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, March Cohort

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Buying recorded courses that leave you with broken code, "magic solutions," outdated libraries, and no one to help you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

Reserve Your Spot for the July 21st Cohort

Limited seats available. Payment plan available. Money back guarantee.

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:

Engaging, live group sessions for real-time answers
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 1,000+ strong community of like-minded beginners to crowd-source answers, code, and strategies
Industry Speakers from firms like Man Group, SigTech, OpenBB, CrunchDAO, ThetaData, and QuantConnect so you can network with industry practitioners
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?

Reserve Your Spot for the July 21st Cohort

Limited seats available. Payment plan available. Money back guarantee.

Nick

Active Trader, November Cohort

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

10 live sessions

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 Week

Get ready for the course

Install the Python Quant Stack, download market data, and connect to Interactive Brokers—all in the first week. All with step-by-step instructions.

Live Session 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'.

Live Session 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.

Live Session 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.

Live Session 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).

Live Session 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.

Live Session 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.

Live Session 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.

Live Session 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.

Live Session 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.

Live Session 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

Reserve Your Spot for the July 21st Cohort

Limited seats available. Payment plan available. Money back guarantee.

Also included: The Recipe Book

Code templates to get you started with algorithmic trading, portfolio optimization, and risk management.

In addition to the Live sessions, 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.

Reserve Your Spot for the July 21st Cohort

Limited seats available. Payment plan available. Money back guarantee.

Nick

January Cohort

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 Live Sessions, 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.

30% off all cohorts

Join future cohorts of the course for new code, templates, speakers, and lectures for 30% off

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

January, March, May Cohorts

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 & Schedule

The cohort starts March 10.

All live sessions are recorded. The replay is available within an hour after the call. Access to the course content and community opens on March 10 at 8:00 am Eastern US time.

Live Session #

Getting the Python Basics Right

We kick off the cohort 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!