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

Product type Paperback

Published in Apr 2021

Publisher Packt

ISBN-13 9781838982881

Length 360 pages

Edition 1st Edition

Languages

Python

Tools

NumPy

Concepts

Data Analysis

Authors (2):

Sourav Ghosh

Jiri Pik

View More author details

Table of Contents (15) Chapters

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

Exploring file operations with pandas.DataFrames

pandas supports the persistence of DataFrames in both plain-text and binary formats. The common text formats are CSV and JSON files, the most used binary formats are Excel XLSX, HDF5, and pickle.

In this book, we focus on plain-text persistence.

CSV files

CSV files (comma-separated values files) are data-exchange standard files.

Writing CSV files

Writing a pandas DataFrame to a CSV file is easily achievable using the pandas.DataFrame.to_csv(...) method. The header= parameter controls whether a header is written to the top of the file or not and the index= parameter controls whether the Index axis values are written to the file or not:

df.to_csv('df.csv', sep=',', header=True, index=True)

We can inspect the file written to disk using the following Linux command typed into the notebook. The ! character instructs the notebook to run a shell command: