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 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

Chapter 5: Data Visualization Using Matplotlib

Data visualization allows comprehending numerical data significantly more easily than reading pure tables of numbers. Getting instant insight into data and the identification of patterns, trends, and outliers are the primary uses of charting libraries.

When deciding which stock may be suitable for which algorithmic trading strategy, creating a chart of the stock price is the first step – some strategies are suitable only for trending stocks, some for mean-reversion stocks, and so on. While numerical statistics are critical, there is no substitute for a well-designed chart.

This chapter introduces us to Matplotlib, a static, animated, and interactive Python visualization library extending the capabilities of NumPy. The pandas library allows direct charting of DataFrames using Matplotlib.

This chapter covers the following main topics: