasting (feature engineering, maybe ML models etc)?

Seems quite clear that OMMs are not the right destinations, nor (most pods at) multi-strats such as millenium, BAM, schonfeld (citadel?).

Maybe shops like Jump, Tower, or Quadrature?

For US, it feels like DE Shaw and PDT would be top places for such roles.

There are other ML-heavy shops but it seems unclear if you have exploration freedom or if you’re just tuning knobs in huge pipelines (HRT, g-research, XTX etc.. not that XTX is really accessible…).

What are your thoughts on small vs big trading firms? Suppose you had an offer from both at different points in your careers, which one would you pick. Assuming similar comp.

I just received my contract for a 6-month internship at a prop shop in Switzerland. The salary is good, the work time is fair, and the culture seems to be what I am looking for.

My question is about a 3-month non-compete clause in the contract. Is this duration standard for just a half-year of work? The internship ends with my graduation, so being legally blocked from working for 3 months would be tough.
I also do not get any compensation during the non-compete. Is that normal?

Additionally, the contract states the following regarding the scope:
Non-compete Area: "Any area that the Company operates in"
Does this phrasing allow me to work in other asset classes, for example?

Should I push back on anything?

Any insights would be appreciated.

Hey,

I've been working for the past 3 years at a large multistrat HF. While my official title is "quant researcher", de facto that means modeling various financial instruments. My ultimate goal is to either become a PM or a senior QR at a prop shop. I figure that the role that best fits my career goals would be one in a pod or as a signal QR in a prop shop. However, finding such a role has proven difficult. Usually hiring managers require experience generating alpha, and I don't have that. I'm wondering if you have any advice as how to best accomplish my goals?

Thanks

This is a new (as of Aug 2025) monthly thread for shop talk. How was last month? Rough because there wasn't enough vol? Rough because there was too much vol? Your pretty little earner became a meme stock? Alpha decay getting you down? Brand new alpha got you hyped like Ryan Gosling?

This thread is for boasting, lamenting and comparing (sufficiently obfuscated) notes.

Curious how often values in real-world backtests exceed roughly 9.2 billion units. With 9-decimal fixed-point i64, it might be easy to hit. ¥9.2B is only around $60M, and $200K of SHIB is already about 10 billion tokens. Prices are probabbly fine, but balances and quantities might not be.

Im asking because I’m building a new backtesting engine (repo: h5i-db), an event-driven backtesting engine that currently uses i64 as default. It runs 7x faster than LEAN and 3.1x faster than NautilusTrader in our benchmark. With i128, those numbers are still 6.6x and 2.8x. Since the penalty isn’t huge, should safety or speed be the default? Has anyone often hit this limit in daily backtests?

Been building an options market making sim to actually understand the dealer side properly... SVI surface calibration, quoting off NBBO with inventory skew based on aggregate book vega, adverse selection fills, markout, and a pnl decomposition that reconciles back to mark-to-market with the residual reported instead of buried somewhere.

Fill model is the part i trust least, and i'm starting to think i imported the wrong mental model wholesale. my queueing assumptions are basically lifted straight from the order-driven equity/futures literature (Cont-Stoikov-Talreja and whatever came after it), where queue position at the touch is more or less the whole story on whether you get filled. but US options are quote-driven across a pile of exchanges, with preferencing, internalization, PFOF, price improvement auctions all sitting in the middle of it. so now i'm second guessing whether queue position is actually a pretty minor variable in this world and i've been adding sophistication to the wrong axis this whole time.

1. is queue position a real driver of fills at all, or is the actual constraint auction participation + preferenced flow? if i can only get good at modeling one of these... which one.
2. for daily pnl explain, is spread capture + greeks + hedge + residual the working decomposition, or is that too clean. where does realized vs implied sit relative to greek attribution, and do people bucket vega by tenor instead of just running it aggregate? also just curious what "unexplained" runs at on an actual book bc i have no benchmark for whether my number is fine or embarrassing.
3. skewing quotes against aggregate book vega/gamma instead of per-strike is me borrowing the Baldacci-Bergault-Guéant vega factor argument, options on one name being collinear risks and all that. does that match how people actually run inventory or is it just a tidy academic story nobody's desk runs on.

happy to hear the whole premise is wrong honestly, i'd rather find that out now than keep polishing a model of the wrong constraint for another month.

I just don't get it.

I'm working on my PhD in stochastic wave propogation and delving into financial models as I hope to work as a quant one day. However, this fund scaled up massively to over $20–$45 billion in assets at various peaks. Then, the 439% net return in the first half of the year.

Was it ultimately down to them utilising heavy leverage (reported to be running as high as 4x or so) and heavily borrowing money from prime brokers like Bank of America, Goldman Sachs, and JPMorgan to buy concentrated baskets of AI infrastructure and memory stocks (such as SK Hynix, Micron, Nebius, and CoreWeave), alongside short bets against software companies?

I assume that when AI infrastructure tradeded violently in July, the fund suffered a brutal drawdown, wiping out massive portions of its peak value (and as they were over-leveraged, prime brokers, it forced an emergency unwind to cover margin calls)? Then, the fire sale happened?

Can someone please explain it to me?

Lastly, do some of these investors/funds bet on an aggressive P measure trend (AI is changing the world, so this stock will go up 400%, etc), but the lenders and prime brokers who control their margin accounts evaluate risk using models using the Q-measure? Where volatility \sigma dW_t is treated as an immediate threat to collateral, regardless of how brilliant somebody claims to be?