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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

Indexing of ndarrays

Array indexing refers to the way of accessing a particular array element or elements. In NumPy, all ndarray indices are zero-based—that is, the first item of an array has index 0. Negative indices are understood as counting from the end of the array.

Direct access to an ndarray's element

Direct access to a single ndarray's element is one of the most used forms of access.

The following code builds a 3 x 3 random-valued ndarray for our use:

arr = np.random.randn(3,3); 
arr

The arr ndarray has the following elements:

array([[-0.04113926, -0.273338  , -1.05294723],
       [ 1.65004669, -0.09589629,  0.15586867],
       [ 0.39533427,  1.47193681,  0.32148741]])

We can index the first element with integer index 0, as follows:

arr[0]

This gives us the first row of the arr ndarray, as follows:

array([-0.04113926, ...
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