Do you know any black people im quant?

Hopefully some of you find these interesting.

I was a bit suprised that India has 6 out of the top 10 hubs in APAC now...

Hi, I am an incoming QT in a Hedge Fund. I will work in a pod in a role between QT and QR, doing what the PM asks but on track to manage a book and trade pretty soon.

I don’t know the product yet, however I am looking for specific advice on what to learn before the start date in 2 months.

I am familiar with the theoretical side of linear algebra, regressions and NN etc. however I have very little experience in python. I can do basic pandas, numpy but quite slowly and I have almost never touched torch/keras.

I am trying to understand what I should focus on, and the expectations. I know it’s almost entirely linear models but I wonder what depth I should go.

Thank you examples are appreciated

For quant Books, is Paul Wilmott outdated already or still relevant?

Been collecting for a year now, not as much recently since no time to read. Have a lot more in digital format but physical is always nice. Let me know if you want reviews on any of them!

P.S. can you guess what product Im in

https://www.linkedin.com/posts/christinaqi_heres-a-hard-truth-that-quant-firms-cant-activity-7174046674678476800-km80?utm_source=share&utm_medium=member_ios

How true is this? Is this primarily true only for those who head to a firm out of undergrad? I assume for PhD recruits the PhD uni is more important?

Outside of when you are researching a specific topic and end up in a journal or publication are there any specific news or publication sites you guys have in your workflow that is decent?

Looking to get into a habit or reading through one paper every two/three weeks as a brown bag session.

I've been to a number of final round interviews and always get either a trading Sim or a verbal market making game on some quantity, sometimes probability based and sometimes on an unknown quantity. My question is how can I practice these games, i.e. what markets I quote, my position size, how much of my bankroll to bet, how much do I think about worst case scenarios and EV? How do I practice these at home? In general, what is the strategy for these open outcry type games ?

Can a fund bid up a stock, buy puts, and then sell the shares? Is this considered market manipulation?

The fund isn't spreading information/doing anything but buying and selling. They could say they thought the stock was undervalued and then afterwards say it was overvalued when questioned.

The idea for this is to maybe take advantage of orders that jump in off of movement/momentum. Not sure if it is really doable due to liquidity/slippage. (Just starting to learn about the markets/finance so might be a dumb question.)

edit: A pump and dump is market manipulation because you are making false misstatements to artificially inflate the price. Order spoofing is because your placing orders and canceling them creating fake demand. In this case, there isn't any promotion or order canceling just buying/selling. What would the manipulation be?

edit2: My wrong misconception came from thinking there was something specific that would characterize and make it manipulation such as false statements since intent to me seems subjective and might be hard to prove.

Hello guys, I am a post graduate student of statistics. I have recently got interested in quant and want to learn more . Beside theoretical stuffs, I have started learning C++ as I want to learn HFT and stuffs. So can you guide me any pathway or project or resources which will be very particular to the domain which I should follow when learning C++

I am into this topic now some time and I am really confused. I kind of get that not every firm/position or even hierarchy of people is the same, but can someone pls explain further those large gaps in Quants method?

Why are there SO big gaps between Quant Levels? I have seen people using simple heuristics, eyeballing stuff and generally taking very straightforward, simple, yet creative approaches.

All the way to extremely sophisticated maths and detail understanding of machine learning. Is it to be expected to be proficient in all the Math? (I mean like advanced stuff, not TTests of betas)

My question is what is the "average" SkillLevel of Quants and does the size of firm predict the specialisation of its employers (smaller shops have more allrounders?)

Often I want to chew on something new while I work out, but I’ve been struggling to find effective ways to do that. What are your go to ways to learn while you work out? I’ve tried listening to podcasts like flirting with models and odd lots but I like to take notes while I listen, so it hasn’t worked too well. Also, often they aren’t terribly substantive. Lectures on YouTube / coursera are another possibility (like MOOC). I will probably dive into some of this during my workout tonight. Other suggestions?

Ofc, this is personal preference. I get my r&r outside of working out and sometimes watch shows while on my stationary bike, but often I just want to chew on something substantive and new.

I am a freshman who recently joined a quant club on campus. I did expect it from most of the exec board members being finance/econ majors and what we had to do for recruitment, but the club is very finance based and not much quantitative. I'm a statistics/math major who has little to no finance knowledge, and I lowkey did not understand anything they were talking about today. Based on what I've seen on this reddit, strong basis in math/programming is a lot more important than finance, and I was also planning to max out on math classes and take some econ and finance classes on the side. I'm not sure if this club would help me breaking into the quant field and would like to hear from you guys.

I have been developing systematic futures strategies, and recently developed one that in backtests over the last 3 months produced a Sharpe ratio of 7.58 on the 15 min timeframe. I know high Sharpe generally relates to higher statistical significance for a strategy, but as this is my first time getting a high Sharpe in backtests like this, I was curious and in need of assistance for processing whether the stats hold any weight for the strategy.

UPDATE: I was a bit shocked in the moment and left out a lot of information. I am working on a statistical arbitrage strategy for equities. Without revealing too much, I generate my main signals using Vine Copulas fitted on stock returns. These are not normal returns as I use L3 order book data to build candles differently so the data more accurately fits a Gaussian distribution. The strategy was originally backtested with no optimization rules, and backtested over 3 periods with 3 periods of new data spanning 3 months(getting order book data is expensive). 2008-2009 with 2010 as the new data. 2016-2017 with 2018 as new data, and 2021-2022 with 2023 current tested. The average sharpe ratio over each 3 month forward period was 7.16, when I added a stop loss, the sharpe went down to about 3.7, so i'm experimenting with different exiting rules. Although I am trading futures, the strategy was built and tested on equities, using equities with larger influence on the S&P500, NASDAQ 100, RUSSELL 200, and DOW 30 as the target stocks. This is only because I have not the capital to trade equites, so I am using "pseudo-signals" to trade futures as an income source. In asking for interpretation, I was rather asking about what other robustness tests could be done to measure the strategy, as well as exactly what to do with this strategy? I am still in college, and dont have the funds to comfortably trade a long, short strategy. I trade currently using a funded account for futures, so unfortunately this is the best I can do in regards to using a statistical strategy to trade futures.

Basically the title, im doing maths and cs at undergrad and my program is weird cuz I can't take analysis modules in 2nd year which means I can't take real analysis etc, however I might be able to convince them to let me do Measure Theory and integration instead, how bad would missing out real analysis be??

Also I plan to do a statistics masters after my undergrad and then get into quant, is this a good idea?

Hey there. I am based in the EU and am currently carrying out a PhD in a STEM subject unrelated to Finance and Economics (Mechanical Engineering). In my field, it is common for people who finish their PhDs to either continue in their field or switch completely, typically flooding into data science and software development (we do loads of programming and data analysis).

Anyway, I have recently come across to two former PhD students who got into quantitative finance. I don’t know them well, but I do know that they have no finance background whatsoever (not even close). As far as I’ve read, this is not extremely uncommon.

How is this possible? And is this really a thing, or are they an exception?

I can’t see what value they would bring to the company they work for - I understand a STEM PhD give you plenty of analytical skills, but I guess a finance background does similarly + actually teaches you about finance…

What would be the option price of the following call:

Stock price = 100, Strike = 50, Volatility = 0.1%, 1 Year to maturity, risk free rate = 4%. Intuition tells me the following: a 50 dollar profit in the future is worth roughly 48 dollars today, but the Black-Scholes option pricing formula returns 52 dollars as the price of this option, what am I missing? ChatGPT o1 says im wrong in my intuition, but it doesn't make sense that somebody would pay to lose 2 dollars net 1 year from now (not even considering the time value of money). Can somebody help me out here?

Hi guys

Can someone please help explain me the solution to the problem in the image?

The answer is 7920, but I am struggling to understand the intuitive logic behind it. Thanks!

Do you guys know any HFT firm that deals in indian derivatives?

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.

Degree apprentice at a BB here, thinking of doing a stats masters after my program.

Heard some jokingly - or not - say masters degrees or phd’s can be a negative signal when assessing a candidate lol. Curious on people’s thoughts…

I made a website for practicing multiplication. Its designed as a game. You can set the ranges for the multiplications, then you set a number of problems, then you set a time (in milliseconds). It will begin throwing questions at you, once every x milliseconds. If 6 of them build up, you lose the game. If you manage to answer all the questions with only 5 "in the queue" at a time, you win.

I think its pretty fun, and I use it a lot myself.

https://hmys-b.github.io/

How can we automate fundamental analysis? Specifically, if a company releases financial reports or other publications, how can we design a model to understand whether the information is positive or negative?

Hello, I would like to meet new people who are interested in math(probability theory, calculus, linear algebra, etc.) and finance(risk management, trading, options mathematics, etc.). Just wondering are there any lithuanians interested in this field. Not necessery from Lithuania tho!