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Unlock promotions, career opportunities, and extra income with Python.
A complete system for getting started with Python for quant finance from scratch. No theory. No jargon. Just practical Python you can use.
File Size: 3.20 GB.
Jason Strimpel – Python for Quant Finance
You know Python can help in a lot of ways:
- Advance your career
- Earn passive income trading
- Improve your trading performance
You know to unlock these goals, you need Python for data, analysis, and trading.
So you took a $19 online course.
You learned how to build a tic-tac-toe game from someone that has never traded, done financial analysis, or even worked in finance.
Or more useless theory that doesn’t get you any closer to your goals.
I’ve been there.
If you’re new to Python, you probably start by googling “python tutorial.” Then you see the 533,000,000 results, scroll for a few seconds, then jump to the first paid ad you see.
You’ve read the blogs, watched YouTube, and taken all the courses.
But actually using Python for quant finance in real life (and not just for toy examples)?
That can seem like something other people figure out, not you.
Instead, you’re…
- Taking courses with no practical application, examples, or real-world projects
- Wasting time on one-size-fits-all tutorials focused on syntax—not quant finance
- Buying recorded courses that leave you with broken code, “magic solutions,” outdated libraries, and no one to help you
- Totally lost with where to focus your attention to get the concrete skills and experience you want
- Stressing out about actually applying what you learn so you can improve your job prospects (or quit your job altogether)
Sound about right?
It’s one thing to “learn Python.” But it’s a completely different thing to use Python for quant finance.
For many of us, getting started with Python is a mystery.
You know there is immense power at your finger tips, but you just can’t quite figure out how to go from theory to practice.
Modules, IDEs, swaps, options, Jupyter, functions, automation, backtesting, loops, classes, CVaR, Sharpe, list comprehensions, walk forward analysis…
People may as well be speaking another language.
Hi there, I’m Jason and I’m the creator of Getting Started With Python for Quant Finance.
I can translate that language for you.
I’ve been trading for over 20 years, a quant for 15, and a daily user of Python for 12.
In October 2022, I started helping other people learn how to put the three together.
Over the years, I’ve helped hundreds of finance professionals, developers, and complete beginners use Python for quant finance. I’ve done this through keynote talks, Meetups, Twitter threads, LinkedIn posts, in-depth articles, newsletters, and my course Getting Started With Python for Quant Finance.
But Getting Started With Python for Quant Finance is not actually a course.
It’s not theory.
It’s not jargon.
It’s not “Hello World”.
Included is an entire framework to get you started with Python for quant finance.
It’s a complete set of step-by-step, proven frameworks that gives you:
- 20 hours of recorded content across 20 modules and 134 lessons
- An experienced, hands-on instructor to guide you every step
- 1,000s of lines of quant code you can use to kick start your projects
- A 1,300+ strong community of like-minded beginners to crowd-source answers, code, and strategies
- A structured, step-by-step path to getting outcomes with Python
- Accountability and support to help you when you hit a speedbump
So what does all that actually mean?
It means that you won’t waste time learning Python you can’t use. It means you’ll get the skills for a new quant job or to start trading from home.
You get the same quant tools I used to analyze $20 billion of derivatives credit exposure, manage $100 million book of CVA, manage a global team of quant engineers, and trade stocks and options.
So if you’re struggling to get started with Python for quant finance, this course is for you.
Sound about right?
What’s Included
Everything you need to start using Python for quant finance, algorithmic trading, and market data analyis.
Inside you’ll find real-time answers, code to get you started, and hundreds of people for networking, sharing ideas, and accelerating your progress. To maximize your investment, you’ll also get video replays, a written course curriculum, and more than $4,500 of free bonuses.
PQN Pro
Support from 1.4K others like you
Get personalized answers fast, detailed code walkthroughs, strategy ideas, and help fixing code bugs. All from thousands of like-minded people.
Onboarding
Get ready for the course
Install the Python Quant Stack, download market data, and connect to Interactive Brokers—all with step-by-step instructions.
Module 1:
Getting the Python Basics Right
If you’re brand new to Python, you’ll fast-track your learning with exactly what you need to know—no overwhelm, no complexity.
Module 2:
The Python Quant Stack
Get familiar with the the most important Python libraries for algo trading and data analysis—Pandas—so you can work with market data.
Module 3:
Algorithmic Trading, Backtesting, and Strategy Formation
Yes! Retail traders can compete. Get a framework to form trading ideas, test them, and get them executed.
Module 4:
Treat Your Backtest Like an Experiment
Understand why most people get backtesting wrong—and the secret of avoiding losing money because of a backtest.
Module 5:
How to Engineer Alpha Factors With Python
Get the tools and techniques professional money managers use to manage portfolios and hedge away unwanted risk.
Module 6:
Prototyping and Optimizing Strategies with VectorBT
Get working code to run millions of simulations with the cutting-edge VectorBT backtesting library.
Module 7:
How to Backtest A Trading Strategy with Zipline Reloaded
Build factor pipelines to screen and sort a universe of 21,000+ equities to build and backtest real-life factor portfolios.
Module 8:
Risk and Performance Analysis with PyFolio and AlphaLens
Get the code to quickly asses strategy risk and performance—including factor performance—and assess alpha decay.
Module 9:
Automate Trade Execution with Python
Connect to your broker, download high-resolution market data, historical data, and automate your trades so you can get to trading, faster.
Module 10:
Double Down on Your Success With More Help and Support
Get expert guidance to take your experience to the next level. More strategies. More code. More support.
Also included: The Recipe Book
Code templates to get you started with algorithmic trading, portfolio optimization, and risk management.
In addition to the 12 modules and 134 lessons, written curriculum, and PQN Pro community, you get self-paced code “recipe.” Each one includes a 20-minute video walkthrough and is packed with code designed to get you started ASAP.
Code for portfolio risk and performance optimization
6 code templates and video walkthroughs to build the foundational risk and performance metrics for improving your trading performance.
Code to price options and derivatives with Python
4 code templates and video walkthroughs to price options and forecast implied volatility for trading edge.
Code to build factor portfolios and hedge beta
4 code templates and video walkthroughs to reduce risk and build portfolios that make money.
Code to automate your trading strategies
5 code templates and video walkthroughs to demonstrate an algo trading system you can modify for your own purposes.
Course Curriculum
Module # 1
Getting the Python Basics Right
We kick off with the very basics of Python. We cover primitive data types, data structures, control statements, functions, and classes. This is a practical but critical introduction to Python!
Module # 2
The Python Quant Stack
The most important library you’ll use is Pandas. You can use pandas for 80%+ of all work you’ll do in quant finance. In this module, we dive deep into several practical examples of using pandas for market data analysis.
Module # 3
Algorithmic Trading for Non-Professional Traders
The harsh truth is most people get algorithmic trading, backtesting, and strategy formation wrong. In this module, you’ll understand how non-professional investors can compete, how to backtest the right way, and the 8-step process for strategy formation.
Module # 4
Treat Your Backtest Like an Experiment
Most people think backtesting is all about optimizing input parameters to maximize profit. That’s exactly the wrong way to backtest. In this module, you’ll see how to statistically test a backtest and shift your framing of backtesting forever.
Module # 5
Prototyping and Optimizing Strategies with VectorBT
VectorBT is an advanced vector-based backtesting framework that simulates millions of strategies in seconds. In this module, we’ll analyze our example crack spread trade and optimize the entry and exit z-score signals.
Module # 6
How to Engineer Alpha Factors With Python
Most people have heard of alpha. Most people even have a concept of alpha. Few have the technical understanding of alpha. In this module, we’ll define alpha, discuss how to hedge beta to isolate it, and build alpha factors to capture it.
Module # 7
How to Backtest A Trading Strategy with Zipline Reloaded
Zipline Reloaded is the most robust event-based backtesting framework available. Zipline Reloaded is great for backtesting portfolio strategies based on alpha factors. In this module, we’ll use Zipline Reloaded to backtest an alpha factor.
Module # 8
Risk and Performance Analysis with Pyfolio and Alphalens
Risk and performance analysis is critical. Luckily for us, a suite of tools plays nice with the Zipline Reloaded backtesting framework. In this module, you’ll get intuition on how to use risk and performance metrics to improve your investing and trading.
Module # 9
Execute Trades on Interactive Brokers With Python
The last step of the algorithmic trading pipeline is executing trades. Unfortunately, it’s tricky to get right. In this module, we’ll build the basic scaffolding for a trading app using the Interactive Brokers API.
Module # 10
Course Wrap-Up and Next Steps
Whether you were writing code every day or missed a few, making it through the course is no easy feat. So, in this final module, we will recap everything we learned and discuss how you can take the next steps to continue your Python journey!
Get access to the entire program (for life)
Join 1,410 finance professionals, Python developers, traders, and complete beginners to use Python for data analysis, derivatives pricing, and algorithmic trading.
$1,000
All the guidance, code, and community support you need. Access for life.
Lifetime access to the PQN Pro community, code updates, code templates, and written course curriculum
Private community of 1,410 finance professionals, developers, and traders who use Python
12 modules, 134 lessons with examples, walkthroughs, and Q&A for students
Q&A to reinforce everything you’ve learned (and make sure nobody gets left behind)
Written curriculum to support all the modules and lessons
Over $4,500 worth of discounts, freebies, and partner deals
40+ Jupyter Notebook code templates with real quant code to help you jump-start your coding including 19 with video walkthroughs
Should you join? Here’s what I think…
Not everyone is right for Getting Started With Python for Quant Finance and I want to make sure I don’t waste your time.
You’ll love this course if:
You want to use Python for getting market data, analyzing the financial markets, backtesting, and automating trading
You’re sick of paying Udemy and Datacamp for courses that are irrelevant to your goals
You want a somewhat opinionated approach to installing Python, writing code, and using the Python Quant Stack
You’re brand new to Python, quant finance, or both
You realize that taking tutorial after tutorial does not guarantee success. You want to learn and adopt of framework that will make you successful using Python
You don’t have time to waste learning a programming language and want to know just want you need
You want step-by-step guidance and structure from someone who’s been in the industry for 23 years
You like specific, hands-on instruction and don’t have time for the fluff
You’ll want a refund if:
You’d prefer to learn the theory behind programming and quant finance and not actually apply anything in practice
You prefer “figuring it out yourself” with a plethora of lessons with no clear path
You’re hoping that buying a course like this will give you trading strategies that will print you money
You’re looking for another Python tutorial that will help you do things like print “Hello World” and the Fibonacci sequence to the screen
You don’t really need to use Python in your field and probably won’t anytime soon
You’re OK with using the tools you have (like Excel) and are unwilling to budge in the slightest.
You’re thinking this course will teach you fundamentals of computer science like memory management.
You want to use Python to brute force optimize backtests and data mine the market (a bit of an inside joke you’ll understand once you dig into the course!)
Hi! 👋 I’m Jason.
My name is Jason Strimpel and I’m the creator of Getting Started With Python for Quant Finance.
I traded my first stock and wrote my first line of code when I was 18.
Since then:
☀️ I traded professionally for a hedge fund and an energy derivatives trading firm in Chicago wracking up several millions of dollars in profit.
️☀️ I was a credit quant looking after $20 billion in credit exposure and managing $100 million of CVA exposure.
☀️ I managed a global, quant engineering team that built all the market risk analytics for a $7 billion derivatives trading business.
☀️ I built and led the data engineering and quant-analyst team for a $60 billion metals trading business.
☀️ I taught myself Python in 2012 to avoid spending $2,000 per year on a MATLAB license after finishing my master’s degree in quant finance.
I trade stocks and options in my free time using Python for data acquisition, automation, and execution.
Me and my son, Tucker.
My quant career has allowed me to live and work in 3 countries (the United States, England, and Singapore) and travel to 41.
I started PyQuant News in 2015 to share what I knew about Python for quant finance.
Nine years later, I’m still at it.
Course Features
- Lectures 0
- Quizzes 0
- Duration 10 weeks
- Skill level All levels
- Language English
- Students 98
- Assessments Yes