What is a quant? (explained by a quant developer)

Summary

In this engaging video, Coding Jesus, a quantitative developer, delves into the fascinating world of quants, or quantitative analysts. He explains what a quant does, the skills needed to become one, and the income they can earn. Coding Jesus distinguishes between his role as a quantitative developer and that of a quant, highlighting the collaborative nature of their work. The video covers both the technical and soft skills required for a quant, including mathematical proficiency, programming skills, trading concepts, and a trader's mentality. Codings Jesus also touches on the importance of innovation and being comfortable with failure in the dynamic world of trading. He finishes by providing insights into the potential earnings of quants and encourages viewers to engage with his content for more insights into the quant and trading professions.

Highlights

• Quants not only design models but trade with them too! 🎯
• Quant developers like Coding Jesus implement strategies created by quants. 🔍
• Python is a popular language among quants for backtesting and data analysis. 🐍
• Having a background in math or finance gives you a leg up in becoming a quant. 📚
• Adaptability and embracing failure are vital traits in the fast-paced trading world. ⚡

Key Takeaways

• Quants are key players in finance, blending math and trading. 📉
• Quantitative developers support quants by implementing trading models. 👨‍💻
• Technical skills for a quant include math, programming, and trading concepts. 🧮
• Soft skills like risk-taking and innovation mindset are crucial for success. 🚀
• Quants can earn between $125,000 and $500,000 annually, based on performance. 💰

Overview

Ever wondered who those sharp, number-crunching minds behind trading models are? Meet the quants! A quant, or quantitative analyst, combines mathematical expertise, programming proficiency, and trading know-how to create models that firms use to predict and profit from market movements. Not only do they focus on developing these models, but they also actively trade based on them, embodying the perfect blend of theory and practice.

Coding Jesus, a quantitative developer, shares his role in working with quants—implementing their sophisticated models into actionable strategies. Unlike quants, who are steeped in analysis and model creation, quantitative developers focus on the technological side, ensuring these models perform seamlessly in the real trading world. This collaborative effort is crucial for the success of any trading operation.

To become a quant, one needs a mix of technical and soft skills. Mastery in mathematics, familiarity with programming languages (like Python), and a solid grasp of trading concepts, such as option pricing models, form the backbone of a quant's skillset. Equally important are soft skills, including risk-taking, innovative thinking, and coping with pressure. Quants must be resilient, learning from failures to refine their models continuously. While challenging, the career path is rewarding both intellectually and financially, with quants earning lucrative salaries based on their contributions to a firm’s success.

Chapters

• 00:00 - 00:30: Introduction and Overview of Quants In this chapter titled 'Introduction and Overview of Quants', the speaker, known as Coding Jesus, introduces the concept of quants. He explains what a quant is, the requirements and skills needed to become one, and the potential earnings in this career path. Coding Jesus clarifies that he is a quantitative developer, which involves creating algorithms for automated trading and developing trading applications for risk management and trading facilitation at his firm.
• 00:30 - 01:00: What is a Quant? A quant, or quantitative analyst, is a professional who employs numerical and statistical methods to back test strategies aimed at generating profit for a firm. Their work extends beyond theory as they actively engage in trading, embodying the strategies they develop.
• 01:00 - 02:00: Role and Skills of a Quant The chapter explores the distinct roles and skills of quants and quantitative developers in a financial setting. Quants are primarily responsible for building and trading on financial models, focusing on data analysis, model creation, and backtesting. In contrast, quantitative developers are more akin to software engineers, collaborating with the quant team to implement the models and algorithms that the quants design.
• 02:00 - 03:00: Comparison: Quant vs. Quantitative Developer This chapter investigates the distinct roles of a Quant and a Quantitative Developer within a financial firm. It emphasizes that while Quants are responsible for creating, backtesting, testing, analyzing, and explaining profitable strategies based on historical data, Quant Developers are tasked with implementing these strategies. Quant Developers might not need extensive knowledge of advanced mathematical concepts used in model building, but they must understand them well enough to implement the strategies effectively.
• 03:00 - 06:00: Technical Skills Required for Quants The chapter titled 'Technical Skills Required for Quants' discusses the competencies needed to be a quantitative analyst, colloquially known as a 'quant.' It highlights both technical and soft skills but primarily focuses on the technical aspect. The speaker begins by pointing out the necessity of being comfortable with numbers, which is typically the first consideration when thinking about the role of a quant.
• 06:00 - 09:30: Soft Skills Required for Quants The chapter 'Soft Skills Required for Quants' emphasizes the importance of mathematical competence and data analysis skills for quants. Quants are expected to handle a large volume of data to build statistical and forecasting models. They need to create strategies, including back-tested strategies, which identify patterns and anomalies. The role requires a strong understanding of mathematical concepts such as kurtosis, skewness, and value at risk. These skills are crucial for successful data analysis and strategy development in a quant's work environment.
• 09:30 - 10:30: Quant's Income and Workflow This chapter discusses the work of quants, particularly focusing on the implementation and testing of models. These models often involve forecasting future financial events, like pricing options with a volatility model. Furthermore, the chapter delves into the educational backgrounds typical of quants, highlighting that they often have advanced degrees in mathematics or financial engineering. However, it notes that a strict mathematical background is not always necessary for quant roles.
• 10:30 - 11:30: Conclusion and Additional Resources This chapter focuses on what it takes to break into the field of quantitative analysis. It suggests that a background or work experience in data analytics can be beneficial. However, it emphasizes that having a strong foundation in math and finance is particularly advantageous, even though finance is not strictly necessary. The combination of skills in math and finance, along with interest, can significantly enhance one's application for a quantitative analyst role.

What is a quant? (explained by a quant developer) Transcription

• 00:00 - 00:30 what's up guys it's coding jesus and in today's video we're going to be taking a look at what a quant is what it takes to be a quant and how much money quants make now for those of you that are new to my channel i'm coding jesus i'm a quantitative developer not a quant i'm a quantitative developer we'll get to that in a sec but i write algorithms to interface with the exchange in an automated fashion and i also write trading applications for traders at the firm that i work at to display risk to allow them to trade etc etc all right guys so let's jump
• 00:30 - 01:00 right into it what is a quant what do they do what skills you need to become a quant and how much money do they make all right guys a quant is another way to describe a quantitative analyst it's the short form for quantitative analyst in a very general sense quants work with numbers to back test various strategies that will make money for the firm now quants aren't only theoretical in nature they don't only work with crunching numbers and data but they also trade themselves so they practice what they preach really
• 01:00 - 01:30 they build the models that will work that a quantitative developer like myself will implement and then they go ahead and trade based off those models as well right guys now that we understand kind of the general gist of a quant how's that different from a quantitative developer like myself well as a quantitative developer i'm more of a software engineer than a data cruncher model creator back tester analyst type right so what i do is i work with the quant team to implement the models and algorithms
• 01:30 - 02:00 that they come up with which have shown via historical data to generate profitable returns for the firm so they think of the strategy they backtest the strategy they test the strategy they analyze the strategy and i go ahead and implement the strategy now that doesn't mean that i need to know all the fancy mathematical concepts that they use when actually building out their models but that does mean that they need to be able to explain it to me and i need to be able to understand those mathematical concepts to actually implement it in whatever
• 02:00 - 02:30 language i'm using to implement that algorithm okay guys now that we understand the difference between quantitative analysts and quantitative software engineer like myself let's talk about what skills are required to be a quant or quantitative analyst there are technical skills and there are soft skills so i'm going to be speaking about the technical skills first there's a couple of technical skills guys but i guess the first thing that would come to mind when you hear the word quant quantitative the first thing that comes to mind is numbers you need to be very
• 02:30 - 03:00 comfortable with mathematical concepts with crunching numbers because your job will require analyzing a tremendous amount of data and building both statistical models and forecasting models for whatever your firm is working on right so you are responsible for creating those strategies that might be some sort of back-tested strategy that recognizes patterns and anomalies and you might need some sort of mathematical understanding of kurtosis skew value at risk etc to be able to
• 03:00 - 03:30 implement and test those models but it might also involve models that forecast the future or attempt to forecast the future that might be something like a volatility model to price options all right guys now that we understand the numbers component let's talk about the education component oftentimes quants come from an educational background that focuses on numbers math and finance that might be a master's in mathematics a masters in financial engineering etc etc now if you don't come from that mathematical background that's still
• 03:30 - 04:00 okay if you have a background or work experience in data analytics that might also help you in the quantitative field if you're looking to break into the quantitative field but it really helps you if you have some sort of background in math and finance right finance isn't a must but if you do have a background and especially if you have an interest in that then that goes a long way when you're applying to become a quantitative analyst okay the third technical skill you should have as a quantitative analyst or applying to become one is an
• 04:00 - 04:30 understanding of various trading concepts now that often involves option theory so understanding the various option pricing models black shoals bachelier et cetera understanding the advantages and disadvantages of those option pricing models goes a long way all right the fourth and probably one of the most critical skills you should have as a quantitative analyst is programming language experience now you're not going to be required to be a developer per se but you will be required to backtest
• 04:30 - 05:00 test analyze and crunch a lot of data using at least one programming language that might be r that might be matlab but oftentimes it's python yes there are c plus quants they are very rare and far in between but most use python for its simplicity the ability to chart data easily the ability to take in and process data and read data easily and just the ability to really iterate over your work easily the last technical skill that i think is very important to be a quant is computer usage you should be
• 05:00 - 05:30 comfortable with excel you should be very comfortable with the bloomberg terminal right so you should be comfortable interfacing with computers now i know i talked a lot about back testing programming analytics etc but quants aren't only theoretical they don't only make models hoping they'll work they actually trade based off their models so maybe half to two three fourths maybe two-thirds of their time they're focusing on building out models but one-third to maybe you know half of their time
• 05:30 - 06:00 they're actually spending trading so they will trade based off their models they will trade various portfolios of instruments maybe they might trade a given you know product group like natural gas or maybe they might trade a given another product group like treasuries but quants will go ahead and actually trade based off the models that they've built so they're not only theoretical they're not only hiding somewhere in a back room and drawing on a whiteboard but they're also implementing the models themselves via the practice of trading all right
• 06:00 - 06:30 guys now let's talk about the soft skills required to be a quantitative analyst the first soft skill is a trader's mentality or a trader's temperament often times like i said you're not only building out models implementing those models passing them on to quantitative developers but you are also trading yourself so you will be required to be able to work well under pressure and have the temperament of a trader to understand when to cut losses to understand when your strategy isn't working and when you need to iterate on that strategy
• 06:30 - 07:00 all right so the temperament is very important and oftentimes employers will give you maybe an iq test or even a psychometric test to see how you handle pressure and in an interview they might ask you extremely difficult questions not necessarily to see how you answer those questions but to see how you vocalize yourself how you think through problems and how you handle the pressure that they are forcing on top of you all right guys the second soft skill is risk taking abilities guys the markets are dynamic they're constantly changing so you're required
• 07:00 - 07:30 to think on your feet and be willing to take some risks even if it means generating small losses as long as you're able to get something out of those losses improve your models understand where you went wrong and then pivot that brings me to my third soft skill which is the innovative mindset oftentimes at a firm that you're working at especially at the firm that you know i'm working on as a quantitative software engineer you're going to see that there is a lot of various models you might have you know model a to model 20 model 100 and this might be a model for the exact same concept it might be a volatility
• 07:30 - 08:00 model it might be some other model that you know quants are developing but when you join these hedge funds in these high frequency trading firms you'll notice you know we're on model 154. why were you on model 154 and how is it different than what model 153 well model 154 is a slight improvement on model 153 so quants are required to not be anchored to what they've create but rather learn from any mistakes that their model generates be proud of the profits that it does
• 08:00 - 08:30 great for the firm but also innovate based off the mistakes that you've learned from the previous model when creating a new model all right guys and the very last thing that a quantitative analyst should be comfortable with is failure you should be comfortable admitting failure understanding where something didn't go well and this comes back to that innovative mindset which involves iterating on your failure to hopefully improve improve and prove to the point where you're succeeding on a constant basis
• 08:30 - 09:00 so be comfortable with failure because markets are always dynamic a trend that existed in the past three months during some given high volatility period might not exist in the next three months if volatility completely drops off so you need to be cognizant of that be comfortable with failure and understand that sometimes failure is expected because if you don't fail well you didn't learn anything and you're not iterating off what you've previously created of course you want to have certain risk mitigation tactics and and management skills in place
• 09:00 - 09:30 you don't want to lose everything and say you know i'm just going to iterate on it and i'll maybe i'll do better next time but you do want to be able to be comfortable with those small losses that small failure so you can iterate later all right guys in terms of what quantitative analysts make they're probably making between 125 and upwards of 500 000 a year and of course your bonus is dependent on how well your models have contributed to the bottom line of the firm whether that's your volatility models maybe your back testing back tested trading strategies whatever
• 09:30 - 10:00 your firm is actually going ahead to implement now in terms of the quants role in the process of what gets done i told you how i interface with the quant but this is the for this is the flow in which a quant interfaces with the exchange or so not with the exchange with the firm that he works at what a quant will do is he will generate the strategies generate the trading ideas generate the models for me like a quantitative developer to go ahead and implement i will implement those tested strategies and those tested strategies might be completely algorithmic so they might
• 10:00 - 10:30 interface with the exchange entirely with no intervention or they might be used by traders at the firm now those traders can be market making market making oriented traders directional traders they might be quantitative analysts themselves but they will then be passed on to the traders at the firm to go ahead and leverage when they're making trades on a day-to-day basis of course alongside various risk management tools etc etc all right guys if you liked the video make sure to give me a thumbs up subscribe to the channel for more i look forward to making another video about what a quantitative developer does
• 10:30 - 11:00 what's required of them on a day-to-day basis how to become a quantitative developer like myself and then another video on how to become a trader not a quantitative analyst which does some trading but to become a trader which focuses 90 of your time on actually trading based off models created and your own given trading knowledge and experience thanks for watching the video guys discord link in the description box below i have a calendly link in the description box below if you want to speak to me one-on-one and i also have a patreon link if you'd like to support me on patreon i have a
• 11:00 - 11:30 link in the description box below guys thanks for watching cheers