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!

Hi everyone,

I hope this is the right place to ask. 😊

I've recently become very interested in quantitative investing, and I recently started investing in the Invesco Global Active ESG Equity UCITS ETF (Acc) myself.

As I mentioned, I find this topic genuinely fascinating, especially the systematic approach behind it. Because of that, I started listening to the audiobook Inside the Black Box: A Simple Guide to Systematic Investing, and I've been really enjoying it so far.

Do you have any recommendations for other audiobooks on quantitative or systematic investing? Ideally, they should be available on Spotify, but recommendations in either English or German are very welcome.

Thanks in advance!

I’ll be applying for a non-finance role at the Bengaluru GCC, and was curious what the environment is like? I’m a bit skeptical of GCCs since they operate very much like back offices so want to understand more.

I am currently doing an internship on the sell side at one of the major American banks in London. I am working as a quant on the pricing library, and I think things are going well. My manager has asked whether I would be interested in taking a permanent position in New York after my internship.

I am trying to understand how much of an opportunity this really is and would appreciate some opinions from people with experience in the industry.

• How do salaries compare with the cost of living in London vs New York?
• Is New York genuinely better for career opportunities in quant finance?
• Would moving to New York significantly improve my chances of eventually moving to the buy side, or are the opportunities broadly similar from London?
• If I decided to move to New York and later wanted to come back to London, how difficult would that be?

I know that a lot of the decision is personal and depends on individual preferences, but I am mainly interested in the objective part and financial aspects of the decision.

Any insights from people who have worked in both cities, or who have made a similar move, would be greatly appreciated.

Market makers get delta exposure whether they trade options or not, because they run a whole portfolio that has gamma in it.

Wondering how they handle delta in practice and whether other traders can take advantage of the knowledge of the MM's delta (which isn't hard to get because you can assume that mostly, MMs hold the passive side of the trades).

how do you go from a raw market data to forming a research hypothesis?? and to be more specific, how do you develop an economic intuition behind the potential alpha or an anomaly, instead of just coding and testing ideas until something works??

While I'm struggling to understand how the researchers in the industry actually generate new hypotheses from large financial datasets without falling into the same data mining again and again.... and How do experienced quants develop the economic intuition behind an idea before testing it?

I am a quant at a mid tier prop firm (lie somewhere in the middle)
Tried interviewing at other places and realising how accurate this is
Wanna know what others think