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

Chapter 4: Data Manipulation and Analysis with pandas

In this chapter, you will learn about the Python pandas library built upon NumPy, which provides data manipulation and analysis methods for structured data frames. The name pandas is derived from panel data, an econometrics term for multidimensional structured datasets, according to the Wikipedia page on pandas.

The pandas library contains two fundamental data structures to represent and manipulate structured rectangular datasets with a variety of indexing options: Series and DataFrames. Both use the index data structure.

Most operations in the processing of financial data in Python are based upon DataFrames. A DataFrame is like an Excel worksheet – a two-dimensional table that may contain multiple time series stored in columns. Therefore, we recommend you execute all the examples in this chapter yourself in your environment to get practice with the syntax and to better know what is possible.

In this chapter, we...