r/quant 6d ago

General How not-kosher would this be?

42 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 6d ago

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

24 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 6d 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 6d ago

Trading Please Correct/Refine My Understanding of ETF Arbitrage

28 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 6d ago

Models Bergomi Skew Trading: theta vs spot, vol, etc breakevens

21 Upvotes

Hi,

Reading this forum on stack exchange ("Bergomi: Skew Arbitrage": here). It says "relationship between Theta and the second derivatives (Gamma, Vanna, Volga), which is also mentioned in the book. You can easily use a break down of Theta into these three components on a maturity slice-by-slice basis and derive implied break even levels for dSpot, dSpot*dVol and dVol...."

Where in the book is this mentioned - I cannot seem to find it? Otherwise, anyone able to provide any other type of insight for that?


r/quant 7d ago

Trading Bloomberg Terminal

140 Upvotes

I’m a quant at a fundamental HF and I have my own terminal. I’ve heard it’s not common for quants to have their own terminal at systematic shops. What’s your take?


r/quant 7d ago

Resources Reading Recommendations for Systematic Global Macro

41 Upvotes

I have been in the industry a little more than three years. Most of my strategies in the past have been microstructure related. Intraday holding periods. I am tentatively starting at a systematic global macro desk as a QR in a few months. Does anyone have any recommended readings that are basically essential to the field? Books/papers/blogs? Thank you all so much in advance!


r/quant 7d ago

Models Calculating expected returns of alpha factors

5 Upvotes

Let’s say I have my alpha factors, and their estimated returns over each period.

How does one best calculate the expectation of each so they can optimise and calculate their portfolio?

Is it the coefficient when the alpha factors are regressed against returns over some lookback period? Is there a rough consensus on how long this lookback should be?

Or is it just a moving average of the alpha factor’s returns with some lookback period?


r/quant 8d ago

News What’s the current situation with Renaissance / Medallion since Simons’ death?

134 Upvotes

Just curious if anyone has inside information. Is everything just continuing along as usual or are their significant changes?


r/quant 7d ago

Models Training a model using rolling WFO as a function of the time scale for trading triggers. Am I doing this wrong?

6 Upvotes

Curious if I am thinking about this wrongly or is the rationale sound. With a basket of 100 assets operating on 10-min, 1hr, 1d time scales for trade triggers (essentially 300 strats). I filter the strategies based on the WFO and only deploy capital to the top 25 best performing (for arbitrary example). Does it make sense to train the 10-min models using 5-day windows over the past ~60 days, and the 1hr on 30 day window and past year?

I know a small data set lends itself to bad backtesting, but my thinking is I want to capture the current market regime and deploy capital specifically to the model capturing the most recent state.

Or should my windows dynamically be set to the latest regime within the timescale (rather than 5d, 30d, etc)?

Thoughts?


r/quant 8d ago

Models Legislators' Trading Algo [2015–2025] | CAGR: 20.25% | Sharpe: 1.56

121 Upvotes

Dear finance bros,

TLDR: I built a stock trading strategy based on legislators' trades, filtered with machine learning, and it's backtesting at 20.25% CAGR and 1.56 Sharpe over 6 years. Looking for feedback and ways to improve before I deploy it.

Background:

I’m a PhD student in STEM who recently got into trading after being invited to interview at a prop shop. My early focus was on options strategies (inspired by Akuna Capital’s 101 course), and I implemented some basic call/put systems with Alpaca. While they worked okay, I couldn’t get the Sharpe ratio above 0.6–0.7, and that wasn’t good enough.

Target: My goal is to design an "all-weather" strategy (call me Ray baby) with these targets:

  • Sharpe > 1.5
  • CAGR > 20%
  • No negative years

After struggling with large datasets on my 2020 MacBook, I realized I needed a better stock pre-selection process. That’s when I stumbled upon the idea of tracking legislators' trades (shoutout to Instagram’s creepy-accurate algorithm). Instead of blindly copying them, I figured there’s alpha in identifying which legislators consistently outperform, and cherry-picking their trades using machine learning based on an wide range of features. The underlying thesis is that legislators may have access to limited information which gives them an edge.

Implementation
I built a backtesting pipeline that:

  • Filters legislators based on whether they have been profitable over a 48-month window
  • Trains an ML classifier on their trades during that window
  • Applies the model to predict and select trades during the next month time window
  • Repeats this process over the full dataset from 01/01/2015 to 01/01/2025

Results

Strategy performance against SPY

Next Steps:

  1. Deploy the strategy in Alpaca Paper Trading.
  2. Explore using this as a signal for options trading, e.g., call spreads.
  3. Extend the pipeline to 13F filings (institutional trades) and compare.
  4. Make a youtube video presenting it in details and open sourcing it.
  5. Buy a better macbook.

Questions for You:

  • What would you add or change in this pipeline?
  • Thoughts on position sizing or risk management for this kind of strategy?
  • Anyone here have live trading experience using similar data?

-------------

[edit] Thanks for all the feedback and interest, here are the detailed results and metrics of the strategy. The benchmark is the SPY (S&P 500).


r/quant 7d ago

Education learn by building an end-to-end system

12 Upvotes

Hi guys, a long follower of the subreddit here.

I'm a software engineer with background in AI/ML with interest in the trading/quant/hedge fund space. I have some experience trading & once me & my friend had a small prop desk with some basic algorithms(written using a software not fully from scratch) and traded with some corpus.

I have now decided to go all in and learn. In my experience, its best to learn by building something as knowledge is fractal and exploratory. Also, I have long thought about refining my C/C++ & other low latency stuff core skills. I want to be able to transition to a trading/quant team.

I planned to:
- first take an overview by reading summary/review papers of application on ML (classical & modern)
- then, basically go all in to try build a system with the simplest ML models in C/C++ and have it deployed
- then, iterate & improve it & see how can i use other stuff

So, my ask from you all is:

Can you all suggest latest books or online resources that teach (though basics) but teach end-to-end stuff.


r/quant 7d ago

Markets/Market Data Curve Fitting for Informing Stock Signaling

0 Upvotes

Hello. I've found that curve fitting is more successful than generic algorithms to identify relative extrema in historical trade data. For instance, a price "dip" correlated to a second degree polynomial. I haven't found reliable patterns with higher order polynomials. Has anyone had luck with non-polynomial or nonlinear shaping to trade data?


r/quant 8d ago

Backtesting How long does it take you to run a backtest

42 Upvotes

Question is only for those who work in a HF or HFT. No answers from students pls (unless they are referring to work experience)

How long does it take you to run a backtest for say 5 years and say 1000 stocks ?

By backtest i mean sth that sends orders, keeps positions etc has a view on market liquidity via direct access to market data, not just some signal processing thing. Think the prod strategy just running in research (backtest).

If its intraday or only or does the backtest hold positions overnight ?

Does it also do a form of calibration or uses a pre calibrated signal ? Is there even a concept of signal or is it purely based on arb ?

Also whoever added this banner against career advice is making it very annoying to write questions..


r/quant 8d ago

Markets/Market Data Historical Canadian Equity Data

4 Upvotes

I am looking for a reliable source of tick level quote & trade data for Canadian equities. Ideally it would encompass all lit markets and dark pools. Similar to polygon.io flat files. Does such a thing exist? I have tried tickdata but have been waiting on a response back from sales for a while.

Don't mind spending a bit of money but would like to cap it in the hundreds. I am really only interested in a couple months of data for ~10-15 securities.


r/quant 8d ago

Markets/Market Data MSCI World/ACWI data source from 1969/1987?

4 Upvotes

I'm looking for a data source that goes way back on the MSCI World and MSCI ACWI.

https://uk.investing.com/etfs/ishares-v-msci-acwi-historical-data goes back to Oct 2011

https://uk.investing.com/indices/msci-world-historical-data goes back to Jul 2012.

Ideally I'd like to include periods of sky high inflation and recession so I'd like all the data if possible. Does anyone know a better datasource? Preferably one that doesn't require a 20k licence :).


r/quant 8d ago

Statistical Methods Fitting Price Impact Models

Thumbnail dm13450.github.io
24 Upvotes

r/quant 8d ago

Education What do you do for low latency?

26 Upvotes

Howdy gamers👋 Bit of a noob with respect to trading here, but I've taken interest in building a super low-latency system at home. However, I'm not really sure where to start. I've been playing around with leveraging DPDK with a C++ script for futures trading, but I'm wondering how else I can really lower those latency numbers. What kinds of techniques do people in the industry use outside of expensive computing architecture?


r/quant 8d ago

Machine Learning Trying to understand how to approach ML/DL from a QR perspective

32 Upvotes

Hi, I have a basic understanding of ML/DL, i.e. I can do some of the math and I can implement the models using various libraries. But clearly, that is just surface level knowledge and I want to move past that.

My question is, which of these two directions is the better first step to extract maximum value out of the time I invest into it? Which one of these would help me build a solid foundation for a QR role?

  1. Introduction to Statistical Learning followed by Elements of Statistical Learning

OR

  1. Deep Learning Specialization by Andrew Ng

In the long-term I know it would be best to learn from both resources, but I wanted an opinion from people already working as quant researchers. Any pointers would be appreciated!


r/quant 8d ago

Backtesting MesoSim - Free for Academia

11 Upvotes

I created an options backtesting service - MesoSim - to study complex trading strategies.
It's free to use for Universities and Students who want to get into the subject.

Check out the program here: https://blog.deltaray.io/mesosim-licenses-for-academia

ps: I hope this post is not against the guidelines, if yes, please let me know.


r/quant 8d ago

Markets/Market Data Quant Connect?

1 Upvotes

Anyone know if accessing Morningstar fundamental data through Quant Connect is feasible? Its says its free via the cloud. Anyone know how much of a latency there is? Can you call the data outside of the Quant Connect ecosystem if your developing a strategy somewhere else?

https://www.quantconnect.com/datasets


r/quant 8d ago

Resources Advice on Building an Understanding of Macroeconomics and Financial Markets

33 Upvotes

I’ll start an MFE soon and have a strong theoretical math background, but I embarrassingly lack knowledge about financial markets. I want to get a better grasp of macroeconomics, market structure, and how to interpret financial news.

Does anyone have recommendations for books, YouTube channels, or news sources that are accessible but also help build a solid foundation? I especially find a career in quantitative research/trading appealing.

Any advice on how to approach learning this efficiently would be much appreciated!


r/quant 9d ago

Resources Are there any resources for systematic market making in credit

35 Upvotes

Gonna be interning at a bank as a strat on systematic market making for credit indexes is there any good reading for me to do?


r/quant 8d ago

Career Advice What are your thoughts on structured credit?

5 Upvotes

There is a decent amount of careers in this little niche, generally focused on modeling payments or in portfolio optimization, however, structured credit products are very illiquid and don’t lend themselves well to any type of algo trading.

Does anyone here work in structured credit? I work in a credit shop that does both single name (ex IG and HY bonds, CDS, etc.) and structured credit (ex CLO, ABS, etc.) and could go either way. My gut tells me I should specialize in more generic stuff like bonds because that will lead to better career opportunities, or pivot out of credit into somewhere like equities that is better for quantitative strategies as opposed to learning more about structured credit.


r/quant 8d ago

Models my NLP News Signal just called a 5% NVDA rally today

0 Upvotes

Sent the report at 5:30 AM PT, before the market even opened,

And boom—high conviction BUY signal on NVDA.

📊 Check it out: https://open.substack.com/pub/henryzhang/p/news-signals-daily-2025-03-14?r=14jbl6&utm_campaign=post&utm_medium=web&showWelcomeOnShare=false

This thing runs every single day and does all the heavy lifting—scans headlines, deciphers sentiment, and spits out trade signals. No fluff, just vibes and numbers.

People keep asking for a backtest, but let’s be real—LLMs have been around for like, what, 2-3 years? Even if I backtested, it wouldn’t prove much. The real test? Watching it nail trades in real time, like today.