Book Image

NumPy Cookbook - Second Edition

By : Ivan Idris
Book Image

NumPy Cookbook - Second Edition

By: Ivan Idris

Overview of this book

<p>NumPy has the ability to give you speed and high productivity. High performance calculations can be done easily with clean and efficient code, and it allows you to execute complex algebraic and mathematical computations in no time.</p> <p>This book will give you a solid foundation in NumPy arrays and universal functions. Starting with the installation and configuration of IPython, you'll learn about advanced indexing and array concepts along with commonly used yet effective functions. You will then cover practical concepts such as image processing, special arrays, and universal functions. You will also learn about plotting with Matplotlib and the related SciPy project with the help of examples. At the end of the book, you will study how to explore atmospheric pressure and its related techniques. By the time you finish this book, you'll be able to write clean and fast code with NumPy.</p>
Table of Contents (19 chapters)
NumPy Cookbook Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Trading periodically on dips


Stock prices periodically dip and go up. We will take a look at the probability distribution of the stock price log returns and try a very simple strategy. This strategy is based on regression towards the mean. This is a concept originally discovered in genetics by Sir Francis Galton. It was discovered that children of tall parents tend to be shorter than their parents. Children of short parents tend to be taller than their parents. Of course, this is a statistical phenomenon and doesn't take into account fundamental factors and trends such as improvement in nutrition. Regression towards the mean is also relevant to the stock market. However, it gives no guarantees. If a company starts making bad products or makes bad investments, regression towards the mean will not save the stock.

Let's start by downloading the historical data for a stock, for instance, AAPL. Next, we calculate the daily log returns (http://en.wikipedia.org/wiki/Rate_of_return) of the close...