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Python for Algorithmic Trading Cookbook

Python for Algorithmic Trading Cookbook

By : Jason Strimpel
4.2 (19)
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Python for Algorithmic Trading Cookbook

Python for Algorithmic Trading Cookbook

4.2 (19)
By: Jason Strimpel

Overview of this book

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.
Table of Contents (16 chapters)
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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.

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