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Jason Strimpel

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

by Jason Strimpel (Author) Format: Paperback

(86)

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Harness the power of Python libraries to transform freely available financial market data into algorithmic trading strategies and deploy them into a live trading environment

Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free

Key Features

Follow practical Python recipes to acquire, visualize, and store market data for market research
Design, backtest, and evaluate the performance of trading strategies using professional techniques
Deploy trading strategies built in Python to a live trading environment with API connectivity

Book Description

Discover how Python has made algorithmic trading accessible to non-professionals with unparalleled expertise and practical insights from Jason Strimpel, founder of PyQuant News and a seasoned professional with global experience in trading and risk management. This book guides you through from the basics of quantitative finance and data acquisition to advanced stages of backtesting and live trading.

Detailed recipes will help you leverage the cutting-edge OpenBB SDK to gather freely available data for stocks, options, and futures, and build your own research environment using lightning-fast storage techniques like SQLite, HDF5, and ArcticDB. This book shows you how to use SciPy and statsmodels to identify alpha factors and hedge risk, and construct momentum and mean-reversion factors. You’ll optimize strategy parameters with walk-forward optimization using VectorBT and construct a production-ready backtest using Zipline Reloaded. Implementing all that you’ve learned, you’ll set up and deploy your algorithmic trading strategies in a live trading environment using the Interactive Brokers API, allowing you to stream tick-level data, submit orders, and retrieve portfolio details.

By the end of this algorithmic trading book, you'll not only have grasped the essential concepts but also the practical skills needed to implement and execute sophisticated trading strategies using Python.

What you will learn

Acquire and process freely available market data with the OpenBB Platform
Build a research environment and populate it with financial market data
Use machine learning to identify alpha factors and engineer them into signals
Use VectorBT to find strategy parameters using walk-forward optimization
Build production-ready backtests with Zipline Reloaded and evaluate factor performance
Set up the code framework to connect and send an order to Interactive Brokers

Who this book is for

Python for Algorithmic Trading Cookbook equips traders, investors, and Python developers with code to design, backtest, and deploy algorithmic trading strategies. You should have experience investing in the stock market, knowledge of Python data structures, and a basic understanding of using Python libraries like pandas. This book is also ideal for individuals with Python experience who are already active in the market or are aspiring to be.

Acquire Free Financial Market Data with Cutting-edge Python Libraries
Analyze and Transform Financial Market Data with pandas
Visualize Financial Market Data with Matplotlib, Seaborn, and Plotly Dash
Store Financial Market Data on Your Computer
Build Alpha Factors for Stock Portfolios
Vector-Based Backtesting with VectorBT
Event-Based Backtesting Factor Portfolios with Zipline Reloaded
Evaluate Factor Risk and Performance with Alphalens Reloaded
Assess Backtest Risk and Performance Metrics with Pyfolio
Set Up the Interactive Brokers Python API

(N.B. Please use the Read Sample option to see further chapters)

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ISBN-10

1835084702
ISBN-13

978-1835084700
Publisher

Packt Publishing
Publication date

August 16, 2024
Language

English
Dimensions

7.5 x 0.92 x 9.25 inches
Print length

412 pages

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Python for Finance Cookbook: Over 80 powerful recipes for effective financial data analysis, 2nd Edition

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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.

Editorial Reviews

Review

“Jason Strimpel's Python for Algorithmic Trading is an exceptional guide for anyone venturing into the world of algorithmic trading. The book’s practicality and hands-on examples, as well as emphasis on open-source tools and packages, stand out with clear, functional code samples that readers can immediately apply to real-world scenarios. The book spends needed time making sure readers are set-up to master skills like the open-source pandas library data manipulation. As the book progresses, Strimpel dives into deeper, denser topics, such as using linear regression to hedge "beta" (volatility), and then from there the books gets into truly advanced material. Overall, Python for Algorithmic Trading strikes a perfect balance between accessibility and technical depth, making it an invaluable resource for aspiring algorithmic traders.”

Rob Underwood, Chief Open Source Officer and Head of Open Source Governance, JPMorgan Chase

About the Author

Jason Strimpel is the founder of PyQuant News and co-founder of Trade Blotter. His career America, Europe, and Asia over the last 20+ years. He previously traded for a Chicago-based hedge fund, was a risk manager at JPMorgan, and managed production risk technology for an energy derivatives trading firm in London. In Singapore, he served as APAC CIO for an agricultural trading firm and built the data science team for a global metals trading firm. Jason holds degrees in finance and economics and a Master's in Capitalize Quantitative Finance from the Illinois Institute of Technology. He shares his expertise through the PyQuant Newsletter, social media, and teaches Getting Started With Python for Quant Finance.

Product details

Publisher ‏ : ‎ Packt Publishing
Publication date ‏ : ‎ August 16, 2024
Language ‏ : ‎ English
Print length ‏ : ‎ 412 pages
ISBN-10 ‏ : ‎ 1835084702
ISBN-13 ‏ : ‎ 978-1835084700
Item Weight ‏ : ‎ 1.53 pounds
Dimensions ‏ : ‎ 7.5 x 0.92 x 9.25 inches
Best Sellers Rank: #688,279 in Books (See Top 100 in Books)
- #97 in Financial Engineering (Books)
- #153 in Computer Algorithms
- #304 in Programming Algorithms
Customer Reviews:
(86)

About the author

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Jason Strimpel

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Jason Strimpel is the founder of PyQuant News and co-founder of Trade Blotter, with a career spanning over 20 years in trading, risk management, and data science.

He previously traded for a Chicago-based hedge fund, served as a risk manager at JPMorgan, and managed production risk technology for an energy derivatives trading firm in London. In Singapore, Jason served as the APAC CIO for an agricultural trading firm and built the data science team for a global metals trading firm. He holds degrees in finance and economics and a Master’s in quantitative finance from the Illinois Institute of Technology.

His career has taken him across America, Europe, and Asia. Jason shares his expertise through the PyQuant Newsletter, social media, and teaches the course "Getting Started With Python for Quant Finance."

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