Welcome back to Sean’s Syntax! Today I’m talking about tech stack for my algorithmic trading project. As I continue to work on this project I’m finding new tools to utilize. I’m going to cover my favorite and least favorite parts of my tech stack, aswell as one that was hard to learn but is worth it.
The best part of my tech stack: Python
Python is my go to tool because it’s simple, easy to read, and works for almost anything. We use it for backtesting strategies, optimizing performance, or analyzing data. Libraries like NumPy, Pandas, and backtesting.py are essential for working with financial data, and pymoo helps with optimization. There’s also a large community supporting the language, which makes learning and troubleshooting easier.
My least favorite part of the tech stack: SQL
SQL is useful but not my favorite. Writing queries, managing databases, and dealing with joins can feel tedious. Unlike Python, where I can be creative, SQL feels more rigid. Still, it’s necessary for handling large datasets, especially when working with historical market data. Even though I don’t love it, I know it’s important for organizing and retrieving financial data quickly.
Difficult to learn but well worth it: AWS
AWS was confusing at first. There are so many services, settings, and best practices. IAM roles, EC2, S3… a lot to learn. But once I got comfortable, I saw how powerful it was. AWS will make it easy to deploy and scale our trading algorithm application. With cloud-based execution, we can run backtests, store market data, and set up automated testing. Learning AWS has been very important to this project.