You're a finance professional. You know Python is eating your industry. And you still haven't learned it.

You've hit a ceiling. And you know it.

Five years ago, you could get by with Excel. A few thousand rows. Some pivot tables. Maybe a VLOOKUP that made you feel clever.

That world is gone.

Today, your colleagues are pulling millions of rows of market data through APIs. Junior analysts half your age are building models in Python that run in seconds — models that take you an afternoon in a spreadsheet. The job postings in your field now list "Python" as a requirement, not a nice-to-have.

You've noticed. Of course you have.

You've probably tried to learn, too. A YouTube tutorial here. A blog post there. Maybe you even started a Udemy course that taught you how to print "Hello World" and calculate Fibonacci numbers — and you thought, what does this have to do with my actual job?

So you stopped. And the gap kept widening.

Here's what nobody tells you about learning Python as a finance professional: the problem isn't Python. The problem is that nobody teaches it in a way that's relevant to your work.

Every free resource. Every generic bootcamp. Every 40-hour online course — they're all built for software engineers. They teach you computer science concepts you'll never use and skip the finance applications you need on day one.

That's the real frustration. Not that Python is hard. But that the path to learning it for finance is fragmented, contradictory, and full of dead ends.

Until now.

I built this course because it didn't exist when I needed it.

I've spent 20+ years in quantitative finance — trading derivatives, managing risk, and building analytics systems at J.P. Morgan, BP Trading, and Rio Tinto.

In 2012, I taught myself Python to avoid a $2,000/year MATLAB license. That decision changed my career.

Since then, I've used Python to trade stocks and options, manage $20 billion in credit exposure, lead quant teams for a $7 billion derivatives trading business, and build analytics infrastructure for a $60 billion metals trading firm.

In August 2024, I published the best-selling Python for Algorithmic Trading Cookbook with Packt Publishing.

Today, I run PyQuant News — a newsletter read by thousands of finance professionals learning Python — and co-founded Quant Science, where I build professional-grade algorithmic trading systems.

Python Foundations is the course I wish existed in 2012. Every section, every example, every code template comes from real-world finance applications. No filler. No computer science theory. Just the Python you'll actually use.

Everything you need to go from zero to writing Python for finance.

Python Foundations is 9 sections, 41 lessons, and 19 hours of instruction — all focused on finance applications. Here's what you'll walk away with.

Section 1: Orientation

What you'll know: Exactly how the course works, what to focus on first, and how to extract maximum value from every hour you invest. No wandering. No wasted time.

Section 2: The Python Tools

What you'll know: Which IDE to use (and why it matters), how the Anaconda distribution works, and the exact toolchain — VS Code, Jupyter, conda — that professional quants rely on. You'll stop second-guessing your setup.

Section 3: Setting Up Python

What you'll have: A fully installed, working Python environment with virtual environments and every library in the Python Quant Stack — configured correctly the first time. This is the section that saves you the 6-8 hours most beginners lose fighting installation errors.

Section 4: Python Code

What you'll master: The 80% of Python you'll use 100% of the time. Syntax, data structures, loops, control flow, functions, and classes — taught through finance-relevant examples, not toy problems. By the end of this section, you read and write Python with confidence.

Section 5: Pandas for Data

What you'll be able to do: Load, clean, transform, and analyze market data using the single most important library in quantitative finance. If you learn one thing from this course, this is it. Pandas is the tool that replaces your spreadsheet.

Bonus 1: Numerical Computing

What you'll have access to: Code walkthroughs and ready-to-use notebooks for NumPy, SciPy, Statsmodels, and Scikit-learn — the libraries that power everything from statistical analysis to machine learning in finance.

Bonus 2: Data Acquisition

What you'll be able to do: Pull real market data programmatically using the OpenBB Platform. No more copy-pasting from websites. No more stale CSVs. You'll build the habit of working with live data from day one.

Bonus 3: Pricing and Optimization

What you'll be able to do: Price derivatives with QuantLib and optimize portfolios with Riskfolio. These are production-grade tools used by working quants — and you'll have practical examples showing exactly how to use them.

Frequently Asked Questions