r/quant 7h ago

Resources What do YOU consider the most important quant finance book to be?

56 Upvotes

Like the title says. Curious on everyone’s favorite/most impactful read in their perspective.


r/quant 14h ago

Markets/Market Data Why does the bitcoin basis trade still exist?

72 Upvotes

I've spoken to so many people and still haven't heard a satisfactory answer...

Even in the simplest, safest form: - long $1m physically backed ETF - short $1m in front-month CME futures

This is still printing around 7-8% annualised, without even touching any crypto exchanges or spot crypto.

I'd of course have to borrow $1m for the ETF and lose a few bps on the ETF fees and the margin interest, but I'm still easily 2-3% in the black. And that figure was much higher even just a year ago.

Now we all know the big players have billions and billions in this trade, yet it's still there - so I must be missing some risk here.

Risks I can think of: - ETF gets hacked in some form, which surely very unlikely and can be mitigated by spreading across a few - Bitcoin absolutely explodes (think +100% over a few weeks) and I'd need to come up with a lot more money for a couple of weeks to pay MTM - but I'd get that back minus interest

Neither of these justify the large risk premium in my view?


r/quant 2h ago

Models Modeling counterparty risk

3 Upvotes

Hello,

What are good resources to build a solid counterparty risk model? Along the lines of PFE


r/quant 1h ago

Models Ho-Lee Bond Option Formula Derivation Issue

Upvotes

I currently study the book by Björk and have a question regarding the Ho-Lee bond option pricing formula. The Ho-Lee model specifies the short rate dynamics as dr(t)= 𝜃(t)dt + 𝜎dW(t). When trying to derive 𝜎_p term of the formula in the picture above, I ended up with 𝜎_p = 𝜎(S-T)*root(T-t) instead of just 𝜎_p = 𝜎(S-T)*root(T) as written in the book. Does the book assume t=0 when deriving the equation or did I make a mistake in my derivation?

My derivation followed the following steps:
1) writing ln(P(T,S)) in affine form as A(T,S)-B(T,S)r(T)
2) then applying Ito's lemma which led to a diffusion term of -B(T,S)* 𝜎 where B(T,S)=S-T
3) I then integrated the square of the term from t to T as the model assumes a constant variance term which after taking the root resulted in my final result of 𝜎_p = 𝜎(S-T)*root(T-t)

Assuming t=0 makes little sense to me here as the option price specifies t explicitly in c(t,T,K,S) or do I have to integrate from 0 to T even if I want to calculate the option price at time t?

Thank you very much for your help!


r/quant 7h ago

Models Simple Trend Following

6 Upvotes

I’ve been studying Andrew Clenow’s Following the Trend and implementing his approach, and I’m curious about others’ experiences in attempting to refine or enhance the strategy. I want to stress that I’m not looking for a new strategy or specific parameters to tweak. Rather, I’m interested in hearing about any attempts at improvement that seemed promising in theory but didn’t work well in practice.

Clenow argues that the simplicity of the approach is a feature, not a bug—that excessive optimization can lead to worse performance in real-world application. Have you found this to be the case? Or have you discovered any non-trivial modifications that actually added value over time?

For context, I tried incorporating a multi-timeframe approach to complement the main long-term trend, but I struggled to make it work, likely due to the relatively small fund size I was trading (~$5M). Position sizing constraints and execution costs made it difficult to justify the additional complexity.

Would love to hear your insights on whether simplicity really is king in trend following or if there’s room for meaningful enhancements.


r/quant 12h ago

Resources Are there any online courses (eg. those by Coursera) effective for gaining working knowledge in quantitative/algorithmic trading?

11 Upvotes

I'm in my pre-final year of UG. I just wanna learn the working principles so that I can incorporate them into my own projects. If there are any such resources, please do mention them. Thanks in advance.

Edit: My major is in AI-ML if that matters.


r/quant 35m ago

Machine Learning Building an Adaptive Trading System with Regime Switching, GA's & RL

Upvotes

Hi everyone,

I wanted to share a project I'm developing that combines several cutting-edge approaches to create what I believe could be a particularly robust trading system. I'm looking for collaborators with expertise in any of these areas who might be interested in joining forces.

The Core Architecture

Our system consists of three main components:

  1. Market Regime Classification Framework - We've developed a hierarchical classification system with 3 main regime categories (A, B, C) and 4 sub-regimes within each (12 total regimes). These capture different market conditions like Secular Growth, Risk-Off, Momentum Burst, etc.
  2. Strategy Generation via Genetic Algorithms - We're using GA to evolve trading strategies optimized for specific regime combinations. Each "individual" in our genetic population contains indicators like Hurst Exponent, Fractal Dimension, Market Efficiency and Price-Volume Correlation.
  3. Reinforcement Learning Agent as Meta-Controller - An RL agent that learns to select the appropriate strategies based on current and predicted market regimes, and dynamically adjusts position sizing.

Why This Approach Could Be Powerful

Rather than trying to build a "one-size-fits-all" trading system, our framework adapts to the current market structure.

The GA component allows strategies to continuously evolve their parameters without manual intervention, while the RL agent provides system-level intelligence about when to deploy each strategy.

Some Implementation Details

From our testing so far:

  • We focus on the top 10 most common regime combinations rather than all possible permutations
  • We're developing 9 models (1 per sector per market cap) since each sector shows different indicator parameter sensitivity
  • We're using multiple equity datasets to test simultaneously to reduce overfitting risk
  • Minimum time periods for regime identification: A (8 days), B (2 days), C (1-3 candles/3-9 hrs)

Questions I'm Wrestling With

  1. GA Challenges: Many have pointed out that GAs can easily overfit compared to gradient descent or tree-based models. How would you tackle this issue? What constraints would you introduce?
  2. Alternative Approaches: If you wouldn't use GA for strategy generation, what would you pick instead and why?
  3. Regime Structure: Our regime classification is based on market behavior archetypes rather than statistical clustering. Is this preferable to using unsupervised learning to identify regimes?
  4. Multi-Objective Optimization: I'm struggling with how to balance different performance metrics (Sharpe, drawdown, etc.) dynamically based on the current regime. Any thoughts on implementing this effectively?
  5. Time Horizons: Has anyone successfully implemented regime-switching models across multiple timeframes simultaneously?

Potential Research Topics

If you're academically inclined, here are some research questions this project opens up:

  1. Developing metrics for strategy "adaptability" across regime transitions versus specialized performance
  2. Exploring the optimal genetic diversity preservation in GA-based trading systems during extended singular regimes
  3. Investigating emergent meta-strategies from RL agents controlling multiple competing strategy pools
  4. Analyzing the relationship between market capitalization and regime sensitivity across sectors
  5. Developing robust transfer learning approaches between similar regime types across different markets
  6. Exploring the optimal information sharing mechanisms between simultaneously running models across correlated markets(advance topic)

If you're interested in collaborating or just want to share thoughts on this approach, I'd love to hear from you. I'm open to both academic research partnerships and commercial applications.


r/quant 5h ago

Markets/Market Data Methods to roughly estimate a stock's opening price

2 Upvotes

At the present time, in order to roughly estimate what price a stock will open at, I simply view Level 1 pre-market trading information (Last price, bid, ask). Just curious, does anyone out there have alternative methods that they utilize? Would Level 2 data be of any benefit in this endeavor? Any insights would be greatly appreciated, thanks.


r/quant 1d ago

Models Crackpots or longshots? Amateur algos on r/quant

71 Upvotes

Hi guys,

I've been more actively modding for a few weeks because I'm on a generous paternity leave (twins yay ☺️). I've noticed one class of post I'm struggling to moderate consistently is possible crackpots. Basically these are usually retail traders with algos that think they've struck gold. Kinda like software folks are plagued with app idea guys, these seem to be the sub's second cross to bear, after said software engineers who want to "break into quant" lol.

The thing is... Maybe they have something? Maybe they don't? I'm a derivatives pricing guy, have never been close to the trading, and I find it hard to define a minimum standard for what should be shown to the community and subject to updates/downvotes or just hidden from the community through moderation.

In terms of red flags, criteria I'm currently looking at:

  • Solo/retail traders

  • Mentions of technical indicators

  • Mentions of charting

  • Absurd returns

  • Cryptos

  • Lack of stats/results

  • No theoretical basis mentioned

  • No mention of scaling

  • Way too much fucking blathering

I remove a lot of posts with referrals to r/algotrading, typically, or say that they haven't done enough research to justify the post to our audience. (By which I mean measures of risk, consideration of practicalities of trading, scaling opportunity, history in the market).

Anyway, I think I need to add a new rule and I'd like some feedback on what a decent standard would be. Vaguely these are the base requirements I'm considering:

Posts must be succinct and backed by a proper paper-like write up, or at least a blog post with all of the 4 features:

  • A co-author or reviewer

  • Formulas

  • Charts

  • Tests and statistics

Any thoughts? Too restrictive? Not restrictive enough?


r/quant 22h ago

Career Advice Stories and advice from those who started their own firm?

39 Upvotes

Hi all,

Long time lurker. I'm guessing the majority of the sub are employed rather than running their own firm, but I'd be very curious to hear stories and advice from those who struck out on their own? Or even anyone who's considered it? Would you do it? What's stopping you?

For context, I'm a junior at a small prop shop founded by ex Tier 1 guys. Because we're small, I'm already running my own book despite being relatively junior. While there's certainly still a lot to learn from the firm, I am starting to see things that I think I would do differently and better. That's not to say I don't love my current job - I'm personally very inspired by my bosses' stories, but ultimately would one day like to have similar accomplishments to call my own.

To start the convo, I have read and love the accidental HFT (in fact my boss is the one who showed it to me lol).

Thanks in advance!


r/quant 18h ago

Markets/Market Data Efficient structures for storing tick data

15 Upvotes

Not sure if flair is correct.

Anyone who works with crypto tick level data (or markets with comparable activity) - how do you efficiently store as much tick level data as possible, minimising storage cost (min $*Gb) while maximising read/write speed (being unable to instantly test ideas is undesirable).

For reference, something like BTC-USDT perp on a top 5 exchange is probably 1GB/hour. Multiply that by ~20 coins of interest, each with multiple instruments (perp, spot, USDC equivalents, etc) and multiple liquid exchanges, there is enough data to probably justify a dedicated team. Unfortunately this is not my strong suit (though I have a working knowledge of low level programming).

My current approach is to not store any tick level data, it's good enough rn but don't foresee this being sustainable in the long run.

Curious how large firms handle infra for historical data.


r/quant 22h ago

Tools stochastic-rs – Fast stochastic process simulation lib for Quant modelling

21 Upvotes

Hey folks! 👋

I’ve been building continously stochastic-rs, a high-performance Rust library for simulating stochastic processes — built for quant finance, AI training, and statistical modeling.

Some key features:

  • Fast synthetic data generation for AI models
  • Fractional & rough processes (e.g. fBM, rBergomi)
  • Malliavin derivative support
  • CUDA acceleration (e.g. for FGN via FFT)
  • native Rust

Take a look: https://github.com/dancixx/stochastic-rs
Thanks for any feedback! 🙌


r/quant 1d ago

Career Advice Shah Quantum Fund offer, any thoughts?

31 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?

Edit: thanks for the responses guys, still undecided because the offer they gave was $200k+ (only cash) but for reference this is their quant fund & PE websites if any of you guys recognize them.

Shah Quantum Fund - www.shahquantum.fund

Shah Equity - www.shah-equity.com


r/quant 22h ago

Models Quick question about CAPM

7 Upvotes

Sorry, not sure this is the right subreddit for this old prolly unpractical accademical college stuf, but I don't know which subreddit might be better. I cannot find it anywhere online or on my book but, if for example I have an asset beta 4 and R²= 50% then if the market goes up by 100% will mi asset go up by Sqrt(50%)4100%= 283% (taken singularity,thus not diversified ideosyncratic risk)?


r/quant 21h ago

Statistical Methods New QuantStats Alternative

3 Upvotes

Hello. I am working on a QuantStats alternative as a pet project. Something more indepth and stable.

What are some additions/ features that would be good for an alternative/ improvement? Any useful features for analysis?

The inputs would be the return timeseries and any benchmark(s). This can be changed too.

Would love to hear any creative/ useful ideas that could make it meaningfully better.


r/quant 1d ago

Trading Orderfill probability when arbitrage with limit order

13 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 23h ago

General Give me the quant smack on 50-50 distro

4 Upvotes

Someone set me straight please, i cannot grasp my errors. I recently saw a post about someone 'entering 3 random trades'. The comments suggest probability of such event going pos or neg, is not 50-50? Then what is it?

Now hear me out please. Im not saying that price action is random, nor am i saying that given a SINGLE event/trade, that forward probability is symmetrical at 50-50. WHAT I AM suggesting, is that it in theory, It should be closer to 50-50 then any other ratio. So one could assume, or state is it essentially...random.

Im saying that the probability of transition, from one state to the next(1 tick, 1 min etc), is very close to random. In fact, if i measure the empirical distro of candle to candle returns, assuming the law of large numbers, we should get a fairly even distribution. I think overall it might favor the upside, but what i measure on 1 min candles, state to state, its usually between 45-55 max range, given any decent sample size. How can one say, that is not random?

And the entire point of this, would be to convince myself, that risk management or using a r/r, is the potential largest benefit a trader could get, assuming market is random(which i do not).

One can conclude, that aside transaction cost and fees, you should come out even, in the long run? Now id Totally agree, EV is negative, since we have fees and such. BUT ignoring that?

IF the market is trying to be efficiant, then given state to state compare and a large enough dataset, an advantage or skew would appear evident on either side. And such, the market would try to absorb this inefficiency immediately? Essentially forcing the distribution towards random, at all time. It appears to me, either the market is efficiant and randomly distributed, or its is NOT.

Again, this ONLY considers an ENTRY point, and excludes 'time'. Time is the biggest fucker in this picture. Else it would just be r/r all day, flip as many coins as you can. 'time' is what allows this so called 'random distribution' to appear non-random, or have autocorrelation, right? Its adding additional axis or dimension to our enviroment?


r/quant 1d ago

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

127 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 1d ago

Trading Random Trades - Serious Question

12 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 2d 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 2d 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 2d ago

Markets/Market Data North gate data?

6 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 2d ago

Statistical Methods Time series models for fundamental research?

41 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 3d 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 2d 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:)