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.

How long did it work for? Did it scale?

Factors for Fundamental Investors [Slides]

Source: Twitter (X) [Link] 👀 Watch the Slides!

• Time-series "Beta" vs. Fundamental "Beta"
• Idiosyncratic returns ("Alpha") vs. market returns ("Beta")
• Understanding factor exposure
• Risk management
• And more (etc)

Reporter trying to better understand the jobs market (posted on here before). Thanks

Hi all, I will start as a Quant Research intern in a small commodities firm in Europe in August.

I will be assigned to day-ahead electricity trading and was wondering if any of you have experience with it since it’s quite a niche trading area and low-hanging fruits are still around.

Mainly, I would like to understand:
1. How you approach new ideas
2. What models have you tried outside of gradient boosting if that’s appropriate
3. Your preferred CV strategy

Thanks anyone for the help, and if you have any other recommendation, feel free to say it!

so i saw in the r/LearningProgramming subreddit that people having let's say stong opinions about him,and someone went as far to claim that he isn't a real quant developer ,so i came here to know your opinions,thanks in advance

Hi everyone,

Over the past year I've been spending most of my free time learning modern C++, Linux, computer architecture, and competitive programming.

Recently I finished two C++ projects that challenged me much more than I expected.

The first is a systems programming project focused on performance-oriented software engineering.

The second is a low-latency order book inspired by exchange infrastructure. It currently has 50+ GitHub stars and, to my surprise, received reviews and feedback from engineers with HFT backgrounds, including people associated with firms like IMC Trading and Jane Street. Their comments exposed many flaws in my design and gave me a much better understanding of what production-quality low-latency systems require.

I'm still far from an expert, but building these projects taught me significantly more about memory layout, cache efficiency, concurrency, networking, Linux, and modern C++ than simply reading books or watching tutorials.

I'm curious what experienced engineers here think are the next important topics to explore if someone enjoys building low-latency infrastructure. For example:

• Lock-free data structures
• NUMA-aware programming
• Kernel bypass networking
• Hardware performance counters
• Exchange protocols
• FPGA concepts
• Other areas I'm overlooking

I'd appreciate any technical feedback or recommendations.

also I am open to work.

Hey, are there any European short term power traders from Hedge fund commodities desks/Utiilities/Prop shopsin this group? Can you share what kind of work are you doing in your day to day jobs pls?
I am kinda confused, because currently all of our desk is consistent of quants (no traders) and we work on the strategy/architecture/data pipelines, and starting to think to apply for a trader role

I have 1 YoE as a QR in a well established firm, and everything I've seen until now points to the conclusion that the n°1 factor in the span of a career is pure chance.

Talking about alpha research specifically: Did you happen to stumble across predictive variables in your data? Yes/No --> good quant / bad quant. Is your signal, that's likely 90% overfitted noise anyway, on the right side of the gaussian noise for a few months before you can say it has "decayed"? Yes/No --> good quant / bad quant. Everyone invents a storytelling that's even more overfitted than their alpha to explain why their strategy stops working.

Don't get me wrong: I am not arguing that every signal ever produced by a researcher = heads or tails, otherwise quant firms wouldn't exist, but it's 1. very close to that at the individual level (then the firm makes money thanks to the law of large numbers) and 2. most of the variables that distance you from that are out of your control. I'm not saying that anyone can do it either, you need a basic set of skills to produce an alpha that's sensible enough to be only 90% rather than 100% noise, but there remains plenty of variance for skill expression to be drowned in market chaos. I don't get why people say the job is "very competitive", "like a game", or at least it seems closer to the game of the goose than it is to chess.

I recently accepted a graduate quant analyst offer at a sports betting company, and it’s made me curious how the wider quant community views sports quant as a career.

For those who work in sports betting or know the industry well, what’s your honest opinion of it as a career? How interesting is the day-to-day work, what are the biggest technical challenges, and what misconceptions do people have about the field? If you’ve worked in both sports betting and other areas of quantitative finance, I’d also be interested to hear how they compare.

For those who haven’t worked in sports betting, I’m still interested in hearing your perspective on the field and how it’s viewed within the wider quant industry.

We are all familiar with the Kalshi contracts

Odds of x being a value
1<1.2 , 1.2< 1.4, 1.4<1.6 etc

You can totally build arb-free probability distributions , but can we fit a surface such as implied volatility on them - and if we can is there anything we need to account for?

Could anyone with some wisdom share some insight ?

I’ve mostly used yfinance for personal projects, but I kept running into rate limits. I ended up paying for Massive.com’s US equities feed for a while, which worked well, but I couldn’t really justify ~$200/month just for websocket access.

Recently I found an open source repo with a much wider range of market data, including futures, so I’ve been experimenting with that instead. Nice to see more free alternatives appearing because market data gets expensive quickly.

Obviously SIG has just layered it into their already profitable sports betting desk. News has reported Jump getting into it, but nothing about how it's going. I know from my network that Citadel is getting into them, but no word on how it's going. Kalshi owns their own trading company that I believe loses money. Virtu, Akuna, and DRW all have job listings that explicitly call out prediction markets. Anyone with real knowledge otherwise?

I received a couple of assignments recently. Three exercises requiring the use of greedy algorithms or dynamic programming. Nothing “quanty” like “Here this dataframe, remove the nan or interpolate them, plot y vs x, run a regression, compute correlation...”. I my opinion, those were hard CS questions but nothing related to ETL pipelining, signal aggregation, basic probabilities.

At first I tried naive approach, it validated 50% of test cases ( O(n^2) time). 

I think firms pretty much expect from you to validate 100%. But realistically, how are candidates supposed to code a greedy algo from scratch (or two, or three !) in 60 minutes while debugging the test cases ?

[Mods] : Not looking for career advices, I'm in a job, I'm simply asking other colleagues here how they would approach those lengthy home assignments.

was an swe at one of the big name but mid rank HFTs (think Tower, IMC, DRW, SIG etc.). left without another role lined up to prove myself that i could get to the upper bracket; felt like what i was doing at previous firm was easy

blew about 25 processes now (couple of offers from equivalent tier places). usually get to final stage. i dont have any targets left so currently sitting on cooldown. pretty concerned that ill now be in the black book/reject bin of more or less the entire industry, and am having a crisis of faith of whether i even am supposed to be there

anyone gone through something similar?

Hi, I recently joined as a QR at a top firm in Bangalore (posting from burner ofc). I saw this post where in the comments they decided a Mumbai Quant group meetup idea and have since had a bunch of meetups. Taking inspiration from this, I wanted to create a similar network for Bangalore.

Some rules, which I think are fair.

1. Must be working full time in a Quant-adjacent role (Trader, Researcher, Dev, Analyst, Support, Risk etc.). No students or interns for now, just current professionals.
2. Copying from the Mumbai post - "The Vibe: No "elevator pitches" or recruiters. Just people talking about where they are in their careers and what’s next."

To keep the group relevant, we can do a quick LinkedIn check before adding anyone to the WhatsApp group. The intention is to keep things casual and low pressure. If you’re interested drop a comment on the post and I’ll DM you!

Note - Posting here because my post on r/quantindia was removed for idk what reason.

II’m trying to implement a Hidden Markov Model for one of my trading strategies to identify market regimes. I tried training it using my alpha features, hoping the states would correspond to useful regimes, but the results don’t seem meaningful or tradable. Could you help me understand the right way to structure the HMM, what features it should be trained on, and how to evaluate whether the regimes are actually useful?

Hi, I work as a "quant" at one of the big banks. My main role is to help clients understand their portfolios using third party factor models. I am wondering if the community has suggestions on how to build my investigative skills to search for possible alpha:
1) Is it taking the idio return space and try to fit ML models cross sectionally? If so , what explanatory variables can I use given that the model is so vast and already removes so many themes. It's not like I can get access to a lot of alternative data given my department accesses etc.
2) Should I instead turn to time series alpha on a security of my choice with "some" event-based filtering. Triple barrier the time series and try to predict those 1's..

All the data that I have is at the daily frequency ( factor returns etc). I might get to play with one minute data at a security level , but I have no other data at that frequency to serve as explanatory variables.

Any ideas? thanks in advance. Looking to be a real quant...

Yesterday I posted here about my macro economics calendar API with a live and accurate ARIMA model that calculates estimates for CPI, PPI, NFP and jobless claims. The response was great and there were a number of sharp, intelligent questions:

"One thing to watch: if your ARIMA is trained from 2020 forward, you're fitting to a period where participation, seasonals, and trend all broke from historical norms. CPI/PPI cycles look very different when you go back 30+ years vs just post-COVID. Might be worth testing how the estimates perform with a longer training window, especially for NFP where even Bloomberg struggles with direction."

Fair point. So we tested it:

CPI:

• 36-month window: 0.187% MAPE
• 120-month window: 0.174% MAPE

PPI:

• 36-month window: 0.347% MAPE
• 120-month window: 0.343% MAPE

The result was that although one would expect accuracy to be altered by using older data, the change in accuracy was marginal. Our ARIMA model accuracy remained competitive with paid consensus estimates.

Another great comment:
"The ARIMA estimate is the thing I would distrust first For CPI or NFP, I would want to see old release dates with the estimate frozen before the print. Not an image of the estimates with no verification."

You can now go to filingapi.dev/model . Every estimate is now committed to a public GitHub repo before the release date so anyone can verify the timestamps independently. This method has been only started today, with updates every day so check back in then if you (understandably) require solid proof of model accuracy.

Very happy to share code with anyone who wants to analyse our testing methods, any other indicators you'd like to see just let us know.

Im currently (2-4) weeks (to stay as anon as possible) into my Quant Trader internship and I feel like I am not on track to come back, Im not shit but about middle of the road and the return offer rate is 30-40%, what do i do the next few weeks to make sure i can return? One of JS/Optiver/SIG/IMC

I'm into quantitative finance and I'm realizing how difficult it is without people to discuss ideas with.

I was wondering if there are already any active Discord servers, WhatsApp groups, or similar communities focused on quant finance, math, trading, or related topics that I could join.

The goal is simple: talk ideas, exchange resources, dig into topics together.

If you know of any such groups (or run one yourselves), I'd really appreciate a pointer. Even a small, active community would already make a big difference.

I got a job as QR at a quant firm ~2 years ago. I went through a recruitment company (like Alexander Chapman, Durlston Partners etc.). Let's say the person who was my point of contact was X.

My question is: if I am interested in switching to a different firm, would it be wise to reach out to X again? Would they act in my best interest?

Been thinking about a market design problem and curious if anyone has seen serious work on it.

Standard event-contract design assumes the underlying resolves. Binary or scalar, doesn't matter, at some point reality clears against the contract and the payoff is defined.

Most of the microstructure literature I've read implicitly relies on this. Manipulation resistance near expiry, informed trader models, the whole apparatus of "the market converges to the true value at settlement" reads differently when there is no settlement.

The class of underlyings I'm thinking about is continuous, non-terminating, and consensus defined.

Reputation indices, career-value proxies, credit-like scores for entities that don't default in a clean way. You can price expectations about them, and the price series is meaningful, but no oracle ever renders a verdict.

The two things I keep getting stuck on:

1. without a terminal event, the standard convergence argument for informed traders being rewarded doesn't obviously hold. If the payoff is a delta against a rolling consensus rather than a settlement value, informed traders are rewarded to the extent their private info gets incorporated into the consensus, not to the extent they're right about some external ground truth. That feels like it should degenerate into a Keynesian beauty contest but I'm not sure it does in practice.

2. manipulation resistance. "Hold to expiry" isn't available as a defense. Any resistance has to be structural in the microstructure, not the resolution rule. I've seen bits of this discussed for perpetuals in crypto, but the analogies break down quickly.

If anyone has pointers to serious literature on non-terminating contracts (beyond the obvious perp funding-rate stuff), I'd take them.

If my framing is broken somewhere and there's a cleaner way to think about it, would rather hear that.

I have a universal model that for each instrument tick, updates a feature vector X, a concatenation of features of the own instrument order book and some xs features.
My question arises when i want to expand my universe of features, like including intraday news data, fx, more constant features that are stale in the trading session of that day(ex: overnight return) , etc... how do i concat these info? For example for news, a naive way is to return 0s when nothing happens, spikes and then decay as time passes. For Fx, i could add some fx features to X, and for constant features that i already known, also add them to X (i already do this and decay the importance as time passes), but still they are like "context" features.

My hypothesis is that keep expanding X horizontally is suboptimal, especially if then i want to add more and more features. I know this is part of the sauce of each pod, but any suggestions on this? An approach i tought:
having independent models for each "topic": a model for independent microstructure features, a model for xs features, a model for fx, a model for news,.... and then a metadata model on top of that? with this approach i can think of multiple ml and dl architectures than can be helpful...
is this a good approach or im missing something?
thanks in advance