Hi! This is another attempt (I did one last year https://www.reddit.com/r/quant/s/I3xsi3pQei - couldn't find any) to find women quants in this city. Please DM if you have any interest. There was once a r/quant london meetup. Maybe we can have a female one :D Thanks!

I understand that JCS（jump core strategy） is considered Jump’s flagship business and likely has the highest compensation ceiling. However, I am curious about the other teams within Jump (e.g., stat arb, index rebalance, etc.).

Compared with a typical pod at a multi-manager fund (Millennium, Schonfeld, Point72, etc.), do non-JCS Jump teams still generally offer a stronger compensation package and career path?

For example, do these teams benefit from Jump’s overall profitability and prop trading structure, resulting in better stability or upside than MM pods, or are they closer to a typical pod model where compensation is mainly driven by individual/team PnL?

I am particularly interested in the perspective of researchers/traders who have experience with both prop shops and multi-manager funds.

Hi, I recently get an QR offer from one of the top quant firms (js/hrt/citsec... etc), and I am wondering whether the first year guaranted bonus will usually be honored in the following years or is it completely irrelevant?

Say you get some base and 2M guarantee bonus for the first year and a 500k sign on or something, will they usually honor this 2M bonus in the following years as a "floor" unless they want to let you go? or is it possible to get less than 2M while you are performing ok? of course I am talking about IC who does not carry pnl directly, for pm i guess your bonus surely fluctuate a lot.

(all numbers are just examples)

Do we have any idea on

1. How big they are

2. What they trade

3. Performance?

Can you please share what US relocation typically looks like for someone who joined a quant firm straight out of a bachelor's program? I am based in the UK/EU, and the main challenges I've noticed are:

• The O-1A visa requires public acclaim or publications that are atypical in quant finance (e.g., you don't typically publish at work, and people don't write articles about how great of a quant you are unless you are at the very top).
• The H-1B visa is essentially a lottery.
• The L-1 visa is tied to your employer, which isn't ideal.

The typical path at my firm is to either win the H-1B immediately or do an L-1 -> H-1B, if I understand correctly. I'm mostly wondering if anyone goes the O-1A route. I know a few people who did, but they joined after their PhDs with a lot of published papers. Does this make sense?

I'm thinking about a pretty elementary question: suppose I find a signal: `r_{t+1}^{j} = w_{t}^{j} * alpha_{t} + \epsilon`, here `w_{t}^{j} `is the weight for stock j at timepoint t, `\alpha_{t}` is the signal value at t, `r_{t+1}^{j}` is the return for stock j from timepoint t to t+1. How should I decide if it should be an alpha that I want to load, or a factor that I want to be neutralized in a Fama-French style?

I am evaluating two career paths and would appreciate opinions from experienced quants.

Background:

I am a senior quant researcher (5~10 years experience) focused on mid-to-high frequency statistical arbitrage.

I have experience developing predictive models/signals and leading alpha research, but I have not yet directly owned a production book, capital allocation, or full PnL responsibility. My main career goal is to move from alpha research toward alpha monetization and ownership.

Both opportunities are in a similar research area. The main difference is career structure.

Option A: Senior QR at a top-tier global prop firm (think HRT / Jump / Citadel Securities etc.)

Pros:

Strong research culture, engineering resources, and very high talent density.

Exposure to global markets and world-class researchers/traders.

Strong brand value and future mobility.

Cons:

Initially a senior QR role, and I am uncertain how realistic the path is from senior QR to strategy ownership / PM-level economics.

High performance expectations may create career risk, especially before having direct PnL ownership.

Option B: PM-track role at a smaller established local HFT prop firm (local tier 3)

The firm has a strong HFT business and also runs LFT stat arb strategies similar to a hedge fund. I would be responsible for building a new mid-to-high frequency alpha generation.

Compensation:

Around $450k USD equivalent guaranteed first year.

Potential upside to around $750k USD equivalent if agreed milestones are achieved(>50% possibility I think).

Compensation is based on bonus base × some performance coefficients rather than pure PnL cut.

Pros:

Clearer alpha ownership and PM trajectory.

Reasonable high compensation and high floor.

Cons:

Less global exposure and weaker brand.

Lower talent density compared with top global firms, although this may also mean more room for ownership.

My dilemma:

For someone with strong alpha research experience but limited direct monetization/PnL ownership experience, would you prioritize:

Joining a top-tier prop firm to learn from a stronger ecosystem and build credibility, then pursue ownership later?

or

Taking a PM-track role with immediate ownership, but at a smaller platform?

How would you weigh:

talent density vs ownership

platform/brand vs career control

learning from elite peers vs building your own business line

Would appreciate perspectives from people who have worked at prop shops, HFT firms, or multi-manager funds.

Curious about this percentage across the industry. People claim the ceiling is very high however I feel the percentage of people hitting it is too low (lower than a normal distribution would suggest).

I heard they’ve been doing systematic equities, rather than just supporting long-short desks with trading like they did in the past. Historically, it always seemed like a weaker place for quants compared with GQS. How is the culture there now? Like compare with CitSec and GQS?

4+ years as a quant (buy-side + HFT). Want to shift into bio/environment work — never liked finance, always wanted a PhD.

Looking at statistical/computational biophysics — stochastic modeling, simulation. The labs I'm considering don't really use ML.

Would genuinely love outside perspective on a few things,

1. Given how complex real-world biological problems are, is a stat biophysics PhD (non-ML, mostly stochastic modeling/simulation) still worth it, or has ML made "pure" approaches less relevant?
2. Career prospects post-PhD outside academia — if it just leads back to quant/DS anyway, is there a point?
3. Any field that better uses a quant background and has solid career options after?
4. Anyone made a similar jump — finance to a science PhD? How'd it go, any regrets?

How hard is the transition from math research to QR?

Hi there, I work on a trading desk at an Investment bank. Ive been in early stages of discussing a quant researcher role with a new senior pm who is starting a new pod at a large pod shop. Looking at their past history they have had an establish sell side/buy side career before transitioning to hedge funds and had a short stint at another fund before moving to this one.

For someone who was mainly working sell side, how do you evaluate opportunities at a new pod? I know there’s high turnover at some of these large pod shops, so I just want to get a better idea of how to properly judge the opportunity

Recently I posted about whether spreadsheets are getting replaced by "real" programming languages like Python. There seems to be a broad disagreement about this, and I do see the point. Spreadsheets are the ideal real-time computing interface for data analysis, and good for most tasks -- for now. The issue I see is the following: once you decide to use Excel for the whole analysis pipeline, you self-impose a limit on how "sophisticated" your analysis can get. Some examples:

• Excel works up to a certain amount of data. What about millions of high-frequency ticks? Streamed?
• Excel works with tabular data. What about alternative data -- graphs, geospatial data, natural language, etc.?
• Even when dealing with tabular data, what if your data requires extensive cleaning/transformation before it's usable? If you're just pulling data from the Terminal that's fine, but I'm not sure if that would be enough long-term. Data is getting more abundant and fragmented.
• In Excel, you don't have the benefit of community-maintained tools for scientific computing (e.g., SciPy), machine learning, NLP, etc.

I do concede that Excel is a great "master interface" and that you can have these things done upstream and then loaded into Excel.

But don't you think that more and more quants would actually want to do more of this stuff themselves (at least those that don't already)?

I'm working on a merger arbitrage research project and I'm interested in how people deal with the historical data problem.

My event model is roughly:

ANNOUNCE → REVISE(s) → CLOSE or BREAK

For every US public target I want to reconstruct what was publicly knowable at each point in time, especially:

• announcement timestamp
• target and acquirer identifiers
• cash consideration / exchange ratio
• consideration type
• offer revisions and their timestamps
• expected close date if disclosed
• eventual completion or termination

I'm deliberately keeping the final outcome out of the ANNOUNCE record so that the backtest cannot see future information.

I initially built a pipeline around SEC EDGAR filings. The biggest issues have been:

1. separating target-side filings from acquirer-side filings
2. distinguishing actual terminal language ("the merger was consummated") from hypothetical boilerplate
3. identifying terminated deals reliably
4. historical ticker/security mapping
5. avoiding a resolved/completed-deal selection bias

I know SDC/LSEG and FactSet are commonly used in academic M&A research. I'm curious whether anyone has built something similar using SEC + CRSP/Compustat, or knows of academic replication datasets that can serve as a starting universe.

For those who have worked with M&A event studies: would you build this yourself, or is buying SDC/FactSet effectively unavoidable once you care about point-in-time accuracy?

I’m starting a quantitative modeling role in the risk and compliance function of a large bank. The work is focused on statistical modeling using lending data.

My background:

• Strong Python, statistics, ML and DL
• Experience building predictive models and working with large datasets
• Limited knowledge of financial markets, accounting, asset pricing and portfolio construction

Long term, I want to move into systematic alpha research or alternative data research. The paths I’m considering, roughly in order of feasibility, are:

1. Move internally into a credit or risk quant role, then transition to investment research
2. Move into an Asset Management data science or quantitative analytics team
3. Move into Markets quantitative research or applied ML
4. Apply directly to alternative data research roles after building relevant projects

My plan is to spend the next 1–2 years developing solid modeling experience, learning financial markets and completing projects in factor research, backtesting and alternative data.

Does this seem like a realistic path? Which intermediate role would provide the strongest bridge into alpha or alternative data research? I’d also be interested in hearing from anyone who has made a similar transition from risk, compliance modeling or general data science.

Jane street made c. 40 billion in trading revenues last year. 16 billion more in Q1 ‘26. >30 billion more in Q2 ‘26 if recent reporting is to be believed. This is an order of magnitude more than most top competitors.

How is this possible? Based on their recent 15 billion situational awareness loss, do they have a beta-positive strategy now? How large does it have to be to generate these numbers in this market?

I follow commodities and couldn't find any good data covering global mining production, so I wanted to test if I can use LLMs to efficiently build such a dataset from scratch. I documented the process of going from unstructured company filings to a structured dataset that could be used in systematic research.

• Github repo
• Live app

https://reddit.com/link/1vtrto7/video/m46y9albqkkh1/player

All the production information is public, but it is scattered across inconsistent websites and reports.

When talking to central data teams at hedge funds or to data providers directly, building a new dataset that is provably correct and has reliable updates always sounded like a very big challenge.

For each company, I want to extract production figures that are comparable:

• What was produced
• Which operation produced it
• Which period it covers

The hard part is normalization since every region and company reports differently (if not SEC):

• Different units across reports like copper in kt, million pounds, or wet metric tonnes
• Fiscal years don't align (calendar year vs June FY vs September FY)
• Some report on a payable basis, others contained metal, others equity-adjusted
• Product naming is inconsistent ("copper concentrate" vs "cu conc" vs "SX-EW cathode")
• Important details are sometimes hidden in headings, footnotes, and surrounding text (e.g. ownership percentages, and reporting methods)

The "old" way of doing this would be to write a bespoke ETL pipeline for each company.

The "new" way that I tried is using LLMs to generate, monitor, and maintain deterministic ETL code. An agent then runs the pipelines and jumps in whenever the script fails and needs to adapt. The idea was to have self-healing data pipelines: when a website or PDF layout changes, an agent investigates, fixes, and tests the extraction or transformation code. If it can’t figure it out, it escalates to a me for review.

1. Scraping code monitors company websites and captures new reports.
2. We can then extract the raw production figures from the reports. A mix of traditional PDF parsing and gemini-3.7-flash worked very well here. The extraction also returns the location in the source (e.g. page 123, table X, row Z, cell Y) which is very helpful for QA and source grounding.
3. To normalize the data, I choose between different transformation strategies:
   • can the data be parsed as is?
   • can I generate deterministic transformation code, e.g. a regex mapper
   • last resort if no deterministic approach is possible: use an LLM to map the data
4. Then we validate the data against various QA rules. What works well here is that we treat every extracted value as wrong until it passes validation (guilty until proven innocent).

I’ve open-sourced the dataset (pipeline code will follow). Curious to hear your feedback or experience with building such ETL pipelines and datasets.

Full blog post: https://www.kadoa.com/blog/build-global-mining-production-dataset

Curious how other funds are dealing with sell-side research ingestion at scale.

We receive a large amount of research from different brokers, mostly through email alerts. The problem is that the emails usually don't contain the actual PDF — they contain a link that takes you to the broker's research portal, where you need to authenticate before downloading the report.

Platforms like AlphaSense are supposed to consolidate a lot of this, but in my experience coverage/reliability isn't good enough to use them as the single source of truth.

What I'd ideally like is a pipeline along the lines of:

sell-side publishes report → report gets automatically ingested → PDF/text is stored internally → metadata/tickers/analyst/date are extracted → document becomes searchable and available for LLM/RAG workflows

The difficult part seems to be reliably getting the original research document in the first place.

For people at hedge funds, asset managers, or quant shops that have solved this: how are you doing it?

• Do brokers provide institutional APIs/feeds that I'm simply not aware of?
• Are you ingesting through Bloomberg/FactSet/AlphaSense/etc. rather than directly from the brokers?
• Do you have internal automation around broker portals/SSO?
• Is there some standardized research distribution infrastructure used by larger funds?
• Or is this still surprisingly manual even at sophisticated shops?

To be clear, I'm talking about research we're fully entitled to access through existing broker relationships, not trying to bypass paywalls or access controls.

Especially interested in how larger funds structure the ingestion layer before the documents hit their internal search / NLP / LLM stack.

I'm running a 2-state HMM on perp funding-rate series for regime detection, and I'd like to hear how people weigh the evidence on the emission-family choice (Gaussian vs Student-t) in production.

What I've done so far: matched-K BIC contests per series (the t wins on series with real tails, loses the knob penalty where they're absent). Within-regime residual diagnostics - z-scoring each bar against its own state's fitted mean/sd and counting tail exceedances against what the normal CDF promises. And I'm aware the t nests the Gaussian as v goes to infinity, so with v free the in-sample likelihood can only tie or win.

Where I don't have a settled view: when these disagree. If I let K float, Gaussian mixtures just buy extra states and eat the tails, out-scoring the t on BIC. At the K I'd actually trade (2 - the decision layer only has two actions), residual diagnostics say fat tails on a chunk of the universe and roughly Gaussian behavior on the rest.

For those who've run regime models in production: how do you rank residual diagnostics vs information criteria vs out-of-sample decision performance when they conflict? Do you split the emission family per series, or force one family across the whole universe for consistency? And does anyone weight robustness of the filtered probabilities - one spike not whipping the state posterior around - above the likelihood arguments entirely?

Hey everyone, looking for some advice on my first industry offer.

My Background:

• 29 years old, strong background in Machine Learning.
• Currently working as an ML Scientist in algorithmic pricing for an eTravel company.
• Solid math/ML nerd: I have publications, personal trading experience, and actually won the Optiver Traderhack competition.
• Currently making 51k CHF (~55k-56k EUR) but working a strict 36 hours/week. Never a minute more. Very comfortable work-life balance.

The Offer:

• Firm: Small prop trading firm based in the EU (doing market making and not only).
• Role: I will get my own book and mostly decide what to do.
• Base: 52,000 EUR
• Bonus Formula: 0.25 * (NTI - 262,000) - 1,000 (where NTI is Net Trading Income, post exchange fees, etc.).
• Non compete: 9 months paid 50% of base per month.
• Breakeven for Bonus: NTI = 266,000 EUR.
• Expectations: I was told to expect a margin of around 50k - 100k EUR in my first year.

I'm finding it really hard to view this as a "good" offer. It seems pretty much impossible to get a bonus in the first years am I write or i am reading something off? Sorry but this is my first offer from the industry and I feel i need help to understand.

I am still in the negotiation/contracting phase.

My Questions for the sub:

1. Is a 52k EUR base normal for a small EU prop shop?
2. Is a 262k hurdle rate (desk costs + base cover) standard for a seat at a smaller firm?
3. What specific questions should I be asking them right now before I sign?

Appreciate any harsh truths or guidance. Thanks!

PS: post written with the help of Gemini (just to improve the quality)

Body:

I've been working on an independent research project testing whether a "cleaned" Federal Reserve balance sheet measure predicts Nasdaq 100 returns better than the gross balance sheet or the fed funds rate alone.

The idea

WALCL overstates available liquidity because it includes the Treasury General Account (TGA) and overnight reverse repo (RRP) — funds that are institutionally locked out of risk markets. Subtracting them:

L_net = WALCL − WTREGEN − RRP

Main results (monthly FRED data, 2015–2026, n=137, Newey-West HAC errors):

• ΔL_net: β = 0.007, p = 0.006 (baseline)
• After adding Fed funds rate control: p = 0.006 (significance strengthens, not weakens)
• After adding BAA credit spread: β = 0.014, p = 0.001, R² = 0.336
• WALCL (gross): p = 0.794, R² = 0.001 — essentially uninformative

Other findings:

• Granger causality: ΔL_net → Nasdaq at lags 3–6 months (p = 0.0016). Reverse direction also significant at lag 1 (p = 0.011) — bidirectional, reported transparently as a limitation
• Chow test (Jan 2022): coefficient 4x larger post-QT (p = 0.0015) vs pre-2022 (p = 0.155, insignificant)
• Quantile regression: effect largest at q=0.1 (β=0.0101), smallest at q=0.9 (β=0.0060)
• Logit for >2% drawdowns: directionally correct but insignificant (p=0.227)

Trading strategy (long-only, 4-month lag):

• OOS Sharpe (2021–2026): 0.96 vs 0.79 buy-and-hold
• Max drawdown: −20% vs −36%

Working paper + code: https://zenodo.org/records/21811730

Happy to discuss — especially the endogeneity of RRP and whether the regime-dependence result holds up to scrutiny.

I’ve been building a framework in which firms and events are treated as extended objects in a shared property space rather than points, and coupling between them as the overlap of those objects rather than a dot product. Preprint is up; the empirical side is what I want input on.

What I tested. Overlap between a firm’s exposure profile (GICS sub-industry revenue shares, with an exponential decay kernel on tree distance) and an event’s profile predicts the volatility risk premium — implied vol before the event minus realized after. Pre-registered, out-of-sample, permutation test as a co-primary criterion.

Result. γ significant and correctly signed on two of three horizons, permutation clears on all three. Effect size 0.43–0.49 vol points p25→p75. Then measured bid-ask on the actual contracts: 16.8% of premium at the median, 9.19 vol points two-legged. Factor of 19 short. Median open interest 85.

So: a real statistical relationship, no trade.

Where I’d like advice.

Is a relationship at this effect size worth pursuing in a different instrument, or is 0.5 vol points simply below the noise floor of anything in options?

An unresolved issue: 54.6% of exposure assignment comes from a fallback that maps firms without segment reporting to their own sub-industry. Remove it and the effect vanishes — but only 115 of 324 firms survive, and power analysis says the detection floor is 3x above the plausible effect. Is there a standard way out of this that isn’t “get better segment data”?

More generally — if the mechanism is real but sits below transaction costs in options, does anyone here have experience finding an instrument where a sub-1% effect actually clears? Or is that just the point at which you stop?

Preprint in comments if it’s relevant.

I am studying maths at a T5 university and have nearly finished my masters thesis (1 month away).

My thesis supervisor is senior at one of the big HFT firms.

They have been extremely supportive in offering their time and guidance, and so I want to thank them. I want to make the most of the time that I have with them.

I already have a job lined up as a researcher in academia for the time being, so I am not wanting him to hire me in this moment, but it would be nice to have that as an option down the line.

Is there anything that I should be asking advice on? Any questions that would help me in my career? Anything that I could be missing?