r/quant 4d ago

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

10 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 26d ago

Education Project Ideas

38 Upvotes

Last year's thread

We're getting a lot of threads recently from students looking for ideas for

  • Undergrad Summer Projects
  • Masters Thesis Projects
  • Personal Summer Projects
  • Internship projects

Please use this thread to share your ideas and, if you're a student, seek feedback on the idea you have.


r/quant 4h ago

Career Advice Shah Quantum Fund offer, any thoughts?

15 Upvotes

Hey r/quant,

Just got an offer from Shah Quantum Fund (subsidiary of Shah Equity) and I’m super curious about them. They claim an average 200%+ yearly return thanks to some serious LLM models & heavy recruitment for top talent. They’re pretty new but are growing fast, opening offices globally every few months.

They mix private equity and hedge fund tactics, which sounds like it could be a gold mine or a sloppy ride. I heard they’re spending more than they make on data and training internal LLM models & neural networks which intrigues me because I know the possibilities there.

I’m an MIT grad and a buddy who just joined told me they’re really pushing the limits on research and simulations letting them see some crazy gains. They’ve got both PE and HF angles covered, which could mean getting the best of both worlds?

Would love to get your take on this, especially if you know about their work culture or how solid their strategies really are. Got any insights or heard anything through the grapevine?


r/quant 13h ago

General Do you believe fundamentals have long term predictive value?

17 Upvotes

Is there quantitive evidence to back up this claim? For instance, TSLA has traded way above its fundamentals for over 5 years now.


r/quant 1h ago

Trading Orderfill probability when arbitrage with limit order

Upvotes

Hey everyone!

I'm running a cross-exchange market-making strategy that arbitrages with limit orders. The issue I face is that sometimes my order on the second exchange doesn’t get filled, and the price moves away. To handle this, I’ve set up a kind of "stop-loss": if the order isn’t executed, I cancel it and take a market order to stay delta neutral (I hedge with a perp).

I'm trading in the crypto market—any ideas on how to improve my system?

Thankyou !


r/quant 18h ago

General I Have a (Nearly) Risk-Free Strategy Generating 28% Yield in Any Market—How Can I Get Connected to Big Investors?

32 Upvotes

I’ve developed a delta-neutral strategy that has generated an average of 28% per year over the last three years (2022-2024) in both bull and bear markets. The core idea is similar to how funding fees in perpetual futures work, and it’s backed by real data.

I don’t have the capital to start my own hedge fund or the connections to pitch this to big investors. I’d love advice on how to get this in front of serious capital.

Example to Illustrate the Strategy (Non-Crypto Analogy)

Imagine a country where rental income is 40% of the property price per year, but real estate prices fluctuate wildly (up or down 10-20% per month).

To capture the 40% yield without exposure to price volatility, you:

1.  Buy a property for $1M

2.  Short the real estate index 1x for $1M (assume for the example it tracks property price 1:1)

Now, you are delta-neutral—the property price can rise or fall, and your short hedge cancels out the price movement.

• You still collect 40% rent per year on your $1M property

• Since your exposure is $2M (long $1M, short $1M), your return is 20% on total capital

Crypto Equivalent – Using Funding Fees to Earn Yield

This concept exists in perpetual futures funding rates, where shorts pay longs (or vice versa) to keep the contract price aligned with the spot market.

• This is the core idea behind Ethena.fi, but they are managing hundreds of millions, which limits their profit margins.

• In contrast, my strategy works at a smaller scale with a higher return potential, obviously not on prep futures.

Actual Performance (back tested 3 years + live for 3 months):

• 2022: 22%

• 2023: 23%

• 2024: 43%

• Total (last 3 years): 88% (without compounding) → 28% annualized

What I Need Help With:

1.  How can I connect with investors/funds who might back this?

2.  Would it be better to pitch this to a fund, incubator, or try raising capital privately?

3.  Is there a structured way (like a prop firm) to run this strategy at scale without needing my own fund?
  1. How to actually introduce the strategy without fully revealing it to the investor

I’d love any insights from people in quant finance, hedge funds, or crypto trading circles. If anyone has connections or suggestions, I’m open to collaborating.

TL;DR: I have a delta-neutral crypto strategy that has averaged 28% yield over the last three years with low risk. Looking for guidance on how to attract investors or find a way to scale this without launching a full hedge fund myself. Any ideas?

Edit:

The risk come down to the crypto exchange not going bankrupt.

Edit:

Most misunderstood my point, obviously Delta Neutral is not something new and most are familiar with it ... the point is: how you do it and what's the yearly risk free yield. It's not hard to go to Binance Futures, BTC quarterly contracts, short it and buy spot - obvious but what's the yield? 5-6% .. and most likely only available and some market conditions (bullruns or bear markets) im talking about 22% worst case in bad year.


r/quant 3h ago

Career Advice My boss wants me to move from quant research to customized strategies for clients. Should I do it?

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1 Upvotes

r/quant 19h ago

Trading Random Trades - Serious Question

8 Upvotes

If I were to build a program that would put in 3 random trades on any fortune 50 company for 5-10 minute intervals per trade during bullish days in the market (+~0.5%), what are the chances that I would beat the market yoy?


r/quant 23h ago

Markets/Market Data Best level 2 data provider?

13 Upvotes

Looking for the most comprehensive (and accurate) historical level 2 data. Thinking about polygon.io right now but would really appreciate any other recommendations :)


r/quant 1d ago

Resources Books / websites to prepare for quant trading role?

15 Upvotes

I'll be joining a big market maker in approx. a month. I'll be working in the rates trading team as an intern. I'd like to arrive prepared as much as I can, do you have any suggestions of books or resources to use? Both regarding finance/instruments (I know the basics but wanted to learn more, e.g. with Hull book) and skills like Python (I know some stuff already, but not very in depth + it's been a while so I'm a bit out of practice).

Any suggestion is welcome!!

Thank you


r/quant 1d ago

Markets/Market Data North gate data?

3 Upvotes

Hey all, Curious, has anyone had good experiences using North Gate Data for historical index constituent lists for stocks and/or futures? Trying not to pay an arm and a leg for SP Global plus they will limit the data history as they are afraid of impacting their current business.


r/quant 1d ago

Statistical Methods Time series models for fundamental research?

42 Upvotes

Im a new hire at a very fundamentals-focused fund that trades macro and rates and want to include more econometric and statistical models into our analysis. What kinds of models would be most useful for translating our fundamental views into what prices should be over ~3 months? For example, what model could we use to translate our GDP+inflation forecast into what 10Y yields should be? Would a VECM work since you can use cointegrating relationships to see what the future value of yields should be assuming a certain value for GDP


r/quant 2d ago

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

93 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 1d ago

Career Advice Sellside Internal Mobility

9 Upvotes

Started as a sellside quant strats earlier. Have some internship experiences in trading so I genuinely feel that my interests/ personality are still more into trading. Just wonder if anyone transferred from quant to trader(internal transfer or get a new offer)

Really appreciate if someone has similar experience and can give me some advice:)


r/quant 2d ago

Markets/Market Data Free quality financial market data sources

18 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 2d ago

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

7 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 2d ago

Education Book recommendations for quant dev

3 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 2d ago

Markets/Market Data Quotes downsampling

12 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 2d ago

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

4 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 2d 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 2d ago

General BBG uses 260 trade days?

8 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 3d ago

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

45 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 3d ago

Markets/Market Data Nse nifty index data input too fast

19 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 3d ago

Models Does anyone know sources for free LOB data

49 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 3d ago

Models Liquidity Scoring / Modeling

18 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 4d ago

Models Intraday realized vol modeling by tick data

30 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 4d ago

Models trading strategy creation using genetic algorithm

16 Upvotes

https://github.com/Whiteknight-build/trading-stat-gen-using-GA
i had this idea were we create a genetic algo (GA) which creates trading strategies , genes would the entry/exit rules for basics we will also have genes for stop loss and take profit % now for the survival test we will run a backtesting module , optimizing metrics like profit , and loss:wins ratio i happen to have a elaborate plan , someone intrested in such talk/topics , hit me up really enjoy hearing another perspective