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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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.