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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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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

Running Zipline backtesting from the command line

For large backtesting jobs, it's preferred to run backtesting from the command line.

The following command runs the backtesting strategy defined in the job.py Python script and saves the resulting DataFrame in the job_results.pickle pickle file:

zipline run -f job.py --start 2016-1-1 --end 2021-1-1 -o job_results.pickle --no-benchmark

For example, you can set up a batch consisting of tens of Zipline command-line jobs to run overnight, with each storing the results in a pickle file for later analysis.

It's a good practice to keep a journal and library of past backtesting pickle files for easy reference.

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