Відео

Hey Dimitri, really important question here. Is Pikachu still your favorite Pokémon?

Hi Dimirtri, Subscriber here. My background is in physics/software. I am 37 and I am wondering whether its too late to pursue a career as a Quant? Keep it up!

Where can i see the full video? Sounds really interesting

@dimitri Bianco , I am starting my MSC financial Mathematics this september from QMUL , I would like to be mentored by you throughout my career , Can i get your email-id so i can ask you a couple of questions? I promise i wont spam you.

Дмитрий Бьянко?)

There was this study that put people between the ages of 50 and 60 something into a program of a typical college student with few classes each week for 6 to 8 weeks. Their level of learning new materials picked up dramatically for this short period, they were not on the level of younger people(not far off either) but also younger people have been learning for 20 years straight. Fascinating study.

Should I put that I roomed with you on my resume? Do you think that would help or hurt my chances 😏

Most people we regard as creative geniuses in the arts and sciences created new ways of thinking and solving problems past the age of 25. Like you said, it’s about lifestyle.

Love reading technical academic books.

Hey Dimitri, I’ve watched a lot of your videos and you have helped me a lot in determining my career goals. I love math and really want to have a career in quant research/modeling. I was thinking about getting an MFE or masters in quantitative finance. I am currently going into my second year of undergrad and I am an applied math major and I was wondering if I should double major in applied math and statistics and possibly even minor in computer science or if it would be overkill and I should just focus on the applied math major. Thank you so much for all your help!

wth you cant just drop that your applying quantum principles to credit modeling and not explain 😭

As a 27yr old with a child and a wife, you have to use ur time wisely and with that time do exactly what you said you would. Otherwise you end up doing nothing.

never!

I applied for computational finance in UCL after completing CS in King’s College London, with a first class, got rejected though, devastated..

But the “fast” thinking of traders has to be acquired through experience. If you decided that you don’t have it early in your career, then you won’t have it

he says the data science side is toxic then proceeds to bash everything they do and insult them. Not the best way to prove your point

Economist working in data science here. Not a physics expert, but I remember from an Econometrics professor who also a physicist the debate re fundamental irreducible uncertainty vs in theory reducible uncertainty. My understanding is that Quantum mechanics revolutionised physics by saying that even in principle uncertainty cannot be reduced (by understanding all the factors, dynamic equations, observing everything) due to Heisenberg uncertainty principle etc...In contrast a lot of scientists probably think that in theory we could observe all factors and all dynamics, it's just very unlikely to ever happen with our cognitive and information acquisition constraints. What I think is relevant re both physics and cs people working in social science or business domains: They're trained in environments where theories and models have much better 'deterministic' explanatory and predictive power. I think this is especially so for cs people getting into data science through machine learning, and has been bugging me looking into data sci. There's a focus on finding a best fitting f(x) in outcome=f(x)+e, and point prediction/forecast when maybe the most interesting things in economics/finance happens in the random e. Standard machine learning also assumes the unknown e's are stat independent from the known x's, while in social sciences correlation between known x's and unknown e's is pervasive. Machine learning is now the dominant statistical branch of data science, with probabilistic causal inference in 2nd place. I think this helps explain the high reported failure rates of machine learning and data science in industry (e.g 85% project failure rate seems like almost a current rule of thumb). Too much focus on single value prediction of an outcome, without enough worry/concern about unknown factors and simultanaeity of multiple equations in a complex system. This might contribute to explaining why around 15 years of big data and data science in business have failed to yield systematic big productivity improvements in the economy (lots of investment and hype in these things, yet labour productivity growth has been quite low at e.g 1% or less in last 15 years->no big data/data science revolution).

Okay I might be biting off more than I can chew here. Did not understand a thing in this video lol. Will revisit in a year to see if I've learned as much as I plan to

162% returns you missed out on

is it okay to take master's degree that is not inclined to your bachelor's degree and find a job that is 'inclined' to your finished masterals?

First email could also simply be vibing. Sometimes I just randomly give compliments to people on the street. Compliments I genuinely mean, but I don't want anything out of it other than to give them the compliment and have the hope that it makes them feel a little better, just for the sake of feeling better.

All of your points are logical, but you only considered it from the perspective of the individual doing it full time as their primary source of income. I have a colleague who programs strategies that aren’t HFT algos but multi day swings at the shortest.

Hello Dimitri, I have 13+ years of experience as a prop trader(discretionary). Can you please guide me on how to advance my trading career. How can I make a shift from a discretionary approach to a quantitative approach? Is there any step by step approach that you would recommend to me?

Need more of these please Dimitri

How do you organize projects where different parts of the code require different virtual environments (due to version conflicts between packages).

3 more steps after validation of development code (validation of output, validation of implementation testing, ongoing performance monitoring)

Is it sill true for the MFE Master ? Did they got better in value, e.i if we did it from haas , NYU etc

Is there a consensus in the quant world whether MacOS or windows or Linux is the best operating system to use?

damn the world is hard... haha

Hi Dimitri. I'm about to start my thesis to finally complete my economics undergraduate. I've been following you over the last 2 years and you helped me to get interested in the topic of quantitative finance. That being said, I'm thinking to research the difference between traditional (Bachelier, Markowitz, Sharpe, Ross, etc...) and modern (AI and ML) methods applied to portfolio management, and try to compare some of their advantages and disadvantages. However, I would really thank you if you can give me an opinion on the topic, given that portfolio management offers a lot of different applications and problems to solve. Thanks a lot Dimitri, keep up the great work!

Hello Dimitri, I graduated from CS degree and about to start my masters in Quantitative Finance. I am reading J.Hull and P.Willmott books to familiarize myself with finance, but I have a feeling I need smth more practical. I’m also looking at quant internships in the US next Summer and application deadlines are approaching (July-September). What can I do to increase my chances? Will recruiters really check my GitHub? Do I need to write any personal projects that relate to quant finance?Thank you!

this is something no body talks about, the risk and cost of a one man army.

Amazing vid fam, getting there💪🏼

thirsty wannabes 😂. keep up the grind

This is such an interesting video! I just graduated with a Master's degree in Quantitative Finance and Actuarial Science from Tilburg University. While awaiting responses for my research master application and job applications, I have been exploring future seminar topics, which led me to the 2024 book "Quantitative Energy Finance" by Fred Espen Benth and Almut E. D. Veraart. I'm currently immersing myself in these models, aiming to address the complexities of energy markets through risk management. I firmly believe that being effective in risk management amidst competitiveness in these markets will ultimately benefit consumers.

Hey Mr. Bianco I'm on the path of getting a computer science engineer degree but I wanted to learn more about finance. I watched your 2019 video reading list but I wanted to ask you are there anymore books you would recommend to read to learn about finance? thank you and a informative video:)

noice

python -m venv env source /env/bin/activate pip install spyder which spyder /../..(path)/spyder would be a better approach because running anaconda and spyder together takes up so much RAM even on my arch linux

Wow really didn't know you could do this!! Certainly got me to think about switching to Anaconda. Love your vids! Please keep them coming❤

This was a really useful video! I'm pretty used to using R in undergrad, but now in my master's program we are using both python and R, so this is a big time help.

I'm in my senior year of high school, if I wanted to work in a hedge fund in the future should I major in business-finance or computer science? I want to major in computer science but I'm afraid that I won't have enough finance background knowledge, since my current background is just trading stocks. What are your views @Dimitri

PDM supremacy!!

Do you think python is good for doing time series analysis? Or would you recommend learning something else like SAS, SPSS etc