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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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Who this book is for

Traders, investors, and Python developers can gain practical insights into designing, backtesting, and deploying algorithmic trading strategies from this book. The three main personas who are the target audience of this content are as follows:

Active traders and investors: Individuals who are already investing in the stock market and want to leverage algorithmic strategies to enhance their trading performance. They will learn to use Python to develop, test, and implement advanced trading models, including acquiring and processing freely available market data with OpenBB and building a research environment populated with financial market data.

Python developers with market interest: Developers with a solid understanding of Python data structures and libraries, such as pandas, who are looking to apply their programming skills to the financial markets. This book will help them bridge the gap between coding and trading by providing practical recipes and techniques used in algorithmic trading. They will learn to identify alpha factors, engineer them into signals, and use VectorBT for walk-forward optimization to find strategy parameters.

Aspiring algorithmic traders: For those who aspire to enter the field of algorithmic trading and have basic experience in Python programming, this book will provide them with the foundational knowledge and tools to start designing and deploying their trading strategies. They will learn to build production-ready backtests with Zipline, evaluate factor performance, set up the code framework to connect and send orders to Interactive Brokers, and deploy trading strategies to a live trading environment using the IB API.

This book will equip you with the skills to acquire and analyze financial data and build and refine algorithmic trading strategies using Python. Whether you are an experienced market participant looking to enhance your technical capabilities or a Python programmer with a keen interest in the financial markets, this book provides actionable insights and techniques to succeed in algorithmic trading.

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