I’m trying to find the best information and I want to know how your guys’s experience is working with the API’s. Let me know in the comments what you think the best brokerages and why for algorithmic trading.

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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.

Hi everyone, I've been fortunate enough to be offered a Trading Internship at Jane Street, for Summer 2025. This subreddit has answered a lot of my questions along the way, and I was hoping to return the favour (with a throwaway for privacy reasons) :) Hope this helps someone!

Curious what percentage of this sub actually works as quants and if not what is your background. Would love to add more options, but 6 is max.

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I’m currently pursuing a PhD in experimental psychology with a focus on human cognition. I’m also specializing in a quantitative analysis concentration, which involves rigorous statistical and data analysis. I’ve been wondering if this background would be sufficient to explore a career in quantitative trading. Are there skills or areas I should focus on developing further to make the transition? Any advice or insights from those in the field would be greatly appreciated!

What would you value the most in terms of career development? I am currently a consultant (since Apr23) at Deloitte in Milan with a focus on model development but after some interviews I got a job offer as a Risk Quant Research at BNP in Lisbon. I am 26 yo and I would like to build a career as a quant but I am not sure if this is enough of a good opportunity: from the specific job description and the interviews with the team it seems that BNP is more aligned with my interest, but given that it is not a central office and given that my salary would basically stay the same I am not sure if I should wait for a better opportunity to come. Plus there is the consequences of the relocation (costs and leaving my current life). What do you think?

As the title says, I am looking for a question/topic that I need to write a topic about. I am a second year graduate student in Math-Econ. I am especially interested in Term Structure modelling and Swap models, but open to anything else! Thanks!

Quantra is offering all their courses for about $3500. Has anyone tried Quantra courses?

Recommend resources on pricing illiquid stock options especially options in india, which are european style options, i was thinking garch or stochastics volatilty, i might be wrong

So from my understanding crypto options are generally not tradable in the United States (as well as some other countries), yet there appears to be some firms that are doing it so is there some loophole I don't know about?

I've heard that Akuna Capital is a very large trader on Deribit and I originally thought that they could do that because they have a Sydney office, but they are advertising for Crypto Options traders in their US office now:

https://akunacapital.com/job-details?gh_jid=6269288

"Join our team as a Crypto Options Trader! Successful applicants will have the unique opportunity to undergo training in Akuna's Sydney office before moving into a full-time position at our Chicago headquarters."

I thought this was the reason that Susquehanna set up SCB because Susquehanna wasn't allowed to trade crypto options in the US. It would be weird to openly put out a job posting for something that's not allowed so is there some workaround to the rules here?

Context: I am just a guy looking forward to diving into a quant approach of markets. I'm an eng. that works with software and control stuff.

The other day I started reading The Elements of Quantitative Investing by u/gappy3000 and I was quite excited to find that the Kalman filter is introduced so early in the book. In control eng., the Kalman filter is almost every-day stuff.
Now, searching a bit more for Kalman filter applications, I found these really interesting contributions:

• TREND WITHOUT HICCUPS - A KALMAN FILTER APPROACH
• Kalman filter demystified: from intuition to probabilistic graphical model to real case in financial markets

Do you know any other resources like the above? Especially if they were applied in real-life (beyond backtesting).

Thanks!

Hello bright minds!

I’m working on a paper that involves swaptions (specifically EONIA-based swaptions across different strikes), and I’ve run into a question I haven’t been able to find an answer to online. Unfortunately, I no longer have access to the Bloomberg terminal where I initially gathered my data, so I’m hoping some of you might be able to help.

For a 'normal' swaption contract, what is the notional amount of the underlying swap that you have the right to exercise?

Thanks in advance!

I have noticed that after many years at top funds, some quants would run their own "whatever Captials" with only one employee.

My question is why. Is there any tax benefit running a sole-proprietor "Capital" vs just trading out of your personal account?

Hi everyone, probably my question it's a bit off topic, but I'm struggling to understand the relation between the basis risk and the hedge ratio. In particular I can't answer to these 2 questions:

1)If the optimal hedge ratio is 1, is the hedge a perfect hedge?
2) When basis risk is zero, the optimal hedge ratio is 1. Is it true?

For 1 I was thinking that an hedge ratio of 1 can be obtained with different combination of the correlation coefficient and the ratio between st dev of deltaS and st dev of deltaF. So we can have an hedge ratio=1 but a correlation coefficient≠1 and this implies that there's basis risk. For the 2, I think that if there isn't basis risk the correlation coefficient must be 1, but can't match this with the hedge ratio being 1

The question I am unsure how to do and what to do.

How do changes in exercise levels from one month before the COVID-19 outbreak to three months into the outbreak and lockdown (May 2020) differ according to both sex and cohort/age?

You will need to consider participants of all four of the cohorts listed above, but exclude MCS parents in your analyses. These can be identified using the variable: CW1_COHORT.

Hint: this question could be addressed with a mixture of descriptive statistics, tabulations, and ANOVA and/or general linear regression.

Can someone please help me with this?

Python seems to be the must-know programming language for research, but I was wondering if Matlab is used?

Python is free, while Matlab is paid, but I don't think the cost of Matlab would be a deterrent for a company that manages large budgets.

Python is very popular for machine/deep learning, but Matlab is also very capable and has plenty of toolboxes and well-tested libraries.

I also think Matlab is faster in some cases and has an equally large and supportive community.

When it comes to visualisation capabilities, Matlab seems clearly superior to me (indeed, Matplotlib emulates Matlab).

A drawback of Python is sometimes its "portability". Running the same code in a different computer can sometimes be problematic, a problem that virtually doesn't exist in Matlab.

Why has Python become the default option everywhere?

https://www.youtube.com/watch?v=X1A2DqtlA98

PART I https://www.reddit.com/r/quant/comments/1b3iidh/life_of_a_quant_in_cfm_equity_background_in/

What is your current percent return for 2024? Im just curious where i sit in the mix. Happy to share if anyone else is interested to see.

I want to experiment with some alternative assets like maybe crypto or forex, which have nothing to do with my work in equities. I'm thinking of building a home NAS to experiment with. But I also want to consider the option if pushing the infrastructure to a cloud provider at later date.

I am thinking I will test locally on a NAS/home infrastructure and if something seems interesting, I can go live on a cloud account later. I don't have a ton of experience building databases and certainly not maintaining them.

Any feedback is welcome on what is most reasonable.

* Should I use local docker containers and then push to S3, etc. when I want?

* Should I just straight install databases (postgres, etc.) on unbuntu and they will be easy to move to an S3 later?

I'm an info sec guy, but interested in quant topics. Since I'm not too deep in down the rabbit hole I was wondering if there are any tools, subscriptions or other deals you're watching for the black Friday season. The infosec community has a list for that maybe something like this exists here too?

https://preview.redd.it/zsyfvfd8wl3e1.png?width=1684&format=png&auto=webp&s=e3f8a9684e97f51f55bde65fd37443ee5e14ae17

So I came across this feature of Option Samurai

What I don't understand is how to calculate this Expected P/L. They have published article for that EV calc

I tried using that method but final P/L value isn't coming same. I tried using https://pypi.org/project/optionlab/

It's giving value EV = 375.90 and POP = 28% while here in screenshot you can see long CALL EV is US$525.34

Did lot of research but not able to crack this one. Does anyone have any idea here, would really appreciate the help here ?

I am working on a school project building a simple fx portfolio using momentum signals. And I am confused about a few things.

• would you construct your signals for a currency or currency pair
• same question but for portfolio optimization

A lot of literature is for equity portfolio optimization. Can someone point me to some interesting quantitative FX research?

Fun question: Inviting folks who have exposure to International Math Olympiad or equivalent in Physics or related fields.

What do you find more challenging - winning an IMO medal or quantitatively solving the market to earn consistent supernormal return. What takes more work, effort, IQ and is overall a harder target to achieve.

For the sake of quantification, I would say solving the market equates to earning over 100% return a year on $10mm book with less than 5% negative days year after year. Something that a good HFT system or a high churn stat arb probably achieves.

Most financial orderbooks on exchanges operate on a price-time priority, meaning that market orders are matched against limit orders with the most favourable price and in situations of equal price, the order which arrived first.

What would be the impact of having a price-size-time priority orderbook, where the most favourable price is still matched first but following the same price, the largest sequential limit orders are put first in the queue before looking at arrival times.

Would this be better off for market participants? I imagine it would wreck the concept of HFT but I don't believe the economic value of squeezing microseconds out of orders is very high. Market making would become a lot more game-theoretical, but ultimately market impact and execution costs should be greatly improved, no?

What are your thoughts on how a widespread adoption of this model would affect markets today?

I am looking for information from someone who actually has worked in options pricing, what kind of model did you use for estimating volatility surfaces?

I just finished my undergrad (major in math) and through my dad’s connections I was able to get an internship at a risk management desk for a trading firm. They mainly do hedging against commodities and are a small desk. Since it’s just an internship and I’m looking to apply for masters and then for quant jobs what should I look to get out of the internship (6 months to 1 year). Since I know the fund manager he told me to tell him what I want to do and they can create projects/ learning opportunities for me that way. It’s a much more personalised internship. So what would your guys’ advice be for what I can do and learn through the next 6-12 months. Thanks!

Hi all, I’ve been working for a bit over a year in the industry. I’ve been working on some new ideas at work, which is largely self-motivated. I’m curious on IP protection and pnl split for developing and running these strategies. Below are some questions in my mind.

1. For strategies developed in house, does the IP of the model usually belong to the firm, or to the quant who created it, or both?

2. What about for strategies that the quant brought into the firm? If it belongs to the quant, how can he prevent the firm from accessing it / reverse engineering it / modifying it and claiming it isn’t the original model under IP protection?

3. If a quant brought strategy A to a firm, then developed strategy B / improved strategy A, how does IP usually work in these cases?

4. Would the pnl split be affected by who owns the IP? How much is the split usually under both cases?

I am curious to know the extent of HFT presence in China.

Is the presence as huge as it is in India? Or due to regulatory concerns major HFTs stay away from this market?

Which international HFT players are most active in this market and any idea about the opportunity available?

TIA