Table of Contents

Chapter 1: Introduction and Installation of Python 9
Introduction to Python 10
Installing Python 12
Different versions of Python 12
Ways to launch Python 13
Launching Python with GUI 13
Launching Python from the Python command line 14
Launching Python from our own DOS window 15
Quitting Python 16
Error messages 16
Python language is case sensitive 17
Initializing the variable 17
Finding the help window 18
Finding manuals and tutorials 19
Finding the version of Python 21
Summary 21
Exercises 22

Chapter 2: Using Python as an Ordinary Calculator 23
Assigning values to variables 24
Displaying the value of a variable 24
Error messages 24
Can't call a variable without assignment 25
Choosing meaningful names 25
Using dir() to find variables and functions 26
Deleting or unsigning a variable 27
Basic math operations – addition, subtraction,
multiplication, and division 28
The power function, floor, and remainder 28
A true power function 30
Choosing appropriate precision 31
Finding out more information about a specific built-in function 32
Listing all built-in functions 32
Importing the math module 33
The pi, e, log, and exponential functions 34
"import math" versus "from math import *" 34
A few frequently used functions 36
The print() function 36
The type() function 36
Last expression _ (underscore) 36
Combining two strings 37
The upper() function 37
The tuple data type 39
Summary 40
Exercises 40

Chapter 3: Using Python as a Financial Calculator 43
Writing a Python function without saving it 44
Default input values for a function 45
Indentation is critical in Python 45
Checking the existence of our functions 46
Defining functions from our Python editor 47
Activating our function using the import function 48
Debugging a program from a Python editor 48
Two ways to call our pv_f() function 49
Generating our own module 50
Types of comments 51
The first type of comment 51
The second type of comment 52
Finding information about our pv_f() function 52
The if() function 53
Annuity estimation 54
Converting the interest rates 55
Continuously compounded interest rate 57
A data type – list 58
Net present value and the NPV rule 58
Defining the payback period and the payback period rule 60
Defining IRR and the IRR rule 61
Showing certain files in a specific subdirectory 62
Using Python as a financial calculator 63
Adding our project directory to the path 64
Summary 66
Exercises 67

Chapter 4: 13 Lines of Python to Price a Call Option 71
Writing a program – the empty shell method 73
Writing a program – the comment-all-out method 75
Using and debugging other programs 76
Summary 76
Exercises 77

Chapter 5: Introduction to Modules 79
What is a module? 80
Importing a module 80
Adopting a short name for an imported module 81
Showing all functions in an imported module 82
Comparing "import math" and "from math import *" 82
Deleting an imported module 83
Importing only a few needed functions 84
Finding out all built-in modules 85
Finding out all the available modules 86
Finding the location of an imported module 87
More information about modules 88
Finding a specific uninstalled module 90
Module dependency 90
Summary 92
Exercises 93

Chapter 6: Introduction to NumPy and SciPy 95
Installation of NumPy and SciPy 96
Launching Python from Anaconda 96
Examples of using NumPy 97
Examples of using SciPy 98
Showing all functions in NumPy and SciPy 102
More information about a specific function 103
Understanding the list data type 103
Working with arrays of ones, zeros, and the identity matrix 104
Performing array manipulations 105
Performing array operations with +, -, *, / 105
Performing plus and minus operations 105
Performing a matrix multiplication operation 105
Performing an item-by-item multiplication operation 107
The x.sum() dot function 107
Looping through an array 108
Using the help function related to modules 108
A list of subpackages for SciPy 109
Cumulative standard normal distribution 109
Logic relationships related to an array 110
Statistic submodule (stats) from SciPy 111
Interpolation in SciPy 112
Solving linear equations using SciPy 113
Generating random numbers with a seed 114
Finding a function from an imported module 116
Understanding optimization 116
Linear regression and Capital Assets Pricing Model (CAPM) 117
Retrieving data from an external text file 118
The loadtxt() and getfromtxt() functions 118
Installing NumPy independently 119
Understanding the data types 119
Summary 120
Exercises 120

Chapter 7: Visual Finance via Matplotlib 123
Installing matplotlib via ActivePython 124
Alternative installation via Anaconda 125
Understanding how to use matplotlib 125
Understanding simple and compounded interest rates 129
Adding texts to our graph 131
Working with DuPont identity 133
Understanding the Net Present Value (NPV) profile 135
Using colors effectively 137
Using different shapes 139
Graphical representation of the portfolio diversification effect 140
Number of stocks and portfolio risk 142
Retrieving historical price data from Yahoo! Finance 144
Histogram showing return distribution 145
Comparing stock and market returns 148
Understanding the time value of money 150
Candlesticks representation of IBM's daily price 151
Graphical representation of two-year price movement 153
IBM's intra-day graphical representations 154
Presenting both closing price and trading volume 156
Adding mathematical formulae to our graph 157
Adding simple images to our graphs 158
Saving our figure to a file 159
Performance comparisons among stocks 160
Comparing return versus volatility for several stocks 161
Finding manuals, examples, and videos 163
Installing the matplotlib module independently 163
Summary 163
Exercises 164


Chapter 8: Statistical Analysis of Time Series 167
Installing Pandas and statsmodels 168
Launching Python using the Anaconda command prompt 169
Launching Python using the DOS window 169
Launching Python using Spyder 170
Using Pandas and statsmodels 171
Using Pandas 171
Examples from statsmodels 173
Open data sources 174
Retrieving data to our programs 176
Inputting data from the clipboard 176
Retrieving historical price data from Yahoo! Finance 177
Inputting data from a text file 178
Inputting data from an Excel file 179
Inputting data from a CSV file 180
Retrieving data from a web page 180
Inputting data from a MATLAB dataset 181
Several important functionalities 182
Using pd.Series() to generate one-dimensional time series 182
Using date variables 183
Using the DataFrame 183
Return estimation 185
Converting daily returns to monthly returns 187
Converting daily returns to annual returns 190
Merging datasets by date 191
Forming an n-stock portfolio 192
T-test and F-test 193
Tests of equal means and equal variances 194
Testing the January effect 195
Many useful applications 196
52-week high and low trading strategy 196
Roll's model to estimate spread (1984) 197
Amihud's model for illiquidity (2002) 198
Pastor and Stambaugh (2003) liquidity measure 199
Fama-French three-factor model 204
Fama-MacBeth regression 206
Estimating rolling beta 207
Understanding VaR 210
Constructing an efficient frontier 211
Estimating a variance-covariance matrix 212
Optimization – minimization 214
Constructing an optimal portfolio 215
Constructing an efficient frontier with n stocks 217
Understanding the interpolation technique 220
Outputting data to external files 221
Outputting data to a text file 221
Saving our data to a binary file 222
Reading data from a binary file 222
Python for high-frequency data 222
Spread estimated based on high-frequency data 227
More on using Spyder 228
A useful dataset 230
Summary 232
Exercise 232

Chapter 9: The Black-Scholes-Merton Option Model 237
Payoff and profit/loss functions for the call and put options 238
European versus American options 242
Cash flows, types of options, a right, and an obligation 243
Normal distribution, standard normal distribution, and cumulative
standard normal distribution 243
The Black-Scholes-Merton option model on non-dividend
paying stocks 247
The p4f module for options 248
European options with known dividends 250
Various trading strategies 251
Covered call – long a stock and short a call 252
Straddle – buy a call and a put with the same exercise prices 253
A calendar spread 254
Butterfly with calls 256
Relationship between input values and option values 257
Greek letters for options 258
The put-call parity and its graphical representation 259
Binomial tree (the CRR method) and its graphical representation 261
The binomial tree method for European options 268
The binomial tree method for American options 268
Hedging strategies 269
Summary 270
Exercises 271

Chapter 10: Python Loops and Implied Volatility 275
Definition of an implied volatility 276
Understanding a for loop 277
Estimating the implied volatility by using a for loop 278
Implied volatility function based on a European call 279
Implied volatility based on a put option model 280
The enumerate() function 281
Estimation of IRR via a for loop 282
Estimation of multiple IRRs 283
Understanding a while loop 284
Using keyboard commands to stop an infinitive loop 285
Estimating implied volatility by using a while loop 286
Nested (multiple) for loops 288
Estimating implied volatility by using an American call 288
Measuring efficiency by time spent in finishing a program 289
The mechanism of a binary search 290
Sequential versus random access 292
Looping through an array/DataFrame 293
Assignment through a for loop 294
Looping through a dictionary 294
Retrieving option data from CBOE 295
Retrieving option data from Yahoo! Finance 297
Different expiring dates from Yahoo! Finance 299
Retrieving the current price from Yahoo! Finance 300
The put-call ratio 300
The put-call ratio for a short period with a trend 302
Summary 303
Exercises 304

Chapter 11: Monte Carlo Simulation and Options 307
Generating random numbers from a standard normal distribution 308
Drawing random samples from a normal (Gaussian) distribution 309
Generating random numbers with a seed 309
Generating n random numbers from a normal distribution 310
Histogram for a normal distribution 310
Graphical presentation of a lognormal distribution 311
Generating random numbers from a uniform distribution 312
Using simulation to estimate the pi value 313
Generating random numbers from a Poisson distribution 315
Selecting m stocks randomly from n given stocks 315
Bootstrapping with/without replacements 317
Distribution of annual returns 319
Simulation of stock price movements 320
Graphical presentation of stock prices at options' maturity dates 322
Finding an efficient portfolio and frontier 324
Finding an efficient frontier based on two stocks 324
Impact of different correlations 326
Constructing an efficient frontier with n stocks 329
Geometric versus arithmetic mean 332
Long-term return forecasting 333
Pricing a call using simulation 334
Exotic options 335
Using the Monte Carlo simulation to price average options 335
Pricing barrier options using the Monte Carlo simulation 337
Barrier in-and-out parity 339
Graphical presentation of an up-and-out and up-and-in parity 340
Pricing lookback options with floating strikes 342
Using the Sobol sequence to improve the efficiency 344
Summary 344
Exercises 345

Chapter 12: Volatility Measures and GARCH 347
Conventional volatility measure – standard deviation 348
Tests of normality 349
Estimating fat tails 350
Lower partial standard deviation 352
Test of equivalency of volatility over two periods 354
Test of heteroskedasticity, Breusch, and Pagan (1979) 355
Retrieving option data from Yahoo! Finance 358
Volatility smile and skewness 360
Graphical presentation of volatility clustering 362

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