Description
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.
Included is an entire framework to get you started with Python for quant finance.
Itâs 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+Â 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
Whatâs Included
Everything you need to start using Python for quant finance, algorithmic trading, and market data analyis.
Inside youâll 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, youâll 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 Brokersâall with step-by-step instructions.
Module 1:
Getting the Python Basics Right
If youâre brand new to Python, youâll fast-track your learning with exactly what you need to knowâno overwhelm, no complexity.
Module 2:
The Python Quant Stack
Get familiar with the the most important Python libraries for algo trading and data analysisâPandasâso 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 wrongâand 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 performanceâincluding factor performanceâand 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 ârecipe.â Each one includes a 20-minute video walkthrough and is packed with code designed to get you started ASAP.
Course Curriculum
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!
The Python Quant Stack
The most important library youâll use is Pandas. You can use pandas for 80%+ of all work youâll do in quant finance. In this module, we dive deep into several practical examples of using pandas for market data analysis.
Algorithmic Trading for Non-Professional Traders
The harsh truth is most people get algorithmic trading, backtesting, and strategy formation wrong. In this module, youâll understand how non-professional investors can compete, how to backtest the right way, and the 8-step process for strategy formation.
Treat Your Backtest Like an Experiment
Most people think backtesting is all about optimizing input parameters to maximize profit. Thatâs exactly the wrong way to backtest. In this module, youâll see how to statistically test a backtest and shift your framing of backtesting forever.
Prototyping and Optimizing Strategies with VectorBT
VectorBT is an advanced vector-based backtesting framework that simulates millions of strategies in seconds. In this module, weâll analyze our example crack spread trade and optimize the entry and exit z-score signals.
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, weâll define alpha, discuss how to hedge beta to isolate it, and build alpha factors to capture it.
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, weâll use Zipline Reloaded to backtest an alpha factor.
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, youâll get intuition on how to use risk and performance metrics to improve your investing and trading.
Execute Trades on Interactive Brokers With Python
The last step of the algorithmic trading pipeline is executing trades. Unfortunately, itâs tricky to get right. In this module, weâll build the basic scaffolding for a trading app using the Interactive Brokers API.
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.
Should you join? Hereâs what I thinkâŚ
Not everyone is right for Getting Started With Python for Quant Finance and I want to make sure I donât waste your time.
Youâll love this course if:
You want to use Python for getting market data, analyzing the financial markets, backtesting, and automating trading
Youâre 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
Youâre 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 donât 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 whoâs been in the industry for 23 years
You like specific, hands-on instruction and donât have time for the fluff
Youâll want a refund if:
Youâd prefer to learn the theory behind programming and quant finance and not actually apply anything in practice
You prefer âfiguring it out yourselfâ with a plethora of lessons with no clear path
Youâre hoping that buying a course like this will give you trading strategies that will print you money
Youâre looking for another Python tutorial that will help you do things like print âHello Worldâ and the Fibonacci sequence to the screen
You donât really need to use Python in your field and probably wonât anytime soon
Youâre OK with using the tools you have (like Excel) and are unwilling to budge in the slightest.
Youâre 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 youâll understand once you dig into the course!)
Get Digital Download ” Jason Strimpel – Python for Quant Finance ” Right Now!
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