Python for Finance

13.59 MB·English

by  Yves Hilpisch

Preface Page: 4
Conventions Used in This Book Page: 5
Using Code Examples Page: 5
O’Reilly Online Learning Page: 5
How to Contact Us Page: 5
Acknowledgments Page: 5
I. Python and Finance Page: 7
1. Why Python for Finance Page: 8
The Python Programming Language Page: 8
A Brief History of Python Page: 8
The Python Ecosystem Page: 9
The Python User Spectrum Page: 9
The Scientific Stack Page: 9
Technology in Finance Page: 9
Technology Spending Page: 10
Technology as Enabler Page: 10
Technology and Talent as Barriers to Entry Page: 10
Ever-Increasing Speeds, Frequencies, and Data Volumes Page: 11
The Rise of Real-Time Analytics Page: 11
Python for Finance Page: 11
Finance and Python Syntax Page: 11
Efficiency and Productivity Through Python Page: 12
From Prototyping to Production Page: 14
Data-Driven and AI-First Finance Page: 15
Data-Driven Finance Page: 15
AI-First Finance Page: 16
Conclusion Page: 17
Further Resources Page: 17
2. Python Infrastructure Page: 19
conda as a Package Manager Page: 19
Installing Miniconda Page: 19
Basic Operations with conda Page: 20
conda as a Virtual Environment Manager Page: 21
Using Docker Containers Page: 22
Docker Images and Containers Page: 22
Building an Ubuntu and Python Docker Image Page: 22
Using Cloud Instances Page: 23
RSA Public and Private Keys Page: 24
Jupyter Notebook Configuration File Page: 24
Installation Script for Python and Jupyter Notebook Page: 25
Script to Orchestrate the Droplet Setup Page: 25
Conclusion Page: 26
Further Resources Page: 26
II. Mastering the Basics Page: 27
3. Data Types and Structures Page: 28
Basic Data Types Page: 28
Integers Page: 28
Floats Page: 28
Booleans Page: 29
Strings Page: 30
Excursion: Printing and String Replacements Page: 30
Excursion: Regular Expressions Page: 31
Basic Data Structures Page: 31
Tuples Page: 31
Lists Page: 32
Excursion: Control Structures Page: 32
Excursion: Functional Programming Page: 33
Dicts Page: 33
Sets Page: 33
Conclusion Page: 34
Further Resources Page: 34
4. Numerical Computing with NumPy Page: 35
Arrays of Data Page: 35
Arrays with Python Lists Page: 35
The Python array Class Page: 36
Regular NumPy Arrays Page: 36
The Basics Page: 36
Multiple Dimensions Page: 37
Metainformation Page: 38
Reshaping and Resizing Page: 38
Boolean Arrays Page: 39
Speed Comparison Page: 40
Structured NumPy Arrays Page: 40
Vectorization of Code Page: 40
Basic Vectorization Page: 40
Memory Layout Page: 41
Conclusion Page: 42
Further Resources Page: 42
5. Data Analysis with pandas Page: 43
The DataFrame Class Page: 43
First Steps with the DataFrame Class Page: 43
Second Steps with the DataFrame Class Page: 44
Basic Analytics Page: 46
Basic Visualization Page: 47
The Series Class Page: 47
GroupBy Operations Page: 47
Complex Selection Page: 48
Concatenation, Joining, and Merging Page: 49
Concatenation Page: 49
Joining Page: 49
Merging Page: 50
Performance Aspects Page: 50
Conclusion Page: 51
Further Reading Page: 51
6. Object-Oriented Programming Page: 52
A Look at Python Objects Page: 53
int Page: 53
list Page: 53
ndarray Page: 53
DataFrame Page: 53
Basics of Python Classes Page: 54
Python Data Model Page: 55
The Vector Class Page: 56
Conclusion Page: 56
Further Resources Page: 56
III. Financial Data Science Page: 57
7. Data Visualization Page: 58
Static 2D Plotting Page: 58
One-Dimensional Data Sets Page: 58
Two-Dimensional Data Sets Page: 59
Other Plot Styles Page: 60
Static 3D Plotting Page: 62
Interactive 2D Plotting Page: 63
Basic Plots Page: 63
Financial Plots Page: 64
Conclusion Page: 64
Further Resources Page: 65
8. Financial Time Series Page: 66
Financial Data Page: 66
Data Import Page: 66
Summary Statistics Page: 67
Changes over Time Page: 68
Resampling Page: 69
Rolling Statistics Page: 69
An Overview Page: 69
A Technical Analysis Example Page: 70
Correlation Analysis Page: 70
The Data Page: 70
Logarithmic Returns Page: 70
OLS Regression Page: 71
Correlation Page: 71
High-Frequency Data Page: 71
Conclusion Page: 71
Further Resources Page: 71
9. Input/Output Operations Page: 73
Basic I/O with Python Page: 73
Writing Objects to Disk Page: 73
Reading and Writing Text Files Page: 74
Working with SQL Databases Page: 75
Writing and Reading NumPy Arrays Page: 76
I/O with pandas Page: 77
Working with SQL Databases Page: 77
From SQL to pandas Page: 77
Working with CSV Files Page: 78
Working with Excel Files Page: 78
I/O with PyTables Page: 79
Working with Tables Page: 79
Working with Compressed Tables Page: 81
Working with Arrays Page: 82
Out-of-Memory Computations Page: 82
I/O with TsTables Page: 83
Sample Data Page: 83
Data Storage Page: 84
Data Retrieval Page: 84
Conclusion Page: 84
Further Resources Page: 85
10. Performance Python Page: 86
Loops Page: 86
Python Page: 86
NumPy Page: 86
Numba Page: 87
Cython Page: 87
Algorithms Page: 87
Prime Numbers Page: 87
Fibonacci Numbers Page: 88
The Number Pi Page: 89
Binomial Trees Page: 90
Python Page: 91
NumPy Page: 91
Numba Page: 91
Cython Page: 92
Monte Carlo Simulation Page: 92
Python Page: 93
NumPy Page: 93
Numba Page: 93
Cython Page: 93
Multiprocessing Page: 94
Recursive pandas Algorithm Page: 94
Python Page: 94
Numba Page: 95
Cython Page: 95
Conclusion Page: 95
Further Resources Page: 96
11. Mathematical Tools Page: 97
Approximation Page: 97
Regression Page: 97
Interpolation Page: 99
Convex Optimization Page: 100
Global Optimization Page: 100
Local Optimization Page: 101
Constrained Optimization Page: 101
Integration Page: 102
Numerical Integration Page: 103
Integration by Simulation Page: 103
Symbolic Computation Page: 103
Basics Page: 103
Equations Page: 104
Integration and Differentiation Page: 104
Differentiation Page: 104
Conclusion Page: 105
Further Resources Page: 105
12. Stochastics Page: 106
Random Numbers Page: 106
Simulation Page: 108
Random Variables Page: 108
Stochastic Processes Page: 109
Variance Reduction Page: 113
Valuation Page: 113
European Options Page: 113
American Options Page: 114
Risk Measures Page: 115
Value-at-Risk Page: 115
Credit Valuation Adjustments Page: 116
Python Script Page: 117
Conclusion Page: 118
Further Resources Page: 118
13. Statistics Page: 119
Normality Tests Page: 119
Benchmark Case Page: 119
Real-World Data Page: 122
Portfolio Optimization Page: 123
The Data Page: 123
The Basic Theory Page: 124
Optimal Portfolios Page: 125
Efficient Frontier Page: 126
Capital Market Line Page: 126
Bayesian Statistics Page: 128
Bayes’ Formula Page: 128
Bayesian Regression Page: 128
Two Financial Instruments Page: 129
Updating Estimates over Time Page: 130
Machine Learning Page: 131
Unsupervised Learning Page: 131
Supervised Learning Page: 132
Conclusion Page: 136
Further Resources Page: 137
IV. Algorithmic Trading Page: 138
14. The FXCM Trading Platform Page: 139
Getting Started Page: 139
Retrieving Data Page: 139
Retrieving Tick Data Page: 139
Retrieving Candles Data Page: 140
Working with the API Page: 141
Retrieving Historical Data Page: 141
Retrieving Streaming Data Page: 141
Placing Orders Page: 142
Account Information Page: 142
Conclusion Page: 143
Further Resources Page: 143
15. Trading Strategies Page: 144
Simple Moving Averages Page: 144
Data Import Page: 144
Trading Strategy Page: 144
Vectorized Backtesting Page: 145
Optimization Page: 145
Random Walk Hypothesis Page: 146
Linear OLS Regression Page: 147
The Data Page: 147
Regression Page: 148
Clustering Page: 148
Frequency Approach Page: 149
Classification Page: 149
Two Binary Features Page: 149
Five Binary Features Page: 150
Five Digitized Features Page: 150
Sequential Train-Test Split Page: 150
Randomized Train-Test Split Page: 151
Deep Neural Networks Page: 151
DNNs with scikit-learn Page: 151
DNNs with TensorFlow Page: 152
Conclusion Page: 153
Further Resources Page: 153
16. Automated Trading Page: 155
Capital Management Page: 155
The Kelly Criterion in a Binomial Setting Page: 155
The Kelly Criterion for Stocks and Indices Page: 157
ML-Based Trading Strategy Page: 158
Vectorized Backtesting Page: 158
Optimal Leverage Page: 160
Risk Analysis Page: 160
Persisting the Model Object Page: 161
Online Algorithm Page: 161
Infrastructure and Deployment Page: 162
Logging and Monitoring Page: 163
Conclusion Page: 164
Python Scripts Page: 164
Automated Trading Strategy Page: 164
Strategy Monitoring Page: 164
Further Resources Page: 164
V. Derivatives Analytics Page: 166
17. Valuation Framework Page: 167
Fundamental Theorem of Asset Pricing Page: 167
A Simple Example Page: 167
The General Results Page: 167
Risk-Neutral Discounting Page: 168
Modeling and Handling Dates Page: 168
Constant Short Rate Page: 169
Market Environments Page: 169
Conclusion Page: 170
Further Resources Page: 170
18. Simulation of Financial Models Page: 172
Random Number Generation Page: 172
Generic Simulation Class Page: 172
Geometric Brownian Motion Page: 174
The Simulation Class Page: 174
A Use Case Page: 175
Jump Diffusion Page: 175
The Simulation Class Page: 176
A Use Case Page: 176
Square-Root Diffusion Page: 177
The Simulation Class Page: 177
A Use Case Page: 178
Conclusion Page: 178
Further Resources Page: 178
19. Derivatives Valuation Page: 180
Generic Valuation Class Page: 180
European Exercise Page: 181
The Valuation Class Page: 181
A Use Case Page: 182
American Exercise Page: 183
Least-Squares Monte Carlo Page: 183
The Valuation Class Page: 184
A Use Case Page: 185
Conclusion Page: 186
Further Resources Page: 186
20. Portfolio Valuation Page: 188
Derivatives Positions Page: 188
The Class Page: 188
A Use Case Page: 188
Derivatives Portfolios Page: 189
The Class Page: 189
A Use Case Page: 190
Conclusion Page: 192
Further Resources Page: 193
21. Market-Based Valuation Page: 194
Options Data Page: 194
Model Calibration Page: 194
Relevant Market Data Page: 194
Option Modeling Page: 195
Calibration Procedure Page: 196
Portfolio Valuation Page: 198
Modeling Option Positions Page: 198
The Options Portfolio Page: 198
Python Code Page: 199
Conclusion Page: 199
Further Resources Page: 200
A. Dates and Times Page: 201
Python Page: 201
NumPy Page: 202
pandas Page: 203
B. BSM Option Class Page: 205
Class Definition Page: 205
Class Usage Page: 205
Index Page: 206