Python Skills for Structured Products SnT or Research (RMBS, CMBS, CMO, CLOs, etc.)
Hi all,
I'm currently preparing myself to move into a structured products Sales & Trading or Research role, specifically focusing on instruments like CMBS, RMBS, CLOs, CMOs, and other ABS. I've noticed that coding skills, particularly Python and potentially other programming languages, have become increasingly valued in these areas, especially for modeling, data analysis, trading automation, surveillance, and day-to-day workflow efficiency.
For those currently in structured products (SnT or Research), could you share your insights on the most practical or valuable coding skills and libraries that you use regularly on the desk?
Specifically:
- Which coding skills or techniques (e.g., pandas data manipulation, NumPy array operations, cash flow modeling, Monte Carlo simulations, VBA, SQL querying, etc.) do you consider critical for structured products roles?
- How do these skills typically fit into your day-to-day workflow?
- What specific libraries, frameworks, or tools (e.g., pandas, NumPy, SciPy, Pyfolio, QuantLib, SQL, VBA, or any proprietary tools) do you most frequently use?
- Can you suggest any resources, courses, or projects that effectively prepare someone aiming to develop intuitive and practical coding skills tailored specifically for structured finance?
Any personal insights or tips on effectively building relevant coding expertise before entering a structured products desk would be greatly appreciated!
To excel in structured products Sales & Trading (SnT) or Research roles, particularly for instruments like CMBS, RMBS, CLOs, and CMOs, Python and other technical skills are indeed becoming essential. Here's a breakdown based on the most helpful WSO content:
1. Critical Coding Skills and Techniques
2. Day-to-Day Workflow Integration
3. Key Libraries, Frameworks, and Tools
4. Resources and Projects for Preparation
5. Tips for Building Expertise
By focusing on these skills and resources, you'll be well-prepared to contribute effectively to a structured products desk.
Sources: Programming/Technical Skills for Finance: SQL and Python, Programming/Technical Skills for Finance: SQL and Python, WSO Python / Machine Learning Courses - NOW AVAILABLE, Using Python to Automate tasks in ER/IB, 0 to pseudo quant real quick - analytical skills for juniors with finance background
Bump
You need to be pretty proficient relative to the other juniors on other desks
Great name - but proficient /in what/?
bump
Bumping this as well. Would also be helpful to know if its possible to break into a structured trading desk (CLOs, CMBS) from a non tech background (econ major at target) if I learn python outside of class.
Depends on role. In S&T I’d expect you’re doing data wrangling and process automation. For Python think more so Pandas and NumPy vs some of the other more “quant” tools you mentioned.
SQL is key for archiving data regardless of role (but pretty easy to learn).
VBA is increasingly used less, but still can be the best solution for certain automation tasks within Outlook/Excel. Plus keep in mind that as an entry level analyst you’ll initially be put on existing processes, so if it’s in VBA you’ll still be expected to figure it out.
You’ll also be using a lot of API/wrappers like Intex and Bloomberg to scrape data, model cash flows, and run analytics. But I don’t think you can practice much of that without a license. Unless you’re covering a very esoteric product I wouldn’t expect to do a ton of cash flow modeling from scratch.
Overall if you want to practice I’d suggest focusing on analysis of large datasets. You’ll likely be dealing with a lot of loan level data (deals can have thousands of loans) or at least a lot of cusips. A lot of stratification and turning large datasets into a digestible summary for more senior guys to make decisions on. And maybe try setting up an automated email or report that goes out based on the results of your code.
If you end up in a quant seat you might be working on some more advanced libraries, but at the start I think it’s unlikely you’re expected to write those kind of scripts from scratch vs running/debugging existing code.
using chatgpt to code... especially for matplotlib or plotly
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