Buy Python for Algorithmic Trading Cookbook: Recipes for designing, building, and deploying algorithmic trading strategies with Python

Python for Algorithmic Trading Cookbook: Recipes for designing, building, and deploying algorithmic trading strategies with Python

Brand: Jason Strimpel

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From the Publisher

How has your experience helped you to write this book?

I've been associated with the markets in some capacity for my entire 20-year career. I traded my first stock when I was 18 (24 years ago!) and started writing code around the same time. Early on, I saw how powerful programming was, and for some reason, it just captured my attention. Back then, we didn't have nearly as many tools, so I really had to figure it all out from the first principles. It ended up being a good bet, considering how computing has revolutionized trading and the markets in general. I did not get a computer science degree or study it in school. I'm entirely self-taught, so I had to become obsessed with marketing and programming (Python specifically) to use it effectively. I maintained that level of intensity for my entire career and brought all that experience to the book.

What part or parts of the book are your favorite, and why?

I'm really excited about the entire book, but if I had to pick one part, it's the backtesting section. Specifically, backtesting factor portfolios with Zipline Reloaded. I was an early user of Quantopian in 2012 and became very familiar with Zipline. I immediately saw how powerful it was and studied the code for years. After Zipline Reloaded was created, I started teaching people how to use it in my courses. It's the most powerful event-based backtesting software we have in open source. I love building new factor and relative value strategies. I love analyzing the results with tools like Pyfolio and Alphalense even more! I spent many years professionally and academically working on factor portfolios. It's amazing to see it all wrapped up in powerful Python libraries.

In what ways have you been active in the algorithmic trading community?

In 2015, I started PyQuant News to share Python and quantitative finance content. Since then, I've built a following of 122K people on Twitter, 55K people on LinkedIn, 27K subscribers to my twice-weekly newsletter, and 2,000 people on YouTube. I share examples of using Python in industry for people just getting started. In November 2022, I launched my course ‘Getting Started With Python for Quant Finance’. Since then, more than 1,300+ students have joined. More recently, I've been invited to podcasts and interviews to discuss my background and how people can get started using Python for quant finance. So far, I've published over 110 blogs about using Python for quant finance, dozens of long-form articles, and several hours of YouTube videos.

What makes this book better than other learning resources about algorithmic trading?

I wrote this book to bring readers on a journey. They start with the basic tools required for algorithmic trading, and they end by implementing a trading system. Since it's in a recipe format, readers can pick and choose the recipes that are relevant to their skill level. That combination is quite different from other books I've read. I also stay away from technical analysis. For most people, technical analysis does not work. Especially people new to algorithmic trading. Instead, I focus on trading edges that professionals use. The combination of end-to-end focus, recipe format, and industry-grade strategies is the biggest differentiator. I also introduce several powerful Python tools in the book, which have recently been introduced to the ecosystem: OpenBB Platform, VectorBT, and ArcticDB. These have not been written about together before. Using them will give readers an edge.