Quant Marathon - Reviews

I have a master's degree in Computational Finance and Risk Management. I spent the bulk of my career on the buy side in Quantitative Research and Portfolio Management, and now I am in investment Risk management. So, running on short portfolios during market disasters and very large funds, I am well aware of the shortcomings of the mean-variance and normality assumptions in many of the traditional finance taught in business schools.

This program is really, from a very theoretical perspective and practical perspective, a vast resource of materials to really relax these assumptions that do not hold up when you need them to. It gives you some tools and theory of how to work with the world differently and address life with fat tails and more sophisticated techniques and algorithms to handle portfolio management, risk management, and research. So, a vast wealth of material in the program stands out and is also quite unified across the program. You can always go back to it, you see something once, and it is referred to a month or a year down the road again, so if you do not fully understand it the first time, I do not think that's too big of a deal.

There is a lot of support in the program like the others talked about; chat boards, live sessions, examples, code, and reading. The program is a lot of reading, but it is very coherent, and I think I became more familiar and comfortable with it over time. The Marathon works well, as a professional, I have a lot of responsibilities and work, it’s a nice pace to encourage me to keep going through the program, but not overwhelming at the same time. It’s not like the Bootcamp where you are trying to capture everything, there is more time to sit with the material, go back and do these examples. It's a tremendous resource and I think it has tremendous value for what's offered in the program.

I'm a VP at *******. My academic background is in Financial Mathematics, and I have been in various Risk and Quantitative Analytics roles at *******.

I have participated in the ARPM program for two years now. One of the things I most enjoyed about it, apart from obviously the very strong theoretical material, is the program's flexibility. It allows you to find the best way to interact with material depending on your preferences. You've got the Lab with all the information, then the live classroom sessions, and also a lot of opportunities for coding if that's something that you enjoy doing.

There is also a great opportunity to speak to participants via forum questions. I have been in a Cohort with other participants, which I enjoyed because it gives a sense of community and some tangible timelines around completing certain steps in the program.
Another thing also is that it was great that whenever my colleagues had any questions or feedback, it's answered very promptly and taken on board, so you feel supported.

There are lots of different options. Depending on your workload, you could change the way you participate. In a busy period, you could select an individual program, for example, so there's a lot of flexibility here.

My background is in computer science. I work in counterparty credit risk.
I heard about this streaming from one of my colleagues; he actually did it a few years ago, passed all the classes, and has a certificate on his LinkedIn. And when he told me, “Jiajie, you have to try this, I kept that in my mind.”

So as ***** employees, we have full access to the ARPM Lab, which means that we can study something from the previous week or a future week.

I joined ARPM one year ago, and it was a great activity during the lockdown.

There are two main advantages to ARPM: the first is plenty of [theory] materials; the second is the python code, you can play around in the environment.

Our team leader put study time on our calendar every Friday to ensure nobody forgets it.
And my line manager really cares about this as well, and we have catchup just to talk about what we have learned.

I have small notes about some main points to remind myself.

People from ARPM support us a lot as well. We have this kind of catchup frequently, and a lot of people ask questions in this chat session, and we can talk about our opinions, and you can even ask questions face to face.

I hope you enjoyed this as much as I do, and it is my pleasure to share my experience.

My background is PhD in statistics. I have been working in ***** credit risk for nearly 5 years now. My motivation for it was to improve my knowledge of financial mathematics, and as you touched earlier, the Lab is really broad in topics it covers.

What I do like about Quant Marathon is the video lectures.
Each page is accompanied by the 18-20 minute video lecture, which gives an overview of the points, looped through by one of the lecturers from ARPM.
This is a very good company for the longer material, so when you have a few minutes, you can view it on your phone, so you ca always stay in touch.
Obviously, repeat the viewing og these video lectures.

The second thing I would like to point out, which is useful, a minor thing, is the inline references to the equations. You can just hover over it, and it pops out.
That is very useful, if you need a quick reminder, and it pulls everything together.

My educational background is Bachelor in Computer Science and Master in Mathematical Trading and Finance. I am working in ***** analytics team, mainly focusing on data science and statistical modeling.

I choose the Quant marathon because I have never seen such a comprehensive set of theory and materials all in one place. The second is all the code in three different languages, Python, Matlab, and R.

ARPM has been very helpful for me in filling in the gaps in terms of mathematical or statistical concepts.

I graduated with an Applied Mathematics and Financial Engineering. I’ve been with ***** for more than two years.

There are many things I like about ARPM, but I think I’ll only mention a couple of them.
One is the enormous amount and the quality of material covered in the program.
The second point which I like is the Live classroom, the organization, and the structure of the classroom itself.

They cover topics that are relevant to the current industry practice and also the depth in which goes to. Interested readers can further dig in and understand the topics in more theory.
It encourages participation, where the team members within the class help each other, and I think this widens the understanding of different topics, given that the group is not homogenous, they have different ideas and you get an understanding from different points of view.

I studied Electrical Electronic Engineering, I’m from an engineering background, Analog and Digital Integrated Circuits, which is also a subset of Electrical Electronic Engineering.
I did my PhD research: I worked on computational finance topics, optimizing accuracy and acceleration of numerical methods for options pricing.
My current role now is in *****. I work in the global risk analytics team.
What I do is create models to forecast expected credit losses for key portfolios within *****.

I joined the ARPM course in February last year, 2021. It's been a very useful course within my team and within *****.

What I really like about the course is the bottom-up theoretical coverage of the topic from the first principles.
Any models you've heard about that you haven't actually might not have used within your own team, you have an opportunity to learn how these models are created right from scratch, for example, the risk identification, what are the variables that fit into this model depending on what model you're looking at.

The video lectures are very good. You have those at your fingertips 24/7.
You can watch the videos at any time at your own pace.
That's something I really like about the course.
I'm sure I've watched some videos several times, not just once.
When you have an opening topic, you can go back and watch the videos as you look through the theory and connect the two, and it's very good.
I've really found that some of the theoretical coverage, once you go through the video, everything just falls in place and it's really clear.

Another thing I like about this course is that it really covers a very broad range of quantitative skills, both on the buy side, sell-side, even insurance side, you can really gain from the modeling approach using Data Science and Quant finance.
It covers very broad topics, if you have any misgivings on what quant finance is, what do people do in credit, what do people do in loss forecasting, and things like that, you get full coverage, this is what I've seen here.

I have seen topics and models I have covered before on my side and topics of models I work with now. I think that's a very good thing.

Another thing I really like about this course is the Python code base.
For every theoretical aspect you cover, there is a corresponding code base to go with the definition.
You don't just learn the theory, you know how to implement it, you have exact specifications of how you implement this model.
Packages within Python call and code functions are already prewritten for you as well.

I'm a senior data scientist at *****. I have a PhD in physics from Yale University. Before joining *****, I was a quant researcher for two investment banks in New York City.

I've been taking ARPM classes for over one year now. I really enjoy taking these classes.
I find them very intellectually stimulating. Classes cover many interesting topics, and if you want to read more about these topics, the ARPM course has a great list of references to original articles and textbooks, which I also like.

Another aspect of this course that I really like is that it has a good mixture of theory and practice.
The course has a good set of Python programs. You can go through them, try them, and by doing so you can grasp theoretical concepts much faster. That's what I usually do: I go through the code and then go back to the theory part and read it completely.

I think this course is very useful and intellectually stimulating, so I definitely recommend it.

I have attended the ARPM Marathon course which I started in 2019. The course was spread out across 4 different areas: data science in finance, financial engineering, quantitative portfolio management and quantitative risk management. The program offers a plethora of sources to access and consolidate knowledge across the modules. There is the online (live) classroom where discussions with lecturers and other participants take place; the video lectures that provide guided overviews of the topics covered during that week; the graded assignments which assess participants’ understanding on various levels (both theoretical and practical). The platform also allows access to the forum where you can post ( and reply to any question and you will have full access to the ARPM Lab. Focusing on the last point (ARPM Lab) it consists of thousands of pages of theory properly organized, accompanied by practical case studies and data animations, but most important thing is that everything is reproducible through the code (predominantly Python based) easily accessible and deeply documented (pseudocode and comments line by line with references to the theory section). It is also possible to create your own Python scripts in Jupyter environment. All these sources are coherent, consistent and linked among each other. Attilio, his team and the various speakers are really world-class and they offer nuanced and in-depth analysis of all the topics covered in the course, which is permeated by a considerably strong academic emphasis: that offers the opportunity to explore and digest the peculiarities behind each and any section while also providing a solid commercial edge thanks to the high number of practical examples available. Overall, I highly recommend participating to the ARPM Marathon to anybody keen on a career in quantitative finance or to any finance professional willing to obtain a stronger quantitative knowledge.

I did the Marathon in 2018. I believe I was in the first or one of the first cohorts to finish the Marathon. There were some minor technical and organizational glitches, nothing serious. The program was nicely structured with live instructors. There was some possibility for (online) networking with other students and of course access to the lecturers for discussing the topics and homework. The program was demanding, with a lot of sessions and homework. You must keep this in mind, especially if you have a full-time job. The exercises were done in Matlab/Python. I used the Marathon also as an introduction to Python. A small negative experience here: there was an introductory Python course and the lecturer was hard to understand. That was an exception - although most of the lecturers were non-native speakers, I usually had no problem understanding them. I guess in principle you could learn most of the stuff by yourself by going over Attiio’s book (Risk and Asset Management). You would miss some of the more modern parts (machine learning etc), access to the code, and lecturers. Also, it pushes you to stay at it – I don’t think I would be able to bring myself to go over the stuff in this intensity. The Marathon fees were paid by the company I work at. For me, it was also possible to do the sessions/homework partly at the office (all the lectures were live, but maybe this changed meanwhile). So, to answer the question, yes, given my remarks above it was worth it for me to do the Marathon.

I had taken one course of the quant marathon back in 2019 and would say it's completely worth it. I would have taken the whole suite of courses if I had more time. One aspect I really enjoyed was the zoom sessions with breakout rooms. They both helped from a learning perspective, as well as providing a sense of being in a “classroom” with other people. The material was very interesting. The contents are quite complementary to what you would find in standard finance textbooks. I have a master's degree in mathematical engineering and have taken courses in financial maths. Despite being familiar with some aspects of the material, there were still a lot of topics that I could dig into more. The online forum is especially useful in case you have any follow-up questions regarding the material in the lab you have not understood well. I personally would have preferred having a textbook instead of the lab, as I am more used to studying with physical objects instead of online material, but maybe that’s because of my age. However, just having the material online was not an issue at all. So overall, yes, go for it, it's completely worth it!

I have participated in the first two courses of the Marathon; Data Science for Finance and Financial Engineering. The reading material organized in the lab is self-contained and very accessible with cross-references that make it easy to navigate around, so you don’t need any old books for support. The downside (if you don’t buy unlimited access to the lab) is that after finishing the course, you won’t have access anymore so if you don’t have a photographic memory, take good notes. There is an extensive code library with code underlying examples, figures, and exercises which I found extremely helpful since I also used the courses for getting started programming in Python. Every week there is homework that is reviewed and graded if submitted. I found that the level of these exercises varied a lot – some weeks I had to spend a lot of time while on other weeks the questions were at a level much lower than the material taught. I definitely found it worthwhile to participate but be aware that it is quite time-consuming because the material covered is rather extensive, especially for the Data Science course. I also followed the math and Python refresher, but in my opinion, that was at a much lower level than the actual courses.

I took the ARPM Quant Marathon program last year and I can tell that it was totally worth it. It was a wild (and tough) ride but I enjoyed it. The thing I liked the most was the large amount of topics covered in the Lab (where you have all the study materials). Every aspect was detailed and well explained and I really appreciated that every “chapter” was filled with examples and code snippets to test (mainly on Python); this comes handy if you are keen on coding and learn better by examples than reading theory. Another interesting aspect was the Q&A forum where students helped each other and shared their thoughts; this was really useful in my experience because the personnel inside ARPM provided clarifications and answers to the most technical doubts I had. Since I took the Quant Marathon program, I have noticed that they have broadened their offer giving more flexibility to the students in terms of courses to follow. At that time we had four courses to follow but now there are seven. In terms of how the program is structured, most of the material revolves around the concept of the “Checklist”: a simple set of steps from data management and handling to financial engineering/modelling to portfolio risk/return management. It helped me a lot to have a guideline to follow gluing together all the material in the Lab. This is by far the most complete program I ever took and I totally recommend it.

From my experience, I can tell the ARPM Quant Marathon is totally worth it and I definitely benefited from the program, which I attended in 2018. The program is built on the “Checklist”, which is the basic theory around which all the extensive program is coherently structured. The learning experience is very detail-oriented and based either on rigorous mathematical and statistical theory or practical applications through lines of codes, which are one of the things I value the most. Practical assignments are scheduled and graded every week. It is a great moment to put into full practice the theoretical concepts that you learn as you progress with the Marathon. Everything is well documented from theory to pseudo-code. The way the program courses are implemented gave me a unique and solid framework for data science, risk management, and portfolio management with lots of practical implementations to learn from. I also refined my coding skills, either in Python or Matlab, in numerous ways from writing pseudo-code to full implementation. Attilio Meucci is an absolutely terrific instructor and he is able to transfer his passion and vision of his work through his lectures. The program is complete and definitely brings value to your quantitative finance skills.

I signed up for the ARPM Quant Marathon program in the fall of 2020. I know some of the other contributors to this thread from the flipped classroom sessions that are part of the ARPM Quant Marathon. The ARPM Quant Marathon deals with both tough theory and how to bring the theory to use for a company’s investment and risk management activities. The math and the models in the courses close the gap between the purely theoretical approach of the mathematical academics (which is sometimes a bit rigid and far from reality) and the application-oriented approach chosen by some economists (that is sometimes a bit sloppy). The courses combine the best of both worlds. The central subject of the ARPM Quant Marathon is a framework that gives you guidance through all quant activities (the enumeration is not complete): Identify suitable risk drivers, choose a suitable mathematical model to describe the joint distribution of the risk drivers, estimate the model with historical data, project the risk drivers to your investment horizon, evaluate possible portfolios, construct and execute an optimal trading strategy. Here is one remark on the depth of mathematical formalism. If you don’t have the mathematical skills of a graduated mathematician, schedule a bit more time to penetrate the material. The math is really on the master level. Personally, I really enjoy the program and I would sign up again.

I have signed up for the ARPM Marathon program since Sept 2020 and I regard it as a highly valuable learning experience. The course week designs are well-balanced with extensive quantitative techniques. The first part focuses on data science for different financial instruments. After massaging the data, the second part concentrates on financial engineering, risk management, and portfolio management at aggregate levels. A big plus is the machine learning applications in finance, are up to date. I feel the examples and case studies are particularly supportive of understanding the modeling. The course structure provides concise and clear visions of quantitative techniques in financial markets. A new course week starts every Monday, the online classroom begins on Thursday, and on Sunday the homework will be handed in and graded by the tutors. Homework is divided into theoretical and empirical parts. I enjoyed the online class sessions very much. Some puzzling problems are solved easily during the discussion sessions meanwhile the tutors are well-informed and down to earth. Many classmates are industry practitioners, their opinions and views expand visions. A variety of codes for Python applications are ready, one of the top features for constant practices. The email reminding system really reminds you of the intensity of this program, time management is crucial, make sure you don’t skip any course week. My background is in economics. The understanding of econometrics is certainly helpful, but it took me time and effort to catch up with postgraduate-level mathematics and statistics. With diligence and determination, surely you will feel rewarded!

I’m currently enrolled in the Quant Marathon for 2020–2021 and have had an excellent overall experience. It isn’t easy and is not meant to be – so if you do decide to enroll, please prepare to put in the hours because that’s also the best way to maximize what you get out of the program. In terms of what’s useful, there is no shortage of things that you’ve seen or that you’ll come across in finance – from generating correlated random variables to understanding time series analysis to derivatives pricing and calibrating implied volatility surfaces. You will definitely find something that you’ll want to understand in greater depth and the ARPM Lab will have enough material to keep you occupied and satisfied. Speaking of examples, there is plenty of worked examples and code available in the ARPM Lab that you can work through line by line – I found this to be extremely helpful. Downsides – the math can be very involved, and in some cases, I found the material to be too technical for me. This is where I found the worked examples to be particularly useful – if I couldn’t grasp the theory entirely, I would at least be able to understand the application. I signed up for the 12-month Quant Marathon course and am nearing the halfway point of the older version of the program (the Data Science for Finance and Financial Engineering modules came first; I’ll be starting on Quantitative Risk and Portfolio Management in a few weeks). Truth be told, I’ve had a look at the newer program and I’d definitely benefit from the Data Science Mathematics module - the ARPM is ever-evolving and upgrading. I paid for this course out of pocket, and don’t regret it one bit. I’ll be signing up for continued access to the ARPM Lab as well once I complete the Marathon.

From first-hand experience, ARPM Quant Marathon is a very unique learning experience, even in an age of ever-expanding options for online training and courses. Really a work of vision and passion, rather than a conventional subject-based online course from a professional professor. Prior to enrolling in the ARPM marathon, I took Attilio’s risk management course at NYU Courant. I can see that he and his team have spent many years since the publication of his textbook (the basis for the Courant’s course) developing and evolving a grand unified theory, if you like, of risk management and portfolio analysis. A full coherent vision of quantitative financial risk management and portfolio optimization is presented, complete with extensive coverage of relevant quantitative foundations starting with the fundamentals such as linear algebra and functional analysis, all the way to more modern subjects such as machine learning. The consistency and coherence of the presentation (including notation) allow the various topics to re-enforce each other and connect in wonderful ways. Add recorded and live lectures, full implementation, and data illustrating selected case studies. fully documented libraries in python, R, and Matlab, active Q&A forums, and graded homework, and you end up with, as I said earlier, a really unique and valuable learning experience. I see more recently that ARPM has started to package the quantitative foundation's course of the ARPM program, which is substantial, as a separate data science offering. This would also be a worthwhile series of courses to take in its own right. The amount of material is quite extensive, and obviously what you will get will be proportional to the effort you put in. This is not an educational program you can enroll in casually, even if you come to it with relevant experience. My pet peeve regarding the program is that you couldn’t print any of the material out. As a die-hard paper and pen (and highlighter) learner, I found that challenging. So, in conclusion, yes, very much worth it.

Hi , I took the ARPM Quant Marathon in 2019, lasting one year and I can share with you my personal experience. First of all, I can confirm that it is really worth it. The fundamental aspect to take into consideration is that it provides you with a complete framework (a real toolbox) that allows you to gain a deep knowledge of each quantitative finance subject covered (Data Science, Financial Engineering, Risk Management, Portfolio Management, etc.), putting all the theory into practice. Indeed, the following are just some of the learning sources available for pursuing the Quant Marathon: you could join online (live) classroom discussions with lecturers and other students, you could watch video lectures, you could do the weekly homework (which will be graded), you will have access to the forum where you can post (and reply to) any question, you will have full access to the ARPM Lab. Focusing on the last point (ARPM Lab) it consists of thousands of pages of theory properly organized, accompanied by practical case studies and data animations, but most important thing is that everything is reproducible through the code (Python in particular) easily accessible and deeply documented (pseudocode and comments line by line with references to the theory section). You could also create your own Python scripts. All these sources are coherent, consistent, and linked to each other. This is to say that from a personal point of view you will gain a very distinct competitive hedge in terms of knowledge (and you could demonstrate it during the interviews and most important during your daily job). In addition, I can tell you that in the last few years the ARPM has become more and more known and spread (I also noticed this from my network of contacts on LinkedIn). The possibility to write in your CV that you had Attilio Meucci (well known both by academic people and practitioners) as a lecturer is an additional competitive edge (and this will increase the probability your CV will enter the shortlist of the interviewers). Finally, I want to stress the fact that this is a real master-level quantitative finance course and not only a certificate.