Market data industry insights with PyQuant News
At Databento, we're fortunate to work with customers and partners with insightful perspectives within the market data industry. Occasionally, we get the chance to chat with industry thought leaders and practitioners to get their insights on what's driving changes in the market data industry, what they're working on, and changes they predict on the horizon.
We spoke with Jason Strimpel, the Founder of PyQuant News, to gain insights on the current challenges in quant finance, advice on breaking into the industry, and why Python is the most popular language for algorithmic trading.
The barrier to entry into the quant finance industry is relatively high and needs more transparency for those just joining. When we asked Jason what he would tell individuals trying to break into the industry, he said, "Start with a single goal in mind. Get laser-focused on solving it. Learn only what you need to solve that specific goal. Backfill knowledge later." Having a goal in mind and learning relevant applications to solve it gives you direction and enough understanding to get started. As Jason mentioned, you can backfill knowledge as you're learning about the goal you have in mind, building a foundation for breaking into the industry.
On the other side of the quant finance industry, we were interested to know what challenges Jason has found. He noted, "People think they need to be trained programmers to use Python as a tool to achieve their goals." It's still possible to start learning Python for a career in quantitative finance. Whether you know a little bit or have no experience at all, educational courses like Jason's "Getting Started With Python for Quant Finance" can give you applicable knowledge needed to succeed in the industry.
For those early in their career, it can be unclear to know what to look for in a market data API provider. Jason kept it simple with his advice and mentioned that it should be: "Free." When you're just starting, it makes sense to put costs at the forefront of your learning. At Databento, we aim to address the accessibility of our users by providing $125 in free data credits to use on our historical data and offer a usage-based pricing model.
Python is widely used across the quant finance industry, and people often recommend it to those just joining. We asked Jason why he thought Python was the most popular language and if there were any drawbacks to using other commonly used languages for quant finance (C++, Rust, Java, etc): "User-friendly, English-like syntax, massive ecosystem, general purpose, dynamically typed, interactive. Drawback is it's dynamically type (e.g. potentially unsafe) and slow (GIL). C++ and Rust are more ubiquitous when speed matters."