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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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Toc

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

Introduction to statsmodels

statsmodels is a Python library that allows us to explore data, perform statistical tests, and estimate statistical models.

This chapter focuses on statsmodels' modeling, analysis, and forecasting of time series.

Normal distribution test with Q-Q plots

An underlying assumption of many statistical learning techniques is that the observations/fields are normally distributed.

While there are many robust statistical tests for normal distributions, an intuitive visual method is known as a quantile-quantile plot (Q-Q plot). If a sample is normally distributed, its Q-Q plot is a straight line.

In the following code block, the statsmodels.graphics.api.qqplot(...) method is used to check if a numpy.random.uniform(...) distribution is normally distributed:

from statsmodels.graphics.api import qqplot
import numpy as np
fig = qqplot(np.random.uniform(size=10000), line='s')
fig.set_size_inches(12, 6)

The resulting plot depicted in...

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