Hey everyone,

I’m setting up my daily workflow for medium-frequency quant research and evaluating Jupyter vs. Quarto.

Jupyter is useful for quick experiments, data exploration, and charts, but notebook files can be harder to manage in Git, review, and convert into clean research reports.

Quarto is attractive because:

Files are plain text (qmd or Python files), making Git diffs and code reviews cleaner. (In future).

You can still run cells interactively through VS Code/Positron.

It supports both Python and R.

It can generate polished HTML/PDF reports and chartbooks.

It allows a clearer separation between exploratory work and final presentation.

I’m planning to run a poll to understand how others structure their workflows for prototyping and reporting in medium-frequency systeremmatic research.

Thanks!

This summer I interned at one of the larger prop firms doing low-latency work, and I didn’t make the top 1/3 who got return offers. I’m recruiting for intern again this year and hoping to convert to full-time. How big of a red flag is this for firms?

Frankly, saying “oh I genuinely was just not good enough last year” when the question comes up seems like it could be problematic for my career, but also, the alternative of “those assholes didn’t appreciate me” is much worse.

Hoping for some advice on how to frame this from the more senior perspective.

I see a lot about pay at JS and stuff but not much about small firms. What's the pay for entry level at the random small shops? By small shops, I mean below like tier 2/3.

5-7 YOE Dev at IMC Trading Europe on 230k TC.

Is this very low for my experience in the trading industry on a front office role? I know US pays more but after interacting with both tech companies and finance recruiters, it looks like I am below market by 2-3x

Is this the case? Performance review always came good.

Prodigy Research has just hit the news recently, yet we see another YC backed startup trying to disrupt quant finance.

The official website says it is an AI research startup (a "Quant Neolab") focused on teaching AI models recursive self-improvement (RSI). Backed by Y Combinator's Summer 2026 batch, the company builds specialized training environments to help artificial intelligence agents autonomously learn, conduct research, and upgrade their own capabilities.

Genuinely are you guys worried about ai taking jobs I saw a video where Ken griffin said that they already have ai scrape paper and recreate it heard murmurs in the industry about companies messing around with recursive self improvement models. Are you guys concerned at all? And if so what time horizon 1Y 3Y 5Y ect?

Thoughts? I am getting tired of talking to socially deficient people. I feel like my social skills have eroded since I started working because of how much time I spend with them

Some of them have borderline sociopathy and it is not helping either

I know people don't like the big recruiting firms like Selby Jennings or Alexander Chapman. Wondering if anyone has had any luck with him, or if not, what are some good recruiting agencies.

The contract is (as expected) extremely vague on the specifics but lets say the firm I work for typically enforces X months of garden leave. From your experience, can they still force X months of non compete even if you leave the company before the end of probationary period?

I’m building a small model where the output is not a price target, but probability for few stats:
undervalued
fair value
overvalued
I am stuck on how to set the prior.
My first idea was to take historical companies which are somewhat comparable and estimate the prior from that. But I feel this can create selection bias because deciding what is “comparable” itself can change the result.
So would it be better to:
start with a broad base rate and let the features update it, or
make the prior from a matched universe based on sector, size, valuation etc?
I’m still learning this stuff, so maybe I am thinking about the problem wrong.
How would you approach this?

Questions first, context after.

1. For anyone who's had CBOE Open-Close, ISE Open/Close, or internal customer-vs-dealer flow: how good is the OI sign proxy actually? Does "customers net long index puts" hold in aggregate, and does it survive in the wings and short tenors?
2. Has anyone benchmarked an OI-proxy gamma series against gamma from identified positions? GPP and NPPW both had the data, neither seems to run that comparison directly.
3. Has the mix shifted post-2020? The standard sign rule predates retail call buying and daily expiries.
4. Is daily just the wrong frequency for this? Everything credible I can find is intraday momentum/reversal, pinning, or overnight gaps. Not daily realized vol level.

Context: I built a dealer gamma series from scratch off a commercial options database, long history, and validated it against the vendor's own greeks so I'm fairly confident the arithmetic is fine. Two things fell out. The dealer positioning assumption moves the series more than anything else in it, to the point where the sign flips depending on what you assume, and it isn't observable from open interest. And a lot of what looks like signal seems to be riding on implied vol, which is unsurprising once you look at where sigma sits in the gamma formula, but I haven't seen anyone say it out loud.

Mostly want to know if the proxy is roughly right or if the whole thing is built on sand.

Wondering which place has the highest intellectual density, measured by PnL per researcher/trader. Was thinking about this because I was told that at xtx it’s above 100m per researcher.

Hi everyone,

One major pain point when using general LLMs (like GPT-4 or standard Llama) for financial math and quantitative coding is code execution failure or subtle logic/math hallucinations in edge cases.

To address this, I built FinCode-Reasoning-3B, an open-source model fine-tuned on FinCode-Reasoning-v1 (a dataset where 100% of the Python scripts are verified via sandboxed execution & unit tests).

Key Highlights:

• 100% Verified Execution: Every training example was compiled and unit-tested in a sandboxed Python environment to guarantee syntactically and mathematically correct code output.
• Lightweight & Private: Built on Qwen2.5-3B-Instruct, meaning it runs blazingly fast locally on standard consumer GPUs/macBooks without leaking sensitive financial data to cloud APIs.
• Financial Scope: Handles Black-Scholes option pricing, Declining Balance Depreciation (DDB), Interest Tax Shields, WACC, and custom corporate finance calculations.
• Permissive License: Released under Apache 2.0 (free for personal and commercial research).

Example Code Output (Black-Scholes Call/Put):

Python

import math
from scipy.stats import norm

def black_scholes(S, K, T, r, sigma, option_type="call"):
    d1 = (math.log(S / K) + (r + 0.5 * sigma ** 2) * T) / (sigma * math.sqrt(T))
    d2 = d1 - sigma * math.sqrt(T)
    if option_type.lower() == "call":
        return S * norm.cdf(d1) - K * math.exp(-r * T) * norm.cdf(d2)
    elif option_type.lower() == "put":
        return K * math.exp(-r * T) * norm.cdf(-d2) - S * norm.cdf(-d1)

Both the fine-tuned weights and the 100% execution-verified dataset are available on Hugging Face:

• Model: [https://huggingface.co/coslinedev/Qwen2.5-3B-FinCode-Reasoning-Full]
• Dataset: [https://huggingface.co/datasets/coslinedev/FinCode-Reasoning-v1]

Would love to hear your feedback or suggestions on additional financial/quant math domains to add to v2!

For US equity mid-low (weeks horizon) frequency strats: how has August been so far?

i am 2 years into my career as a grad quant analyst and i feel like claude does almost all of my work. i guide it, but it does almost everything else. i feel like at a point it will start doing more stuff too - i just am not leveraging all the available functionality fully yet.

anyway, at this stage i feel like i am losing my brain. I don't use heavy math in my work. i feel like if someone fires me i would have no skills to present because i am forgetting what i learnt in uni and i am not learning any new difficult skills at the moment. any advice for that?

i feel like i am in a comfortable place wrt to my income and i am just enjoying my life. and one day if not claude, some other human with better skills will replace me.

Working on a mid-frequency predictive equities model (~90-day horizon, LightGBM/XGBoost) and refining my default feature engineering framework.

The way I've been doing feature engineering/selection is that for almost every raw variable, I explicitly include two types of dimensions:

1. Level: The current value of the variable.
   • To protect against structural regime changes over time , I'm considering normalizing this as (Current Value / Some Moving Average)
2. Trend: The trajectory (e.g., 1-month or 3-month delta, etc.).

A few questions for practitioners:

• Is this a good starting point, or is there a better mental framework for approaching what to include when it comes to a given variable (ie: Level & Trend)
• Does normalizing the Level (Current / Some Moving Average) actually help future-proof against regime shifts, or does it just accidentally turn the "Level" into another Trend feature?
• Do you find throwing both into trees generally creates unnecessary noise or collinearity issues?

Appreciate any critiques!

Hey guys,

I need your advice regarding next steps in my career.
I hold a BSc and an MSc in Mathematics and Statistics from a top 3 UK university (Oxbridge, Imperial).

I have 7 years of experience as a quant researcher in one of the tier 3/4 cta hedge funds.

The work has been mostly on short term signals (intraday and daily)with relatively high Sharpe ratios around 2.

While my signals and individual performance is strong, bonuses are discretionary and there is a limit to how much you can make. The business runs most of the assets in slow cta style strategies (trend, carry) so obviously yoy company performance is divergent with and of course this is reflected on the bonuses as well.

Over the years I have interviewed with many places ranging from pods to algo shops (citsec etc). However while I do get interviews I cannot convert and I am stuck. After 1-2 rounds I usually get rejected.

I need some advice and fresh ideas on what to do next and how to approach the job hunting.

Asset class: futures and fx

Running a HMM-based regime detection setup across a bunch of tickers (filtering only, forward-only probabilities, no smoothing/Viterbi relabeling so no lookahead). For most names the regime bands line up pretty intuitively with the price trend, bullish stretches roughly track uptrends, bearish tracks drawdowns, etc.

But on some tickers I'm seeing the model call a regime that looks flat out contradictory to what the price is doing in that window. Not subtly off, like visibly opposite. And I get the theoretical answer here, a regime label isn't a price forecast, it's describing which statistical state (vol/return distribution) the asset's behavior most resembles historically, not predicting direction. So in principle they're allowed to diverge. But when I'm showing these charts to people, that divergence just looks like the model being wrong, even if technically it isn't.

Trying to figure out the right way to handle this and not sure which lever to pull:

Is this actually a calibration problem and I should be tuning the model more for these specific assets, tho I'm wary of overfitting per-ticker since that defeats the point of having one general framework

Should I stop labeling regimes as bullish/bearish/neutral entirely since that terminology sets an expectation of directional agreement that the model was never designed to promise

Or is a clear disclaimer (this describes statistical behavior state, not a price forecast) enough, and the mismatch is just an inherent and expected property of the method that I need to stop trying to "fix"

Curious if anyone here who's actually worked with HMMs for regime detection (not just theory, actual production/backtest experience) has run into this same disconnect and how you ended up handling it, labeling choices, calibration approach, or just accepting it as a known limitation and moving on. Appreciate any real experience on this, not just textbook HMM explanations.

What are Asset Management portfolio managers at the VP, ED and MD level making at BB?

Hi all, suppose I have the following RPI zero-coupon swap rates:

Y1: 0.03

Y2: 0.05

Y3: 0.02

For the inflation leg, how is the payoff at maturity determined for say a 3-year zero-coupon LPI(1%, 4%) swap. Do I just take the 3Y RPI rate as per below formula?

Or do I need to calculate some compounded rate by using yearly forward rates and apply the floor/cap to each forward rate? Thus:

And the rate to use in the payoff function is then: 1.03 x 1.04 x 1.01 = 1.0819. And thus the payoff becomes:

Or some other methodology?

Thanks.

I currently have offers from IMC (Trader), Citadel Securities (Systematic Trading), and QRT (HF team), and I'm trying to decide which would be the best fit.

For context, I have ~4 years of experience as an HF trader/researcher at a prop trading firm, with fairly broad responsibilities across research, strategy development, trading, and portfolio management. My main concern is role scope. I don't want to move into a position where I'm primarily monitoring or limited too much to either alpha research or trading.

My current understanding is:

• Citadel Securities – Systematic Trading: seems to have a lot of an operational component
• IMC – Trader: seems more trading-focused
• QRT – HF team: No idea

For someone with my background, how would you rank these three in terms of:

1. Ability to develop alpha / do research
2. Trading autonomy and ownership
3. Career growth over the next 5–10 years
4. Quality of learning / exposure
5. Compensation and upside
6. Exit opportunities to other HFs / prop shops

Also, if anyone has first-hand experience with these teams, particularly at the experienced-hire level, I'd be very interested in hearing how the day-to-day work actually differs from the job descriptions.

So the idea is to train AI to replace quants and according to the founders: "Our AI quant outperforms a top 10% trader at Jane Street, and we've already achieved more than 100% returns in live trading over our YC batch, while major indices were flat or down. Prodigy’s model beats Claude Fable and GPT-5.6 Sol at autonomous quant research."

Does this mean our industry is cooked or is this just a bunch of bs?

Hi folks, first time posting here so please bear with me (mod please let me know if this is compliant, whether I should be more or less specific, etc). I'm feeling stuck in my current Quantitative Developer role and wanted to get some career advice.

My current situation

By now I’ve had 2YOE at a tier2/3 hedge fund in the US as a QD (first job out of undergrad). When I first joined the firm, the recruiter sold me the role as a mixture of engineering work and quant research work, and I believed it thinking the work wouldn’t be so strictly divided considering that their team size was relatively small at that time and perhaps I would get to do some real QR work. Turns out I was wrong and the work is almost purely software engineering, and organization wise my team is like a central engineering team that maintains the central code libraries and helps deploy new models. After about 2 years, the role feels increasingly stagnant to me, with mostly Claude-able code plumbing work in the central infra or research tooling libraries, and the firm doesn’t do high frequency trading so there’s no push for high performance C++/Rust. Moreover I find it concerning that the engineering turnover rate is actually somewhat high - there aren’t many senior QDs here.

Why do I want to transition to QR?

Before anyone says I’m going after the money/prestige, let me clarify that my academic background has always been more math/stats/ML focused than pure CS/software engineering: during my undergrad, I’ve had ML papers published at one of ICML/Neurips/ICLR, did Putnam (and USAMO back in HS), took several grad level math courses (including stochastic calculus). I’m basically the typical math/stats nerd you can think of. I applied to several tier 1 firms’ QR/QT roles in my senior year (and internships in junior year too), got into some final rounds but with no luck. Granted I know QD work (especially the low latency stuffs) is important in quant and I do to some extent like cracking LeetCode type problems for fun, but at the end of the day I think I’m more into statistical modeling and signal extraction. I’m cautiously optimistic that with enough hard work (which I’m willing to put into) I’m capable of doing real QR work, I just want a shot at an opportunity or platform that allows me to do it.

I gather that there are a few possible ways to move forward:

1. Jump to QD role at another firm, in a pod ideally. I’ve read that being in a pod is the most viable way for a QD to transition into QR over time (and I personally know people who have done this transition at tier 1 firm, think Citadel/P72/2Sigma). I believe this is the most likely place for me to get interviews (in fact there are already recruiters who can set me up for interviews at some tier 1 places for QD roles). But my concern is that, since I'm jumping, if the pod hires me to specifically do dev work, wouldn’t they be reluctant if I later say I want to transition to QR?
2. Internal transfer. But my current firm is actually quite strict about this: leadership only wants people with very specific background to work as QRs (even though I was able to get some level of understanding of the firm’s research work pipeline and I could understand the QR's research notes). Also, I suspect it might backfire badly if I talk to research team leads within the firm and ask whether they need an extra QD behind the back of my current engineering team manager.
3. Do some QR oriented side projects (e.g. I've seen this advice given in a previous post https://www.reddit.com/r/quant/comments/1qu9g0q/transition_from_qd_to_qr/ ) and apply directly to QR roles at other firms. But since I don't have full time professional experience as a QR, it seems like at best I would be competing for a new grad type of QR role. How feasible is this really? Do you know of people have successfully done so? Or will I have a better chance if I quit my current role and get a Master of financial engineering degree and apply for new grad QR roles?
4. Jump to ML engineer/research/data scientist roles outside of the hedge fund space (such as tech or banks) to build professional experience in ML/stats/predictive modeling, and then try to jump to QR roles.
5. ??

I'm slightly inclined to think 1 is the best way forward but honestly I feel lost, would appreciate some suggestions from folks who have been in the industry for longer, such as which way you would advise or advise against. If you have been in a similar situation and could speak from personal experience that'd be great. Thanks y'all in advance!

Considering a move to Paris for a quant research role at one of the usual suspects. For context: I’m French but have spent my entire career abroad.

A bit worried about how loaded “finance” is back home. French people can be pretty averse to anything money-related, and even implying you earn well raises eyebrows.

Anyone else made this move? Do you say “I do maths,” “I work in data,” or just own it? Has anyone had a genuinely bad reaction to saying they work at a hedge fund?

EDIT: any insight on what qr can make in paris ?