My channel's purpose is to shine a positive light on the often difficult to navigate world of algorithmic trading by breaking down publicly accessible resources into digestible chunks for those interested. As a quantitative developer, I hope many of you will find my insight useful, educational, and informative. I also like cryptocurrency, and code.
Looks like it’ll be a project that makes a streamlit app which plots the volatility surface of an options chain (ie finding implied vol using black-scholes + some numerical method)… just my guess though
Depends on what you mean. Tons of institutional hedge funds get slapped all the time. If you mean quant firms? They're usually too small to move the market. Renaissance wasn't even 1% the size of Berkshire. The SEC isn't going to waste time chasing guppies when people are getting attacked by sharks.
@@kagakai7729 i think both the interviewer and the interviewee dont know what they are talking about when they refer to market minipulation by wallstreet that disadvantages retail traders. asking about market manipulation by quant traders is a pretty dumb question.
@@Ventryx Yeah, that why I always find it strange when they compare day trade and quant trade. They should use TA trader vs Quant Trader, it makes more sense like that. Although, Quant strat tells you longer term is more favorable.
There’s maybe like 20 “good” quant firms that hire a lot of new grads. These consist of firms that actually have good performance and are good places to work at. By “firms” I include MM, prop shops, hedge funds, etc. Balyasny (BAM) Citadel/Citadel Securities DE Shaw DRW Flow Traders Headlands Hudson River Trading Jane Street Jump Trading Millennium/WorldQuant Optiver PDT Point72/Cubist Renaissance Technologies Susquehanna (SIG) Squarepoint Tower XTX Two Sigma Virtu There’s probably others and probably smaller firms that are good, but the above are all great (not all S tier but most A tier). Edited this list from Gappy list.
scam,, they tricked me to put so much money, and I did, I lost it all for almost 3 months, until I saw a review here, about alex victim help at mail dt cm then he got back my funds, lately I won the battle.
Honestly not a complicated question. So basic it’s actually funny to throw the rest of the vid behind patreon. But this shows that maybe quant coding interviews aren’t crazy at all.
I’m 43 and have 18+ years in C++ and 10/18 is trading systems. I still learn things by doing/implementing basic stuff like Coding Jesus mentioned. It is vital. Keep up the great work. Cheers.
Currently in risk management and I handle derivatives. Came as an investment analyst. They liked my modelling from my last job. So far so good and work life balance is very good. Helps me with law school lots vs my last job where I sometimes worked till the sun showed up. Might go to another industry after I pass the bar! Would recommend it if you wanna pursue the CFA esp lvl 2 since you ‘could’ be pricing derivatives every day and need to know the math to sense check it. You also need to know the greeks!
I like when you share knowledge on what exactly quants do, but for this particular subject you throw a lot preassumptions without solid understanding. In the beginning you said that the daily trader's risk management is simply stop loss, and the prop traders have a "real" risk management because they use greeks, and so on so on... Instead of all that talking, you could just say that day traders care where market moves, and prop traders (option traders) care what's the volatility. If you guys use all that terabytes of past data to predict the risk level, then whats the difference in comparison to day traders who use less or more complicated models to predict the prices? I know I know... there are a lot more factors if it comes to prop trading, but generally both groups can have solid Risk management and theres no bullshit in putting stop loss, becuase big firms do the same thing but execute it differently (because they base their tactic on much higher volumes). I respect what you're doing here and thanks for that, but this time just wanted to give you some other perspective. Thanks.
I work in Accounting at a quant/prop trading firm (originally as an accountant, now as an accounting automation pm). No idea how to feel about this tier list lmao
(TL;DR Not saying anything about you or the candidate or this channel, but more constructive feedback on how the question could be better ) What a stupid question to ask in a quant interview (for the original firm that used this question) - There's no substance - it's like asking someone to write a linked list in an interview - sure you can roll out something that works - but what did you as an interviewer learn about the candidate boundaries in terms of ability to think through hard problems and multi-layered problem solving ? The topic of the question is actually decent - lots of areas to explore - but the actual question and what it asks the candidate too simplistic - Of course the title says it is for a junior quant dev - what differentiates a junior dev from a senior ? not sure what the expectations for the skill level of this role are - so maybe the question is warranted for the level - but it's still quite easy anyone who's ever taken a data structures course can pull this off without breaking a sweat - not sure how it helps as useful selection criteria For example a better question to ask around this topic would be Imagine you need to store and look up items based on keys - but you have insanely high insert requests order of 1million per second - there's no way so much info would fit on a single machine - how would you overcome a challenge like this This would lead to more interesting discussions around - do we need the data to be retrieved fast - is it okay to have slow reads ? - once a machine is full - how do you orchestrate inserts to a new machine ? - how can you distribute the load across multiple sites ? - what about the latency of operations - are the clients that insert all geographically in the same region ? - is there caching to reduce load ? - how frequently does the cache get invalidated ? read after write consistency ? - what happens if one of the machines containing keys goes down ? can you recover ? - how would you add new machines ? - is it possible to have redundancy ? - what about compaction ? can you archive keys that haven't been used for more than x days / months ... - what are the properties of the hash function, collision rate, throughput ? - what about a distributed hash table ? ... And all these questions have both a system design AND a coding / algorithms angle to them which makes it quite engaging
I was intrigued to watch this video wondering if there would be themes of signal processing, reduced order modelling, numerical methods, etc. This just looks like a coding problem for a software engineer.
Thats the second biggest problem the first biggest problem is the ilusion of controll "you are your own bank " quantum decription cough oh sorry your money is gone....
