You're reading from Python for Algorithmic Trading Cookbook Recipes for designing, building, and deploying algorithmic trading strategies with Python

Product type Paperback

Published in Aug 2024

Publisher Packt

ISBN-13 9781835084700

Length 404 pages

Edition 1st Edition

Languages

Python

Tools

Zipline

Concepts

Data Analysis

Author (1):

Jason Strimpel

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Table of Contents (16) Chapters

Preface

1. Chapter 1: Acquire Free Financial Market Data with Cutting-Edge Python Libraries FREE CHAPTER

2. Chapter 2: Analyze and Transform Financial Market Data with pandas

3. Chapter 3: Visualize Financial Market Data with Matplotlib, Seaborn, and Plotly Dash

4. Chapter 4: Store Financial Market Data on Your Computer

5. Chapter 5: Build Alpha Factors for Stock Portfolios

6. Chapter 6: Vector-Based Backtesting with VectorBT

7. Chapter 7: Event-Based Backtesting Factor Portfolios with Zipline Reloaded

8. Chapter 8: Evaluate Factor Risk and Performance with Alphalens Reloaded

9. Chapter 9: Assess Backtest Risk and Performance Metrics with Pyfolio

10. Chapter 10: Set Up the Interactive Brokers Python API

11. Chapter 11: Manage Orders, Positions, and Portfolios with the IB API

12. Chapter 12: Deploy Strategies to a Live Environment

13. Chapter 13: Advanced Recipes for Market Data and Strategy Management

14. Index

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Preface

Algorithmic trading is the art of using statistical models, programming, and math to trade financial assets. With the vast volumes of data available in today’s markets, it is essential to have powerful tools at your disposal to remain competitive. In Python for Algorithmic Trading Cookbook, you’ll get code to design, backtest, and deploy your own algorithmic trading strategies with Python. Python is not only accessible and easy to learn, but also boasts thousands of powerful libraries that can help you implement sophisticated trading strategies.

Many resources available today cover basic trading strategies largely focused on technical analysis. Unfortunately, these strategies often fail over the long run. This book aims to bring professional techniques and tools to non-professionals through small, digestible recipes. These recipes will not only guide you in creating, testing, and deploying algorithmic trading strategies, but build a strong foundation in the tools and techniques to prepare for creating, testing, and deploying algorithmic trading strategies.

In this book, I will share insights and methodologies drawn from my 20+ years of experience in algorithmic trading and Python programming. I have taught the techniques presented in this book to over 1,000+ students in my course, Getting Started with Python for Quant Finance. The course has been praised for its practical application of Python to algorithmic trading. Whether you are a seasoned programmer looking to expand into trading or a trader aiming to enhance your technical skills, this book is built to provide you with the tools and knowledge you need.

With Python, you can easily access financial data, perform complex calculations, and backtest your models efficiently. This book will guide you through the entire process, from setting up your Python environment to implementing advanced trading algorithms.

By the end of this journey, you will have a robust toolkit to develop and refine your own trading strategies. As the financial markets evolve, so too must our approaches. With the right knowledge and tools, you can navigate these changes and find opportunities that others might miss.