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Jason Strimpel – Python for Quant Finance

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Jason Strimpel – Python for Quant Finance

Jason Strimpel - Python for Quant Finance

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.

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.

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  • Buying recorded courses that leave you with broken code, “magic solutions,” outdated libraries, and no one to help you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?

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.

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.

Title slide from a presentation: 'How to Build Trading Strategies: Step-by-Step', branded with the PyQuant News logo in the corner.

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

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!

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.
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.

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