Genuinely curious. It feels like a really good close to top tier company but general in this subreddit as well as r/quant is gets shit on pretty often. What's the reason behind that?

Has anyone heard about them? or any things to know?

Just looking at pod shop returns and so much money has gone into space in hope of allocating to next Citadel or Millennium but the returns are just not there. Balyasny has grown AUM like crazy to $38bn but are flat for 2026. They did 15% gross returns in 2023 and 2-3% net and are running a crazy 12% pass through. But Walleye, Verition and others also flat to barely up. Just wondering if this is peak pod shop? Even Ken Griffin is moving away from space with centralized commodities biz and that big portfolio trade vs Situational Awareness. Also so many pod shops are underperforming the guys who started in quant and going into discretionary (DE Shaw, QRT) these days https://rupakghose.substack.com/p/peak-pod-shop-and-balyasny-blues?r=1qelrn&utm_medium=ios

i've been slowly building out this database over time and it's gotten pretty big 279 million rows, about 17GB.

Daily prices on 8,000 stocks across US, China, India, and international markets, minute level FX and crypto data going back over a decade, futures, COT positioning data back to the 80s, and macro data for some countries going back to the 1850s (mostly useless that far back but it's there).

a small breakdown

I already went through and cleaned up the obvious stuff fixed split adjustments that were broken, checked for survivorship bias in the stock universe, killed some duplicate/junk data, that kind of thing.

Now I'm just sitting here with all this data and honestly running out of ideas on what to actually do with it. If you had this, what would you build first? Cross-market stuff, some kind of macro overlay, actually using the COT data for something real genuinely curious what people who do this seriously would go for.

Hi,

I am a quant at a well known hft, and it has been around 5-6 years now.

Though the past few years have been good, great learning, good bonuses, and we were able to scale up at quite a few places succesfully. But recently there has been stagnation in terms of learning and future growth seems bleak.

I am writing this post to ask about suggestions about changing firms with around this yoe, what to aim for, how to go about it. And if anyone has experienced this, please do share your experience as well. Thankyou

I mean I know what the role of a Quant Trader is but what does a Quant traders day-to-day on their trading desk look like? How does probability, expected value, pattern recognition being used by Quant traders?

hear people say some firms like headlands specifically have good execution, what exactly does that mean?

Genuinely curious if others out there have experience working with extremely inept PMs?

A recent hire at my firm has strategies akin to that of a teenage youtube day trader. Their signals are entirely price based and their processes lack any statistical rigour.

Even worse, I know that they have been lying about backtest results to management. They are aware of bias and cherry picking yet still quote the results to higher ups. They supposedly have a decent track record but from what I gather, its pretty opaque.

I have heard quants work with non-technical PMs relatively frequently but this feels next level.

How common is this? Are PMs everywhere like this? Should I run?

Tldr;

Bad PMs - how common? How bad?

Hey everyone. I wanted to share an architectural solution to a mathematical edge case that blew up one of my risk engines a while back, specifically when pricing Knock-Out Barrier options and calculating second-order Greeks.

When we don't have closed-form analytical Greeks (like in most path-dependent exotics), we rely on Central Finite Differences:

Gamma ≈ [V(S_0 + dS) - 2V(S_0) + V(S_0 - dS)] / (dS)^2

To do this efficiently in a Monte Carlo simulation without the variance tearing the derivatives apart, the standard practice is Common Random Numbers (CRN). We apply the exact same stochastic shock Z to the base path, the upper-bumped path, and the lower-bumped path inside the hot loop.

The Discontinuity Problem:

Barrier options possess a step-function discontinuity. Let's say we have a Down-and-Out Put. If the barrier H is 85.0, and our initial spot S_0 drops to 85.0001.

When we calculate the Greeks, the numerical bump -dS forces the lower-bumped path to instantly breach the barrier. The payoff evaluates to strictly 0.0.

Because Gamma divides by (dS)^2 (a microscopically small number), the sudden absolute drop in the V(S_0 - dS) term is interpreted by the algorithm as infinite convexity. Your engine outputs a Gamma of 999,999.0 or -infinity. If you have an automated delta-hedging script hooked to this output, it will violently over-leverage your portfolio trying to hedge a mathematical ghost.

The Algorithmic Solution:

I realized that catching this after the matrix computation was too late and computationally wasteful. The check needs to be embedded directly at the C++ core before the finite difference execution.

If the absolute distance between the Spot and the Barrier is less than or equal to 2 * dS, the boundary is breached by the numerical bump. We must flag the state as unstable and force the engine to yield NaN for Gamma, while preserving the Fair Value and Delta calculations.

Implementation & Testing it out:

I ended up building a dedicated C++ OpenMP pricing engine to handle these massive matrices because Python/NumPy was choking on the GIL when simulating 50M+ paths with barrier logic. I wrapped it behind a Python SDK.

If anyone is backtesting exotic portfolios and wants to see how this discontinuity handling works in practice (or just needs to compute 100 million paths in ~3 seconds), I made a Google Colab notebook demonstrating it.

You can run the stress test directly in the browser here:

https://colab.research.google.com/github/Prometheus-Quant-Engineering/prometheus-quant-examples/blob/main/03_HPC_Asynchronous_Polling_Stress_Test.ipynb

The SDK is open source (pip install prometheus-qengine). Let me know how you guys handle step-function discontinuities in your own proprietary risk engines, always looking to optimize the core loop further.

This is a very interesting firm: there are nearly no public discussion about this firm, but somehow when I talked to friends (or just in general people in the industry) they are aware of it, which is surprising.

I understand that they are very small and probably very secretive but I expect more discussion about them: like how competitive are they in the market right now? They are big in crypto and they are planning to expand in equity, but how good are they exactly? Compared to other firms what are the main differences? At what level are they operating compared to their competitors?

I am a quant on a fixed income desk. I primarily work with rates products, sometimes help with credit desks as well.

I have an opportunity to transition in the same firm to a fixed income trader role where the book mostly consists of structured products on bonds, cds. And some treasury options.

I have been working with these products for a while in my capacity as a quant.

I am not able to decide if I should take this up or not ? I find myself excited by the prospect of working as a trader, but I wager it comes with it's own downsides. And I don't know how it would be for long term career. I would be more like a quant who can trade as well.

Please share any suggestions you have. Thanks.

Hi all, I would like to change position in sometime and I am soon to reach 3 yoe as a swe/qd

Honestly the biggest barrier for me would probably be leetcode, completely forgot how to do those, are they still relevant?
Someone that recently made the move with this years of experience can share some insight on how the interviews are?
Can I expect something similar to what I had when I was trying to join as a new grad?

I was just doing some research on their website and came across this page. What other quant firms have incredibly obscure or random subdivisions like this?

Does anyone have any techniques or advice on transaction execution and quantitative assessment of optimal transactions in corporate bonds?

Hi everyone, has your firm changed its approach given the recent CPT suspension? If CPT remains suspended, are companies considering internship alternatives ( non US offices ? ) Curious to hear from anyone directly involved in quant.

Hey all, doing market research on structured products and how traders actually access it today and what they'd want to see better.

~5-mins survey, no pitch. Will share the results back with anyone who fills it out so you can see how the rest of the market answered

DM me if you want to chat about it directly.

I heard about something pretty crazy over the summer from a friend who works at a big quant firm.

Two students from Yale university who were dating each other were interviewing for a QR internship at the same firm. They are from a big lab in Yale’s biostatistics program. According to my friend, they were essentially helping each other during the interview process and both ended up receiving offers!!!

The firm eventually figured it out. Both their offers were reportedly revoked.

What really got me thinking wasn't even the specific incident, but the broader issue of fairness in recent recruiting. Especially, AI tools are widely used in our daily life.

Quant interviews are already extremely competitive. There are thousands of students spending months, even a year, preparing for probability, statistics, coding, brainteasers, and mental math. If someone gets an offer because they found a way to collaborate during an interview when everyone else is expected to compete individually, that's obviously unfair, not just to domestic applicants, but to every other applicant who played by the rules.

This also made me think differently about the recent debate around international students, CPT, and university policies.

If companies and universities want to maintain a level playing field, maybe stricter verification and interview integrity measures aren't necessarily a bad thing.

Curious what people think: should companies be doing more to prevent interview collaboration/cheating, especially for extremely competitive internships like QR?

please share with us

This is a 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.

I've built a small set of models that put a probability on a 10%+ S&P 500 drawdown over the next 1, 3, 6 and 12 months, from macro and credit inputs (ISM, the yield curve, high-yield spreads, financial conditions, and a few others). One ridge-penalised logit per horizon, estimated walk-forward, so every point in the track record was scored with only the data available that month. This is really a methodology question, and the model is just the concrete case.

The out-of-sample AUCs are 0.60, 0.69, 0.74 and 0.68 across the four horizons. On paper that reads fine. The part I keep coming back to is the denominator. A 10%+ drawdown is rare, so across the out-of-sample window there are only six to eight independent episodes, and the 6- and 12-month models are largely scoring the same ones twice, with heavily overlapping, autocorrelated labels. When I bootstrap the AUC by episode rather than by month, the intervals are wide: the six-month one runs from the low 0.5s to the mid-0.8s. So the six-month headline looks fragile rather than fake, but I want to know how people handle the inference properly.

The specific questions:

1. Event count and dependence. With about six to eight independent events and overlapping multi-horizon labels, per-month AUC and its usual variance are overstating precision. Is an episode-level bootstrap the right correction, or is there something more principled (a block bootstrap, a DeLong test adjusted for clustering, a Bayesian setup with a sensible events prior)?
2. Label construction. The label is a forward 10%+ drawdown within the horizon window, which makes adjacent months' labels highly dependent and the horizons non-independent of each other. Is there a cleaner label for rare-event forecasting that doesn't manufacture this autocorrelation?
3. Regime dependence. It catches the slow, macro-driven falls (2022, the GFC, the dot-com unwind) with some lead, and by construction it misses the fast ones (COVID, an LTCM-style shock), because monthly macro data can't see them coming. How would you evaluate a model that is honestly conditional on regime, without either cherry-picking the regimes it works in or marking it down for shocks no macro model could catch?

Two things that already survived my own poking. Dropping high-yield spreads costs the model nothing, and the spread on its own is a coin flip at six and twelve months, so it isn't a credit signal with extra steps. And it is out-of-sample only, no re-fitting with hindsight, with the weak horizons kept in; nothing is dropped for looking bad.

Mostly I want the event-count problem attacked. If the honest answer is that six to eight events cannot support a confident AUC at any horizon, I would rather hear that than dress it up.

For anyone who wants to reproduce the numbers, the full write-up and the live model, with a downloadable monthly probability series, are on my site, agreeableinvestments.com; my own research, shared as educational, not advice.

Curious if the dopamine rush causes you to basically never miss any work. Do you use your PTO?

I've been reviewing a rules-based systematic stock strategy and found an execution issue that I think may be more important than tweaking the signal itself.

I'm deliberately leaving out the exact signal formula, ranking logic and parameter thresholds because my question is about research methodology and execution, not strategy replication.

Current evidence is still very small:

- 7 fully closed paper trades

- 4 winners / 3 losers

- Win rate: 57.1%

- Profit Factor: 1.52

- Intended reward:risk: roughly 2R

I obviously don't consider 7 trades evidence of persistent alpha.

The interesting part appeared when I reconstructed planned entry vs actual fill.

Every completed trade was filled above its planned entry.

A few examples:

Trade A

Planned entry: 466.79

Actual fill: 469.69

Entry deviation: +0.62%

Result: +1.84R

Trade B

Planned entry: 67.27

Actual fill: 68.61

Entry deviation: +1.98%

Result: +1.80R

Trade C

Planned entry: 36.88

Actual fill: 37.67

Entry deviation: +2.14%

Result: +1.71R

Trade D

Planned entry: 181.05

Actual fill: 188.73

Entry deviation: +4.24%

Result: -1.53R

Trade E

Planned entry: 19.92

Actual fill: 20.43

Entry deviation: +2.57%

Result: -1.36R

The stop and target levels were based on the original planned trade.

So when the actual entry was worse:

- distance to the target became smaller

- distance to the stop became larger

In other words, a trade designed around roughly +2R / -1R was mechanically distorted by execution.

The winners ended up around +1.7R to +1.8R, while some losers expanded to roughly -1.3R to -1.5R.

This makes me think I need to separate two questions:

1. Does the signal itself have positive expectancy?

2. Is the real-world execution implementation destroying part of that expectancy?

For the next stage I'm freezing the signal logic rather than optimizing it further.

I'm collecting new paper trades while recording, for every trade:

- planned entry

- actual fill

- entry deviation %

- planned stop

- planned target

- realized R

- time to fill

- setup category

- market regime

- exit reason

I'm also excluding any historical trade where I cannot verify a complete lifecycle from entry through exit rather than guessing the missing result.

My questions for people who have dealt with this:

1. How would you separate signal quality from execution quality statistically?

2. Would you run two parallel versions:

   - an idealized signal-price model to estimate raw signal expectancy

   - a realistic fill model to estimate executable expectancy?

3. Is R-space a better framework for this comparison than raw PnL?

4. With such a small sample, what would you measure now, and what would you refuse to conclude yet?

5. What diagnostics would you use to determine whether larger entry deviation is genuinely degrading expectancy rather than this just being noise from a tiny sample?

6. Would you freeze all signal parameters while testing different execution methods, or is there a better experimental design?

7. At what point would you start segmenting results by setup type or market regime without falling into small-sample overfitting?

I'm less interested in finding a prettier backtest and more interested in identifying exactly where the strategy leaks between signal generation and realized execution.

Any criticism of the experimental design is welcome.

Do companies typically require references from your current employer? Is it okay to use your old references (e.g. your phd advisors/collaborators) for early career people (~2yr experience)?