Have increasingly seen pod offers asking for python skills for junior equity analyst positions. Wonder if pod guys use python on daily basis and if you think mastering python is currently a must for a pod analyst.
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Bump. Curious what use cases would be (esp since MMs have data science teams / quants tp help pods)
Yes and Yes.
Can you share some use cases?
Quant job is go build algorithms, they are very pricey. Data scientist job is how to best create and make data easily usable and accessible.
An analyst’s job is to visualize and do the actual work.
Look up Rich Falk stuff on linkedin, pretty clear examples how of a pod analysts builds summary / models. Everything he builds could be better visualized and built in python (by the next gen of analysts). Beyond that lots of panda examples online as well.
A friend of mine has worked at 2 pod shops frequently discussed here for the last 5 years and no joke ~50% of his job is just coding in python to constantly tweak their models, granted he works in credit not equities so maybe there's a delta there to be aware of. I've asked him the same question about if all the pods at his shops have used it and he said there are almost always dedicated engineers working on the data side but having the skillset can only help you for both getting the role itself and performing on the job.
I don’t understand what is he using python for that he can’t use excel for — esp when not constantly working through massive alt data sets like in equities (this guys in credit)
What makes you say excel is the ideal tool for processing large data sets at scale? It can lag when I'm opening a sell-side model (granted my work computer is almost certainly not as impressive as what a HF is working with) vs at a HF he's dealing with waaaaay more data sets and running complex algos for various regressions and other analyses, all while having to take in real-time quote data. The other big thing about Python is you just have more control, you're not limited to what formulas excel already has and can just build whatever you need, plus the automation capabilities for dealing with variables at the margins when running at scale is just vastly better from what I understand.
It’s very useful, but not at all necessary or common for fundamental analysts. Things like scraping data and automating repetitive tasks and updates is a big boost to workflow in pods where time is $. I ended up self teaching just the basics as needed, when an important project I was working on simply got too big for excel and we didn’t want data science team to copy it for everyone else. That said, do not practice it at the expense of basic fundamental modeling to break in, if you want to be a fundamental analyst
How did you self teach? Please can you list the exact tools you used and how much time you spent on each?
I had big data I needed to collect, aggregate, transform, weight by other datasets and get slices below 1m rows so they could fit in my excel model. Because excel models are still ideal for the practical day to day role of fundamental analysis. I tried to put the data in Microsoft Access, but it didn’t work well. So I tried do it all in python, using Anaconda, Jupiter notebook, xlswriter and beautiful soup. To scrape tables and automate downloading and transforming and joining a bunch of csvs. It all was simple things that could be done in excel if there were less rows. Ultimately I had a bunch of sql queries that basically renamed and joined into one true lookup tables. The stuff I do is technically probably horrible, no indexing, I don’t know how to do anything beyond what I specifically need to do to practically forecast earnings. I’m not a programmer - I understand that the data guys are super proud of how much more optimal the code their visualizations of stuff that doesn’t matter to me forecasting / buying / selling stocks on fundamentals. Much respect for it, but more important to know what questions to ask the data and what should be done with info, than have the most technically great programming form - for my role that is
following
also interested. how did everyone learn? udemy, books, bootcamp, etc?
I’m still skeptical - if you really need this python to cut so much data is it really necessary to derive your fundamental thesis? Can’t you make the point using the 5% of the data that matters instead of giant python accessible models. If I think about it every thesis has a few points that can illustrated in a very simple few charts
Sure but how do you isolate that 5% of usable data from the original larger dataset? That was my main use case
Outside of general financial modeling, which Excel can be superior for just given standards/readability to non-technical users, Python is significantly better for most analyses. Want to look at the relationships between multiple time series - Python is faster and more robust; want to do some historical analyses of various trade structures, Python is faster and easier to scale when looking at a large universe of ideas. I would say it's a must have for any macro/FICC/vol trader.
If you are trading equities/credit based on fundamental analysis, I think you can get away without knowing any coding, but even then you'd probably find it very helpful. You have some alt data that you want to check if it's useful - how would you go about doing that in Excel? You are etrying to look for relationships between a set of forward looking indicators that you have access to and key output KPIs that drive stock performance in your universe - another situation where python is probably a better infra to do the analysis.
This doesn't mean you need to be an actual "developer" or take any CS classes in school. It doesn't matter whether you know about memory allocation or the difference between a stack and a queue. Learn basic pandas, numpy, and get used to googling when you're stuck, just like you did when you learned excel. It's very doable for someone with the basic STEM skills to get a job in finance.
1. How long do you think this basic training in Python would take?
2. What would be the best way to learn the aspects of Python that matter for the purposes of fundamental analysis?
3. How much easier does ChatGPT make it to write/modify the relevant Python code?
Work for a very small PE shop, but get very targeted solicitations from recruiters working on pod shops like Bridgewater, Milennium, Citadel, etc. (I did CS and finance undergrad and recruiters specifically mention that is the skill set for fundamental analysts these days).
I have no business being in those processes given my small PE/boutique background, semi-target background, if that, but funds are in a war on analytics talent.
Do you mind sharing what kinds of roles you get contacted for? Currently in a similar role and have a similar CS/Econ background as you and have always thought about a potential jump to quant-type roles. Do you get contacted for 2nd class / support type roles or quant researcher / data science roles at these places?
"pod shops like Bridgewater" - nice -_-
Calm down nerd, as you can clearly see, I don’t give a shit
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