Coding in discretionary AM
I was wondering what sort of ways one can apply their programming skills in the AM industry. I'm not exactly interested in applications for quantitative portfolio management, as it is self-evident what kind of skills this would require.
I'm more so interested to know what examples of automating processes with the use of programming languages other people may have come across? I can imagine that making certain SQL queries on a regular basis can be automated with a simple python script, but what else have others been doing?
Also, have you seen an increased demand for analysts with moderate programming abilities, or do you feel that these types of candidates have better cards in an application process than their competitors with no working knowledge?
I'm still early-ish on in my career, but basic to intermediate programming skill seems to be a skillset that is increasingly desired by employers in Asset Management. Most roles I've looked at have an understanding of Python/R as a desired trait / plus.
In terms of utilization on the job, there's a huge amount of functionality that you can get out of Python/R and SQL that you simply can't get from Excel, even for those working in traditional roles. I was recently involved in a project building a data pipeline that extracted and cleaned market data from an API using Python/SQL, before it was ingested into PowerBI and presented to PMs for analysis. These kind of projects are huge value adds, in large part because your average PM in their 40s/50s with a non-technical background doesn't have the time to go and learn how to program.
I went out of my way to learn these skills over the past 18 months and they've given me a huge leg up professionally. If you have the mind for it (and frankly I don't think learning basic python is that hard), learning a bit of programming is one of the best ROI projects you can work on.
My use cases:
I know and use or have used SQL, SAS, Python, R, Excel, etc.
IMO it's very useful to know at least SQL, but adding Python or R will just save you a ton of time with the ability to automate your work or through raw processing power.
I don't think we are quite at the point where non-quant programming will open doors in finance, but I can earnestly say if your skills are legit you will definitely gain the respect of your coworkers/boss.
Edit: I have to add that I generally dislike using Excel for the following issues that languages like Python or R don't have. Excel crashes far too often, and the memory limit is laughable. Usually the result of pushing it's capabilities to the limit and expecting the entire organization to be able to use it. Different versions of Excel mean some will have functions that others will not (Looking at you XLOOPUP and SEQUENCE). Final nail in the coffin is that Excel is not reproducible, ironic since we are managing billions of dollars of other peoples money and less mistake prone alternatives are available.
@whatsgucci Thanks for your insights on specific tasks. Regarding the production level PDFs, are you referring to producing materials for internal stakeholders, clients or both? I guess what I'm interested to know is whether you just built the infrastructure to generate a PDF for the internal Investment Committee (or something similar) on demand, or whether the quality is of a level that your organization considers sending out the materials to clients. What would be some examples of use cases?
I've been considering learning how to create web-hosted portfolio-reporting dashboards using python. I guess taking the next step and learning how to generate PDFs from there on makes a lot of sense.
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