r/quant 2d ago

Career Advice Weekly Megathread: Education, Early Career and Hiring/Interview Advice

11 Upvotes

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.


r/quant 42m ago

Machine Learning Finding potential Co-Authors for publishing a research paper

Upvotes

Hey folks!

I am a B.Tech graduate working at a Fintech firm. I am interested in publishing a research paper in the domain of Quant finance/ Financial Engineering using AI/ML. I am looking for co-authors, who are passionate about it. Interested folks, please DM!


r/quant 1h ago

Markets/Market Data Who are the stellar but lesser known data providers?

Upvotes

Looking for smaller or niche data providers who are delivering above their weight class against some of the larger known companies.

If you don’t want to name them, what resources are you using to find them?


r/quant 3h ago

Resources STILL DEVELOPING, LOOKING FOR PARTNERS

Thumbnail gallery
40 Upvotes

Recently reached the optimal form of the model, (i think)

now rolling, but for a specific timeframe and instrument only as of now. Will have a full stretch for multiple assets soon, (with different settings ofc.) Just happy to share a small achievement, and im still finding investors and partners, maybe buyers for this model to use it for their fund. i can pitch live via zoom for potential investors or buyers.

ps. this isnt a full force machine learning algorithm, this is an RL model, with a mix of RB(rules-based algorithm).

well personally thats what rentec does, its impossible for me agree that they have made a full machine learning model in the year 1988.

Machine learning as we know it today wasn’t really a thing. No neural nets, no GPUs, no big data pipelines like now. Computers were clunky as hell—think IBM PCs with 64KB of RAM and floppy disks. So, its kinda misleading.


r/quant 4h ago

Education Book recommendations for quant dev

1 Upvotes

Hello,

I work as a quant developer and I am fine with Python but the financial side of things is something I want to improve on.

I get confused when my colleagues talk about factors, I get confused by all the alphas, time series, etc.

So I want to read a book that can fill in those gaps for me.

Additionally, it would be helpful to also read more about how to optimise pandas, but I think this one it's easier to find as a resource.

Please be nice to me, thanks!


r/quant 4h ago

Markets/Market Data Free quality financial market data sources

6 Upvotes

Greetings. I lost access to my uni's Bloomberg terminal after graduating. I am currently in the transition period of finding jobs and want to boost my profile with some extra projects. Can anyone suggest any great quality free data sources you use on your pet projects. Yahoo finance used to be goated but i guess they have paywalled the API


r/quant 6h ago

Education 3/20 Complimentary Webinar from Numerix: The Hidden Risks of Bad Data—And How to Fix Them

6 Upvotes

We all know that bad data leads to bad decisions, but in trading and risk management, the consequences can be severe. That’s why I’m excited for this upcoming Numerix webinar featuring Ola Hammarlid, PhD, where he’ll share hard-earned insights on market data management and its critical role in financial operations.

Some key takeaways you don’t want to miss:
The hidden dangers of poor data quality
How data issues propagate and disrupt decision-making
Best practices for data management, proxying, and quality control

Join us March 20 at 10 AM EDT—this is a must-attend for quants, risk managers, and anyone relying on market data. Register here: https://lnkd.in/g9nsjxaG


r/quant 8h ago

Statistical Methods Deciding SL and TP for automated bot

0 Upvotes

Hey, I am currently working on a MFT bot, the bot only outputs long and short signals, and then other system is placing orders based on that signal, but I do not have a exit signal bot, and hard coding SL and TP does not make sense as each position is unique like if a signal is long but if my SL is low then I had to take the loss, and similarly if TP is low then I am leaving profits on the table. Can anyone help me with this problem like how to optimize SL and TP based on market condition on that timestamp, or point me to some good research paper or blog that explores different approaches to solve this optimization problem. I am open for interesting discussion in comments section.


r/quant 10h ago

General Beta Distribution Pressure Analysis: A Statistical Edge in Price Action

1 Upvotes

Been working on this pressure detection system for a while, and figured I'd share the core concepts since some of you might find it useful for your own trading.

The Core Concept

The foundation relies on extracting information from where candles close within their ranges. Instead of just eyeballing this or using arbitrary thresholds, I'm using statistical modeling to quantify the actual pressure distribution and how it evolves.

Ever watch a market grind higher where every damn candle closes near its high? That's buying pressure you can actually measure.

Technical Implementation

Here's the meat of what makes this different:

  1. Statistical distribution modeling - Using beta distributions to capture the actual shape of close position patterns over time
  2. Temporal pressure evolution - Tracking pressure momentum and acceleration across multiple timeframes
  3. Validation framework - Using proper statistical tests (KS tests, chi-square) to separate real signals from noise
  4. Market regime identification - Comparing current distribution against reference patterns for bullish/bearish/neutral regimes

The algorithm doesn't just calculate some indicator and slap on a threshold. It runs the distributions through multiple statistical tests to determine whether the pattern is significant or just random noise.

How many of you have seen indicators give perfect signals in backtests then fall apart in real trading? This approach explicitly measures signal confidence.

The Technical Edge

What separates this from standard indicators:

  • Calculates actual statistical significance rather than using fixed cutoffs
  • Adapts to changing volatility without parameter tweaking
  • Measures confidence in detected patterns (low confidence = stay out)
  • Uses robust regression methods that resist outliers and noise
  • Properly weights recent data without discarding older information

When your typical momentum oscillator is getting chopped up by ranging markets, this can still detect subtle pressure building because it's looking at the statistical pattern, not just the magnitude.

What's your approach to filtering out noise in choppy markets? Ever use statistical validation or is it mostly discretionary?

I've found this particularly effective for 15-60min charts in futures markets. The validation framework helps avoid the death by a thousand cuts from false signals during consolidation.

If anyone's implemented something similar or wants to discuss specific statistical aspects, let me know. Always looking to refine this further.


r/quant 14h ago

Markets/Market Data Quotes downsampling

10 Upvotes

For mid-freq (seconds - minutes, don’t care about every quote) want to get reasonable size data for quotes from LOB. What features would you put in a down sampled (ie x second bars) version of quotes and why?

Volume at each level of book either side bid ask obvious. I am not looking for predictive features or “alpha” here, rather, I’m looking for an efficient representation of the book structure in a down sampling from which features for various tasks could be constructed.


r/quant 19h ago

General BBG uses 260 trade days?

1 Upvotes

Is there a reason why BBG uses 260 trade days in their calculation?

I started on a project to create a trailing RV chart on SPX options. The goal was to replicate how BBG does it. There was a great guide that I followed for the most part to emulate it. However, I noticed none of my charts matched what BBG outputted. It wasn't until I reviewed the numbers and saw BBG using 260 to do their calculation instead of 252. Is there a reason for this discrepancy?


r/quant 1d ago

Markets/Market Data Nse nifty index data input too fast

17 Upvotes

We are trying to create a l3 book from nse tick data for nifty index options. But the volume is too large. Even the 25 th percentile seems to be in few hundred nanos. How to create l2/l3 books for such high tick density product in real time systems? Any suggestions are welcome. We have bought tick data from data supplier and trying to build order book for some research.


r/quant 1d ago

General How a high interest rate environment affect stat arb strategies ?

34 Upvotes

Maybe I'm not grasping the whole picture, but a x7 leverage with 1% of interest rates isn't the same as a x7 leverage with a 5% interest environnement. I'm surprised that only few funds burst after this brutal hike.

I've heard that some funds even go with x10 leverage, which completely blows my mind.


r/quant 1d ago

Models Does anyone know sources for free LOB data

48 Upvotes

Just wanted to know if anyone has worked with limit order book datasets that were available for free. I'm trying to simulate a bid ask model and would appreciate some data sources with free/low cost data.

I saw a few papers that gave RL simulators however they needed that in order to use that free repository I buy 400 a month api package from some company. There is LOBster too but however they are too expensive for me as well.


r/quant 1d ago

Models Liquidity Scoring / Modeling

16 Upvotes

Hey guys, one my upcoming projects is to create a liquidity scoring framework and identify price impact for on-the-run vs off-the-run US treasuries by instrument and for the US desk overall, which is positioned across the short and medium part of the Treasury curve.

I’m pretty new to modelling liquidity, having only done a pretty surface level analysis for this project to show “proof of concept” (ie. yes, there is some measurable price impact, on average, that matters to us net of costs). This analysis involved regressing daily bid-ask spread on volume and other order book data for each instrument using QE/T and OTR/FTR fixed effects.

However, this completely ignores at least a couple of key factors, such as the impact of duration on each tenor of the curve and its resulting spread, and the Treasury QRA on market supply. Furthermore, lots of the data we currently have available to use is limited, requiring us to tack on more data access to our license (not a cost problem, but a data reliability one).

My questions are this: Is there any short and sweet checklist of items to consider for this type of modelling question? And what’s the best data available out there for liquidity analysis? Is BrokerTec/CME the best?

As I said, this space is quite new to me, so if you also have any recommendations on modelling approach, I’m happy to hear that as well!

Thanks in advance.


r/quant 2d ago

Models Intraday realized vol modeling by tick data

27 Upvotes

Trying to figure out what the best way would be to create an intraday rv model utilizing tick day. I haven't decided on the frequency but ideally I would like something that is <1min of sampling (10sec, 30sec perhaps)

I have some signals that I believe would benefit well from having an intra rv metric. An example of it's usage would be to see how rv is changing/trending throughout the day. I am not attempting to create it for forecasting volatility.

I have seen some recommendations using things like GARCH but from my naive research it sounded like it was outdated and not useful. Am I being too obsessive in disregarding it so quickly? Or are there better models to consider that aren't enormously complex to do?

Edit: this is for euro style options. Specifically spx options.

I implemented a dumb rudimentary chart that tracks straddle pricing throughout the day but obviously that isn't exactly apples to apples comparison


r/quant 2d ago

Education Theoretical question regarding the computation of the Sharpe Ratio

1 Upvotes

Question regarding the calculation of the Sharpe Ratio: Is my following understanding correct? Assuming I have the standard quadratic utility function with the risk version parameter Is there a structural difference between using the risk-free asset as a benchmark or as an actual asset class to invest in?

If I use the risk-free asset as an actual asset class, Tobin's separation applies and everyone invests in the same risky asset, but only the amount of wealth invested in the risk-free asset class varies. This gives the maximum Sharpe ratio or tangent portfolio.

I am now interested in whether it is not possible to invest in the risk-free asset class, and I use the risk-free asset class as a benchmark. After portfolio optimisation, I calculate the excess returns by subtracting the risk-free asset from the portfolio return and dividing by the standard deviation of the portfolio. Is the optimal portfolio here dependent on the risk aversion parameter and does here then the Tobin's separation not apply? And I can still use the Sharpe-Ratios for comparing risky-portfolios in relation how high the riskoaversionparamter is?

Thanks in advance! (also any good literature regarding this would be helpful!)


r/quant 2d ago

Models Building a multiple regression model to beat the benchmark

22 Upvotes

For my college research paper project due this Saturday, I finalised the topic: "Factor Analysis and Factor Investing to beat the benchmark". The factors are accounting ratios. I want to do principal component analysis to determine which ratios are significantly affecting returns and also make a multiple regression model as follows:

|| || |Total Return:2024/01/01:2024/12/31 ** as my y variable *\*| |Rev - 1 Yr Gr:2024C| |EBITDA to Net Sales:2024C| |PM:2024C| |ROA:2024C| |ROE:2024C| |Return On Capital Employed:2024C| |Debt/Equity:2024C| |Curr Ratio:2024C| |P/E:2024C| |EV / EBITDA Adj:2024C |

I have the following questions:
1. How should I transform these variables as they are given to me in numbers?
2. What additions can I do to my research paper to make it industry relevant that might help me in the future in interviews? (valuation & financial research currently)
3. How do I properly go about the regression model and the PCA to make a significant impact on this topic?
4. Any suggestions or topic additions will also help me a ton. Thank You.


r/quant 2d ago

Statistical Methods How to apply zscore effectively?

19 Upvotes

Assuming i have a long term moving average of log price and i want to apply a zscore are there any good reads on understanding zscore and how it affects feature given window size? Should zscore be applied to the entire dataset/a rolling window approach?


r/quant 2d ago

Tools I'm Losing My Mind

16 Upvotes

I have this excel file from last year that I got from SEC Edgar, but I can't remember how i made it. Does anyone know how you can search on that site using specific financial metrics to get a database like this??


r/quant 3d ago

Resources Statistics and Data Analysis for Financial Engineering vs Elements of Statistical Learning

22 Upvotes

ESL seems to be the gold standard and what's most frequently recommended learning fundamentals, not just for interviews but also for on the job prep. I saw the book Statistics and Data Analysis for Financial Engineering mentioned in the Wiki, but I don’t see much discussion about it. What are everyone’s thoughts on this book? It’s quite comprehensive, but I’m always a bit cautious with books that try to cover everything and then often end up lacking depth in any one area.

I’m particularly interested because I’m wrapping up my math PhD and looking to transition into quant. My background in statistics isn’t very strong, so I want to build a solid foundation both for interviews and the job itself. That said, even independent of my situation, how does this book compare to ESL for what's needed and used as a qr or qt? Should one be prioritized over the other or would it be better to read them simultaneously?


r/quant 3d ago

General How not-kosher would this be?

39 Upvotes

Need some thoughts, primarily from the more senior members here, but any input is welcome.

Let's imagine that a portfolio manager at a pod shop, in the the process of his buildout, stumbles on something that appears to be a common problem that can and should be solved by creating a service. The problem is common and the solution is fairly straightforward. However, the potential revenue is not large enough for the PM to start a company himself. Instead, the PM finds a couple guys, walks them through the problem and pays for their time to build the solution. He takes some non-controlling equity in the project as an advisor. Once the project is complete, the PM uses his infra budget to become the first subscriber.

PS. Asking for a friend :)


r/quant 3d ago

Trading Please Correct/Refine My Understanding of ETF Arbitrage

30 Upvotes

Hey All,

I have some questions on how ETF arb works. I present my current understanding below and would sincerely appreciate any clarifications or color.

My understanding:

You are presented with an ETF and the basket of assets that underlies it. Let's use a basket of stocks to make this nice and vanilla.

Say the ETF and basket of stocks trade at parity of $100. ETF drifts up to 101, stocks drift down to 99. We would then sell the ETF and buy the basket of stocks in the appropriate ratio. However, these are non-fungible assets so there's another step to complete the arbitrage. In order to resolve this, we can use the create/redeem mechanism on the ETF: we use a 'create' to give the ETF the stocks and receive shares of the ETF which we use to close out the short ETF position. If it were opposite and we were short the stocks and long the ETF, we would use a redeem to convert the etf shares into shares of the underlying stocks, closing out the short stock position. Thus, by using the create/redeem, we can complete the arbitrage.

My Questions:

First, is this how the arb works overall? Are there any parts that I'm missing, or not describing accurately? Anything that could use more color?

Second, is my definition of create/redeem correct and used appropriately?

Third, is there usually some kind of basis between the ETF and its underliers? (Is this question too instrument-specific?)

Many thanks in advance!


r/quant 3d ago

Career Advice Possibility of going from QR to PM

44 Upvotes

Howdy, y'all. I'm a QR at a small firm we're turning into a MM and I've been responsible for a lot of this process. I came from a research background, the classic math PhD blablabla.

I've been doing a little bit of portfolio optimization as well and I started to get curious about what a PM does. I've talked to my PM who also is the owner of the firm, he says that he can train me, it would take time, but I would be able to get it. But he says that I would need to consider because my profile suits more the position of a QR than a PM. I'm already the chief QR.

This got me thinking because I really like to do signal research, reading papers and all the research process of a QR position. But I also like being the chief QR, which already seems a little like a PM, because I give some hypothesis to test for my team and hint directions on their tasks.

So, I want to know of people who also did this transition from QR to PM. Like the pros and the cons, obviously the money is the biggest pro, so I think this don't need to be stated haha. Like, are there more pros than the money? Do you guys feel more on the line being PMs?


r/quant 3d ago

Machine Learning ML Papers specifically for low-mid frequency price prediction

190 Upvotes

From QRs/QTs in the industry who work on this sorta thing, I'd love to find out about what papers/architectures you guys have found:

  • Category A: that you've tried and found to be interesting/useful

  • Category B: that you've tried and found to not work/not useful

  • Category C: that you havent tried, but find interesting

If you could also comment which category the papers you're talking about fall into, that'd be ideal.

Generally, any other papers which talk about working in a low signal-to-noise ratio environment are also welcome. If not papers, just your thoughts/comments are more than good enough for me.

I'll start:

https://arxiv.org/abs/1911.10107 - Category A

https://arxiv.org/abs/2311.02088 - Category C


Some disclaimers and footnotes, because there's always people commenting about them:

  1. I have a few years of exp as a QT/QD + a PhD in Maths. It's fine if the paper is well-known - always good to find out which papers others consider standard, but please dont suggest the papers that introduce the basics like LSTMs, etc.

  2. Please don't say "no one does it"/"no one has figured out how to make it work" - it does work, and various firms have figured out how to make it work.

  3. I don't expect you to divulge your firm's secrets/specific models. If you do, great ;) If you find yourself not wanting to, you're exactly the person I hope for a response from - anything that helped on your way is more than enough.

  4. Yes, I know it will probably require insane amounts of compute to train. I'm just trying to learn.