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 7: Financial Market Data Access in Python

This chapter outlines several key market data sources, ranging from free to paid data sources. A more complete list of available resources can be obtained from https://github.com/wilsonfreitas/awesome-quant#data-sources.

The quality of algorithmic trading models' signals fundamentally depends on the quality of market data being analyzed. Has the market data been cleaned of erroneous records and is there a quality assurance process in place to rectify any errors as they occur? If there is a problem with the market data feed, how quickly can the data be corrected?

The following free data sources described are suitable for learning purposes, but not fit for purpose as regards professional trading – there may be a very low limit on the number of API calls per day, the APIs may be slow, and there is no support and no rectification of the data should it not be correct. In addition, when using any of these data providers...