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.

Start Using Python for Quant Finance

Join 1,000+ alumni that are using Python to make money.

"This was the best course I have taken in my 28 year career in finance."

Zarko

Finance Professional

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Buying recorded courses that leave you with broken code, "magic solutions," outdated libraries, and no one to help you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.

Payment plan available.

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?

Payment plan available.

Nick

Active Trader

The course gave me the materials I needed with a mentor to guide me along the way to achieve my end goal of landing an active trader role. Jason gets straight to the point and avoids all the fluff that other courses use that wastes a tremendous amount of time.

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.

40 code templates

Lifetime access

12 modules

Written content

PQN Pro Community

Real-time support

PQN Pro

Support from 1,000+ others like you

Get personalized answers fast, detailed code walkthroughs, strategy ideas, and help fixing code bugs. All from thousands of like-minded people.

Screenshot of a large virtual meeting with dozens of participants, each shown in their own frame, smiling and gesturing to the camera, representing a diverse group of individuals from various backgrounds.

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.

Screenshot of Python code: It shows imports for pandas and a module from openbb_terminal.sdk. Variables are defined for storing stock and futures data paths with 'stocks.h5' and 'futures.h5' respectively, and setting up tickers for 'SPY' and root for 'ES'.

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.

Infographic displaying a matrix of categories and tools used in quantitative finance. Categories include Research Environments, Numerical Computing, Data Visualization, Machine Learning, Risk & Optimization, and Algo Trading, each paired with relevant tools like Jupyter, NumPy, pandas, and Interactive Brokers.

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.

Color-coded heatmap displaying the relationship between various bullish and bearish trading patterns. Each cell represents the correlation between patterns, ranging from dark purple (low correlation) to bright yellow (high correlation).

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.

Graph titled 'Strategy - Return Quantiles' displaying box plots for return distributions on daily, weekly, monthly, quarterly, and yearly intervals from January 2010 to August 2023. Each time period shows variability and median returns through box and whisker plots in different colors.

Module 6:

Prototyping and Optimizing Strategies with VectorBT

Get working code to run millions of simulations with the cutting-edge VectorBT backtesting library.

Data visualization slide featuring a histogram of total returns and box plots for different trading strategies, color-coded as SL, TS, and TP. The slide includes Python and Veles logos, emphasizing the use of these tools in analysis.

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.

Analytical visualization featuring a 3D color density plot to represent data dimensions, accompanied by a flowchart explaining the process flow in data analysis using Zipline library. The flowchart details steps involving data input, processing with functions like 'AverageDollarVolume' and 'MeanReversion', and output based on specific conditions.

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.

Financial analysis dashboard featuring a series of charts and graphs: Cumulative Returns vs. Benchmark, Distribution of Monthly Returns, Daily Active Returns, Rolling Beta to Benchmark, and Strategy's Worst 5 Drawdown Periods. Each chart provides detailed metrics comparing a strategy against a benchmark from 2010 to 2023.

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.

Screenshot of the Interactive Brokers trading platform displaying detailed market data for Facebook (FB) stock. The interface includes multiple panels showing the stock's bid and ask prices, a chart of 10-minute candlesticks, and various market indicators. Additional panels show real-time market news and a summary of other major stocks and currencies.

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.

Jason, participating in a virtual meeting, appears engaged and expressive, surrounded by bookshelves filled with various books in his home office

Payment plan available.

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 options trading

Code for portfolio management

Code for risk management

Code for algo trading

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.

Collection of finance graphs and charts presenting various metrics such as cumulative returns, volatility, Sharpe ratio, and drawdown periods over a span from 2010 to 2024.

Code to price options and derivatives with Python

4 code templates and video walkthroughs to price options and forecast implied volatility for trading edge.

3D visualization of implied volatility surface for AAPL stock, showing a mesh of blue lines with historical data points marked in red, on a dark grid background.

Code to build factor portfolios and hedge beta

4 code templates and video walkthroughs to reduce risk and build portfolios that make money.

3D scatter plot visualizing financial data points in various colors to represent different metrics, with arrows indicating trends. Adjacent is a table of performance metrics comparing a benchmark and a strategy.

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.

Screenshot of a trading platform with multiple sections displaying live stock prices, charts, and trading options. Icons for Python, Pandas, and NumPy libraries are shown, indicating their use in data analysis.

Payment plan available.

Nick

Trader

I've explored most of the quantitative finance courses out there and found this to be the most practical and efficient course currently available. If you want to quickly increase your confidence and productivity with Python applied to trading strategies then take this course.

Also included: $4,500 of free bonuses

Broker credits, partner deals, discounts, and data. All part of the course.

In addition to the video content, code templates, access to the PQN Pro community, and written course curriculum, you get over $4,500 worth of discounts, freebies, and partner deals.

Complex trading dashboard displaying multiple charts and data sets for S&P 500 futures, including price movements, volume indicators, and market indexes.

Up to $1,000 of IBKR

Get up to $1,000 in IBRK stock when you open and fund an Interactive Brokers account.

Diagram showing options trading strategies, including Long Call, Short Call, Long Put, and Short Put, with potential gains and losses detailed.

Free cheatsheet

Breakevens are the prices of the underlying where you start to make or lose money.

Cover of 'The 47-Page Ultimate Guide to Options Pricing Theory', featuring a bright yellow background with mathematical equations.

30% off theory ebook

Get the theory behind the Black-Scholes model, binomial trees, the Greeks, and implied volatility.

Icon of a money bag with a dollar sign, set against a gradient orange background.

50% off Python Foundations

If you need more help with Python, enjoy 50% off my new Python Foundations course.

Graphic illustrating thetadata's cloud network connected to data sources from Nasdaq, IEX, Cboe, and NYSE.

70% off options data

High-resolution, real-time, streaming equity and options data right to your Python API.

Advertisement for Trade Blotter showing a laptop screen with the app interface for managing investment risks and profits.

50% off access

Automatically analyze your trades to manage risk, monitor performance, and make more money.

Screenshot of data analysis software showing multiple charts, including a line graph tracking model predictions and bar charts of model accuracy over time.

QuantConnect access

Access to hedge-fund quality data and multi-asset backtesting on an integrated platform.

Collage of book covers about trading and investment strategies, including titles like 'How to Make Money in Stocks' and 'Algorithmic Trading'.

Reference library

Dozens of the best books on Python, markets, trading, and quant finance at your fingertips.

Rich

Hedge Fund Manager

Great course whether you're a beginner or an experienced Python user. The shared notebooks alone are worth multiples of the course cost. I signed up for round 3!

Course Curriculum

Module #

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 #

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 #

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 #

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 #

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 #

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 #

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 #

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 #

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 #

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!

Jeff Pancottine

Hedge Fund Trader

This was by far the best trading class I have ever taken.

Get access to the entire program (for life)

Join 1,000+ 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 1,000+ 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

Payment plan available.

Rey

Financial Analyst

I have no coding experience and no technical background. I went from no direction with Python to having a clear path in a month.

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!)

Nic P

Options Trader

I consider it one of the best investment decisions I have taken this year. Five weeks ago I didn't know what quant finance was and I only knew some basics of Python. Since I took the course, a new world has opened to me which will boost my options trading.

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.

Jason smiles at the camera with his young son riding on his shoulders in a playful home setting.

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.

Frank Zirnkilton

Investor

I learned much from this course about both Python and quant finance, all of which was dependent upon the incredible support provided by Jason individually and by the PQN community. Highly recommend.

Frequently Asked Questions

What if I run out of time to get everyone done?

You have lifetime access to the PQN Pro community, the self-paced modules, code notebooks, and written course curriculum. That way, you can follow along at your own pace!

I'm bad at math and feel completely out of my depth. Is this course for me?

You can go at your own pace. Plus you have 1,300+ people that can quickly help you debug your code, answer questions, and get you unstuck to keep learning.

How can I trust you?

I built a business on trust. I am public online and have tied my personal avatar on social media platforms (including LinkedIn where my posts are monitored) to the PyQuant News avatar. 1,000+ people have trusted me to get them outcomes with Python. You can too.

Why is your course so expensive when I can take the Udemy one for $19?

Here's the dirty secret of Udemy, Datacamp, Coursera, Udacity, Code Camp, and others: Their business model requires you to take course after course after course. So they teach the most general topics to the broadest audience possible to get the most users on their platform. Their incentives are not aligned with yours. Mine are: Get you outcomes with Python for quant finance. I lose if you can't use what you learn.

Does your code work in my market?

Things like conditional value at risk, factor investing, and options valuation work in all markets. The code templates are meant for you to modify as you need to!

Do I need an Interactive Brokers account?

No! Of the 40 code templates, 3 of them are specific to Interactive Brokers. 99% of the course is applicable to any broker.

I'm completely new to Python. Will this work for me?

It helps if you have some exposure to programming Python before taking the course. The first Live Session covers the basics of Python and the second Live Session covers the basics of pandas, but it ramps up quickly from there.

I'm an experienced software developer. Will this work for me?

Yes! I've had people in my course that were software engineers for 30 years. These folks can quickly use their experience for quant finance.

What if I'm not happy with the course? Can I get my money back?

I'm assuming that if you're considering buying Getting Started With Python for Quant Finance, then you likely have been following me on LinkedIn or Twitter. I don't run ads, so the chances that you've stumbled upon this product through some Facebook or Google advertisement is zero.

I've spent the last 5 years building a reputation online as someone who delivers the highest quality products. I have 1,300 students, a less than 0.1% refund rate, and over 100 5-star reviews with an aggregate score of 4.97 out of 5.

This course is packed with 20 hours worth of instructional content. It's the thing I wish existed when I started back in 2012.

This course is not for people who are on the fence. It's not for "tire kickers" or folks who are just "poking around."

It's for people who are familiar with my work, understand how important quality is to me, and want to put in the work necessary to see the results.

If that's not you, please don't buy the course. If you're just "curious" and want to watch a few lessons and quit, please don't buy the course.

If you're committed to the process and want to learn everything that I've learned over the last 15 years, there's zero chance you'll be disappointed.

Thank you for understanding.

What's the time commitment

Life is busy. I know you don't have time to work on Python all day. That's why this course is designed to be done in less than 1 hour per day.

Bobby G

Data Analyst

This course helped unlock outcomes because of the community aspect - people asking questions to each other and sharing their code. There were multiple circumstances where I got stuck and was able to pass through by asking questions and leveraging other people's code. The community is the biggest differentiator.

Ready to finally get started?

$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 1,000+ 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

Payment plan available.