Book Image

Python Data Analysis Cookbook

By : Ivan Idris
Book Image

Python Data Analysis Cookbook

By: Ivan Idris

Overview of this book

Data analysis is a rapidly evolving field and Python is a multi-paradigm programming language suitable for object-oriented application development and functional design patterns. As Python offers a range of tools and libraries for all purposes, it has slowly evolved as the primary language for data science, including topics on: data analysis, visualization, and machine learning. Python Data Analysis Cookbook focuses on reproducibility and creating production-ready systems. You will start with recipes that set the foundation for data analysis with libraries such as matplotlib, NumPy, and pandas. You will learn to create visualizations by choosing color maps and palettes then dive into statistical data analysis using distribution algorithms and correlations. You’ll then help you find your way around different data and numerical problems, get to grips with Spark and HDFS, and then set up migration scripts for web mining. In this book, you will dive deeper into recipes on spectral analysis, smoothing, and bootstrapping methods. Moving on, you will learn to rank stocks and check market efficiency, then work with metrics and clusters. You will achieve parallelism to improve system performance by using multiple threads and speeding up your code. By the end of the book, you will be capable of handling various data analysis techniques in Python and devising solutions for problem scenarios.
Table of Contents (23 chapters)
Python Data Analysis Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Glossary
Index

Testing for random walks


The random walk hypothesis (RWH) just like the efficient-market hypothesis (refer to the Examining the market with the non-parametric runs test recipe) claims that the market cannot be beaten. The RWH stipulates that asset prices perform a random walk. You can in fact generate pretty convincing stock price charts just by flipping a coin repeatedly.

In 1988, finance professors Lo and MacKinlay constructed a test for the RWH using the natural log(arithm) of asset prices as data. The test specifies the log prices to drift around a mean (7.9). We expect price changes for different frequencies (for instance, one-day and two-day periods) to be random. Furthermore, the variances (7.10 and 7.11) at two different frequencies are related, and according to the following equations, the corresponding ratio (7.12) is normally distributed around zero:

How to do it...

The code is in the random_walk.ipynb file in this book's code bundle:

  1. The imports are as follows:

    import dautil as dl...