Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Hands-On Financial Trading with Python

You're reading from   Hands-On Financial Trading with Python A practical guide to using Zipline and other Python libraries for backtesting trading strategies

Arrow left icon
Product type Paperback
Published in Apr 2021
Publisher Packt
ISBN-13 9781838982881
Length 360 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Authors (2):
Arrow left icon
Sourav Ghosh Sourav Ghosh
Author Profile Icon Sourav Ghosh
Sourav Ghosh
Jiri Pik Jiri Pik
Author Profile Icon Jiri Pik
Jiri Pik
Arrow right icon
View More author details
Toc

Table of Contents (15) Chapters Close

Preface 1. Section 1: Introduction to Algorithmic Trading
2. Chapter 1: Introduction to Algorithmic Trading FREE CHAPTER 3. Section 2: In-Depth Look at Python Libraries for the Analysis of Financial Datasets
4. Chapter 2: Exploratory Data Analysis in Python 5. Chapter 3: High-Speed Scientific Computing Using NumPy 6. Chapter 4: Data Manipulation and Analysis with pandas 7. Chapter 5: Data Visualization Using Matplotlib 8. Chapter 6: Statistical Estimation, Inference, and Prediction 9. Section 3: Algorithmic Trading in Python
10. Chapter 7: Financial Market Data Access in Python 11. Chapter 8: Introduction to Zipline and PyFolio 12. Chapter 9: Fundamental Algorithmic Trading Strategies 13. Other Books You May Enjoy Appendix A: How to Setup a Python Environment

Reviewing the key Zipline API reference

In this section, we will outline the key features from https://www.zipline.io/appendix.html.

For backtesting, the most important features are order types, commission models, and slippage models. Let's look at them in more detail.

Types of orders

Zipline supports these types of orders:

Figure 8.9 – Supported order types

Figure 8.9 – Supported order types

The order-placing logic is typically placed in the handle_data method.

The following is an example:

def handle_data(context, data): 
    price_hist = data.history(context.stock, "close", 
                              context.rolling_window, "1d")
    order_target_percent(context.stock, 1.0 if price_hist[-1] > price_hist.mean() else 0.0) 

This example places an...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image