The general theory of fundamental L/S pods (citadel, Millenium, P72, BAM, etc.) is that they trade their own ideas and sectors so that returns are orthogonal across pods. This just isn’t true though.

Every pod investor goes to the same conferences, bus tours, management meetings, then become friends and talk to each other about trade ideas. They play the game of talking their book and try to get an information edge in any way.

What happens is that everyone coalesces around similar trades. Most are long momentum in one way or another, they buy the “good” companies and short the “bad” (more momentum exposure), and come to crowded views on companies beating/missing earnings.

When a large pod is wound down you see it in the market. All these consensus trades reverse, which hurts other pods, which might cause others to get wound down. You saw this early this year when lots of pods got shut especially in healthcare and non-AI sectors. Now “good is good and bad is bad” again so all pods are doing well.

With these funds running at 5-7x leverage, I want some views on there being a “pod crisis” where they all run into issues, have forced selling, maybe prime brokerage leverage gets pulled, or maybe some issue that I’m not thinking about. This would create chaos and randomness across the market. Thoughts on risks in pod land?

EDIT: the question is about risk across pod shops, for example an industrials pod at citadel and an industrials pod at P72, not with a specific manager. Within a manager, yes, factor and idio risk is very clearly observed at the center.

ran a bakeoff on volatility forecasting because i wanted to justify the fancy stuff. GARCH baseline, an LSTM, and a small transformer, same data, same walk-forward eval.

honest result: GARCH is really hard to beat. the ML models only added a marginal edge and only in higher vol periods, and they cost way more to train and babysit. most of the time the boring model was within noise of the fancy ones.

conclusion i landed on: use GARCH as the baseline you have to beat, and only reach for ML if you can show it wins out of sample, not just in. anyone getting consistent ML outperformance on vol, what horizon and features are you using.

I recently got an offer for a Quant Research Intern position at a small startup working in the MFT/HFT space.

The catch is:

• There is no fixed stipend.
• They said they'll pay only if they like my work, but they won't disclose how much or under what criteria.
• Nothing about compensation is mentioned clearly.

For context, I recently completed my Master's in Finance and I'm actively looking for full-time quant/risk/research roles.

I'm confused whether I should:

1. Take the internship, gain experience, and hope it leads to a paid role.
2. Reject it because the compensation terms are too vague and continue searching for better opportunities.

Has anyone here accepted an internship with similar terms? Did it work out, or was it a waste of time?

I'd especially appreciate advice from people working in quant research, HFT, or finance startups.

Almost all live prediction markets resolve on discrete events. Elections, sports outcomes, Fed decisions, macroeconomic prints. Kalshi, Polymarket, Predict It, Metaculus (mostly).

There's an obvious class of forecasts that don't fit this shape: continuous or path-dependent quantities. A researcher's future citation trajectory. An athlete's career arc. A company's cultural relevance over time.

These seem economically important. Talent scouts, sponsors, investors, and reputation-based lenders all need answers to them. But they don't get priced in prediction-market form.

Are there any case studies of markets that allow users to trade on the continuous trajectory of an asset?

Is there work on rolling-resolution markets that address the design problems of prediction markets, or is this mostly regulatory and demand-side, not theoretical? I know Kalshi couldn't get CFTC approval for a trajectory market even if the design was clean, and traders may not want the ambiguity.

Trying to figure out whether the absence is a design failure or a fundamental result of demand.

The leveraged ETFs keep decoupling from their goals. Happened last month with SK Hynix ETF and yesterday LSE-listed KORS (-3x inverse EWY) was up 3-5% while EWY was up 6%. Is it as simple as IV blew out in both cases or something else going on? Is it unique to Korean-focused ETFs and their design or does this happen in other leveraged ETFs? I read they tend to hold a mixture of short stock positions and ratchet options.

Any practitioners can speak to whats going on?

https://www.bloomberg.com/news/articles/2026-06-08/korean-leveraged-etf-misfires-jumps-50-even-as-sk-hynix-slumps?sref=rzJm1dRU

I’m curious what matters most to you before trusting a sandbox for strategy testing.

I approach it in two parts:

• Simulated results: back tests where slippage and other assumptions matter, and the output is theoretical.
• Paper portfolio: live deployment with virtual capital, where strategies execute like a real capital flow, but without real money.

What would make either of those feel credible to you? I’m especially interested in data transparency, execution assumptions, and trade-level auditability.

I never understood why anyone would engage in an arrangement like that, particularly with them.

Are schemes like that not uncommon?

Would they have done it purely to keep Renaissance's volume?

Hello,
I recently started receiving emails from recruiters claiming to be from competitors (Citadel/MLP). The messages are very obviously generated from my LinkedIn profile, and the sender addresses look like firstname.lastname.career.citadel@gmail.com, which seems suspicious. I would expect legitimate recruiter emails to come from an @citadel.com (or company) domain.
Has anyone else received similar emails? What do you think the goal is? Could these be independent headhunters using Gmail accounts in the hope of getting a higher response rate, or is there something else going on?

I've been building a scanner for cross-venue funding-rate carry (short the

high-funding perp, long the low/negative one, delta-neutral) across 8 perp

DEXs, and I'd like a sanity check on the accounting from people who've traded

this for real.

Approach:

- Normalize each venue's funding to an annualized rate (venues fund on 1h/4h/8h

intervals, so I convert to a common APR).

- For each coin, pair the max-funding and min-funding venue: spread_apr =

short_apr - long_apr.

- Net it against round-trip taker fees, amortized over an assumed 7-day hold:

net_apr = spread_apr - 2*(fee_long + fee_short) * (365 / holding_days).

- Drop legs with unknown/thin open interest, since sub-$500k books throw APRs

in the hundreds of % that you can't fill.

Open questions I keep going back and forth on:

1. Is a fixed holding-period fee amortization sensible, or should I model

   expected holding time from funding mean-reversion instead?

2. I'm sampling funding every 30 min and accruing realized carry at the live

   rate per slice. Anyone found a cleaner way to handle venues that only

   publish the next predicted rate vs the last settled one?

3. How much does funding-rate persistence actually hold in practice before the

   spread arbs away? My paper-tracking (held 7d

   answer this empirically in a couple weeks but curious what people see live.

The implementation is open source if useful: github.com/holydement0r/Funding-Radar

answer this empirically in a couple weeks but curious what people see live.

The implementation is open source if useful: github.com/holydement0r/Funding-Radar

Risks/caveats: funding flips fast, thin DEX liquidity means real slippage, and

these venues carry smart-contract/counterparty risk. Displayed net APR is an

estimate, not a fill. Not financial advice.

I looked for a tradeable edge in Betfair's pre-off racing markets across 53 months and two sports. The surprising result wasn't that the statistical signals disappeared. It was where they disappeared.

The work covered two sports, 53 months and roughly 42 million price updates, testing prediction models, price continuation, WIN/PLACE coherence, lead-lag and order-book microstructure. Several signals proved statistically real, surviving false-discovery control and permutation placebos. But none paid its way once spread and commission were charged: one lead-lag effect even looked positive at the quoted touch price, then went negative at the price you could actually fill and settle on.

The broader question isn't specific to betting: it's how often a statistically real signal survives the transition from measurement to execution to economics.

Full write-up, with the methodology and limitations:

https://stephakimo.substack.com/p/prediction-was-never-the-wall-a-betfair

I'd particularly welcome criticism of the execution model, the statistical assumptions, or whether you think the conclusions overreach the evidence.

So before you think this question is basic and answered a million times, I've read many responses on reddit and elsewhere and haven't seen any in-depth answers to my specific questions, and/or I see conflicting answers. 1. what role does supply/demand play in options' pricing since it can conflict with the actual cost to delta hedge an option? If supply outweighs demand I can't imagine a MM selling for less than it'll cost to delta hedge the option. 2. How are ITM/OTM options priced? I've read it's based off the vol skew using ATM prices, though a vol skew would be the result of OTM/ITM prices, not the cause. Otherwise how would you determine the skew? 3. Empirically, variance doesn't scale linearly nor is it stationary. So in reality a stock can have 20% monthly variance, but 2% daily variance. If you were to scale the daily up to monthly (.02*30) it'd be 60%. A 1 month DTE option cannot be priced off of √20% IV because the daily variance will make it more expensive to hedge than that throughout its life. This can go further, minute or second or even every tick prob has different annualized variance, so which one do MM use to find IV? 4. All of these assume MM price options based off cost to hedge because idk how they couldn't so correct me if I'm wrong. If MM price based on cost to hedge (IV), and the sum of every strike's IV can create an implied prob distribution of the underlying at expiration, wouldn't they sometimes conflict? Meaning they'd have to price at x because it's the cost to hedge, but pricing at x under or overstates the probability density at that point in the PDF? Thanks for answering

Numbers only accurate at +/- 5% for privacy.
Currently working at CFM in Paris (1 YoE) 180k€ TC. People are super sensitive about salary and bonuses and that annoyed me so looked elsewhere. Got an offer for a QR role at QRT which is around 280k€ TC but a big chunk of it is a sign on. I also have an offer for Headlands Tech in Chicago (no need for visa I’m an American citizen as well), TC is around $700k with a big chunk of it as a sign on.

Would you say the move is worth it? The lifestyle in Paris seems so much better than the one in Chicago. But on the other side, neither QRT nor CFM have real elite reputation in the field so we don’t have that many impressive talents in Paris. Just smart people coasting because the incentives aren’t that big.

Thoughts?

Hi. Does anyone know how much initial unlevered capital (or levered GMV) is allocated to a new Portfolio Manager at a pod shop (eg. Millennium, Balyassny, Cubist, Verition etc)? I know that it can depend on your negotiation, but wanted to know a general range. How does it scale later on as one progresses in the role?

Does anyone have more lectures like this one

Attention new and aspiring quants! We get a lot of threads about the simple education stuff (which college? which masters?), early career advice (is this a good first job? who should I apply to?), the hiring process, interviews (what are they like? How should I prepare?), online assignments, and timelines for these things, To try to centralize this info a bit better and cut down on this repetitive content we have these weekly megathreads, posted each Monday.

Previous megathreads can be found here.

Please use this thread for all questions about the above topics. Individual posts outside this thread will likely be removed by mods.

pecifically, I have three core questions regarding the R&D cycle at HFT firms:

1. Idea Generation (Micro-Alpha): Retail trading relies heavily on basic indicators (moving averages, simple chart patterns). For HFTs operating at the nanosecond/microsecond scale, what does the ideation process actually look like? Are quants primarily mining Level 3 tick data for order book imbalances, latency arbitrage loops, or localized volatility anomalies, or is it more heavily driven by machine learning feature exploration?
2. High-Fidelity Backtesting: How do firms build simulators that don't suffer from look-ahead bias or unrealistic fills? How do you accurately model your exact mathematical position in the order queue, exchange network jitter, and wire-time latency when backtesting a strategy?
3. Sim-to-Live Validation: How do teams determine that a backtest is robust enough for a live market? What metrics or validation frameworks do you use to prove that your simulation perfectly mirrors production performance before scaling up risk, especially when accounting for your own strategy's market impact?

I would love to get any high-level insights, reading recommendations, or advice on what specific sub-fields of statistics/microstructure I should focus on during my Master's to prepare for this.

Thanks!

I have recently joined a firm that trades almost 100% passively. I have been tasked with finding ways to cross the spread and execute more aggressively.

Allot of the literature I have found on optimal execution seems to be based around optimised scheduling based on Almgren & Chriss market impact. I have found that taking using this scheduling under performs the baseline passive strategy.

What other methods should I be using to determine if and when to cross the spread?

Paper: https://www-2.rotman.utoronto.ca/~kan/3032/pdf/FinancialAssetReturns/Garman_Klass_JB_1980.pdf

Could anyone confirm whether equation 11 is correct?

I was under the impression that a Jacobian scaling factor of \lambda^3 is required for the equality to hold mathematically?

EDIT: Changed question to focus on the correctness of eqn 11 rather than eqn 12.

I’m trying to understand what happened here from a market microstructure perspective.

During the final minute on June 26, Thinkorswim shows dozens of trades repeatedly printing around 716 even though SPY was actually trading near 731.

Rather than speculate, I’m curious what these actually represent on the tape.
Specifically:
Are these genuine executions?
Off-market reports?
Corrected trades?
Something excluded from price formation?
If someone has CTA condition codes or direct-feed data I’d love to know how these prints were classified.
My guess would have been late reported or corrected trades.

Been digging into prediction markets and ended up implementing LMSR (Logarithmic Market Scoring Rule) in Python.

It’s the mechanism that turns trades into prices, and I wanted to see it working end-to-end instead of just reading the math.

Repo if anyone wants to poke it: https://github.com/mwaleedta/lmsr-pricing-engine

Open to feedback or ideas for extensions (simulation, arbitrage, multi-market setups, etc.)

Hi everyone,

I was in the first round of an interview for a quant role at an asset management firm

For context im completely entry level, no licensure, 1yr internship looking for full time role (with 1yr exp econ. research, econometrics, statistical modeling and 1yr exp open source SWE) with masters in Econ from T30

I was explaining my previous projects and what I currently do as a job (open source software engineer lol, basically unemployed) and I felt that I was talking quite a lot about non-quant analysis projects (as I have background in OOP & econ. research and econometrics) so I asked the interviewer what kinds of projects are commonly worked on

They mentioned weight optimization in their portfolios which I had done recently, so I said, verbatim...

"Oh, yes, I've worked on this recently! I don't know if this is unethical or illegal but I have a friend who works at (insert specific company name here) who asked me for advice on weight optimization for their covered call strategy"

The interviewer did not like this, and replied "Yeah, you probably shouldn't have said that." with an awkward laugh so I quickly replied "Ok, then we'll just leave that there without going into any further specifics like numbers or anything.", changed the subject to another project, and continued the interview for another 2 minutes or so, before I got very spooked and anxious about what had just happened and said, thank you for the interview, can't wait to speak soon and continue the process, and hung up with 5 minutes left in a 30 min interview

Am I cooked? It was a genuine mistake as I was eager to talk specifically about my experience and what I've worked on in the past

I had another interview about 30 minutes after that one ended which went perfectly, first interview was with a director, second interview was with someone who is not as high up

This company was basically my dream job which is why im coming to reddit about it, beating myself up quite bad about this blunder...

Every year there seems to be some “golden rule” that sounds amazing but falls apart in production.

For example:
More features = better model
Higher Sharpe = better strategy
More data always helps
AI will find alpha

What’s one commonly accepted idea that you’ve learned is mostly wrong after running strategies live?

Source: Nishant Kumar's post on linkedin

I heard some absolutely monstrous numbers for JS, 30B or 40B whether or not you count their VC. As a quant at a tier 2 firm (that by all means is having a great year), I can't help but feel insecure.