#248 – January 28, 2018
This summary is an attempt to shed some light on modern quantitative trading since there is limited information available for people who are not already in the industry. Hopefully this is useful for students and candidates coming from outside the industry who are looking to understand what it’s like working for a quantitative trading firm. Job titles like “researcher” or “developer” don’t give a clear picture of the day-to-day roles and responsibilities at a trading firm. Most quantitative trading firms have converged on roughly the same basic organizational framework so this is a reasonably accurate description of the roles at any established quantitative trading firm.
I was looking for a tutorial/book that would teach me how to start to use FFmpeg as a library (a.k.a. libav) and then I found the "How to write a video player in less than 1k lines" tutorial. Unfortunately it was deprecated, so I decided to write this one.
Fellow Engineers: This is where your money comes from
My worldview is so far misaligned with the framing of these questions that it’s hard for me to engage with them directly. If you’re an engineer building a product or service, and you care about getting paid, I think the most important thing you should be thinking about is where does that money come from?
Coding Interviews and Snake (the game) Have This One Thing in Common
As much as we’d love to write a full article on Snake, this post is actually covering the latter, less glamorous, yet very important, sliding window technique, and answers questions like: Why do we, and other programmers, consider it a fundamental algorithm? Why does it comes up so often in coding interviews? How was it used in Snake, or other ‘real life’ applications? What are some common coding interview questions that can be answered (better) using the sliding window technique?
How to solve 90% of NLP problems: a step-by-step guide
Whether you are an established company or working to launch a new service, you can always leverage text data to validate, improve, and expand the functionalities of your product. The science of extracting meaning and learning from text data is an active topic of research called Natural Language Processing (NLP).