Essential Coding Skills for S&T Roles
Hi everyone,
I'm exploring opportunities in Sales and Trading and would like to enhance my coding skills to better align with industry requirements. Could anyone share insights on the specific coding skills and Python libraries that are most valuable in S&T roles? Additionally, I'd appreciate recommendations on the best ways to learn these skills independently, including any tools or paid resources that could expedite my learning.
Thanks in advance for your help!
75% of the code I wrote this summer on a rates vol desk was data fetching/cleaning logic lol. the "cool quanty stuff" is usually just one liners from scikit-learn, statmodels or scipy so like 5%. the other 20% was fidgeting with matplotlib and/or plotly to make it look pretty. if you have access, practice pulling data with blpapi-python. if you dont have bbg, yahoo finance will do and make a pretty chart. understand the basics of how a rest api works - basic http theory and shit. anything more advanced than that the quant team builds it. python is not a hard thing to learn, like with all things, you're just gonna need to put in the time
Any recommended reading/resources? STEM background but now in fundamental ER so out of practice. Want to grab some low hanging fruit since my team is v non technical
https://www.freecodecamp.org/news/how-to-use-rest-api/
Can you authenticate through that Bloomberg API or do you need to be logged into Bloomberg on your local machine for it to connect to the data?
to actually answer ur qs:
data fetching: requests, httpx, asyncio, boto3
data cleaning/wrangling: pandas/polars
database/sources: bbg, aws s3, kdq, arctic, ur banks own infra
math: numpy/scipy
regressions/pca: statmodels, scikit-learn
charting: matplotlib for static, plotly for interactive
if you havent taken an intro to cs class at ur school, sign up for one next semester. theres plenty of "intro to quant finance with python" github repos out there to learn from
learning all that might be a bit overkill if ur just an intern and/or not on a quanty desk. echoing the data fetching stuff but the desk will prob have some stuff already built out. also, knowing the ins/outs of ur analysis is more important than if ur able to shove it into a python library. so spend a good chunk of ur prep understanding the theory behind the statsmodels/sklearn models ur using ie the math.
What would you need to land a flow desk?
probs just regression but understand it well. know when to use ols vs tls/odr, know the assumptions ur making, know the data ur shoving in.
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