Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Numpy Beginner's Guide (Update)

You're reading from   Numpy Beginner's Guide (Update) Build efficient, high-speed programs using the high-performance NumPy mathematical library

Arrow left icon
Product type Paperback
Published in Jun 2015
Publisher
ISBN-13 9781785281969
Length 348 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
Arrow right icon
View More author details
Toc

Table of Contents (16) Chapters Close

Preface 1. NumPy Quick Start 2. Beginning with NumPy Fundamentals FREE CHAPTER 3. Getting Familiar with Commonly Used Functions 4. Convenience Functions for Your Convenience 5. Working with Matrices and ufuncs 6. Moving Further with NumPy Modules 7. Peeking into Special Routines 8. Assuring Quality with Testing 9. Plotting with matplotlib 10. When NumPy Is Not Enough – SciPy and Beyond 11. Playing with Pygame A. Pop Quiz Answers B. Additional Online Resources C. NumPy Functions' References
Index

Time for action – calculating the Average True Range


To calculate the ATR, perform the following steps:

  1. The ATR is based on the low and high price of N days, usually the last 20 days.

    N = 5
    h = h[-N:]
    l = l[-N:]
  2. We also need to know the close price of the previous day:

    previousclose = c[-N -1: -1]

    For each day, we calculate the following:

    The daily range—the difference between the high and low price:

    h – l
    

    The difference between the high and previous close:

    h – previousclose
    

    The difference between the previous close and the low price:

    previousclose – l
    
  3. The max() function returns the maximum of an array. Based on those three values, we calculate the so-called true range, which is the maximum of these values. We are now interested in the element-wise maxima across arrays—meaning the maxima of the first elements in the arrays, the second elements in the arrays, and so on. Use the NumPy maximum() function instead of the max() function for this purpose:

    truerange = np.maximum(h - l, h - previousclose...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image