r/quant

• Which models do you use most frequently, and for what tasks?
• Which models have delivered the most practical value versus being primarily academic?
• How important are classical statistical models compared to modern ML methods?
• Are tree-based models still dominant, or is deep learning becoming more prevalent?
• If you were starting over today, which models would you prioritize learning?

Industry practitioners are invited to comment on any of the above. Thanks in advance.

Hello just wanted to get your thoughts on the efficiency of markets, especially mega cap stocks and large cap stocks of DM, as EM and mid small can be structurally less efficient.

I hear a lot that "trading large stocks is meaningless as price discovery is such a big incentive that there is no mispricing left". But on the other way large LS PMs at Millenium Balyasny Citadel etc do ( seem ) to provide some pure idiosyncratic returns by being sector and / or geographic specialists.

Also I hear as an argument in favor of inefficiencies, that market makers do not really participate in price discovery per se, as they just want a lot of volumes and are ready to accept adverse selection, and thus not really targeting a "faire price" but more a price that will make them money.

Last thing I wonder is, is there really some edge in month long trades, and is it really possible to identify ex ante the catalysts that could make something rally because the market was not seeing it ?

Currently in middle office role at platform shop and looking to move to another platform shop. Curious how non-competes usually work in practice. does your employer decide the actual enforced NC length when you leave and have you sign an agreement confirming it? Also, does the new employer typically ask for that document to verify your NC length, or do they usually just rely on your representation?

I am currently building a backtesting engine for a short-term options strategy and hitting a major roadblock regarding data architecture and API endpoint design with the providers I have tried so far (e.g., CuteMarkets, Massive).

I want to reconstruct the cross-sectional market state of the entire SPY options chain at specific points in time in the past.

Specifically, my backtester loops day-by-day through the last few years of historical daily market closes. For each day, it needs to look at the underlying price, draw a box around the strikes (e.g., 80% to 120% of spot), find contracts expiring within a N-day lookahead window (e.g., 10 days), and save their end-of-day market metrics (Bid, Ask, Volume, OI, Implied Volatility, Greeks) for that exact day.

The providers I have looked at treat their options chain snapshots as "live/current data only." Their endpoints look like /v1/options/chain/SPY but don't accept any historical as_of or timestamp parameters.

Instead, they only allow you to pull an historical reference index of what contracts existed on a past date (using /v1/options/contracts?as_of=2023-05-22), but that response completely lacks market quotes. To get the actual pricing, they expect you to point-query the individual bar/historical quote endpoint for every single contract discovered sequentially for that one date.

When dealing with SPY daily expiries and dozens of strikes, this approach means making hundreds of individual HTTP requests for just a single historical trading day. It completely destroys rate limits, causes massive latency, and feels structurally wrong for bulk historical research.

My questions for the community:

1. Am I misunderstanding how to utilize these APIs, or is the lack of a bulk point-in-time /chain?as_of=... query parameter standard across retail/mid-tier option APIs?
2. Which data providers natively support a bulk point-in-time options chain query for past dates where I can pass a specific date and get the whole grid’s metrics at once? (Looking for alternatives to Cutemarkets/Massive that are budget-friendly for indie devs).
3. If you have solved this without expensive institutional feeds (like ThetaData or Databento bulk files), what architectural ingestion pattern did you use? Did you just suck it up and parallelize thousands of individual contract bar requests?

Hi all, was posed this trading brainteaser recently.

Assuming you had to buy 10 units of A by end of the month. The benchmark to beat would be the average of the closing price of last 5 trading days of the month.

How should we go about sizing buys and the timing of the buys?

Assume 0 trading cost/slippage and asset class agnostic. Thanks!

I’m a dev in the US at a firm with a long paid non compete. I’m currently looking to leave, either for another firm in the industry or switch to tech. I wouldn’t mind having some time off tbh.

My firm does give long non competes for people without anything lined up, and stops enforcing/paying early if you start working outside the industry.

Do most people with a non compete:
- Only leave with another offer in hand
- Leave without an offer, then recruit while waiting out non compete

It does feel like I’d have more leverage if I’m currently employed while recruiting. On the other hand, I worry how much of a disadvantage it is for me if every firm has to weigh waiting out my non compete. Also it would be nice to have more time to prep for interviews while being off.

Lmk if you went through this and how it went for you!

https://preview.redd.it/ok6moo6q5i1h1.jpg?width=1674&format=pjpg&auto=webp&s=165acbd231342c7a81f754f0e84301e2f62ec6c1

https://preview.redd.it/emq1xp6q5i1h1.jpg?width=1441&format=pjpg&auto=webp&s=7b5f3adf8bccc2b17df04fbae75be840bc4db8f4

https://preview.redd.it/efiolo6q5i1h1.jpg?width=1384&format=pjpg&auto=webp&s=c140e0ff4800156527ca826bb5e46226fad77f22

https://preview.redd.it/ciovxs6q5i1h1.jpg?width=2462&format=pjpg&auto=webp&s=d1040505bac17ef28a7009a630c199a18d32103b

I’ve been building a project called MeetProba for students preparing for quant interviews.

The idea came from a frustration I had while preparing myself: probability resources are often either too theoretical, poorly structured, or not really aligned with what gets asked in quantitative finance interviews.

And even when you find good exercises, the solutions are often not detailed enough or skip important reasoning steps.

So I started building a platform specifically focused on:

• combinatorics
• random variables
• stochastic processes
• Markov chains
• Brownian motion
• and other probability topics commonly used in quant interviews

The main idea is to make preparation more structured and interview-oriented through:

• carefully selected exercises
• detailed step-by-step solutions
• roadmap/dependency graphs inspired by NeetCode
• progression between topics

The platform is currently free to use.

I attached a few screenshots of the current version and would genuinely love feedback from people preparing for quant roles or probability-heavy interviews.

https://meetproba.com

https://www.wsj.com/finance/regulation/flurry-of-suspicious-oil-trades-worth-800-million-triggers-regulatory-probe-71e959ce

From the article:

>The CFTC is interested in at least three firms as part of its inquiry, according to documents viewed by the Journal and one of the people. The London-based investment firm Qube Research & Technologies earned about $5 million of adjusted gains on those trades, the documents show, while Forza Fund Ltd. netted roughly $10 million. Totsa, the trading arm of the French oil company TotalEnergies, posted a roughly $200,000 profit.

I guess Qube learned how to detect Trump's insiders?

For people working in quant / systematic trading:

How is power/energy trading generally viewed as a long-term quant career path?

More specifically, for someone with a PhD + ML/statistical research background trying to enter quantitative research, is power trading considered:
\- a strong entry point into systematic trading/quant research,
\- or a more specialized track that can become limiting later?
\- or it depends on the mission?

I’d be especially interested in perspectives regarding transition opportunities later toward broader systematic hedge funds / HFT / ML-driven quant research roles.

Thanks!

I'm reading Modern Pricing of Interest-Rate Derivatives: The LIBOR Market Model and Beyond by Riccardo Rebonato which came out in 2004 but SOFR has replaced LIBOR since 2023, but there's loads of old useful books that use LIBOR rate pricing certain assets. If I swapped LIBOR with SOFR, does that really change anything?

Edit: I'm new to this stuff

So the Carr-Madan paper is quite old at this point, but I've rarely, if ever, heard of any of the large banks using these sorts of techniques to actually price derivatives, structured products (I wonder if they could be used for rates products? I don't see why not) and the like in production. I would have thought they'd be a very popular innovation given the computational saving, but I only ever hear of the usual numerical techniques (FDM, Monte Carlo etc.). Does anyone know if they're used? Which banks, if you don't mind sharing? If not, why not? I don't really see a down side aside from actually having to derive the forward transform of your payoff and underlying process yourself for each non-standard product, which I guess could make development longer compared to Monte Carlo where you pretty much know what you need to simulate straight away and so going from concept to working code is probably relatively quick as there's no derivation step in between (I imagine). I wouldn't even imagine this is a probably for pricing well-known classes of derivatives like vanilla options and the popular exotics.

I'm going to say something that will annoy people.

The vast majority of people who call themselves quantitative traders are doing something much simpler: they're running optimization algorithms on historical data, finding parameter sets that performed well in the past, and calling it an "edge."

That's not quantitative finance. That's curve-fitting with extra steps.

Real quantitative trading starts with a hypothesis about WHY a market inefficiency exists. Then you test if the data confirms it. Then you ask whether that inefficiency can persist given how many other people have now found it.

Most people skip the first step entirely. They just run the optimizer and take whatever comes out.

The tell: ask someone why their strategy should theoretically work in the future. If they can't give you a clear answer that doesn't involve the phrase "because it worked in the past" it's curve-fitting.

I'm happy to be wrong about this. What's the counterargument?

Trying to understand something specific about the Indian equity market and curious if anyone here has dug into this.

The pattern: systematic strategies on NSE/BSE-listed equities show reasonable signal at short horizons (intraday to 5 days). Past 30 days, out-of-sample performance collapses. This is well-documented anecdotally in the Indian quant community but I haven't seen rigorous analysis of why.

Two competing hypotheses:

Data problem: Indian markets lack the alternative data layer that US quant funds use to anchor medium-term signals. No credit card transaction data, no structured e-commerce signals, no job posting intelligence for listed companies. Without macro regime anchors and company-level demand signals, models have nothing to latch onto past the short-term noise.

Structural problem: Indian market microstructure makes medium-term alpha structurally difficult regardless of data; retail-dominated order flow, lower institutional participation in mid/small cap, liquidity constraints that make systematic positioning impractical past a certain size.

My instinct is it's both but the Data problem is more solvable than the Structural problem. Has anyone actually tested alternative data signals on Indian equities with enough rigor to know whether they add medium-term predictive power? Or is the consensus that it's primarily a Structural problem?

QR here with ~6 YOE. Experience building and operating systematic strategies in MFT. I have a significant amount of raw futures data and lots of time on my hands (NC).

Recently, I've been seeing a lot of complaints on this sub about the quality of posts. I thought it might be of interest to a nonzero amount of people on here to follow along the end to end process. (This has no intention of ever going live, or provide investment advice in any form, please don't sue).

The way I imagined it was setting up a fresh github account and posting code (not raw data, sorry) with a weekly write up which would be completely open to suggestions, roasts, or anything the LARPers might have to say.

And no, this would not be vibe coded slop. Initial thoughts?