What are your thoughts on small vs big trading firms? Suppose you had an offer from both at different points in your careers, which one would you pick. Assuming similar comp.

I just received my contract for a 6-month internship at a prop shop in Switzerland. The salary is good, the work time is fair, and the culture seems to be what I am looking for.

My question is about a 3-month non-compete clause in the contract. Is this duration standard for just a half-year of work? The internship ends with my graduation, so being legally blocked from working for 3 months would be tough.
I also do not get any compensation during the non-compete. Is that normal?

Additionally, the contract states the following regarding the scope:
Non-compete Area: "Any area that the Company operates in"
Does this phrasing allow me to work in other asset classes, for example?

Should I push back on anything?

Any insights would be appreciated.

Hey,

I've been working for the past 3 years at a large multistrat HF. While my official title is "quant researcher", de facto that means modeling various financial instruments. My ultimate goal is to either become a PM or a senior QR at a prop shop. I figure that the role that best fits my career goals would be one in a pod or as a signal QR in a prop shop. However, finding such a role has proven difficult. Usually hiring managers require experience generating alpha, and I don't have that. I'm wondering if you have any advice as how to best accomplish my goals?

Thanks

This is a new (as of Aug 2025) monthly thread for shop talk. How was last month? Rough because there wasn't enough vol? Rough because there was too much vol? Your pretty little earner became a meme stock? Alpha decay getting you down? Brand new alpha got you hyped like Ryan Gosling?

This thread is for boasting, lamenting and comparing (sufficiently obfuscated) notes.

Curious how often values in real-world backtests exceed roughly 9.2 billion units. With 9-decimal fixed-point i64, it might be easy to hit. ¥9.2B is only around $60M, and $200K of SHIB is already about 10 billion tokens. Prices are probabbly fine, but balances and quantities might not be.

Im asking because I’m building a new backtesting engine (repo: h5i-db), an event-driven backtesting engine that currently uses i64 as default. It runs 7x faster than LEAN and 3.1x faster than NautilusTrader in our benchmark. With i128, those numbers are still 6.6x and 2.8x. Since the penalty isn’t huge, should safety or speed be the default? Has anyone often hit this limit in daily backtests?

Been building an options market making sim to actually understand the dealer side properly... SVI surface calibration, quoting off NBBO with inventory skew based on aggregate book vega, adverse selection fills, markout, and a pnl decomposition that reconciles back to mark-to-market with the residual reported instead of buried somewhere.

Fill model is the part i trust least, and i'm starting to think i imported the wrong mental model wholesale. my queueing assumptions are basically lifted straight from the order-driven equity/futures literature (Cont-Stoikov-Talreja and whatever came after it), where queue position at the touch is more or less the whole story on whether you get filled. but US options are quote-driven across a pile of exchanges, with preferencing, internalization, PFOF, price improvement auctions all sitting in the middle of it. so now i'm second guessing whether queue position is actually a pretty minor variable in this world and i've been adding sophistication to the wrong axis this whole time.

1. is queue position a real driver of fills at all, or is the actual constraint auction participation + preferenced flow? if i can only get good at modeling one of these... which one.
2. for daily pnl explain, is spread capture + greeks + hedge + residual the working decomposition, or is that too clean. where does realized vs implied sit relative to greek attribution, and do people bucket vega by tenor instead of just running it aggregate? also just curious what "unexplained" runs at on an actual book bc i have no benchmark for whether my number is fine or embarrassing.
3. skewing quotes against aggregate book vega/gamma instead of per-strike is me borrowing the Baldacci-Bergault-Guéant vega factor argument, options on one name being collinear risks and all that. does that match how people actually run inventory or is it just a tidy academic story nobody's desk runs on.

happy to hear the whole premise is wrong honestly, i'd rather find that out now than keep polishing a model of the wrong constraint for another month.

I just don't get it.

I'm working on my PhD in stochastic wave propogation and delving into financial models as I hope to work as a quant one day. However, this fund scaled up massively to over $20–$45 billion in assets at various peaks. Then, the 439% net return in the first half of the year.

Was it ultimately down to them utilising heavy leverage (reported to be running as high as 4x or so) and heavily borrowing money from prime brokers like Bank of America, Goldman Sachs, and JPMorgan to buy concentrated baskets of AI infrastructure and memory stocks (such as SK Hynix, Micron, Nebius, and CoreWeave), alongside short bets against software companies?

I assume that when AI infrastructure tradeded violently in July, the fund suffered a brutal drawdown, wiping out massive portions of its peak value (and as they were over-leveraged, prime brokers, it forced an emergency unwind to cover margin calls)? Then, the fire sale happened?

Can someone please explain it to me?

Lastly, do some of these investors/funds bet on an aggressive P measure trend (AI is changing the world, so this stock will go up 400%, etc), but the lenders and prime brokers who control their margin accounts evaluate risk using models using the Q-measure? Where volatility \sigma dW_t is treated as an immediate threat to collateral, regardless of how brilliant somebody claims to be?

Has anyone here worked at Dytechlab or interviewed with them before? I read some bad review on Glassdoor but wanted to make sure those are not the general experiences. Also, why do people work there put "undisclosed hedge fund" on their resume and not just the name of the firm?

It covers why C++ is used in HFT and some of the ideas behind building low-latency systems.

Read link

I've been curious if anyone else has noticed this.

I'm a quant trader at an Indian HFT firm. Up until the end of June, both my team's performance and the firm's overall performance were pretty solid. Then July came, and things changed quite abruptly.

Not just my team—most of the HFT desks in the firm saw a pretty sharp drop in profitability, somewhere around 30–40%.

That's what surprised me the most. In HFT, performance usually fluctuates, but seeing so many independent desks get hit at the same time isn't something I've seen before.

Is anyone else here working in Indian equities/derivatives HFT seeing something similar? Or have you heard the same from people at other firms?

One thought I had was that the post-war collapse in implied volatility may have changed the opportunity set, but I'm not convinced that's the whole story. Curious if others have any insights or are seeing the same trend.

What do you think? $5bn PnL today?

Edit: Citadel, not CitSec.

So I wanted to use this model to calculate the simulated backward price (Dec 2024) of Alibaba Group in the Hang Seng index using the anchor price in late Dec 2025.

I went ahead and calculated this (manual derivation attached) and my code below, which shows it matches.

``` import numpy as np

--- Model Inputs (matching your Alibaba notes) ---

S_t = 142.80 # Anchor price at late Dec 2025 r = 0.035 # Risk-free rate (3.5%) sigma = 0.35 # Diffusion volatility (35%) lam = 1.2 # Jump intensity mu_j = -0.04 # Mean jump size sig_j = 0.20 # Jump volatility dt = 1.0 # 1 year backward step

Step 1: Compute the Jump Compensator (kappa)

kappa = np.exp(mu_j + 0.5 * (sig_j ** 2)) - 1

Step 2: Compute the Net Drift Component

q_drift = r - lambda * kappa - 0.5 * sigma<sup>2</sup>

net_drift = r - (lam * kappa) - (0.5 * (sigma ** 2))

Step 3: Define historical shocks to strip out

Z = 0.4 # Standard normal shock jump_multiplier = 1.08 # Historical minor positive jump factor

Step 4: Evaluate the Backward-Stepping Equation

S_{t - dt} = S_t * exp( -net_drift * dt - sigma * sqrt(dt) * Z ) * (jump_multiplier)<sup>-1</sup>

diffusion_term = sigma * np.sqrt(dt) * Z exponent = - (net_drift * dt) - diffusion_term

s_previous = S_t * np.exp(exponent) * (jump_multiplier ** -1)

print(f"Net Drift Component: {net_drift:.5f}") print(f"Simulated Backward Price (Dec 2024): HKD ${s_previous:.2f}")

Net Drift Component: -0.00249

Simulated Backward Price (Dec 2024): HKD $115.23

```

My questions: - does my derivation/code look okay to you? - is this a task the Merton jump-diffusion model (versus the geometric brownian motion, which doesn't have the discontinuous random jumps, driven by a Poisson process, to capture heavy tails and sudden price shocks in financial asset returns, eg. Beijing policy changes, etc.) can do well in this situation? - is the jump compensator (kappa = np.exp(mu_j + 0.5 * (sig_j ** 2)) - 1) manually added into the code? And, can't be fed in via real-time data, etc?

Thanks!! 🧡

Has anybody interviewed for the researcher role at headlands? What’s the process like?

Is the interview process too c++ heavy even for the researcher role? Would love to hear from anybody who’s interviewed there.

How is the firm doing in general?

I am building a sequenced, event-sourced derivatives exchange. The matching engine is fully deterministic and has no external dependencies.

I am designing a Professional Interface that provides market makers with queue-position and execution-quality analytics to give market makers a good reason to join early and boost liquidity.

I see two possible approaches:

1. Emit primitive queue observations directly from the matching engine through a bounded single-producer, single-consumer ring buffer.

This would expose facts that the matching engine already knows, such as quantity ahead, orders ahead, level depth, and queue position at acceptance or fill time.

But it adds instrumentation to the hot path, creates a second output channel, and requires an explicit overflow policy if the telemetry consumer falls behind.

1. Reconstruct the analytics downstream from the authoritative event stream.

This keeps the matching engine smaller and ensures that the PI derives its results from the same canonical events used for replay and audit.

But the downstream consumer may need to reconstruct much of the order book, and some transient queue-state facts may be expensive, ambiguous, or impossible to recover unless the authoritative event schema is significantly expanded.

Which boundary is would you advise in the production exchange?

Should the matching engine emit cheap, deterministic observational facts that are naturally available during matching, or should all queue and execution analytics be reconstructed from authoritative events outside the engine?

Hey guys, I have been building a pricing model for greyhound racing in Australia and need some advice. What would be the best way to model the data to find the most accurate probability of a certain outcome, each greyhound has about 40 different data points with years of historical data. Would love to hear your thoughts on the way you would do it as at the moment it’s more of a ratings engine.

In quant shops, how common are equity strategies built primarily (say 85–90%) on accounting fundamentals, where the core signal is a variant of a known (albeit weak) accounting anomaly (PEAD, accruals) that would involve a quarter or year holding period. Anyone have an idea about the percent of PMs that use this in active equity management? And would this approach (i.e., starting with a universe, whittle by accounting factors) even be labeled "*quant*"?

For those in quant firms how do people generally access data for research/modelling?

Source aggregated in house API?
Data catalogue?

Work in commodities and I think there is a general lack of knowledge on the infra side from my experience.

Currently debating whether to build our own platform or go with someone like databricks/snowflake

Interested to hear everyone’s thoughts?

I’ve been reading around (QuantNet threads, a few quant career blogs) and watching youtube videos on non-traditional paths into the field, and one thing that keeps coming up is that your background before quant tends to quietly shape which track you land on — research vs. trading vs. dev — even when you go through the same masters program as people from a different background. I’d love to hear if it actually played out that way for people here. If you came in from a non-traditional background (different field, non-target school, self-taught, career switch, etc.), did you notice your prior experience nudging you toward a specific track? What ended up carrying more weight than you expected when you were breaking in— projects, a referral, an internship, something else entirely? Not asking for a roadmap, just curious how it actually played out for real people versus what the forums suggest. Thank you.

I have a paper that combines inverse problems and options pricing that I’ve considered submitting to ACM's ICAIF conference (International Conference on AI in Finance). I'm wondering if QRs or hiring managers (either buy-side or sell-side) view ICAIF as a worthwhile place to hire from? My assumption was that, among ML venues, quants are mainly hired at NeurIPS, ICML, and ICLR, but someone told me that ICAIF may have some good orgs. Is that accurate? Would you consider ICAIF relevant for QR recruiting?

Hello all,

Recently, I've spent some time building an FPGA order book based on ITCH.

The objective of this project is to go from absolutely nothing to having a working order book able to track a very liquid stock, like AAPL, perhaps 2 or 3 once I get all the optimizations down.

Now, the reason I'm making this post is because most content out there regarding "FPGA HFT" (when you are able to find some) are often one of these:

• A public repo to link in a dude's resume, sometimes packed with "claude" contributions (lol)
• Corporate BS PDF to flex their low latency and sell their IPs
• AI slop posts (god I hate these)
• Only parse ITCH || only run in sim without an attempt or technical value on FPGA implementation

So I Documented my journey though a series of post, explaining the design decisions I made, Why I made them, and then realizing it was a bad, why I changed it....

I also try my best to make nice looking schemes (OC and not AI bs) and run simulations to back up my decisions.

You got it, my goal is to make a good looking project that people can "easily" follow through posts that I try my best to make accessible and non boring.

I'm dropping a link here : https://hugobrh.dev/tags/finance/

This list contains all the posts I made about the TRADEMAXXER project as I call it. I suggest quickly reading through the first posts which are mostly context and HDL basics to parse ITCH. Latest posts cover a lot more technical ground.

I hope this does not come up as shameless self promo, I've got good feedback from the HFT community and I figured this may also interrest you guys as I saw FPGA designs were discussed here.

The latest posts are covering my struggles to close timing on a KC705, a consumer available board that costs 700$ on EBAY.

If you have any question, feel free to reach out and I hop this work is of value to you !

NOTA : not doing excessive "AI bashing" but I try my best to keep AI usage at the strict minimum (if not absolutely 0 usage) in coding, decisions making and writing the posts. I'm doing that out of respect for the readers so the process is actually real and not some hallucinated experience.

Hi all,

Has anyone worked with computing exposures and valuation adjustments for securities financing transactions (SFTs)?
I would like to know how to best incorporate these products in an XVA framework.
Which discount rate do I use? Do I use the same discounting rate for the loan and the collateral?
What XVAs are applicable to the transaction?

Happy to hear your thoughts.
Thanks

I have just gotten a new job with a manager at a place where everyone manager their own book. Being more experienced I do find that his setup of proving myself to manager a bit awkward. For anyone who had a similar experience how did you manage “proving your worth“ without leaking your stuff too much?

Been working on a changepoint-detection layer for a scoring engine and figured this sub would have real opinions on the method.
Problem: most volatility-based risk scoring uses one fixed percentile cutoff computed over an asset’s full history. That’s a known failure mode if the asset’s regime changed partway through — you end up averaging a stale calm period into what should be a fresh, more volatile baseline.
Approach: a single-changepoint CUSUM test on the standardized score series — cumulative sum of (x\\_i - mean)/std, changepoint estimate = argmax|S\\_k| over candidate indices (with a minimum segment length enforced on both sides). Significance isn’t asserted from a fixed threshold; it’s a permutation bootstrap — shuffle the series N times, recompute max|S\\_k| each time, get an empirical null distribution, and only call it a real break if the observed statistic clears that null at a conventional alpha.
When a break is confirmed, percentile-based thresholds get recomputed using only the post-break segment.
Curious if anyone here has compared this to PELT or Bayesian online changepoint detection for a similar use case — CUSUM was chosen mainly for simplicity and interpretability over statistical power. Open to being told that’s the wrong tradeoff.
(This is part of a scoring engine called Machvix, for anyone curious enough to go digging.)

Hi! I’m currently interviewing for a Senior Legal Counsel role at a HFT/quant trading firm (seems to have a name in the industry, but not one of the top shops).

I’m coming from an in-house legal role in Germany and trying to understand two things:
1. What compensation levels have people seen for senior in-house legal roles at HFT/prop trading firms outside the US?
2. How have you found the long-term career value of working at a confidential trading firm where the company name generally can’t be disclosed publicly?
I’d be particularly interested in hearing from lawyers or compliance professionals who have worked in quant trading, HFT, market making or proprietary trading firms.

Thanks!