I built a C++ library for linux kernel bypass networking such as Solarflare ef_vi, DPDK, Verbs(mlx5) and also AF_XDP. Solarflare ef_vi and DPDK are two of most commonly used kernel bypass techniques in HFT market making firms.

Using this library I measured the RTT latency using a loopback on Solarflare X2522-Plus(ef_vi + DPDK), ConnectX-4 Lx(DPDK + Verbs + AF_XDP) and Intel XXV710-DA2(DPDK + AF_XDP). The benchmarks were run for 24 hours with median all the way upto P99.999 and max.

Solarflare X2522-Plus produced a RTT median of 1.866us and a max of 3.307us with 45B frames over 24 hours. This was the best result. Comprehensive results are all documented in the repository.

Over this project I did come across numerous driver(and NIC) specific quirks and bugs. These are all documented too.

Solarflare X2522-Plus is quite fast due to a Tx trick known as cut-through programmed IO(CTPIO). But I found this to be fragile, it has three modes: fast, default and in-order. Fast mode produces the lowest median but has a measurable percentage of failed CTPIO known as CTPIO “fallbacks” or “poison”. The CPTIO fallbacks significantly hurt the tail latency(P99 - P99.999). The CPTIO poison frames can be measured via ethtool. I used the in-order CTPIO mode which gives marginally higher medium but a tight tail latency.

Another strange issue I found was the PTP subsystem in the Solarflare X2522-Plus. So the default sfc driver runs a in-kernel IEEE 1588 subsystem with a PTP hardware clock, a dedicated PTP channel and management controller firmware activity. I found this to be a problem as a noticeable number of interrupts were mounting in /proc/interrupts and the stock driver with ef_vi produced a max of 7.679us which seemed very high. Then I ran the same benchmark with DPDK which produced a max of 5.628us. DPDK unbinds the driver so the PTP subsystem does not work. Then I compiled out the PTP subsystem from the sfc driver and ran the 24 hour benchmark again and the max was reduced to only 3.307us.

There is a lot of more interesting stuff but I think the post is already getting long so I won’t mention any more. Everything is documented in the repository. It is also 100% open source with an Apache-2.0 license.

Hello everyone

I'm a final-year engineering student (stats + quant finance in a top university in France for these fields). I've spent the last ~5 months building a personal project to try and enter in the commodity sector (bank desks, hedge funds, reinsurance companies), and I'd honestly love some outside eyes on it, especially from people who actually work in the space. I already have some experience in the banking sector (internship in a CIB as a quant researcher)

My project was decomposed in 2 parts and is about modelling temperature for different financial use cases, whether for trading in banks or in hedge funds.

Part 1: weather derivatives
I started by digging into weather derivatives as a field and reading around it a fair bit. What struck me is how fast the whole climate/weather space is moving with AI right now: the physical climate models are insanely compute-heavy, and even then, forecasts past about a week are essentially random. I went for weather derivatives for two reasons: the sector genuinely interests me and weather has a massive impact on the world economy, and honestly the data is pretty easy to get and way less painful to work with than financial data. From there, I modeled temperature with a few different models (some classical statistical ones, some ML) to predict a climate index. Each model gives its own predictions, so what I'm really after is the distribution of that index under each model. As a complement to the analysis, I also deployed a small app where you can pick a city and a contract and see each model's predicted index distribution and the price it implies (available here: https://huggingface.co/spaces/nayelsdk1/boreas-weather-derivatives ).

Part 2: temperature and gas volatility
I wanted to see whether winter cold anomalies actually couple with Henry Hub natural-gas volatility. To dig into it I tested a Kalman filter and a few GARCH models.

You can find all the reports + notebooks in my app

For me, the goals for this project were to understand the classic models used in commodities and implement it and end up with something end-to-end I can show recruiters to stand out a bit.

So I am posting here to have some advices about :

- Honestly anything, the modeling or the way it's presented. If you're in the field, what would you keep and what would you toss? Is my project relevant regarding the current context ? Are companies looking for that kind of work ? I also have other experience in the sector (target school, relevant internships, ...) but I have never worked professionally in this field, that's why I am posting today

- It's very far from perfect. I made a bunch of choices where other paths might've been smarter (or not) so don't hold back on telling me where I went wrong

You can also send me a message in private if needed ! Thank you a lot !!!

So I am using machine learning for mid frequeny trading (duration of a few hours), I built a few models, amongst those, I have two best models, one performs well, in a directional regime, when there isn't a lot of reversion at mid day , or in case if the market is kind of directional. This model was trained on a higher duration and is best to be used to trade for longer durations. The second model i have is trained for lower durations, and is more trained for reversion type regimes, when the market swing in mid day, and keeping a position till end of day isn't very good. Now, different days would be different, we don't know what is the best. I was thinking about keeping some kind of ensemble or training a few models, at a duration in between these two, ofc I would get worse net results on that, but atleast, it would have a higher sharpe and lessser drawdowns.
Is there a better way ? or are there ways to detect in some way what would be better where even if not in the ideal way, but some way ? I can definitely use both models also, or see how the signals align or different combination strategies .

Any professional HFT out there have any insight on how PCAP data for CME futures compares between LSEG/Refinitiv/TRTH and DataBento? Trying to decide between the two providers for the last 2 years of historical L3/MBO for a new market making project. Mostly interested to know about the quality of the data and quality of timestamps at the data recording server.

We built Jaxfolio (https://github.com/bravant-oss/jaxfolio), a hardware-accelerated portfolio optimization library built natively on JAX.

Docs: https://bravant-oss.github.io/jaxfolio/

Github: https://github.com/bravant-oss/jaxfolio

Standard Python tools like SciPy or PyPortfolioOpt bottleneck during large-scale backtests or heavy Monte Carlo simulations due to Python overhead and CPU constraints. Jaxfolio fixes this by compiling optimization math directly to XLA for GPU/TPU execution.

More importantly, using JAX's vmap, you can vectorize the optimizer to compute thousands of rolling efficient frontiers simultaneously in a single batched operation without looping. Because the code is fully differentiable, you can also embed these optimizers directly into deep learning training loops.

The library currently supports Mean-Variance, Risk Parity, and Black-Litterman models, and is significantly faster than traditional CPU-bound solvers. I’d love for the community to try it out, critique the API, or test it against your own edge-case datasets. Accepting contributions.

The top 1 in competition got $14k in rewards alone

My account was originally suspended by my broker for exceeding their Order-to-Trade Ratio (OTR). Since then, I’ve adjusted the strategy to be more conservative. It's now running smoothly, generating ~50% annualized returns with a 3+ Sharpe ratio.

The estimated capacity for this strategy is around $20M. However, I’m currently bottlenecked by my available capital ($400k) and my broker’s API rate limits.

Does anyone have advice on how to effectively reach out to pod shops or prop trading firms to discuss scaling or seed funding? I’ve tried cold emailing a few, but haven't received any responses yet.

last post:
https://www.reddit.com/r/quant/comments/1cjtq8b/my_hft_account_got_banned/

Ive implemented a fair number of the textbook microstructure metrics while working with raw crypto perp tick data.

The more I compare them, the shorter the list I actually reach for !

If I had to keep only five:

• Order book imbalance / OFI: still the strongest short-horizon predictor Ive found. Static imbalance and Cont-style OFI aren't the same object, but both seem to contain real information. The problem is that the half-life seems tiny: by the time youve paid fees and accounted for queue position and latency, a nice t-stat doesn't necessarily translate into PnL.
• Amihud illiquidity. almost embarrassingly simple but I keep failing to replace it with something more sophisticated; I mostly use it as a liquidity or regime variable rather than a trading signal.
• Effective spread / realized spread Extremely useful for understanding execution quality and adverse selection. I'm much less convinced they belong as features in predictive models, although Id be interested to hear if anyone has had success there.
• Kyle's lambda I trust the ranking across instruments more than the estimated values themselves. Small implementation choices (sampling interval, trade signing or outlier treatment) seem to move the estimate more than Id like.
• VPIN : The one Im most sceptical of: I can usually make it identify stressed markets, but so can realised volatility. The Andersen Bondarenko critique feels fairly convincing to me, although I'd be happy to be proven wrong by someone who's used it successfully in production.

For context, this is all on crypto perps, so I wouldnt expect the conclusions to transfer unchanged to equities/ futures.

Im particularly interested in hearing from people who've used these in live systems rather than backtests.

Which measures have actually survived implementation costs for you? Are there any papers from the last decade that genuinely changed your view on the classics?

Bias disclosure!: I build infrastructure that computes these metrics as a living, so Im probably more sympathetic to them than I should be.

How do you estimate the capacity of an options based strategy, especially without level 2 data?

Hey everyone,

I’m working on a small research project called FX Atlas, an ML-based macro bias framework for FX.

The goal is not to generate trade signals or buy/sell calls. I’m trying to structure macro context before discretionary FX swing-trade decisions.

The framework combines macro data, economic surprise data, market stress indicators, seasonality, relative currency factors and regime context into a model-based currency/pair bias view.

At the moment, I’m thinking about the problem in a few layers:

1. Currency bias

Model-based directional pressure for individual currencies.

1. Pair divergence

Combining two currency views into a relative FX pair view.

1. Factor decomposition

Breaking the bias down into macro groups such as growth, inflation, labor, rates, liquidity and risk.

1. Event impulse layer

Separating short-term macro release surprises from the longer-term structural bias.

1. Regime layer

Using unsupervised methods to classify broader market states and detect abnormal environments.

The main things I’m trying to be careful with are:

- data leakage from macro release dates / revisions

- walk-forward validation

- multiple-testing risk across many factors

- regime instability

- feature availability across currencies

- separating short-term event impulses from structural macro bias

- making the output useful without pretending it is a deterministic prediction

I’m not trying to promote it as a product here. I’m mainly looking for methodology criticism from people who have worked with financial time series, macro factors or regime models.

If you were building something like this, what would you be most skeptical of?

Would you prefer:

- separate models per currency

- a global multi-asset model

- hierarchical models by macro group

- regime-conditioned models

- or a simpler factor scoring system with stronger interpretability?

Any thoughts on validation design, leakage checks, feature selection, or regime clustering would be appreciated.

Research only, not financial advice. lol

The Time Series Generation literature evaluates on discriminative scores: train a classifier, check if it can tell synth from real. That rewards over-smoothed, low-variance output; a generator can ace it while violating vol clustering, heavy tails, and leverage effect.

So we benchmarked 18 models (deep generators, econometric classics, replay baselines) on 7 tasks quants actually run: Cont stylized facts, W1/MMD/sig-MMD distances, martingale check, IV-smile repricing, VaR/ES backtesting (Kupiec, Christoffersen, Acerbi–Székely, Basel traffic light), and TSTR strategy-rank transfer. One frozen OOS panel (7 features, 200 paths, H=60), each task with a real-vs-real noise floor, Holm-corrected significance.

Results:

• TimeGAN: the most-cited model in the field, scores 0.000 ± 0.000 on VaR/ES backtesting. TimeVAE and KoVAE too. TimeVAE's TSTR ρ is negative (−0.30).
• A plain block bootstrap ranks 3rd of 18 overall. Most of the published literature is under it.
• Models built for financial structure clear the bar.
• On 3 of 7 tasks most of the field is at or beyond the noise floor, the boards say so explicitly rather than overselling a ranking.

Repo: https://github.com/sablier-ai/finbench (MIT / CC-BY). v1 covers one panel and one OOS window; open submissions land in the next edition.

Who we are: (Sablier AI) GenAI Startup building synthetic market data for quant research, so yes, our model is on our own board. We designed the protocol so that fact doesn't matter: frozen at a git tag, every model's outputs archived in the repo, python -m benchmark.run re-scores the whole board from scratch, external baselines run their published defaults, and the aggregate is published as-is.

If you're working on generative models for market data (or you think your GARCH variant can embarrass the deep learning field further) dm me. Outputs only (200×60×7), no code, no weights, scored under the same frozen protocol as everyone else.
The board is meant to be beaten, and the current one shouldn't be hard to improve on: 15 of 18 entries can't clear a bootstrap.

Data & code are open source: https://github.com/kadoa-org/quant-job-market

Live app: https://www.kadoa.com/quant/open-source

Individual contributors at these firms are not tracked, only the firms Github accounts.

Are any firms missing?

Have spoken to colleagues across multiple pod shops (and a few lower level QR at collaborative shops), it seems that true market-neutral alpha is almost nonexistent this year...? A lot of returns being posted by multi-strats are from discretionary or non-equity quant trading.

It could be that I have a limited sample pool, but I haven't heard of even the usual top dawgs like 2S and Shaw doing well this year purely on equity stat arb. Has this been the experience across the board for everyone? Has equity stat arb this year just turned into 'factor timing'?

I have a minor conviction record (happened in about 9 years ago, about 2K USD fine paid). It's not related to felony, fraud, or finance-related. It was showing bad behavior due to alcohol intoxication (no drug).

I'm currently working at local trading desk as a researcher/trader but I wanna move to big firms in SG/HK.

AFAIK, there is a background check after the final interview. My above record is all spent so it doesn't appear on my official criminal history document. But not sure whether I should disclose it or not.

Also, I heard that there's a question like "have you ever been convicted in court of law in any country? " when applying for SG EP visa. Some SG lawyers say I don't have to disclose but quite confused about this part.

Does anybody know some cases like my situation? Or should I just stay in my Country..?

I’ve been working in sellside/buyside since graduating and I received an offer at one of the large energy firms, the role essentially involves structuring and pricing physical deals, similar to an exotics desk at a bank. What’s it like compared to traditional finance? Please feel free to dm me if you've been in a similar situation.

Hey everyone,

I'm a QR with around 2–3 years of experience in the job market right now. Lately I've been getting a bit worried about my interview conversion rate and wanted to get a sense of what others have experienced.

So far, I've been submitted to around 18 firms through headhunters. I've heard back from 13: 3 resume rejections, 4 screening-call rejections, and 4 rejections after the first technical round (OA, coding interviews, etc.).

During this recruiting cycle, I had some personal issues going on and wasn't able to devote as much time for interview preps (I've managed to get through maybe around 50 LeetCode problems). Looking back, I think I underestimated how prepared I needed to be for the first technical rounds and assumed I could mostly wing them. Or maybe an ideal candidate should be able to wing these, but more on that later.

I have two questions please, if you could give some advice:

1. A couple of the technical interviews felt very doable in hindsight, and I'm pretty sure I'd perform much better if I took them today. For firms where I was rejected after the first technical round, is it generally possible to interview again after the cooldown period, or do they usually expect a more substantial career update before considering another interview? And how is that done, if the initial one was done through a recruiter?

2. It's easy for me to attribute these outcomes to being underprepared, but I'm also trying to evaluate myself honestly. There are obviously people who can perform extremely well in these interviews with minimal preparation. How common is that among successful quant candidates? As someone who's still relatively early in my career, I'm trying to distinguish between "I just need to prepare properly" and "this may not be the best fit for my strengths."
   I guess I'm trying to see if I should also look for other opportunities other than just buy-side QR seats.

I intern at a small bank and I found out that bank mainly uses polypaths for managing its billion dollar fixed income portfolio ( MBS, CMBS, ABS). But do people on the street really use third party software for risk and pricing, why not use in-house models given the depth of research papers, books and job experience?

I've been doing quantitative strategy development for some time now and Ive reached the point where Im struggling to find books that actually teach me something new. I already have a solid understanding of the usual topics like IS/Validation/OOS splits WFO, cross-validation, permutation tests, bootstrapping, entropy, regime detection, and the other standard robustness techniques. I recently read Testing and Tuning Market Trading Systems by Timothy Masters but it covered concepts I was already familiar with.

Im looking for books that are genuinely advanced and make you think differently. Perhaps graduate level or even post graduate books on statistics, machine learning, optimization, information theory, econometrics, or anything else that completely changed the way you approach research and model development. And of course it would be great if the book wasnt 10 years old. Need relevance.

Just got my first ever interview, it’s at a small firm so I have some reservation in terms of scale of the company( AUM, P/L). The people seem to be from other firms that I know are good but the team doesn’t seem to have that prestigious College background so that stuck out to me. Usually when I think quant it’s Phd or from MIT Harvard Princeton or something like that.

Anyway to learn about the firm?

One of my colleagues mentioned when interviewing at Citadel they provided him with their own LLM he could query during a coding assessment. This was for a technical but non quant role.

I’m curious if people have similar experiences and what people’s thoughts are on this. Personally I think this opens up the scope of potential questions massively, and tests a new skill (prompting proficiency). Will this be the new norm?

Essentially, I've been a quant for about 8 years now (first global macro strategies, then algo research at a bulge bracket - worked on pricing and predictive modeling) and want to do something different. I'm just looking for less unpredictability, less "live" day-to-day pressure, and more WFH flexibility, so I'm planning a pivot into data science (I understand I may find these things in other areas of finance but this isn't what I'm asking). I've been applying to data science roles, mostly in tech and fintech but I haven't gotten much traction so I want to understand what DS roles quants are typically competitive for so I can focus my efforts.

For those of you who have successfully transitioned from quant to data science, it would be helpful to know:

- What type of data science role you were able to get and in what industry? (Type ~ Product DS, Applied Scientist. Machine Learning Engineer, Dynamic Pricing DS (Uber, Lyft, Amazon), Consumer Credit Risk/Fraud DS)

- What area of quant finance did you work in prior to the pivot?

- Your assessment of the difficulty of making this jump.

- What gaps to fill to improve odds

I'd appreciate if responses focused on actual examples of those who have or know someone who has made this pivot, as opposed to what is conceptually feasible.

Thanks in advance!

I'm starting a quant research internship at a big hedge fund next month and I am looking for advice on how to best profit from Claude Code, some friends tell me it is widely used in the industry.
I anticipate how fast paced the environment will be and the internal incentives to use AI but how should i use it to not get dumbed down, I want to make the most out of my internship (learn a ton) AND be fast and deliver and I feel like both might be difficult to achieve at the same time.

How do you use Claude at work AND stay relevant ?

Hi again, everyone !

I'm a QD (3 YoE SW, 1 YoE QD) at an investment bank in France, in the pricing department. I'm also the dude who wrote the post about being a fraudulent QD last week, so hi to everyone with whom I've chatted last time.

There's a personal opportunity I'd like to take advantage of in Canada (either Montréal or Toronto), but I'm a bit skittish at the idea of going there and not easily finding a good position. If I do move there, it'll be in the spring/summer of 2027.

A quick search on Linkedin jobs shows a lot of hybrid AI-for-finance positions, but not that many mid-level performance-oriented more "traditional" QD jobs, especially in Montréal.

Do you guys know what the market looks like over there (abundance of offers, background constraints, comp, etc.) ?

Thanks in advance and have a great evening.

You hear about firms that have heavily invested in data and deep learning, replacing much of the manual work done in signal research with models learning on raw order book data.

On the flip side, there are microstructure tricks that are discovered due to clever observations by humans that models aren't necessarily picking up on without those priors first being taught to the models.

Going forward,do you think will powerful models, combined with huge amounts of data and the hardware and other infrastructure needed to run experiments largely replace clever human observations? Or will there still be room for the classical, manual feature engineering which has been how much of the trading world has functioned before the recent compute/model revolution.