Python for Finance
SECOND EDITION
Python for Finance
Mastering Data-Driven Finance
Yves Hilpisch
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Boston Farnham Sebastopol
Tokyo
Python for Finance
by Yves Hilpisch
Copyright ? 2019 Yves Hilpisch. All rights reserved.
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978-1-492-02433-0
[MBP]
Table of Contents
Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii
Part I.
Python and Finance
1. Why Python for Finance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
The Python Programming Language
A Brief History of Python
The Python Ecosystem
The Python User Spectrum
The Scientific Stack
Technology in Finance
Technology Spending
Technology as Enabler
Technology and Talent as Barriers to Entry
Ever-Increasing Speeds, Frequencies, and Data Volumes
The Rise of Real-Time Analytics
Python for Finance
Finance and Python Syntax
Efficiency and Productivity Through Python
From Prototyping to Production
Data-Driven and AI-First Finance
Data-Driven Finance
AI-First Finance
Conclusion
Further Resources
3
5
6
7
8
9
9
10
11
11
13
14
14
18
23
24
24
28
31
31
iii
2. Python Infrastructure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
conda as a Package Manager
Installing Miniconda
Basic Operations with conda
conda as a Virtual Environment Manager
Using Docker Containers
Docker Images and Containers
Building an Ubuntu and Python Docker Image
Using Cloud Instances
RSA Public and Private Keys
Jupyter Notebook Configuration File
Installation Script for Python and Jupyter Notebook
Script to Orchestrate the Droplet Setup
Conclusion
Further Resources
35
35
37
41
45
45
46
50
51
52
53
55
56
57
Part II. Mastering the Basics
3. Data Types and Structures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
Basic Data Types
Integers
Floats
Booleans
Strings
Excursion: Printing and String Replacements
Excursion: Regular Expressions
Basic Data Structures
Tuples
Lists
Excursion: Control Structures
Excursion: Functional Programming
Dicts
Sets
Conclusion
Further Resources
62
62
63
66
69
71
74
75
75
76
78
80
81
82
84
84
4. Numerical Computing with NumPy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
Arrays of Data
Arrays with Python Lists
The Python array Class
Regular NumPy Arrays
iv
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Table of Contents
86
86
88
90
The Basics
Multiple Dimensions
Metainformation
Reshaping and Resizing
Boolean Arrays
Speed Comparison
Structured NumPy Arrays
Vectorization of Code
Basic Vectorization
Memory Layout
Conclusion
Further Resources
90
94
97
98
101
103
105
106
107
110
112
112
5. Data Analysis with pandas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
The DataFrame Class
First Steps with the DataFrame Class
Second Steps with the DataFrame Class
Basic Analytics
Basic Visualization
The Series Class
GroupBy Operations
Complex Selection
Concatenation, Joining, and Merging
Concatenation
Joining
Merging
Performance Aspects
Conclusion
Further Reading
114
114
119
123
126
128
130
132
135
136
137
139
141
143
143
6. Object-Oriented Programming. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
A Look at Python Objects
int
list
ndarray
DataFrame
Basics of Python Classes
Python Data Model
The Vector Class
Conclusion
Further Resources
149
149
150
151
152
154
159
163
164
164
Table of Contents
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