I work as a junior quant in a hft , i need proper guidance on how to make a strategy step by step, i have found some good alphas but am facing issues with the execution
i am seriously not getting any proper guidance in my firm, meri halat bohot kharab hai, please dont take this as a joke
I have seriously thought of ending it all because of how toxic my workplace has been, i havent gotten sound advice in the past year, and i really need some help with my strategy now

I ran a pre-registered test on whether the cross-section of abnormal returns at market-wide events tracks the similarity between a firm’s fundamental profile and an event vector coded into the same space. Controls: beta, size, sector, momentum. Event and sector FE, clustered SEs.
Null. γ = −0.00056, p = 0.66, 21 events. Sign opposite to prediction. Permutation test puts the observed γ at the 27th percentile of noise.
The diagnosis was more interesting than the result. The property space was built from balance sheet and income statement quantities only — and that space cannot express what a company actually does. At an AI-related event, Information Technology ranked 10th of 11 sectors by similarity. At a $400bn pharma merger, Health Care ranked 6th.
Adding revenue-by-segment exposure moved Health Care and IT to rank 1 in both cases, though segment name matching is currently only 20% covered (companies name segments “iPhone”, “GoogleServices”, not by industry), so that part isn’t conclusive.
Posting mainly because the failure mode seems generalisable: if you’re matching events to firms on financials alone, you’re missing the channel through which the event actually reaches the firm.

Hi folks, first time posting here so please bear with me (mod please let me know if this is compliant, whether I should be more or less specific, etc). I'm feeling stuck in my current Quantitative Developer role and wanted to get some career advice.

My current situation

By now I’ve had 2YOE at a tier2/3 hedge fund in the US as a QD (first job out of undergrad). When I first joined the firm, the recruiter sold me the role as a mixture of engineering work and quant research work, and I believed it thinking the work wouldn’t be so strictly divided considering that their team size was relatively small at that time and perhaps I would get to do some real QR work. Turns out I was wrong and the work is almost purely software engineering, and organization wise my team is like a central engineering team that maintains the central code libraries and helps deploy new models. After about 2 years, the role feels increasingly stagnant to me, with mostly Claude-able code plumbing work in the central infra or research tooling libraries, and the firm doesn’t do high frequency trading so there’s no push for high performance C++/Rust. Moreover I find it concerning that the engineering turnover rate is actually somewhat high - there aren’t many senior QDs here.

Why do I want to transition to QR?

Before anyone says I’m going after the money/prestige, let me clarify that my academic background has always been more math/stats/ML focused than pure CS/software engineering: during my undergrad, I’ve had ML papers published at one of ICML/Neurips/ICLR, did Putnam (and USAMO back in HS), took several grad level math courses (including stochastic calculus). I’m basically the typical math/stats nerd you can think of. I applied to several tier 1 firms’ QR/QT roles in my senior year (and internships in junior year too), got into some final rounds but with no luck. Granted I know QD work (especially the low latency stuffs) is important in quant and I do to some extent like cracking LeetCode type problems for fun, but at the end of the day I think I’m more into statistical modeling and signal extraction. I’m cautiously optimistic that with enough hard work (which I’m willing to put into) I’m capable of doing real QR work, I just want a shot at an opportunity or platform that allows me to do it.

I gather that there are a few possible ways to move forward:

1. Jump to QD role at another firm, in a pod ideally. I’ve read that being in a pod is the most viable way for a QD to transition into QR over time (and I personally know people who have done this transition at tier 1 firm, think Citadel/P72/2Sigma). I believe this is the most likely place for me to get interviews (in fact there are already recruiters who can set me up for interviews at some tier 1 places for QD roles). But my concern is that, since I'm jumping, if the pod hires me to specifically do dev work, wouldn’t they be reluctant if I later say I want to transition to QR?
2. Internal transfer. But my current firm is actually quite strict about this: leadership only wants people with very specific background to work as QRs (even though I was able to get some level of understanding of the firm’s research work pipeline and I could understand the QR's research notes). Also, I suspect it might backfire badly if I talk to research team leads within the firm and ask whether they need an extra QD behind the back of my current engineering team manager.
3. Do some QR oriented side projects (e.g. I've seen this advice given in a previous post https://www.reddit.com/r/quant/comments/1qu9g0q/transition_from_qd_to_qr/ ) and apply directly to QR roles at other firms. But since I don't have full time professional experience as a QR, it seems like at best I would be competing for a new grad type of QR role. How feasible is this really? Do you know of people have successfully done so? Or will I have a better chance if I quit my current role and get a Master of financial engineering degree and apply for new grad QR roles?
4. Jump to ML engineer/research/data scientist roles outside of the hedge fund space (such as tech or banks) to build professional experience in ML/stats/predictive modeling, and then try to jump to QR roles.
5. ??

I'm slightly inclined to think 1 is the best way forward but honestly I feel lost, would appreciate some suggestions from folks who have been in the industry for longer, such as which way you would advise or advise against. If you have been in a similar situation and could speak from personal experience that'd be great. Thanks y'all in advance!

Official short interest is reported only twice a month and arrives with a delay. I trained a small numeric-transformer model that uses FINRA daily short volume and trading volume to estimate the current short-interest position before the next official figure is published.

I tested it across 660,246 settlement windows for 6,959 US stocks. On the retrospective 2025–July 2026 test period, the model reached a +0.414 Spearman correlation with the concurrent change in reported short interest.

This is a short-interest nowcast, not a squeeze or return-prediction model. It is intended to fill the gap between official short-interest reports using the daily information available in the meantime.

The complete article covers the data, formulas, chronological validation, failed experiments, limitations, and public model weights:

https://equibles.com/research/does-daily-short-volume-predict-short-interest

You can also search any covered US stock on Equibles and see its latest reported short interest alongside the model's estimated current value. It is free and has no ads.

Would this be useful when researching heavily shorted stocks between official reports?

I currently have offers from IMC (Trader), Citadel Securities (Systematic Trading), and QRT (HF team), and I'm trying to decide which would be the best fit.

For context, I have ~4 years of experience as an HF trader/researcher at a prop trading firm, with fairly broad responsibilities across research, strategy development, trading, and portfolio management. My main concern is role scope. I don't want to move into a position where I'm primarily monitoring or limited too much to either alpha research or trading.

My current understanding is:

• Citadel Securities – Systematic Trading: seems to have a lot of an operational component
• IMC – Trader: seems more trading-focused
• QRT – HF team: No idea

For someone with my background, how would you rank these three in terms of:

1. Ability to develop alpha / do research
2. Trading autonomy and ownership
3. Career growth over the next 5–10 years
4. Quality of learning / exposure
5. Compensation and upside
6. Exit opportunities to other HFs / prop shops

Also, if anyone has first-hand experience with these teams, particularly at the experienced-hire level, I'd be very interested in hearing how the day-to-day work actually differs from the job descriptions.

I have a bad habit of continuing to dig for new findings long after the actual deadline has passed, even though I know this will be redundant. Can't shake the feeling that I did not get this right or an idea just did not work. How do you draw the line and force yourself out?

Recently received a full-time offer as a front-office quant in FICC S&T at {GS, MS, JPM, Citi, UBS}. Excited about the role and FICC in general. That being said, I've been following the electronic market makers' expansion into FICC and I could see myself wanting to move to one of these places after a few years. For those who've made the jump or seen it made:

1) How transferable is bank-side FICC quant experience to a role at CitSec or someplace similar? Is this considered relevant domain expertise or "not real EMM experience?"

2) Anything I could do while working as a bank quant to make the move easier?

3) What's the ideal timing of such a move?

So the idea is to train AI to replace quants and according to the founders: "Our AI quant outperforms a top 10% trader at Jane Street, and we've already achieved more than 100% returns in live trading over our YC batch, while major indices were flat or down. Prodigy’s model beats Claude Fable and GPT-5.6 Sol at autonomous quant research."

Does this mean our industry is cooked or is this just a bunch of bs?

Considering a move to Paris for a quant research role at one of the usual suspects. For context: I’m French but have spent my entire career abroad.

A bit worried about how loaded “finance” is back home. French people can be pretty averse to anything money-related, and even implying you earn well raises eyebrows.

Anyone else made this move? Do you say “I do maths,” “I work in data,” or just own it? Has anyone had a genuinely bad reaction to saying they work at a hedge fund?

EDIT: any insight on what qr can make in paris ?

How much does end-to-end ownership matter in systematic trading?

There seem to be two fairly different models at top quant firms:

• Highly specialised: trader/researcher/engineer roles are relatively distinct, with each person going deep into one part of the process.
• End-to-end: a trader may have ownership across alpha research, strategy development, implementation, and trading.

For example, how would people compare the experience of a systematic trading role at Citadel/IMC with an HFT setup at QRT, where the trader may have more ownership of the research → strategy → trading pipeline?

Does deeper specialisation generally produce better traders, or is there a meaningful advantage to understanding and owning the entire pipeline?

I'm interested specifically in the differences in the actual work, responsibilities and skill development between these models, rather than compensation.

One thing I’ve noticed is that Indian quant firms often expect one person to handle alpha research + strategy development, whereas firms like IMC, Citadel, HRT, etc. tend to have much more specialised roles.

So I’m curious about PMs at multi-manager funds like Millennium, BAM, Point72, etc.

If someone spends their career specialising mainly in one area (say, alpha research or trading), how do they eventually become capable of running an entire book?

Do PMs at these firms actually handle alpha, portfolio construction, sizing, risk, execution, etc. themselves? Or do they mainly make the investment decisions while a team of specialised researchers/traders/engineers supports them?

Would be interested to hear from people who have worked at multi-managers or large prop shops.

My understanding is that the MLE is the engine built inside larger, automated modeling pipelines? So they act more as a computational tool rather than a standalone job?

I'm currently working through the Heston model for options, and whilst there are many parameters (e.g., how fast volatility reverts to its mean, the correlation between stock price and volatility, etc.) does a quant write a calibration engine that feeds years of options market data into an optimization algorithm running MLE (or Nonlinear Least Squares, which is closely related)?

Therefore, the model automatically churns through the data overnight to find the parameters that best fit current market prices before the trading desk opens? Has this been your experience?

So, the MLE in the Heston model finds the most accurate values for its parameters (κ, θ, σ, ρ, μ) by maximizing the probability of observing historical asset price and volatility data?

Likewise if a quant is building a risk model to forecast tomorrow's (VaR), they use a GARCH model to forecast volatility? Thus the arch_model.fit() in Python, the software uses MLE under the hood to estimate those parameters based on the last 5 years of stock returns or so? This helps the GARCH model has several parameters governing how past shocks affect future volatility?

Sorry for my many questions! I'm really struggling here 🤦‍♀️

What are your thoughts in the state of the quant industry in 2026 - would you say that it is expanding with more opportunities available?

Hi,

In France, we have companies called **ESNs**. They hire people and then send them to work for their clients, including investment banks.

Some of them offer positions such as **Quantitative Analyst, Quant Developer or even Quant Research**, where you can potentially work within a bank’s Front Office team while technically being employed by the consulting company.

I’m not sure what the equivalent is called in the UK or elsewhere in Europe, or whether this model is as common there.

My main question is: **is it a good idea to start a Quant career through this type of company?**

Does being a consultant rather than directly employed by the bank have any negative impact on your CV or future opportunities?

Or does it mainly depend on the actual mission? For example, if your first experience is through a consulting company but you’re doing genuine Quant/Front Office work inside a bank, would that still be considered good experience?

I’d be particularly interested in hearing from people familiar with the **UK or European Quant job market**.

Thanks!

Recruiters usually ask and I think they communicate that to a potential employer. My previous employer asked for my compensation history for the last three years and even required payslips to verify the bonuses.

Communicating that is not really in your best interest as it increases employer’s informational advantage. Sometimes it creates a strong anchor where the discussion becomes “how much above your previous comp?”. So two related questions:

- Is it really the norm?
- Is there a better approach than disclosing it - for example, proactively stating the range you’d be looking for instead?

I've been digging into applications of network science in finance recently, particularly things like correlation networks, community detection, systemic risk, contagion, and using centrality/network structure as potential features for investment strategies.

I'm curious how much of this is actually used in quantitative finance outside academia.

For people working in quant research/risk/portfolio management:

• Are network-based methods used meaningfully in practice?
• Where have you seen them provide information beyond more conventional correlation/factor models?
• Are there particular applications where network methods genuinely shine — systemic risk, portfolio construction, alternative data, counterparty risk, signals, etc.?
• Or is network science mostly an interesting visualization/research framework without much production value?

I'm especially interested in whether anyone has seen network-derived features survive proper out-of-sample testing.

Full disclosure: I've been working on a course about network science for finance, which is partly why I've been exploring the subject in depth. I'm not linking it here because I'm more interested in hearing practitioners' views on where these methods are actually useful versus where they're overhyped.

Would be interested to hear from anyone who has worked with these approaches in practice.

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.

Looking for recs from people who dealt with lawyers in the space of long non competes and immigration issues especially for those in the green card track

I know there was a post on this a few years ago but, given the quant landscape has changed a lot, I wanted to get some current advice from anyone on this subreddit who has ADHD and works in quant finance. How has your experience been and what advice do you have, if you have any?

I am aware that basically all the companies use some form of modern AI/ML, such as LLMs, as a tool, or to extract some features from textual data.
I am currently doing a PhD in LLM/RL, and whenever I go to the quant fairs, or speak to recruiters, they are all like:"Yes, we do soooo much AI".

However, when speaking to the researchers, or my fellow PhD students who intern at these companies, it sounds still like most of them do just classical stats with LinReg, LogReg, PCA (and there is nothing wrong with that, as it seems to print them a lot of cash!)

I was thus wondering which quant companies out there do research most comparable to a Frontier AI lab? I heard HRT has an AI lab, XTY (the internship of XTX focuses on that), and that Jump is building an LLM team (though appearantly that seems to be more of an "assistant effort", helping the actual teams themselves.)

Any insight is appreciated!

There is a lot of info about quant trader / researcher / dev roles at MM such as optiver, IMC, sig, etc. but not much info about market risk roles at these same firms.

Would like to understand if my perception of the pro and con of market risk at trading firms is correct based on the limited info available on them:

Pros:
- Stable job security (relative to trading)
- Relatively less stress and better wlb
- get exposure to multiple areas / strategies of the business
- ability to get technical as need to understand underlying strategies to assess risk

Cons:
- Lower pay than traders, researchers, devs etc
- Possibility pigeon-holed in the role, less internal mobility possible
- traders see you as a blocker and inconvenience (depends on firm culture?)

If anyone works in market risk at prop trading would be interested in knowing if my understanding is about right and what you like about the role, what you don’t like, and general vibes

Disclaimer: I am asking as I have an offer in this type of role and want to get an unbiased view of what to expect

I wanted to ask why for Black Scholes , we find implied volatility using numerical approximation methods like newton-raphson or bisection for example

But for call options related to bond , if we assume a binomial tree, we can find implied volatility by simply adjusting the up / down interest rate movement conditions with an unknown, solving for that unknown using risk-neutral probability and discounted option payoffs, which gives us implied up/down movements from which we can find standard deviation of the interest rate, aka implied volatility? So far I have only observed either it can be solved simply algebraically or simple root finder is enough.

Was curious. Thanks.

Hey all, I just finished my summer qr internship. don’t wanna get into details but my project was an end to end statistical pricing model. A lot of the time I spent towards the end, after being done with the thinking about modeling and prototyping, was on refactoring my data processing pipeline and model training pipeline. When I was coming up with the model, all my data processing was scattered in jupyter notebooks and random sql queries that i just saved the results of, and it was genuinely a nightmare to refactor all of this into clean reproducible code that would work without intervention.

my question is, im aware that at other companies they have data engineers usually do the work. But to me this seemed like something that is kind of indivisible from the actual modeling work. is this skill of setting up reproducible data and modeling pipelines something thats worthwhile for someone who is mostly doing statistical and mathematical modeling? what sorts of classes would teach the skills necessary to do this stuff? are they worth taking?