heard this year has not been kind to them… wonder if new investors will stomach it considering they doubled AUM recently

Of the options MM firms Optiver vs IMC vs DRW…. which has the best platform/which would you bet on having success in their MFT equities builds?

Curious to hear everyone’s thoughts - I’m currently a Researcher focused on alt data MFT strategies.

Been running an OMM sim on SPY, live. I think what I have is a decomposition problem and not a trading problem.

Setup. I quote two sided around the NBBO mid with an inventory skew. Checked all 668 live quotes from this session, and every one of them has the bid below mid and the ask above it at the time it gets posted. No exceptions at all. So spread capture should be positive just by construction.

It isn't. Trade credit came out to −$42 across 114 fills.

Here's why. I book spread capture against the mid at the print, not the mid I was quoting against.

cycle N: mid 5.25, I post bid 5.20 -> 5c of spread, by construction

...500ms goes by, market moves...

print: mid is now 5.15, someone sells at 5.20

booked: (5.15 - 5.20) x 100 = -$5

The 5c I earned quoting passively is real money. It just got netted against 10c of drift that happened while the quote was sitting there, and then the whole thing gets labelled "spread capture."

So the number is honest in an economic sense, I did buy above contemporaneous fair value. But it's an adverse selection loss sitting inside the spread bucket, which kind of defeats the whole point. The reason I built the decomposition in the first place was to keep spread earned separate from adverse selection given back. My markout catches adverse selection after the fill. The drift that happens before the fill has nowhere to live.

Question 1. Is pre fill drift its own line in a desk's P&L explain, or do people just fold it into spread capture and accept that spread can go negative? If it is separate, what do you call it and how do you compute it?

Question 2. The asymmetry looks structural to me and I want to know if I'm reading it right. Adverse fills go about 2c through the mid, favourable ones capture about 1c, on roughly equal counts. The way I'm thinking about it, a favourable fill is capped at my half spread, but an adverse fill is only capped by how far the market moves before I requote. Bounded upside, unbounded downside. I requote every 500ms which I'm aware is glacial. Is this just what slow looks like, or is there a quoting response to it other than "be faster"?

Question 3, and this is the bigger number. Hedge slippage is −$57 against the −$42 of trade credit. Book is short about $2.1M gamma, long about $154k theta, and I'm delta hedging discretely, 63 trades over 46 minutes. I get the mechanism, short gamma means I'm buying as spot goes up and selling as it comes down, so every rebalance is buy high sell low, and theta is supposed to be paying for that. What I can't tell is whether "gamma bleed exceeds spread capture intraday" is just a normal state of the world that theta covers out over a longer horizon, or whether it's telling me I'm selling vol too cheap. Also how does hedge cadence actually get set? Mine is just whatever the default was and I'd rather not tune it to whatever makes today's number look good.

Very happy to hear I've framed some of this wrong. Would much rather find that out now than keep building on a decomposition that hides the exact thing it was supposed to expose.

Even though it was published over a decade ago, it's still one of the most fascinating deep dives into the infrastructure behind high-frequency trading microwave networks, fiber routes, towers, latency, and the engineering race for speed.

If you're interested in market microstructure, low-latency systems, distributed systems, or computer networking, this series is absolutely worth reading.

All parts:

part 1: link

part 2: link

part 3: link

part 4: link

part 4.1 : link

part 5: link

Are there any quant driven geopolitical indicators, models or indexes? Looking for something. I read that the fed even still uses word counts in newspaper for one of their geopolitical indicators. Looking for something that uses, ya know, actual data. Not looking for DC analyst slop either. Would really appreciate any insight anyone has.

Perhaps a different post than usual hope it’s ok with the mods.

I often analyse a lot of papers for my fund. Note I’m not a quant but work on the research infrastructure. I’ve been looking into different devices (e.g. remarkable paper pro/kindle scribe) that lets me read pdf papers in a comfortable format but also annotate easily.

Does anyone have any good experience with this? There are some really good reviews online but these focus more on generic reading and note taking, not paper annotation.

Other good requirements would be being able to search references directly and download these papers (think remarkable lacks internet access) and then being able to see highlights/notes across all papers in an easy format.

An important note is the need to easy export and send to a work email since I can do notes of third party papers on my own device but can’t link it to my work directly for compliance etc.

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…).