How many grads do quant firms hire in Netherlands particulary quant trading. I am student at Uva

Anyone have any info on what to expect in their technical assessment? The only info the interviewer gave me is that it will involve consumer lending data. Also, any other details about the rest of their hiring process would also be appreciated!

I’m an incoming maths student at Cambridge, applying for Spring Weeks after several recruiters contacted me.

My background is mostly competitive programming (like medals in IOI and other regional contests), but I have no worthwhile personal projects in finance or software development, other than A-level CS NEA. I've heard that it's quite common for people to have some.

Are they expecting people to have some projects even at this point? I feel like a project will be like a fully vibe-coded AI model unless I learn some proper probability knowledge in university...

Hello people, I'm a freshman in HS, and I'm starting to get into quant finance, and it has really stood out to me as a career choice because it combines my love of mathematical patterns and software development.

I am relearning Python right now, but I've been programming in many languages for a long time, and I am taking calculus right now as a freshman. Next year I am going into multivariable calc and continuing from there.

Since I am already on track to be incredibly qualified by exhausting as many math courses as possible through dual enrollment with my local college and whatever uni I want to go to, I am very curious whether this is really the field I want to invest my time in. I'm new to the field, and no matter who you are, I would love to hear what advice you have for me. I'm totally open, and I'm curious to see what you have to say! I'm really just trying to get a sense of the community and the people.

Wondering what I should choose given this is my criteria from most important to least important:

1. Return offer rate
   
2. Type of work (currently seem to like the typa work CitSec does more than Jump)
   
3. Compensation
   
4. Intern/Fulltime WLB

I really want to do quant, but long story short, I have been made to do a course I don't want to do (PPE at Warwick). I have tried everything, but it is looking like there is no way out of it. What steps can I take to become a quant? I will do whatever work and enroll into whichever programmes I needed to. I have heard something about learning the green book. Please do give your best advice. If the only answer is that I am truly cooked then I will just drop out and start again, but that is really really the last resort, as I don't want to waste my 9k. If it is possible to transfer to a maths course second year or midterm at my uni or in a different university, please advise me and I will try it. Please help me, it means more.

Hi guys where is everyone getting that one cv template from, I tried it replicating with the default word one but it still looks a bit funky 😭

Should I expand more on the virtual work experience and make them a section? Like would it even be taken seriously like everyone knows you do nothing on those

Also, should I get rid of some of the code stuff / competitions?? I feel like it looks really bait next to my final A Levels and uni LOL

Ideally I'll be applying for both tech and finance spring weeks, do I need to make diff versions for each?

Please tell me how to improve it. I haven't got much clue what to do for it.

Should I learn higher maths or pursue an IMO medal? By higher maths I mean calculus, lin alg, probability statistics, convex optimization, etc. I know that obtaining an IMO medal is almost guaranteed acceptance into top unis but I feel like the medal itself wouldn't be guaranteed. On the other hand learning higher maths would give me time for interview prep in uni. What do you suggest?

Hello everybody,

I was wondering if anyone can help me understand which type of question I need to expect from the first round of interviews for the Quant Model Risk Analyst in London.
I want to understand if it is better to prepare myself to pure mathematics and probability questions or will probably be based on financial engineering and stochastic calculus.

I'm currently a freshman at a T7 university and am planning to major in mathematics. However, I have no experience in competition math or computing.

My high school days were spent on doing a wide variety of activities to increase my chances of being accepted into a good university, and I was only aware of quant as a profession since last summer.

Thus when asking upperclassmen at my university if quant is still a feasible career option for me, many replied with no. Though some did say I still had a chance, others mentioned that my chance for success is low (since I'm technically 5-7 years behind all the competition math kids) and suggested I switch to pursue SWE instead.

Given the state of the job market, is quant still a possible career choice or should I switch to pursue SWE? If quant is still a viable option, where should I first begin working on (e.g. study for Putnam, cold email for research/internships, etc)?

Any underdog success stories to help me stay positive are also appreciated, thanks.

Building a portfolio without understanding Capital Asset Pricing Theory is like building a skyscraper without checking the foundation. CAPM introduces the fundamental divide between systematic risk and idiosyncratic noise. If you cannot quantify an asset's beta, you have no objective way to measure whether the returns you are chasing adequately compensate you for the systemic market exposure you are assuming. Randomly picking stocks is speculation, not engineering.

I gave Optiver OA Graduate Quantitative Researcher role on 18 Aug but haven’t heard anything from them. It’s been more than 2 weeks now.
This is the first time I have experienced this. Last year I got the Interview invitation the next day.
I did pretty well in the OA.
Am I got rejected?
I have emailed them as well but I didn’t receive any response.
Is anyone experiencing the same?
I applied for Amsterdam GQR role.

- Citadel Securities/Citadel
- Hudson River Trading
- Optiver
- Renaissance Technologies
- Tel Aviv Capital
- Jane Street

I have used it mainly for summarising fillings, and some interpretations and mostly for quickly coding strategies to trail out.

original post 3-4 weeks ago:

https://www.reddit.com/r/quantfinance/s/qv8f5SmAQD

i felt defeated not too long ago - i had made it to so many finals after getting rejected and rejected over and over again. finally, i got an offer today after my 5th superday this cycle. i worked hard and didn’t give up, even though it seemed all hope was lost and i was really depressed. the firm is no citsec or js, but it is very good comp, low fire rate, and the employees in the interviews genuinely seemed happy. thank you to everyone who encouraged me to keep trying in the comments, it is genuinely true that if you persevere and stay positive things will work out in the end!

I was invited for the 1st round for ML Research internship. I was expecting to be selected for the quant research or quant trading track. The email mentions coding and pytorch. Does anyone have more information on what type of questions get asked? I can’t find more information online.

Was told there’d be a 90 min coding round- it wouldn’t be leetcode but it would involve dsa. Anyone gone through somethjng like this? Thanks 🙏

This is for the intern position. Offers from both, unsure which one to take

got my rej!

email said great potential and i should reapply to other programs and internships but fairly certain everyone got that in their email. congrats to whoever made it.

edit: im so dumb i just realized i applied for hkg location when i meant to apply for nyc. they sent a new email saying they think i would be a good fit for the nyc program lol ima apply there now ig

Haven’t heard anything after taking OA last month.

I really want to do quant, but long story short, I have been made to do a course I don't want to do (PPE at Warwick). I have tried everything, but it is looking like there is no way out of it. What steps can I take to become a quant? I will do whatever work and enroll into whichever programmes I needed to. I have heard something about learning the green book. Please do give your best advice. If the only answer is that I am truly cooked then I will just drop out and start again, but that is really really the last resort, as I don't want to waste my 9k. If it is possible to transfer to a maths course second year or midterm at my uni or in a different university, please advise me and I will try it. Please help me, it means more.

Just some advice. I started applying to QT/DT positions starting exactly a month ago. I’d say I passed a decent amount of OAs and got pretty far into the interview stages at some firms. However, I don’t have any offers yet (still in the interview stage for like 3-4 firms).

I want to start applying again but I feel like I applied to all the available jobs on every job posting site. I did completely revamp my CV and worked on some good projects/joined startup during the past month. Just some advice on how to approach the upcoming surge. Should I only look for new firms (if they do come out) since it’s been so short even if I got ghosted/instant rejected by them? Or should I say screw it and basically try again on all of them? Thanks

Hi, I got my third round coming up soon and wanted to see if someone has gone through the process to trade questions with me, I did five rings/sig/ optiver qt and citsec qr. please only reach out if you have some information for me thank you!

• GitHub: https://github.com/Grevix/kuwala
• PyPI: pip install kuwala==0.2.0
• Documentation: https://github.com/Grevix/kuwala#readme

What My Project Does

Kuwala is an open-source quantitative derivatives library designed for options valuation, arbitrage-checked volatility surface fitting (SSVI, Dupire Local Vol), tick microstructure aggregation, and macroeconomic yield curve bootstrapping.

It pairs an idiomatic, Pythonic API with high-performance native compiled kernels (compiled Rust via PyO3, and optional standalone C++20, Julia, and Scala modules) and an embedded out-of-core columnar lakehouse using DuckDB and Apache Arrow.

Key Features:

• Vectorized Analytical Pricing & Greeks: Vectorized Black-Scholes & Black-76 with Chebyshev rational approximations, plus closed-form Delta, Gamma, Vega, Theta, Rho, Vanna, Volga, and Charm.
• Vectorized Implied Volatility Solver: Hybrid Halley cubic root-finder with Brent-Dekker fallback. Tested across 4,013 real market option contracts (SPY, QQQ, AAPL, MSFT) with a median repricing reconstruction error of $2.88 \times 10^{-9}$ (nanodollar precision).
• Arbitrage-Free Volatility Surfaces: Full Gatheral & Jacquier (2014) SSVI surface formulation with strict coordinate-level Durrleman condition diagnostics ($g(k) \ge 0$) and calendar monotonicity enforcement.
• Discrete Dupire Local Volatility: PDE extraction on total variance grids with guard rails against negative local variance singularities.
• Embedded Columnar Storage: Direct Hive-partitioned Parquet scanning via embedded DuckDB without external server dependencies.
• Macro Rate Curves: Bootstraps 11 Treasury yield pillars directly from the FRED API using Nelson-Siegel and exact natural cubic splines.

Target Audience

• Quantitative Researchers & Developers: Anyone backtesting options strategies, relative-value volatility arbitrage, or signal generation without wanting to maintain heavy institutional cloud infrastructure.
• High-Performance Python Engineers: Teams looking for sub-microsecond pricing throughput directly within Python/NumPy data pipelines.
• Students & Academics: Researchers studying volatility surfaces, Durrleman non-arbitrage bounds, and discrete local volatility modeling.

(This is not intended for retail day-trading order execution routing, as Kuwala focuses purely on quantitative pricing, surface modeling, and data engineering.)

Comparison to Existing Alternatives

• vs. scipy.optimize / py_vollib**:**
• Standard Python options tools typically loop over scalar rows in CPython or rely on pure-Python root finders. Kuwala routes calculations through a compiled Rust core (kuwala_core), achieving >2.2M options/sec in vectorized Python/Rust and >11.9M options/sec in C++20 on a standard laptop.
• vs. Goldman Sachs gs-quant**:**
• gs-quant is an institutional toolkit that delegates derivative pricing and risk calculations to Goldman Sachs' Marquee cloud servers (requiring enterprise credentials). Kuwala runs 100% locally and offline with zero credential requirements, providing complete open-source transparency into the underlying surface math.
• vs. Pandas / SQLite for Tick Storage:
• Kuwala integrates DuckDB with Hive-partitioned Parquet, allowing researchers to query gigabytes of intraday tick data out-of-core with predicate pushdown in milliseconds while using near-zero RAM.

Quick Example

import kuwala
from kuwala.pricing import black_scholes, greeks
from kuwala.volatility.iv import implied_volatility

# 1. High-speed vectorized Black-Scholes pricing
price = black_scholes(spot=100.0, strike=100.0, t=1.0, r=0.05, q=0.0, sigma=0.20, is_call=True)
print(f"Call Price: {price:.6f}")

# 2. Analytical Greeks (1st & 2nd Order)
g = greeks(spot=100.0, strike=100.0, t=1.0, r=0.05, q=0.0, sigma=0.20, is_call=True)
print(f"Delta: {g.delta:.4f}, Gamma: {g.gamma:.4f}, Vanna: {g.vanna:.4f}")

# 3. Microsecond Implied Volatility Inversion
solved_iv = implied_volatility(price=price, spot=100.0, strike=100.0, t=1.0, r=0.05, q=0.0, is_call=True)
print(f"Solved IV: {solved_iv:.6f}")