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Numpy Beginner's Guide (Update)

Numpy Beginner's Guide (Update)

By : Ivan Idris
2 (1)
Buy this Book
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Numpy Beginner's Guide (Update)

Numpy Beginner's Guide (Update)

2 (1)
By: Ivan Idris
Buy this Book

Overview of this book

This book is for the scientists, engineers, programmers, or analysts looking for a high-quality, open source mathematical library. Knowledge of Python is assumed. Also, some affinity, or at least interest, in mathematics and statistics is required. However, I have provided brief explanations and pointers to learning resources.
Table of Contents (16 chapters)
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Preface
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Preface
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What is NumPy?
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History
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Why use NumPy?
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Limitations of NumPy
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What this book covers
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What you need for this book
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Who this book is for
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Sections
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Time for action – heading
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Conventions
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Reader feedback
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Customer support
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1
1. NumPy Quick Start
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1. NumPy Quick Start
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Python
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Time for action – installing Python on different operating systems
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The Python help system
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Time for action – using the Python help system
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Basic arithmetic and variable assignment
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Time for action – using Python as a calculator
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Time for action – assigning values to variables
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The print() function
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Time for action – printing with the print() function
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Code comments
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Time for action – commenting code
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The if statement
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Time for action – deciding with the if statement
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The for loop
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Time for action – repeating instructions with loops
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Python functions
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Time for action – defining functions
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Python modules
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Time for action – importing modules
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NumPy on Windows
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Time for action – installing NumPy, matplotlib, SciPy, and IPython on Windows
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NumPy on Linux
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Time for action – installing NumPy, matplotlib, SciPy, and IPython on Linux
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NumPy on Mac OS X
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Time for action – installing NumPy, SciPy, matplotlib, and IPython with MacPorts or Fink
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Building from source
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Arrays
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Time for action – adding vectors
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IPython – an interactive shell
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Online resources and help
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Summary
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2. Beginning with NumPy Fundamentals
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2. Beginning with NumPy Fundamentals
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NumPy array object
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Time for action – creating a multidimensional array
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Time for action – creating a record data type
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One-dimensional slicing and indexing
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Time for action – slicing and indexing multidimensional arrays
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Time for action – manipulating array shapes
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Time for action – stacking arrays
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Time for action – splitting arrays
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Time for action – converting arrays
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Summary
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3. Getting Familiar with Commonly Used Functions
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3. Getting Familiar with Commonly Used Functions
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File I/O
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Time for action – reading and writing files
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Comma-seperated value files
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Time for action – loading from CSV files
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Volume Weighted Average Price
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Time for action – calculating Volume Weighted Average Price
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Value range
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Time for action – finding highest and lowest values
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Statistics
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Time for action – performing simple statistics
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Stock returns
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Time for action – analyzing stock returns
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Dates
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Time for action – dealing with dates
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Time for action – using the datetime64 data type
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Weekly summary
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Time for action – summarizing data
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Average True Range
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Time for action – calculating the Average True Range
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Simple Moving Average
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Time for action – computing the Simple Moving Average
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Exponential Moving Average
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Time for action – calculating the Exponential Moving Average
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Bollinger Bands
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Time for action – enveloping with Bollinger Bands
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Linear model
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Time for action – predicting price with a linear model
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Trend lines
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Time for action – drawing trend lines
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Methods of ndarray
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Time for action – clipping and compressing arrays
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Factorial
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Time for action – calculating the factorial
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Missing values and Jackknife resampling
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Time for action – handling NaNs with the nanmean(), nanvar(), and nanstd() functions
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Summary
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4. Convenience Functions for Your Convenience
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4. Convenience Functions for Your Convenience
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Correlation
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Time for action – trading correlated pairs
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Polynomials
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Time for action – fitting to polynomials
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On-balance volume
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Time for action – balancing volume
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Simulation
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Time for action – avoiding loops with vectorize()
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Smoothing
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Time for action – smoothing with the hanning() function
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Initialization
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Time for action – creating value initialized arrays with the full() and full_like() functions
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Summary
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5. Working with Matrices and ufuncs
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5. Working with Matrices and ufuncs
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Matrices
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Time for action – creating matrices
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Creating a matrix from other matrices
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Time for action – creating a matrix from other matrices
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Universal functions
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Time for action – creating universal functions
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Universal function methods
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Time for action – applying the ufunc methods to the add function
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Arithmetic functions
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Time for action – dividing arrays
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Modulo operation
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Time for action – computing the modulo
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Fibonacci numbers
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Time for action – computing Fibonacci numbers
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Lissajous curves
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Time for action – drawing Lissajous curves
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Square waves
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Time for action – drawing a square wave
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Sawtooth and triangle waves
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Time for action – drawing sawtooth and triangle waves
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Bitwise and comparison functions
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Time for action – twiddling bits
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Fancy indexing
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Time for action – fancy indexing in-place for ufuncs with the at() method
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Summary
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6. Moving Further with NumPy Modules
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6. Moving Further with NumPy Modules
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Linear algebra
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Time for action – inverting matrices
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Solving linear systems
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Time for action – solving a linear system
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Finding eigenvalues and eigenvectors
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Time for action – determining eigenvalues and eigenvectors
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Singular value decomposition
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Time for action – decomposing a matrix
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Pseudo inverse
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Time for action – computing the pseudo inverse of a matrix
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Determinants
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Time for action – calculating the determinant of a matrix
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Fast Fourier transform
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Time for action – calculating the Fourier transform
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Shifting
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Time for action – shifting frequencies
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Random numbers
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Time for action – gambling with the binomial
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Hypergeometric distribution
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Time for action – simulating a game show
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Continuous distributions
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Time for action – drawing a normal distribution
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Lognormal distribution
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Time for action – drawing the lognormal distribution
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Bootstrapping in statistics
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Time for action – sampling with numpy.random.choice()
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Summary
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7. Peeking into Special Routines
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7. Peeking into Special Routines
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Sorting
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Time for action – sorting lexically
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Time for action – partial sorting via selection for a fast median with the partition() function
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Complex numbers
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Time for action – sorting complex numbers
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Searching
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Time for action – using searchsorted
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Array elements extraction
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Time for action – extracting elements from an array
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Financial functions
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Time for action – determining the future value
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Present value
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Time for action – getting the present value
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Net present value
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Time for action – calculating the net present value
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Internal rate of return
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Time for action – determining the internal rate of return
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Periodic payments
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Time for action – calculating the periodic payments
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Number of payments
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Time for action – determining the number of periodic payments
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Interest rate
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Time for action – figuring out the rate
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Window functions
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Time for action – plotting the Bartlett window
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Blackman window
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Time for action – smoothing stock prices with the Blackman window
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Hamming window
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Time for action – plotting the Hamming window
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Kaiser window
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Time for action – plotting the Kaiser window
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Special mathematical functions
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Time for action – plotting the modified Bessel function
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sinc
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Time for action – plotting the sinc function
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Summary
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8. Assuring Quality with Testing
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8. Assuring Quality with Testing
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Assert functions
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Time for action – asserting almost equal
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Approximately equal arrays
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Time for action – asserting approximately equal
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Almost equal arrays
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Time for action – asserting arrays almost equal
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Equal arrays
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Time for action – comparing arrays
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Ordering arrays
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Time for action – checking the array order
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Object comparison
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Time for action – comparing objects
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String comparison
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Time for action – comparing strings
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Floating-point comparisons
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Time for action – comparing with assert_array_almost_equal_nulp
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Comparison of floats with more ULPs
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Time for action – comparing using maxulp of 2
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Unit tests
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Time for action – writing a unit test
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Nose test decorators
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Time for action – decorating tests
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Docstrings
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Time for action – executing doctests
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Summary
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9. Plotting with matplotlib
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9. Plotting with matplotlib
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Simple plots
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Time for action – plotting a polynomial function
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Plot format string
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Time for action – plotting a polynomial and its derivatives
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Subplots
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Time for action – plotting a polynomial and its derivatives
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Finance
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Time for action – plotting a year's worth of stock quotes
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Histograms
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Time for action – charting stock price distributions
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Logarithmic plots
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Time for action – plotting stock volume
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Scatter plots
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Time for action – plotting price and volume returns with a scatter plot
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Fill between
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Time for action – shading plot regions based on a condition
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Legend and annotations
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Time for action – using a legend and annotations
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Three-dimensional plots
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Time for action – plotting in three dimensions
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Contour plots
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Time for action – drawing a filled contour plot
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Animation
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Time for action – animating plots
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Summary
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10. When NumPy Is Not Enough – SciPy and Beyond
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10. When NumPy Is Not Enough – SciPy and Beyond
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MATLAB and Octave
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Time for action – saving and loading a .mat file
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Statistics
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Time for action – analyzing random values
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Sample comparison and SciKits
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Time for action – comparing stock log returns
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Signal processing
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Time for action – detecting a trend in QQQ
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Fourier analysis
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Time for action – filtering a detrended signal
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Mathematical optimization
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Time for action – fitting to a sine
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Numerical integration
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Time for action – calculating the Gaussian integral
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Interpolation
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Time for action – interpolating in one dimension
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Image processing
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Time for action – manipulating Lena
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Audio processing
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Time for action – replaying audio clips
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Summary
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11. Playing with Pygame
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11. Playing with Pygame
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Pygame
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Time for action – installing Pygame
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Hello World
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Time for action – creating a simple game
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Animation
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Time for action – animating objects with NumPy and Pygame
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matplotlib
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Time for Action – using matplotlib in Pygame
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Surface pixels
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Time for Action – accessing surface pixel data with NumPy
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Artificial Intelligence
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Time for Action – clustering points
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OpenGL and Pygame
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Time for Action – drawing the Sierpinski gasket
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Simulation game with Pygame
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Time for Action – simulating life
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Summary
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A. Pop Quiz Answers
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Chapter 1, NumPy Quick Start
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Chapter 2, Beginning with NumPy Fundamentals
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Chapter 3, Getting Familiar with Commonly Used Functions
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Chapter 4, Convenience Functions for Your Convenience
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Chapter 5, Working with Matrices and ufuncs
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Chapter 6, Move Further with NumPy Modules
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Chapter 7, Peeking into Special Routines
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Chapter 8, Assuring Quality with Testing
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Chapter 9, Plotting with matplotlib
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Chapter 10, When NumPy Is Not Enough –Scipy and Beyond
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B. Additional Online Resources
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B. Additional Online Resources
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Python
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Mathematics and statistics
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C. NumPy Functions' References
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C. NumPy Functions' References
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Index
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Index

Stock returns

In academic literature, it is more common to base analysis on stock returns and log returns of the close price. Simple returns are just the rate of change from one value to the next. Logarithmic returns, or log returns, are determined by taking the log of all the prices and calculating the differences between them. In high school, we learned that:

Log returns, therefore, also measure the rate of change. Returns are dimensionless, since, in the act of dividing, we divide dollar by dollar (or some other currency). Anyway, investors are most likely to be interested in the variance or standard deviation of the returns, as this represents risk.

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