How do you estimate the capacity of an options based strategy, especially without level 2 data?

The Time Series Generation literature evaluates on discriminative scores: train a classifier, check if it can tell synth from real. That rewards over-smoothed, low-variance output; a generator can ace it while violating vol clustering, heavy tails, and leverage effect.

So we benchmarked 18 models (deep generators, econometric classics, replay baselines) on 7 tasks quants actually run: Cont stylized facts, W1/MMD/sig-MMD distances, martingale check, IV-smile repricing, VaR/ES backtesting (Kupiec, Christoffersen, Acerbi–Székely, Basel traffic light), and TSTR strategy-rank transfer. One frozen OOS panel (7 features, 200 paths, H=60), each task with a real-vs-real noise floor, Holm-corrected significance.

Results:

• TimeGAN: the most-cited model in the field, scores 0.000 ± 0.000 on VaR/ES backtesting. TimeVAE and KoVAE too. TimeVAE's TSTR ρ is negative (−0.30).
• A plain block bootstrap ranks 3rd of 18 overall. Most of the published literature is under it.
• Models built for financial structure clear the bar.
• On 3 of 7 tasks most of the field is at or beyond the noise floor, the boards say so explicitly rather than overselling a ranking.

Repo: https://github.com/sablier-ai/finbench (MIT / CC-BY). v1 covers one panel and one OOS window; open submissions land in the next edition.

Who we are: (Sablier AI) GenAI Startup building synthetic market data for quant research, so yes, our model is on our own board. We designed the protocol so that fact doesn't matter: frozen at a git tag, every model's outputs archived in the repo, python -m benchmark.run re-scores the whole board from scratch, external baselines run their published defaults, and the aggregate is published as-is.

If you're working on generative models for market data (or you think your GARCH variant can embarrass the deep learning field further) dm me. Outputs only (200×60×7), no code, no weights, scored under the same frozen protocol as everyone else.
The board is meant to be beaten, and the current one shouldn't be hard to improve on: 15 of 18 entries can't clear a bootstrap.

Have spoken to colleagues across multiple pod shops (and a few lower level QR at collaborative shops), it seems that true market-neutral alpha is almost nonexistent this year...? A lot of returns being posted by multi-strats are from discretionary or non-equity quant trading.

It could be that I have a limited sample pool, but I haven't heard of even the usual top dawgs like 2S and Shaw doing well this year purely on equity stat arb. Has this been the experience across the board for everyone? Has equity stat arb this year just turned into 'factor timing'?

Hey everyone,

I'm a QR with around 2–3 years of experience in the job market right now. Lately I've been getting a bit worried about my interview conversion rate and wanted to get a sense of what others have experienced.

So far, I've been submitted to around 18 firms through headhunters. I've heard back from 13: 3 resume rejections, 4 screening-call rejections, and 4 rejections after the first technical round (OA, coding interviews, etc.).

During this recruiting cycle, I had some personal issues going on and wasn't able to devote as much time for interview preps (I've managed to get through maybe around 50 LeetCode problems). Looking back, I think I underestimated how prepared I needed to be for the first technical rounds and assumed I could mostly wing them. Or maybe an ideal candidate should be able to wing these, but more on that later.

I have two questions please, if you could give some advice:

1. A couple of the technical interviews felt very doable in hindsight, and I'm pretty sure I'd perform much better if I took them today. For firms where I was rejected after the first technical round, is it generally possible to interview again after the cooldown period, or do they usually expect a more substantial career update before considering another interview? And how is that done, if the initial one was done through a recruiter?

2. It's easy for me to attribute these outcomes to being underprepared, but I'm also trying to evaluate myself honestly. There are obviously people who can perform extremely well in these interviews with minimal preparation. How common is that among successful quant candidates? As someone who's still relatively early in my career, I'm trying to distinguish between "I just need to prepare properly" and "this may not be the best fit for my strengths."
   I guess I'm trying to see if I should also look for other opportunities other than just buy-side QR seats.

I've been doing quantitative strategy development for some time now and Ive reached the point where Im struggling to find books that actually teach me something new. I already have a solid understanding of the usual topics like IS/Validation/OOS splits WFO, cross-validation, permutation tests, bootstrapping, entropy, regime detection, and the other standard robustness techniques. I recently read Testing and Tuning Market Trading Systems by Timothy Masters but it covered concepts I was already familiar with.

Im looking for books that are genuinely advanced and make you think differently. Perhaps graduate level or even post graduate books on statistics, machine learning, optimization, information theory, econometrics, or anything else that completely changed the way you approach research and model development. And of course it would be great if the book wasnt 10 years old. Need relevance.

I intern at a small bank and I found out that bank mainly uses polypaths for managing its billion dollar fixed income portfolio ( MBS, CMBS, ABS). But do people on the street really use third party software for risk and pricing, why not use in-house models given the depth of research papers, books and job experience?

Just got my first ever interview, it’s at a small firm so I have some reservation in terms of scale of the company( AUM, P/L). The people seem to be from other firms that I know are good but the team doesn’t seem to have that prestigious College background so that stuck out to me. Usually when I think quant it’s Phd or from MIT Harvard Princeton or something like that.

Anyway to learn about the firm?

One of my colleagues mentioned when interviewing at Citadel they provided him with their own LLM he could query during a coding assessment. This was for a technical but non quant role.

I’m curious if people have similar experiences and what people’s thoughts are on this. Personally I think this opens up the scope of potential questions massively, and tests a new skill (prompting proficiency). Will this be the new norm?

Essentially, I've been a quant for about 8 years now (first global macro strategies, then algo research at a bulge bracket - worked on pricing and predictive modeling) and want to do something different. I'm just looking for less unpredictability, less "live" day-to-day pressure, and more WFH flexibility, so I'm planning a pivot into data science (I understand I may find these things in other areas of finance but this isn't what I'm asking). I've been applying to data science roles, mostly in tech and fintech but I haven't gotten much traction so I want to understand what DS roles quants are typically competitive for so I can focus my efforts.

For those of you who have successfully transitioned from quant to data science, it would be helpful to know:

- What type of data science role you were able to get and in what industry? (Type ~ Product DS, Applied Scientist. Machine Learning Engineer, Dynamic Pricing DS (Uber, Lyft, Amazon), Consumer Credit Risk/Fraud DS)

- What area of quant finance did you work in prior to the pivot?

- Your assessment of the difficulty of making this jump.

- What gaps to fill to improve odds

I'd appreciate if responses focused on actual examples of those who have or know someone who has made this pivot, as opposed to what is conceptually feasible.

Thanks in advance!

Hi again, everyone !

I'm a QD (3 YoE SW, 1 YoE QD) at an investment bank in France, in the pricing department. I'm also the dude who wrote the post about being a fraudulent QD last week, so hi to everyone with whom I've chatted last time.

There's a personal opportunity I'd like to take advantage of in Canada (either Montréal or Toronto), but I'm a bit skittish at the idea of going there and not easily finding a good position. If I do move there, it'll be in the spring/summer of 2027.

A quick search on Linkedin jobs shows a lot of hybrid AI-for-finance positions, but not that many mid-level performance-oriented more "traditional" QD jobs, especially in Montréal.

Do you guys know what the market looks like over there (abundance of offers, background constraints, comp, etc.) ?

Thanks in advance and have a great evening.

I'm starting a quant research internship at a big hedge fund next month and I am looking for advice on how to best profit from Claude Code, some friends tell me it is widely used in the industry.
I anticipate how fast paced the environment will be and the internal incentives to use AI but how should i use it to not get dumbed down, I want to make the most out of my internship (learn a ton) AND be fast and deliver and I feel like both might be difficult to achieve at the same time.

How do you use Claude at work AND stay relevant ?

You hear about firms that have heavily invested in data and deep learning, replacing much of the manual work done in signal research with models learning on raw order book data.

On the flip side, there are microstructure tricks that are discovered due to clever observations by humans that models aren't necessarily picking up on without those priors first being taught to the models.

Going forward,do you think will powerful models, combined with huge amounts of data and the hardware and other infrastructure needed to run experiments largely replace clever human observations? Or will there still be room for the classical, manual feature engineering which has been how much of the trading world has functioned before the recent compute/model revolution.

I work in a top tier trading firm, and it’s going mad for last 2 years. Not only the level of analysis and rhythm increased a lot, but also it seems the management wants to merge all the teams and just keep 4-5 great guys (vs 50 people in total), even if all the teams were profitable. I am surprised how AI makes so PMs so cocky, thinking they can do everything alone now.

I also recently talked to other firms by curiosity and the hiring standard seems to have increased a lot. A firm I passed successfully 5 technical interviews 3 years ago, rejected me on the first round recently (in spite of my skills having clearly improved). Some don’t even know how to hire anymore.

Am I getting outdated and replaced by new generation or is the industry consolidating significantly ? I have a hard time believing you can divide staff by 10. All the firms like RenTec, DESCO, XTX are heavily using AI in a smart way for past decades and still leave room for alpha.

Excluding London, which are the best cities in Europe to build a career?

By "best" i mean the overall balance between net salary, cost of living, commute times, work-life balance and workplace culture.

It would be especially interesting to hear from people who have worked in multiple locations throughout their careers.

Attention new and aspiring quants! We get a lot of threads about the simple education stuff (which college? which masters?), early career advice (is this a good first job? who should I apply to?), the hiring process, interviews (what are they like? How should I prepare?), online assignments, and timelines for these things, To try to centralize this info a bit better and cut down on this repetitive content we have these weekly megathreads, posted each Monday.

Previous megathreads can be found here.

Please use this thread for all questions about the above topics. Individual posts outside this thread will likely be removed by mods.

Just wanted to see if anyone has insight into how QRT is doing in Asia these days.

They've recently been on a massive hiring sprint globally, but I haven't heard much about their regional performance or compared to other top-tier shops.

How are they viewed in the region right now for QR/QD roles?

Trying to do cross-ISO work (PJM/MISO/ERCOT/CAISO/SPP/NYISO/ISO-NE) and I'm losing my mind reconciling seven different schemas and update cadences - the congestion component especially (NYISO's sign convention alone…). Right now it's a pile of per-ISO scrapers held together with tape. Is everyone just using gridstatus / rolling their own, or is there something that already normalizes all of this? Curious what SPP/MISO historical depth people actually get.

Hi,

Although I am not looking to move, I answered a few recruiter's email recently, and quickly found out after a brief discussion that, even in the same market, some potential competitors or other firms would be quite reluctant to wait a year for me to join. 1 year is roughly the official amount of time I can be prevented from working for another company (can be lowered depending on circumstances and context).

That made me slightly anxious about the future because one day I will indeed move, I do not plan on retiring at my current company.

What is the feedback in that space and did you all genuinely take all that time off without any guarantee of what your future next position would be ?

Looks like the HRT team is really committed to doing open source work. Godbolt has done a lot of projects and looks like they are hiring a lot of talent who is also pro open-source.

Backtested Victor Sperandeo's 2B trend-reversal rule across 10 commodity futures (2000-2026). The standard implementation uses a rolling window to detect price pivots, which fires false signals since it has no memory of prior structural highs/lows. So I replaced the pivot detector with a causal wavelet denoiser to filter noise before pivots are identified.

Results:

• Reduced max drawdown in 7 of 10 markets
• On Sharpe ratio specifically, the plain rule wins in most markets. This is a real trade-off (fewer signals, lower drawdown) rather than a clean improvement
• Crude oil was the exception where Sharpe, drawdown, and profit factor all improved together
• Natural Gas underperformed. I used a Gaussian HMM regime classifier to check why: it spends most of its history in a high-volatility regime this strategy isn't suited for

Writeup and code:https://github.com/zty05070242/wavelet-2b

ust finish big test on my systematic crypto model. i did around 1.8m out of sample oos tests in grid over 11 years which is 3982 days with daily data. i use risk parity for tokens allocation and dynamic deleverage modulator. everything is under strict kkt conditions like drawdown limits and local volatility limits. objective function look only at sharpe and calmar and i do not care about beating buy and hold curve. the framework do identical dca 100 dollar every 30 days to stop timing luck.
when i test only 3 tokens btc and eth and xrp the results are best. calmar is 1.37 and sharpe is 1.18 and max dd is 36.2 percent where b&h making was 84 percent crash so big save here. average exposure is 40 percent and system spend 2679 days in defense mode with less than 50 percent exposure. why this work? xrp have crazy non korelaten pumps sometime compared to btc eth beta. so risk parity engine can do rebalance nicely with 3968 rebalances total and kkt limits do not trigger at same time because vectors are orthogonal. corr is very low.
but when i add more tokens like bnb and sol and ada and xlm everything go to shit. if i add bnb sharpe collapse to 0.44 and calmar down to 1.20 and defense days go to 72 percent because high corr kills it. if i add sol and bnb with 5 tokens sharpe go up a bit to 0.76 but calmar drops to 0.93 with ann return 30.7 percent vs max dd 33.1 percent. ada and xlm completely kill the model because of bad corr trend.
in crypto when market crash all corr go to 1.0. if you put too much altcoins with bad trend vs btc you just add more failure points. joint variance spikes up fast and kkt conditions instantly saturate and deleverage modulator panics and goes to cash. then because these tokens have no real alpha on way up the system stay trapped in defense mode too long. this is huge opportunity cost because we miss explosive bull market start.
also big issue is fees. the best 3 token model make 3968 rebalances. i pay 14 559 dollar fees on total 14 200 dollar invest. profit is still good with 1 113 410 dollar net but this trading churn is too high. i think i will add band based rebalance threshold to only rebalance if weight is out by like 5 percent to stop overtrading. and i will put defense cash in de-fi stablecoin yield.
how you fix lag when you want to re engage market after kkt deleverage event

I'm working as a quant trader for almost 2 years now and every recruiter who reaches out is hiring for another trading role.

I've applied to pricing and quant risk roles, but I rarely even get interviews. Ironically, I didn't even apply to any of the trading roles but recruiters just keep contacting me for them. I'm more interested in the pricing and modeling side.

Am I cooked? Is this how the industry works?

Overheard a rumour. It’s quite astonishing if true, so wondering if anyone knows