r/quant

I never understood why anyone would engage in an arrangement like that, particularly with them.

Are schemes like that not uncommon?

Would they have done it purely to keep Renaissance's volume?

Hello,
I recently started receiving emails from recruiters claiming to be from competitors (Citadel/MLP). The messages are very obviously generated from my LinkedIn profile, and the sender addresses look like firstname.lastname.career.citadel@gmail.com, which seems suspicious. I would expect legitimate recruiter emails to come from an @citadel.com (or company) domain.
Has anyone else received similar emails? What do you think the goal is? Could these be independent headhunters using Gmail accounts in the hope of getting a higher response rate, or is there something else going on?

I am building a automated alpha CREATION ENGINE for making alphas in world quant brain, previously the process is too manual taking too much time so I want to automate the thing which will help platform as well as consultant

So if anyone interested in helping I will give it to them for free

Numbers only accurate at +/- 5% for privacy.
Currently working at CFM in Paris (1 YoE) 180k€ TC. People are super sensitive about salary and bonuses and that annoyed me so looked elsewhere. Got an offer for a QR role at QRT which is around 280k€ TC but a big chunk of it is a sign on. I also have an offer for Headlands Tech in Chicago (no need for visa I’m an American citizen as well), TC is around $700k with a big chunk of it as a sign on.

Would you say the move is worth it? The lifestyle in Paris seems so much better than the one in Chicago. But on the other side, neither QRT nor CFM have real elite reputation in the field so we don’t have that many impressive talents in Paris. Just smart people coasting because the incentives aren’t that big.

Thoughts?

So before you think this question is basic and answered a million times, I've read many responses on reddit and elsewhere and haven't seen any in-depth answers to my specific questions, and/or I see conflicting answers. 1. what role does supply/demand play in options' pricing since it can conflict with the actual cost to delta hedge an option? If supply outweighs demand I can't imagine a MM selling for less than it'll cost to delta hedge the option. 2. How are ITM/OTM options priced? I've read it's based off the vol skew using ATM prices, though a vol skew would be the result of OTM/ITM prices, not the cause. Otherwise how would you determine the skew? 3. Empirically, variance doesn't scale linearly nor is it stationary. So in reality a stock can have 20% monthly variance, but 2% daily variance. If you were to scale the daily up to monthly (.02*30) it'd be 60%. A 1 month DTE option cannot be priced off of √20% IV because the daily variance will make it more expensive to hedge than that throughout its life. This can go further, minute or second or even every tick prob has different annualized variance, so which one do MM use to find IV? 4. All of these assume MM price options based off cost to hedge because idk how they couldn't so correct me if I'm wrong. If MM price based on cost to hedge (IV), and the sum of every strike's IV can create an implied prob distribution of the underlying at expiration, wouldn't they sometimes conflict? Meaning they'd have to price at x because it's the cost to hedge, but pricing at x under or overstates the probability density at that point in the PDF? Thanks for answering

Hi. Does anyone know how much initial unlevered capital (or levered GMV) is allocated to a new Portfolio Manager at a pod shop (eg. Millennium, Balyassny, Cubist, Verition etc)? I know that it can depend on your negotiation, but wanted to know a general range. How does it scale later on as one progresses in the role?

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.

pecifically, I have three core questions regarding the R&D cycle at HFT firms:

1. Idea Generation (Micro-Alpha): Retail trading relies heavily on basic indicators (moving averages, simple chart patterns). For HFTs operating at the nanosecond/microsecond scale, what does the ideation process actually look like? Are quants primarily mining Level 3 tick data for order book imbalances, latency arbitrage loops, or localized volatility anomalies, or is it more heavily driven by machine learning feature exploration?
2. High-Fidelity Backtesting: How do firms build simulators that don't suffer from look-ahead bias or unrealistic fills? How do you accurately model your exact mathematical position in the order queue, exchange network jitter, and wire-time latency when backtesting a strategy?
3. Sim-to-Live Validation: How do teams determine that a backtest is robust enough for a live market? What metrics or validation frameworks do you use to prove that your simulation perfectly mirrors production performance before scaling up risk, especially when accounting for your own strategy's market impact?

I would love to get any high-level insights, reading recommendations, or advice on what specific sub-fields of statistics/microstructure I should focus on during my Master's to prepare for this.

Thanks!

I have recently joined a firm that trades almost 100% passively. I have been tasked with finding ways to cross the spread and execute more aggressively.

Allot of the literature I have found on optimal execution seems to be based around optimised scheduling based on Almgren & Chriss market impact. I have found that taking using this scheduling under performs the baseline passive strategy.

What other methods should I be using to determine if and when to cross the spread?

Every year there seems to be some “golden rule” that sounds amazing but falls apart in production.

For example:
More features = better model
Higher Sharpe = better strategy
More data always helps
AI will find alpha

What’s one commonly accepted idea that you’ve learned is mostly wrong after running strategies live?

I am currently researching an extremely interesting niche.

If we consider an event lets say Take Profit (TP) onto an option lattice (Contracts x Timestep), not as a fixed label point, but passing it into a kernel to form signals that linger away, they are essentially echoes from a point. Certain areas of this lattice are major attractors with abundant TP events emit many echoes. At the end of the day we add up these echoes to form another lattice called the Gamma Lattice.

Mean Gamma Lattice across 250 0DTE options

This is how a randomized lattice looks like:

Randomized Lattice

Depending the Kernel you use, this could take n arguments.

With these arguments you should be able to build the entire Lattice at any point which includes all events till the endpoint.

My custom built kernel takes in 3 arguments based on which I could build the entire Gamma Lattice at any point.

My current research point is: With this, I tried simulating the days in this fashion:

At point t, of current timestep, search top-K days with similar market features (I bundled the features based on their nature and ran a cosine similarity algorithm).

After you get the similar days, curate a Probability Density Function of these Kernel Parameters and run a small N-Monte Carlo to build N Gamma Lattices. Average them out and build percentile scores of Gamma scores based on past X expiries (no lookahead).

By reconstructing the lattice, you can predict based on these scores before hand.

I ran a small pilot run (because the computations gonna take hours) using Fable 5 with event being TP = 100% (Trades doubling) after careful audit of the logic:

This is just a small pilot run without any model learning and just Monte Carlo.

While this doesn't demonstrate any real market edge, its definitely interesting atleast for me.

Please let me know from a peer review perspective. Thank you.

Hi everyone,

I was in the first round of an interview for a quant role at an asset management firm

For context im completely entry level, no licensure, 1yr internship looking for full time role (with 1yr exp econ. research, econometrics, statistical modeling and 1yr exp open source SWE) with masters in Econ from T30

I was explaining my previous projects and what I currently do as a job (open source software engineer lol, basically unemployed) and I felt that I was talking quite a lot about non-quant analysis projects (as I have background in OOP & econ. research and econometrics) so I asked the interviewer what kinds of projects are commonly worked on

They mentioned weight optimization in their portfolios which I had done recently, so I said, verbatim...

"Oh, yes, I've worked on this recently! I don't know if this is unethical or illegal but I have a friend who works at (insert specific company name here) who asked me for advice on weight optimization for their covered call strategy"

The interviewer did not like this, and replied "Yeah, you probably shouldn't have said that." with an awkward laugh so I quickly replied "Ok, then we'll just leave that there without going into any further specifics like numbers or anything.", changed the subject to another project, and continued the interview for another 2 minutes or so, before I got very spooked and anxious about what had just happened and said, thank you for the interview, can't wait to speak soon and continue the process, and hung up with 5 minutes left in a 30 min interview

Am I cooked? It was a genuine mistake as I was eager to talk specifically about my experience and what I've worked on in the past

I had another interview about 30 minutes after that one ended which went perfectly, first interview was with a director, second interview was with someone who is not as high up

This company was basically my dream job which is why im coming to reddit about it, beating myself up quite bad about this blunder...

I heard some absolutely monstrous numbers for JS, 30B or 40B whether or not you count their VC. As a quant at a tier 2 firm (that by all means is having a great year), I can't help but feel insecure.

Hey everyone, I’m an aspiring quants. Mods I understand that there is a mega threat for questions regarding general advice when it comes to education hiring etc but I have a separate unique question to the situation I found myself in.

I’ve been applying for jobs at let’s say middle office level at quant firms, and been successful in getting interviews and moving through the stages. Not got to offer stage yet anywhere but at the moment I’m in the process at HRT, Optiver and Quadrature.

I don’t have a degree. I have a background in data and building models and algos at retail banks in fraud detection etc. I have always wanted to be a quant but my barrier has always been the degree itself. I can’t fund the degree myself (uk degrees are abhorrently expensive).

During an interview at Optiver, the hiring manager asked me directly why I seem to be applying for a trading analyst role and not a front office quant role directly. I’ve taken part in IMC prosperity 4, as well as WorldQuant comps.

My answer was about the barrier to entry, and from there his reaction as well as the other interviews reaction and their demeanour in general became weird. Almost like an inquisitive but reserved way.

My question is, why ask that when it is evident that my lack of degree prevents me from getting a quant role, and then would this affect my chances of being successful in the interview, and if so should I refrain from mentioning my quant aspirations in the future ?

Sorry for the long post

TLDR; applying for quant adjacent roles as no maths degree, specifically was asked by Optiver why I didn’t apply directly for a quant role as opposed to a middle office role. My honesty may have worked against me.

Anyone with new ideas to be included in quant currently building a hurst model on an individual level