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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

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Product type Paperback
Published in Apr 2021
Publisher Packt
ISBN-13 9781838982881
Length 360 pages
Edition 1st Edition
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Authors (2):
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Sourav Ghosh Sourav Ghosh
Author Profile Icon Sourav Ghosh
Sourav Ghosh
Jiri Pik Jiri Pik
Author Profile Icon Jiri Pik
Jiri Pik
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Toc

Table of Contents (15) Chapters Close

Preface 1. Section 1: Introduction to Algorithmic Trading FREE CHAPTER
2. Chapter 1: Introduction to Algorithmic Trading 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

Importing market data into a Zipline/PyFolio backtesting system

Backtesting depends on us having an extensive market data database.

Zipline introduces two market data-specific terms – bundle and ingest:

  • A bundle is an interface for incrementally importing market data into Zipline's proprietary database from a custom source.
  • An ingest is the actual process of incrementally importing the custom source market data into Zipline's proprietary database; the data ingest is not automatically updated. Each time you need fresh data, you must re-ingest the bundle.

By default, Zipline supports these bundles:

  • Historical Quandl bundle (complimentary daily data for US equities up to 2018)
  • .csv files bundle

We will now learn how to import these two bundles in more detail.

Importing data from the historical Quandl bundle

First, in the activated zipline_env environment, set the QUANDL_API_KEY environment variable to your free (or paid)...

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