Which sites can provide historical data for India 10year government security which is monthly. Im assuming yield implies total return data (including dividends/interest) reinvested. Im a newbie so pls excuse if im missing any additional details

Someone posted about it so I feel obligated to post too. I felt like i was scammed paying 3.5 K for a mediocre course that was overhyped. The TB questions were from basica quant books you can find online so it didnt really do much. Plus no solutions for all. I’m embarrassed because I had put my internship savings for this.

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.

Hey everyone,

I've created an Excel sheet with the full 13F list and each security's name, CUSIP, and Ticker. This sheet has been an incredible resource when analyzing 13Fs, as I can reference CUSIPs or Tickers, depending on what my data has. If anyone is interested in this sheet, please feel free to PM me.

Hey everyone,

I'm interested in understanding the distribution of HFT returns in cryptocurrency markets. How does the cryptocurrency market compare to developed or emerging stock markets from an HFT perspective?

Thanks

I’m trying to understand how to model the expected move size for an underlying due to events (like earnings, interest announcements etc) using ATM vols.

Assume we know the ATM vols for options expiring in t1 days and t2 days. For simplicity, let us assume that the ATM vols (v1 and v2) are already annualized.

I know that there’s an important event happening between t1 and t2. I want to understand what is the size of the move the market is pricing in, given these vols.

Thanks in advance!

Hi! I'm trying to construct a smile from market prices of options for USDINR. My basic strategy is to obtain the ATM forward price using call-put parity (bypassing the requirement for interest rates as I'm assuming they would already be priced in), then obtaining IV using BS model for ATMF strike as well as 25-Delta Call and Put options (using some interpolation here as the tradeable strikes may not necessarily be the same.) I then plug these values into the Vanna-Volga model to obtain the smile. My questions are:

1. Do I need to modify anything here?
2. Is the IV always lowest at AMTF in FX options?
3. If so, then how do I make sure that my curve fitting takes this into account during the calculations for 25 Delta options?
4. How do I make sure that the curve I generate is arbitrage free?

Thanks for any help.

I bought TheQuantGuide's interview prep course (thequantguide.com) for $3499. I’m creating a throw-away account and writing with a few months delay because (1) I’m embarrassed for having paid for the course and (2) I don’t want the creators of the course to identify me (they know my real name and other personal information).

Summary: The course is not literally a scam, but they use dishonest marketing and it’s comparable to other resources which are cheaper/free and better. I bought the Quant Guide course based on very positive reviews on reddit and a blog post by someone called “Jack Doherty”. In hindsight, I could have realized these reviews were fake if I had done more research

Initially I signed up for the intro call through a link on their website. I received an invitation from thequantguide@gmail.com, which didn’t include any name. I had an intro call with someone who introduced himself as Aaron. Aaron claimed to have worked as a quant for Citadel for 3 years. He seemed very convincing and charismatic. Even though I was doubting myself, he said I have a high chance of getting a job at a top tier firm if I put enough effort and read all their materials.

I couldn’t find Aaron on LinkedIn or any other social media. Aaron didn’t disclose his last name. In fact, in all my future interactions with them I never saw anyone’s full name - the person’s name was always “The Quant Guide” or just a first name. When I asked to speak to a previous participant as a reference, Aaron said he wants to protect the privacy of each participant and hence he can’t do it. This seemed suspicious to me, but because of the good online reviews, I still ended up buying the course.

After payment, you get a link to create an account on https://course.thequantguide.com, where you can log in to view the course content. I will review each part separately. All the course materials can be accessed from this website.

Video lessons

There were 16+ hours of video lessons and mock interviews as the website advertises. The videos and explanations are OK. They’re similar to what they have on their YouTube channel. The lessons explaining theoretical concepts (combinatorics, expected value, mental math tricks) are nothing special - you can find lots of similar materials online for free. The videos explaining solutions to brain teasers were more useful, but there are free (and IMO better) versions of this, for example this playlist: https://www.youtube.com/watch?v=nBzy5LDHqjY

Question banks

They have a long list of interview questions. They’re mostly questions from Glassdoor and other places with solutions which sometimes have mistakes in them. There were some unique ones that I hadn’t seen elsewhere.

The list of questions hasn’t been updated since I enrolled (more than a month). I had a total of 13 interviews at 5 top tier companies (Jane Street, IMC, DRW, Optiver, SIG). I was asked more than 30 different brain teasers in those interviews and none of them was in the question bank. On the other hand, 3 of the questions I was asked were on Glassdoor, which is a better source if you want to know the most up-to-date questions.

Get-the-interview guide

This has some very ordinary advice that you could find elsewhere.

Feedback on resume

They gave me feedback on my resume, mostly to remove parts that were less impressive so that my most impressive achievements stand out more. I did hear back from most of the companies I sent my resume, but it’s hard to say if it was thanks to their feedback.

Warning signs I didn’t notice

Overall, I think this is an OK resource which they put some effort in, but there’s nothing that you can’t find elsewhere for cheaper/free. I feel embarrassed and guilty for having paid so much money for this. Below I list the signs I should have noticed in case someone is still not convinced after reading my review.

Fake reviews

As of now, I found 2 reviews of the course:

The Quant Guide Interview Prep Course: Review

• This one is written by “Eric Caldwell” on LinkedIn.
• The account has no other activity on LinkedIn, almost nothing in his profile and only 10 connections.

https://www.linkedin.com/pulse/quant-guide-interview-prep-course-review-eric-caldwell-1qttc/

My Experience with The Quant Guide (Detailed Review)

• This one is by “Jack Doherty”. This review is the only article on the blog.
• I could not find any Jack Doherty working at an HFT firm on LinkedIn or elsewhere online.

In addition to this, there are positive comments on reddit by these users: classof2023, thenameisbobbishello, Long-Direction8628, kingwebber24.

All 4 have almost no other activity besides posting good comments about the Quant Guide and some low-effort comments on other threads.

Long-Direction8628 wrote 8 months ago that he bought the course “last year”, even though the website didn’t exist yet. When he got called out, he wasn’t able to explain it.

Classof2023 says absurd things like:

• "You'd be surprised... quants are hungry for money - I'm surrounded by 7 figure earning traders which do wild things to save and on top of that have side hustles."
• "I mean I'm with you, but these guys will stay in the office an extra 2 hours just to get a free dinner voucher"

Besides these, I found no place on the internet where someone said anything good about the course.

Other messages on reddit

There are a couple of comments on reddit of people who said they paid for the course and it wasn’t great, and one who calls it a scam.

Aaron told me this year he had been working for Citadel for 3 years. He told to another commenter last year he’d been working for Citadel for 3.5 years, and another commenter that he works for Jane Street.

The business is registered at the same address as another company that scammed dozens of people (https://www.reddit.com/r/quant/comments/15du0cm/comment/jvslors/)

Dishonest marketing

They have a fake countdown timer for a discount on their course which resets every day.

Relevant threads:

• https://www.reddit.com/r/quant/comments/18743l9/thequantguide_legit_or_scam/
• https://www.reddit.com/r/quant/comments/15du0cm/thequantguides_the_ultimate_quant_interview/
• https://www.reddit.com/r/quant/comments/191zfz1/the_quant_guide_interview_prep_course/

How's the gender-dynamics in this industry? I'm pretty curious and kinda intimidated. Are there instances where women have been discriminated in this?
I'm well aware that hfts solely focus on competence and delivering results so there's no diversity hiring.
What's the male:female ratio at your firm?

Hi guys, I feel like I’m stuck and need some career advice. I'm currently in a middle office role at an investment bank, heavily involved with quant research and P&L explain analysis. I really want to learn more and pursue a career in this field.

Should I do a master's in financial engineering or keep making connections while at my current position for advancement in my career?

Thanks in advance for your advice!

Hi guys, my background is FX HFT, and I’ve only recently started looking at us equity market data So I’d really appreciate your patience here. Here’s the question, I can easily subscribe to public trades from the various exchanges but I’m also interested in seeing OTC trades (eg robinhood trading retail size with citSec in AAPL), is there a single service that these trades are all reported to, multiple services? Is it real-time? Are we talking micro-seconds, milli-seconds, hours…for OTC trades to print here? What data-centre? Multiple data centres?

Many thanks in advance.

I do have a math degree from about ten years ago but I'm rusty now. During my degree I did study some graduate level probability (about 3/4 of Durrett's textbook) but tbh at this point I couldn't tell you e.g. what a sigma algebra is without looking it up. I work as a data scientist and am half decent in coding. I recently built a (crypto) market making algorithm that's been profitable in production for a few weeks now. It's primitive but seems to work. I'm interested in learning how it's done in industry and my only portal to that is scientific papers, all of which seem to demand knowledge of SDE and Stochastic Control. So I'm wondering how I can get up to speed for these papers without wasting much time learning stuff that won't help me. Do I need to work through all of Durrett as a prerequisite or can I skip some of it? Also wondering which papers are must-reads for market making. Is Avellaneda-Stoikov still relevant or outdated now? Thanks in advance

Hello! I've searched extensively online but haven't found a substantial paper related to event volatility modeling, which is distinct from the base Black-Scholes volatility, and at some places implied volatility=base volatility + event volatility. I wonder if anyone here has insights on this topic. Is stochastic volatility applicable to event modeling? Alternatively, could events be represented as jumps in the SVJ (Stochastic Volatility with Jumps) model? Thanks in advance for any guidance!

I don’t believe there’s any point in practicing on Leetcode anymore, if, say, you’re a PhD student now, trying to enter the industry in the next 4-5 years. Divoting more time to actual research / skilling up with AI may be more productive.

https://thedigitalbanker.com/ai-is-coming-for-wall-street-banks-are-reportedly-weighing-cutting-analyst-hiring-by-two-thirds/#:~:text=Big%20banks%20on%20Wall%20Street,software%20under%20nicknames%2C%20sources%20said.

PS. The purpose of the post is to not argue the normative. I don’t care if firms still do or do not choose to interview on Leetcode questions. The purpose is to be informative, whether it will or not.

Hi,

I have been offered a role in inventory management strats division at Goldman Sachs.Any idea what kind of algorithms I would be dealing with? Is it even a front office quant role?

What’s driving the strong Q1 2024 performance for quant hedge funds across the board? I know momentum, short interest, and crowding signals in general have done well but that doesn’t seem like it could explain why everyone has such good returns.

The strategy is to simply short Bitcoin/BTC and go long QQQ in equal size at the market open, and close both legs at the close. That is all. Four trades.

This is discussed in more detail here, with back tests https://greyenlightenment.com/2024/03/14/how-to-break-even-shorting-bitcoin-in-a-bull-market/ I am surprised this anomaly of Bitcoin being weak during market hours gotten no media or academia coverage at all especially given it has persisted since mid 2022. It's like the 'night effect' for stocks, but with bitcoin.

For example yesterday:

https://i.imgur.com/AWJFen4.png

​

As you can see, QQQ is green and Bitcoin sharply reversed at $70,700 (using BITO as a proxy). It works as well now at $69k as it it did when Bitcoin was at $25k last year.

And today

https://i.imgur.com/JuPaEdx.png

Both BTC and Nasdaq down a lot today but Bitcoin far weaker.

This method works because Bitcoin and QQQ are highly correlated, but Bitcoin has a tendency to suddenly/inexplicably drop 2-4% intraday when the stock market is open or otherwise lag QQQ, so basically being short Bitcoin and long QQQ means you can profit from this. It's like having a tip jar: Most people tip little or nothing, but occasionally someone will tip $20, which is analogous to when BTC suddenly dumps intraday and my short position is open.

In explaining why this particular timeframe of shorting Bitcoin during market hours is profitable, hedge funds and institutions take advantage of added liquidity during market hours to sell Bitcoin. This could be the U.S. government selling its Silk Road coins on Coinbase, for example. Or Grayscale redemptions. Also, cryptocurrency regulatory news such as lawsuits, which almost always is negative, drops during weekday mornings, not during weekends or the afternoon, so being short means taking advantage of this too.

This goes to show how good methods and market patterns/anomalies can still be found that can persist for years. You don't have to be some genius working at Renaissance or Jane Street to do this or have a PhD. Just basic pattern recognition goes a long way. There is always stuff waiting to be uncovered. Now scale this up across thousands of possible pairs among hundreds of assets, and then automate it , and you can see how these companies are quite profitable.

Hi everyone,

We're considering taking a big step with our statistical arbitrage Python library, ArbitrageLab, by open-sourcing it. ArbitrageLab has been a cornerstone of our work at Hudson & Thames, providing robust tools for financial analysis. You can check out more about it here: ArbitrageLab.

This isn’t just about releasing the code; it’s about fostering a community that can sustain and grow this resource. However, transitioning to open-source is a significant undertaking, especially in terms of maintenance and ongoing development.

Here’s where we need your input and help:

1. Community Maintenance and Enhancement: If you have experience in Python, financial algorithms, or managing open-source projects, we would love to collaborate. We’re looking for community members interested in contributing to maintaining and enhancing the library.
2. Support through Donations: To kickstart this transition and cover initial maintenance costs, we are considering setting up a platform for community donations. We’re interested in hearing your thoughts on this or any experiences you might have with similar initiatives.
3. Ideas and Feedback: If you have suggestions or want to share your thoughts on how an open-source project like this could be structured to benefit everyone, we’re all ears. Your feedback is crucial in shaping how we proceed.

We want to ensure that this move is as transparent and community-focused as possible. Please let us know your thoughts, or if you're interested in getting involved in any way. We believe that with your support, ArbitrageLab can become a valuable community-managed resource.

Please feel free to reach out directly via email at opensource@hudsonthames.org. We are eager to discuss how you can contribute to this exciting project. Your expertise, financial support, or innovative ideas can help shape the future of ArbitrageLab.

Thanks for reading and for your potential support!

Best regards,

Jacques Joubert

Co-founder at Hudson and Thames

I would love to know how you made the switch / how easy was it.

I’d be really good at this. I’m a social science PhD and this type of work (finding a new way to measure/predict XYZ) was where I excelled in academia (published in top journals, etc). I’m better at this than, say, optimization. What firms hire these roles and what job descriptions/ titles should I look for?

Hi everyone. Im trying to model the distribution of a PPV (Proportion of positive values) feature generated by kernels in a transformer. My PPVs look like the following:

*FYI: I used a package in python to fit various different types of distribution, but I have a feeling I would have to tweak some parameters of distributions or even define it piecewise or truncate something to make a more accurate estimated distribution.

https://jmp.sh/s/Q5MSF0alGOwZwi0KweUw

It looks pretty much like a normal distribution but with peaks on both tails(ends). Do you guys happen to know some type of distribution that I could tweak to model this data?

Thank you so much!

Undergraduate math major here, hoping on becoming a quant some day. I'm currently in a numerical analysis class and we have to pick a project to work on. I wanted to estimate estimate Black-Scholes using finite difference, but I'm not allowed to since Black-Scholes is a PDE.

Are there any similar ODEs I could approximate? Or any other ideas of things I could do?

Hello!

​

Undergraduate math major here, hoping on becoming a quant some day. I'm currently in a numerical analysis class and we have to pick a project to work on. I wanted to estimate estimate Black-Scholes using finite difference, but I'm not allowed to since Black-Scholes is a PDE.

Are there any similar ODEs I could approximate? Or any other ideas of things I could do?

​

Thanks!

Hello people of the subreddit. I am a second year Student in IT engineering. I do plan to do masters in quant finance ( cause I feel that's good to end up as a quant). I plan to study for masters and work in Australia ( currently i am in India). Can you guys tell me what the quant/ finance situation there is?? how is the quant job market there?? and which universities there would be the best for the masters.

I was reading about the options market and its boom in our country (India). I was reading about how many local HFTs have emerged in India and how their earnings are increasing exponentially each year. Also how bigger global HFTs like JS, Citadel, Optiver, XTX , Tower, Jump and other global HFTs have started their operations in India recently.

If I look at the picture of India for now, the market is looking crowded and I have few friends in Indian HFTs who are saying that it's getting difficult to discover new alpha. Considering how global HFTs have established low latency infra and have top quant researchers, it seems that the competition in Indian markets will increase further and so have has happened in International markets earlier.. But let's say like a new age firm for eg XTX or any HFT for example, we hear they take market share from old established companies at times. So is it possible in future for new firms to establish themselves in such a way to gain market share form published firms and how can one this happen? I understand in UHFT domain, there will be limitation in hardware prowess to an extent after some time. Does this also mean that discovering alpha will be difficult for MMs?

Also as the competition intensifies in this domain and it gets difficult to earn a share of the pie, how does new UHFTs (especially MMs) establish themselves in terms of latency and strategies. And can there be any instance in future that a new age firm can dethrone old establish firms?

Honestly a bit lost - where do people go after being in the industry? Been in the industry for 3+ years, but it just seems like there's not a lot of demand for traders in my region (APAC) at the moment, even after talking to a couple firms, they're just not hiring much.

Quantitative Researchers of big trading or hedge funds, how much work are you doing as a solo researcher -- just doing your research with close to no help at all from other researchers?

Let's say you're a junior-middle quantitative researcher, how much help would you usually get from your superiors?

And if you're not working as a solo researcher, how different are your and your teammates' tasks, and how much do they need to be invested in your work?

Hello

Does anybody know how pay works for execution desks? Is there a tracking error type payout or something like that?

Thanks

Hi, I’ve been adding features extracted from an equity fundamentals dataset to my daily alpha model (LGBM) and have come across the following problem:

some features (i.e. earnings surprise) are only meaningful once per quarter. However, the model obviously needs daily values for all features to spit out a daily prediction. LGBM can handle missing values, it learns which side of the decision tree is best to propagate them to when the variable in question is missing. I was wondering though if there is a better way to use/think about these features, perhaps decaying the value since its announcement.. I couldn’t find much literature on this and was wondering if anyone has any ideas to share or if i’m missing the right key words to lookup?

Thanks!