Index

A

• ActivatePython installation
  • URL / Installing matplotlib via ActivePython
• American call
  • used, for estimating implied volatility / Estimating implied volatility by using an American call
• American option
  • about / European versus American options
  • versus European option / European versus American options
• Amihud's model for illiquidity (2002)
  • about / Amihud's model for illiquidity (2002)
• Anaconda
  • URL / Installation of NumPy and SciPy
  • Python, launching from / Launching Python from Anaconda
• Anaconda command prompt
  • used, for launching Python / Launching Python using the Anaconda command prompt
• Anaconda installation
  • URL / Alternative installation via Anaconda
• Anderson-Darling test / Tests of normality
• annotate() function / Graphical representation of the portfolio diversification effect
• annualized return distribution
  • estimating / Distribution of annual returns
• annual percentage rate (APR) / Examples of using SciPy
• Annual Percentage Rate (APR) / Converting the interest rates
• annual returns
  • daily returns, converting to / Converting daily returns to annual returns, Merging datasets by date
• annuity
  • estimating / Annuity estimation
• ARCH
  • about / The ARCH model
  • (1) process, simulating / Simulating an ARCH (1) process
• ARCH (1) process
  • simulating / Simulating an ARCH (1) process
• arithmetic average
  • about / Using the Monte Carlo simulation to price average options
• arithmetic mean
  • versus geometric mean / Geometric versus arithmetic mean
• array
  • looping through / Looping through an array, Looping through an array/DataFrame
  • logic relationships / Logic relationships related to an array
• array manipulations
  • performing / Performing array manipulations
• array operations
  • plus operation, performing / Performing plus and minus operations
  • minus operation, performing / Performing plus and minus operations
  • matrix multiplication operation, performing / Performing a matrix multiplication operation
  • item by item multiplication operation, performing / Performing an item-by-item multiplication operation
• arrays
  • working with / Working with arrays of ones, zeros, and the identity matrix
• Asian options
  • about / Using the Monte Carlo simulation to price average options
  • advantages / Using the Monte Carlo simulation to price average options
• Asset-Backed Security (ABS) / Introduction to Python

B

• barrier options
  • pricing, Monte Carlo simulation used / Pricing barrier options using the Monte Carlo simulation
  • Up-and-out option / Pricing barrier options using the Monte Carlo simulation
  • Down-and-out option / Pricing barrier options using the Monte Carlo simulation
  • Up-and-in option / Pricing barrier options using the Monte Carlo simulation
• bear spread with calls trading strategy / Various trading strategies
• bear spread with puts trading strategy / Various trading strategies
• binary file
  • data, saving to / Saving our data to a binary file
  • data, reading from / Reading data from a binary file
• binary search
  • about / The mechanism of a binary search
• binomial tree (CRR) method
  • about / Binomial tree (the CRR method) and its graphical representation
  • graphical representation / Binomial tree (the CRR method) and its graphical representation
  • for European options / The binomial tree method for European options
  • for American options / The binomial tree method for American options
• binomial_grid() function / The p4f module for options, Binomial tree (the CRR method) and its graphical representation
• Black-Scholes-Merton option model
  • about / European versus American options, The Black-Scholes-Merton option model on non-dividend paying stocks
• Bondsonline
  • URL / Open data sources
• bootstrapping
  • without replacements / Bootstrapping with/without replacements
  • with replacements / Bootstrapping with/without replacements
• Breusch
  • about / Test of heteroskedasticity, Breusch, and Pagan (1979)
• bs_call() function / The p4f module for options, Estimating the implied volatility by using a for loop
• built-in functions
  • listing / Listing all built-in functions
• bull spread with calls trading strategy / Various trading strategies
• bull spread with puts trading strategy / Various trading strategies
• Bureau of Labor Statistics
  • URL / Open data sources
• butterfly with calls trading strategy / Various trading strategies, Butterfly with calls
• butterfly with puts trading strategy / Various trading strategies

C

• calendar spread
  • about / A calendar spread
• calendar spread trading strategy / Various trading strategies
• call
  • pricing, simulation used / Pricing a call using simulation
• call buyer
  • about / Cash flows, types of options, a right, and an obligation
• call option
  • about / Cash flows, types of options, a right, and an obligation
• candlesticks
  • used, to represent daily price / Candlesticks representation of IBM's daily price
• capitalize() function / The upper() function
• CAPM
  • about / Linear regression and Capital Assets Pricing Model (CAPM), Pastor and Stambaugh (2003) liquidity measure
  • Fama-French three-factor model / Fama-French three-factor model
  • Fama-MacBeth regression / Fama-MacBeth regression, Estimating rolling beta
  • rolling beta estimation / Estimating rolling beta
  • VaR, using / Understanding VaR
• cash flow
  • about / Cash flows, types of options, a right, and an obligation
• CBOE
  • option data, retrieving from / Retrieving option data from CBOE
  • URL / The put-call ratio
• ceil() function / Activating our function using the import function
• Census Bureau
  • URL / Open data sources
• certain files
  • displaying, in specific subdirectory / Showing certain files in a specific subdirectory
• clipboard
  • data, inputting from / Inputting data from the clipboard
• closing price
  • and trading volume, viewing / Presenting both closing price and trading volume
• Cluster / A list of subpackages for SciPy
• CND() function / Writing a program – the empty shell method
• colors
  • using / Using colors effectively
• comment-all-out method
  • about / Writing a program – the comment-all-out method
  • example / Writing a program – the comment-all-out method
• comments types
  • first type comment / The first type of comment
  • second type comment / The second type of comment
• compounded interest
  • defining / Understanding simple and compounded interest rates
• Constants / A list of subpackages for SciPy
• Consumer Price Index (CPI) / A useful dataset
• continuously compounded interest rate
  • about / Continuously compounded interest rate
• Cook Pine Capital
  • URL / Estimating fat tails
• CPI (consumer price index) / Choosing meaningful names
• CQ dataset
  • about / Spread estimated based on high-frequency data
• CSV file
  • data, inputting from / Inputting data from a CSV file
• CT dataset
  • URL / Python for high-frequency data
  • about / Python for high-frequency data
• cumulative standard normal distribution
  • about / Cumulative standard normal distribution, Normal distribution, standard normal distribution, and cumulative standard normal distribution
• current price
  • retrieving, from Yahoo! Finance / Retrieving the current price from Yahoo! Finance

D

• %d / The tuple data type
• daily price
  • representing, candlesticks used / Candlesticks representation of IBM's daily price
• daily returns
  • converting, to monthly returns / Converting daily returns to monthly returns, Converting daily returns to annual returns
  • converting, to annual returns / Converting daily returns to annual returns, Merging datasets by date
• data
  • retrieving, from external text file / Retrieving data from an external text file
  • inputting, from clipboard / Inputting data from the clipboard
  • inputting, from text file / Inputting data from a text file
  • inputting, from Excel file / Inputting data from an Excel file, Inputting data from a CSV file
  • inputting, from CSV file / Inputting data from a CSV file
  • retrieving, from web page / Retrieving data from a web page
  • inputting, from MATLAB dataset / Inputting data from a MATLAB dataset
  • outputting, to external files / Outputting data to external files
  • outputting, to text file / Outputting data to a text file
  • saving, to binary file / Saving our data to a binary file
  • reading, from binary file / Reading data from a binary file
• DataFrame
  • used, for working with time series / Using the DataFrame
  • looping through / Looping through an array/DataFrame
• dataset
  • URL / A useful dataset, Selecting m stocks randomly from n given stocks
• datasets
  • merging, by date / Merging datasets by date
  • n-stock portfolio, forming / Forming an n-stock portfolio, T-test and F-test
• data types
  • about / A data type – list, Understanding the data types
• date
  • datasets, merging by / Merging datasets by date
• datetime.date.today() function / Retrieving historical price data from Yahoo! Finance
• date variables
  • used, for working with time series / Using date variables
• default input values
  • used, for functions / Default input values for a function
• default precision
  • choosing / Choosing appropriate precision
• del() function / Deleting or unsigning a variable
• delta
  • about / Greek letters for options
• delta_call() function / Greek letters for options
• delta_put function / Greek letters for options
• dictionary
  • looping through / Looping through a dictionary
• different correlations
  • impact of / Impact of different correlations
• different shapes
  • using / Using different shapes
• dir() function
  • using, for finding variables / Using dir() to find variables and functions
  / Importing the math module, Checking the existence of our functions, Showing certain files in a specific subdirectory, Showing all functions in NumPy and SciPy
• dir2() function / Default input values for a function, Showing certain files in a specific subdirectory
• DOS window
  • Python, launching / Launching Python from our own DOS window
  • used, for launching Python / Launching Python using the DOS window
• Down-and-out option / Pricing barrier options using the Monte Carlo simulation
• DuPont identity
  • working with / Working with DuPont identity

E

• e (2.71828) / The pi, e, log, and exponential functions
• Economics module / Module dependency
• effective annual rate (EAR) / Converting the interest rates
• efficiency
  • measuring, by time spent / Measuring efficiency by time spent in finishing a program
• efficient frontier
  • constructing / Constructing an efficient frontier
  • variance-covariance matrix, estimating / Estimating a variance-covariance matrix
  • variance-covariance matrix optimization / Optimization – minimization
  • optimal portfolio, constructing / Constructing an optimal portfolio
  • constructing, n stocks used / Constructing an efficient frontier with n stocks
  • finding, based on two stocks / Finding an efficient frontier based on two stocks
  • constructing, with n stocks / Constructing an efficient frontier with n stocks
• empty shell method
  • about / Writing a program – the empty shell method
  • describing / Writing a program – the empty shell method
• Enter key / Finding the help window
• enumerate() function / Net present value and the NPV rule, The enumerate() function
• equal means test
  • performing / Tests of equal means and equal variances
• equal variances test
  • performing, sp.stats.bartlet used / Tests of equal means and equal variances
• error message
  • about / Error messages
  • abcde variable, error message / Can't call a variable without assignment
• error messages / Error messages
• European option
  • about / European versus American options
  • versus American option / European versus American options
• European options
  • with known dividends / European options with known dividends
• Excel file
  • data, inputting from / Inputting data from an Excel file, Inputting data from a CSV file
• existence
  • checking, of functions / Checking the existence of our functions
• exit modes / GJR_GARCH by Glosten, Jagannanthan, and Runkle (1993)
• exotic options
  • about / Exotic options
  • Monte Carlo simulation, using / Using the Monte Carlo simulation to price average options
  • barrier options pricing, Monte Carlo simulation used / Pricing barrier options using the Monte Carlo simulation
• expiration dates
  • retrieving, from Yahoo! Finance / Different expiring dates from Yahoo! Finance
  • URL / Different expiring dates from Yahoo! Finance
• external text file
  • data, retrieving from / Retrieving data from an external text file

F

• F-test
  • about / T-test and F-test
• Fama-French dataset / Lower partial standard deviation
• Fama-French three-factor model
  • about / Fama-French three-factor model
• Fama-MacBeth regression
  • about / Fama-MacBeth regression, Estimating rolling beta
• fat tails
  • estimating / Estimating fat tails
• Federal Reserve Bank Data Library
  • URL / Open data sources
• Fftpack / A list of subpackages for SciPy
• fGarch / Simulating a GARCH (p,q) process using modified garchSim()
• figure
  • saving, to file / Saving our figure to a file
• file
  • figure, saving to / Saving our figure to a file
• File | New Window Ctrl + N / Writing a program – the comment-all-out method
• fin101() function / Using Python as a financial calculator
• finance related Python modules
  • Ystockquote / Module dependency
  • Quant / Module dependency
  • trytond_currency / Module dependency
  • Economics / Module dependency
  • trytond_project / Module dependency
  • trytond_analytic_account / Module dependency
  • trytond_account_statement / Module dependency
  • trytond_stock_split / Module dependency
  • trytond_stock_forecast / Module dependency
  • Finance / Module dependency
  • FinDates / Module dependency
• financial calculator
  • Python, using as / Using Python as a financial calculator
• FinDates module / Module dependency
• first type comment / The first type of comment
• floating strikes
  • lookback options, pricing with / Pricing lookback options with floating strikes
• floor function
  • using / The power function, floor, and remainder
• for loop
  • about / Understanding a for loop
  • used, for estimating implied volatility / Estimating the implied volatility by using a for loop
  • IRR, estimating via / Estimation of IRR via a for loop
  • assigning through / Assignment through a for loop
• from math import * / Comparing "import math" and "from math import *"
• functions
  • print() function / The print() function
  • type() function / The type() function
  • upper() function / The upper() function
  • default input values, using for / Default input values for a function
  • existence, checking / Checking the existence of our functions
  • defining, from Python editor / Defining functions from our Python editor
  • activating, import function used / Activating our function using the import function
  • displaying, in NumPy / Showing all functions in NumPy and SciPy
  • displaying, in SciPy / Showing all functions in NumPy and SciPy
  • finding, from imported module / Finding a function from an imported module

G

• GARCH
  • about / The GARCH (Generalized ARCH) model
  • process, simulating / Simulating a GARCH process
  • (p,q) process simulating, modified garchSim() used / Simulating a GARCH (p,q) process using modified garchSim()
• GARCH (p,q) process
  • simulating, modified garchSim() used / Simulating a GARCH (p,q) process using modified garchSim()
• garchSim() R function / Simulating a GARCH (p,q) process using modified garchSim()
• genfromtxt() function / The loadtxt() and getfromtxt() functions
• geometric average
  • about / Using the Monte Carlo simulation to price average options
• geometric mean
  • versus arithmetic mean / Geometric versus arithmetic mean
• GJR_GARCH() function / GJR_GARCH by Glosten, Jagannanthan, and Runkle (1993)
• Glosten model / GJR_GARCH by Glosten, Jagannanthan, and Runkle (1993)
• Google Finance
  • URL / Open data sources
• graph
  • texts, adding to / Adding texts to our graph
  • mathematical formulae, adding to / Adding mathematical formulae to our graph
  • simple images, adding to / Adding simple images to our graphs
• Greek letters
  • for options / Greek letters for options
• GUI
  • used, for Python launching / Launching Python with GUI

H

• head() function / Retrieving option data from Yahoo! Finance
• hedging strategies
  • about / Hedging strategies
• help() function / A true power function, Finding out more information about a specific built-in function, Using the help function related to modules, Generating random numbers from a standard normal distribution
• help(round) function / Finding out more information about a specific built-in function
• help function
  • using / Using the help function related to modules
• help window
  • finding / Finding the help window
• heteroskedasticity / Test of heteroskedasticity, Breusch, and Pagan (1979)
• high-frequency data
  • about / Python for high-frequency data
  • TAQ database / Python for high-frequency data
  • retrieving, from Google Finance / Python for high-frequency data
  • TAQ / Python for high-frequency data
  • TORQ database / Python for high-frequency data
  • spread estimation based / Spread estimated based on high-frequency data
• High minus Low (HML) / A useful dataset
• histogram
  • used, for displaying return distribution / Histogram showing return distribution
  • about / Histogram for a normal distribution
• historical price data
  • retrieving, from Yahoo Finance / Retrieving historical price data from Yahoo! Finance
  • retrieving, from Yahoo! Finance / Retrieving historical price data from Yahoo! Finance
• HML (High Minus Low) / Selecting m stocks randomly from n given stocks

I

• IBM option data
  • URL / Retrieving option data from CBOE
• if() function / The if() function
• implied volatility
  • about / Definition of an implied volatility
  • estimating, for loop used / Estimating the implied volatility by using a for loop
  • estimating, while loop used / Estimating implied volatility by using a while loop
  • estimating, American call used / Estimating implied volatility by using an American call
• implied volatility function
  • based on European call / Implied volatility function based on a European call
  • based on put option model / Implied volatility based on a put option model
• imported module
  • short name, adopting for / Adopting a short name for an imported module
  • all functions, dispalying / Showing all functions in an imported module
  • deleting / Deleting an imported module
  • location, finding / Finding the location of an imported module
  • functions, finding from / Finding a function from an imported module
• import function
  • used, for activating functions / Activating our function using the import function
• import math / Comparing "import math" and "from math import *"
• in-and-out parity
  • about / Barrier in-and-out parity
• indentation
  • in Python / Indentation is critical in Python
• input values, and option values
  • relationship / Relationship between input values and option values
• installation
  • Python / Installing Python
• Integrate / A list of subpackages for SciPy
• interest rates
  • converting / Converting the interest rates
• Internal Rate of Return (IRR) / Understanding the Net Present Value (NPV) profile
• International Business Machines (IBM) / Introduction to Python
• Interpolate / A list of subpackages for SciPy
• interpolation technique
  • about / Understanding the interpolation technique, Outputting data to external files
• intra-day graphical representations
  • about / IBM's intra-day graphical representations
• intra-day pattern
  • URL / IBM's intra-day graphical representations
• Io / A list of subpackages for SciPy
• IRR
  • defining / Defining IRR and the IRR rule
  • estimating, via for loop / Estimation of IRR via a for loop
• IRR rule
  • defining / Defining IRR and the IRR rule
• isnan() function / Estimation of IRR via a for loop
• item by item multiplication operation
  • performing / Performing an item-by-item multiplication operation
• items() function / Looping through a dictionary

J

• Jagannanthan model / GJR_GARCH by Glosten, Jagannanthan, and Runkle (1993)
• January effect
  • testing / Testing the January effect
• join() function / Converting daily returns to monthly returns

K

• Kolmogorov-Smirnov test / Tests of normality
• kurtosis / Estimating fat tails

L

• LaTeX
  • URL / Adding mathematical formulae to our graph
• LEGB rule / A true power function
• len() function / The tuple data type, Retrieving historical price data from Yahoo! Finance
• Linalg / A list of subpackages for SciPy
• linear equations
  • solving, SciPy used / Solving linear equations using SciPy
• linspace() function / Understanding how to use matplotlib
• list data type
  • about / Understanding the list data type
• lo() function / Writing a program – the comment-all-out method
• loadmat() function / Inputting data from a MATLAB dataset
• loadtxt() function / The loadtxt() and getfromtxt() functions
• logic relationships / Logic relationships related to an array
• lognormal distribution
  • graphical presentation / Graphical presentation of a lognormal distribution
• long-term return forecast
  • about / Long-term return forecasting
• lookback options
  • pricing, with floating strikes / Pricing lookback options with floating strikes
• loss function
  • for call option / Payoff and profit/loss functions for the call and put options
• lower partial standard deviation
  • about / Lower partial standard deviation

M

• M.P. Visser / Graphical presentation of volatility clustering
• manuals, Python
  • finding / Finding manuals and tutorials
  • online tutorials / Finding manuals and tutorials
  • PDF version / Finding manuals and tutorials
• market returns
  • and stock, comparing / Comparing stock and market returns
• mathematical formulae
  • adding, to graph / Adding mathematical formulae to our graph
• math import * / "import math" versus "from math import *"
• math module
  • importing / Importing the math module
  • pi (3.14159265) / The pi, e, log, and exponential functions
  • e (2.71828) / The pi, e, log, and exponential functions
• MATLAB dataset
  • data, inputting from / Inputting data from a MATLAB dataset
• matplotlib
  • installing, via ActivatePython / Installing matplotlib via ActivePython
  • alternative installation, via Anaconda / Alternative installation via Anaconda
  • using / Understanding how to use matplotlib
  • URL / Finding manuals, examples, and videos
• matplotlib module
  • installing / Installing the matplotlib module independently
• Matplotlib module / Finding out all the available modules
• matrix multiplication operation
  • performing / Performing a matrix multiplication operation
• mean() function / Geometric versus arithmetic mean
• meaningful variable names
  • choosing / Choosing meaningful names
• min_value variable / Implied volatility based on a put option model
• module
  • about / What is a module?, More information about modules
  • importing / Importing a module
  • short name, adopting for / Adopting a short name for an imported module
  • log() function, importing / Importing only a few needed functions
  • exp() function, importing / Importing only a few needed functions
  • sqrt(), importing / Importing only a few needed functions
  • built-in modules / Finding out all built-in modules
  • available modules, finding / Finding out all the available modules
  • specific uninstalled module, finding / Finding a specific uninstalled module
  • dependency approaches / Module dependency
• Monte Carlo simulation
  • using / Using the Monte Carlo simulation to price average options
  • used, for pricing barrier options / Pricing barrier options using the Monte Carlo simulation
• monthly returns
  • daily returns, converting to / Converting daily returns to monthly returns, Converting daily returns to annual returns
• m stocks
  • random selection, from n given stocks / Selecting m stocks randomly from n given stocks
• multiple IRRs
  • estimating / Estimation of multiple IRRs

N

• n-stock portfolio
  • forming / Forming an n-stock portfolio, T-test and F-test
• Ndimage / A list of subpackages for SciPy
• Nested (multiple) for loops
  • about / Nested (multiple) for loops
• Net Present Value ( NPV) function / The enumerate() function
• normal distribution
  • about / Normal distribution, standard normal distribution, and cumulative standard normal distribution
  • drawing / Normal distribution, standard normal distribution, and cumulative standard normal distribution
  • random samples, drawing from / Drawing random samples from a normal (Gaussian) distribution
  • n random numbers, generating from / Generating n random numbers from a normal distribution
  • histogram / Histogram for a normal distribution
• normality test
  • about / Tests of normality
• normdist() function / Writing a program – the empty shell method
• np.argmin() function / The x.sum() dot function
• np.array() function / Examples of using NumPy
• np.irr() function / Understanding the Net Present Value (NPV) profile
• np.linspace() function / Interpolation in SciPy
• np.min() function / The x.sum() dot function
• np.npv() function / Examples of using SciPy
• np.random.normal() function / Understanding how to use matplotlib
• np.size() function / Examples of using NumPy
• np.std() function / Examples of using NumPy
• NPV
  • about / Net present value and the NPV rule
  • defining / Understanding the Net Present Value (NPV) profile
• NPV() function / Examples of using SciPy
• NPV profile
  • about / Understanding the Net Present Value (NPV) profile
  • colors, using / Using colors effectively
  • different shapes, using / Using different shapes
• NPV rule
  • about / Net present value and the NPV rule
• npv_f() function / Estimation of IRR via a for loop
• n random numbers
  • generating, from normal distribution / Generating n random numbers from a normal distribution
• n stocks
  • used, for constructing an efficient frontier / Constructing an efficient frontier with n stocks
  • efficient frontier, constructing with / Constructing an efficient frontier with n stocks
• NumPy
  • installing / Installation of NumPy and SciPy, Installing NumPy independently
  • functions, displaying in / Showing all functions in NumPy and SciPy
• NumPy, using
  • examples / Examples of using NumPy
• numpy.random function / Generating random numbers from a standard normal distribution
• NumPy module / Finding the location of an imported module

O

• % operator / The power function, floor, and remainder
• Odr / A list of subpackages for SciPy
• OLS regression
  • using / Examples from statsmodels
• one dimensional time series
  • generating, pd.Series() function used / Using pd.Series() to generate one-dimensional time series, Using date variables
  • date variables, using / Using date variables
  • DataFrame, using / Using the DataFrame
• open data sources
  • Yahoo! Finance / Open data sources
  • Google Finance / Open data sources
  • Federal Reserve Bank Data Library / Open data sources
  • Russell indices / Open data sources
  • Prof. French's Data Library / Open data sources
  • Census Bureau / Open data sources
  • Bondsonline / Open data sources
  • U.S. Department of the Treasury / Open data sources
  • Bureau of Labor Statistics / Open data sources
  • Yahoo! Finance, downloading from / Open data sources
• optimal portfolio
  • constructing / Constructing an optimal portfolio
• optimization
  • about / Understanding optimization
• optimization, variance-covariance matrix
  • about / Optimization – minimization
• optimize / A list of subpackages for SciPy
• option data
  • retrieving, from CBOE / Retrieving option data from CBOE
  • retrieving, from Yahoo! Finance / Retrieving option data from Yahoo! Finance
  • retrieving, Yahoo! Finance / Retrieving option data from Yahoo! Finance
• over-the-counter (OTC) / Exotic options
• own module
  • generating / Generating our own module

P

• p4f module
  • for options / The p4f module for options
• Pagan
  • about / Test of heteroskedasticity, Breusch, and Pagan (1979)
• Pandas
  • installing / Installing Pandas and statsmodels
  • used, for data manipulation / Using Pandas, Examples from statsmodels
• Pandas module / Introduction to Python
• Pandas pickle format
  • URL / The mechanism of a binary search
• Pastor and Stambaugh (2003) liquidity measure
  • about / Pastor and Stambaugh (2003) liquidity measure
• path
  • project directory, adding to / Adding our project directory to the path
• Path Browser / More information about modules
• path function / Adding our project directory to the path
• payback period
  • defining / Defining the payback period and the payback period rule
• payback period rule
  • defining / Defining the payback period and the payback period rule
• payoff function
  • for call option / Payoff and profit/loss functions for the call and put options
• pd.DataFrame() function / Using the DataFrame
• pd.interpolate() function / Understanding the interpolation technique
• pd.ols function / Estimating rolling beta
• pd.read_clipboard() function / Inputting data from the clipboard
• pd.read_csv() function / Retrieving data from a web page
• pd.Series() function / Using pd.Series() to generate one-dimensional time series
  • used, for generating one dimensional time series / Using pd.Series() to generate one-dimensional time series, Using date variables
• permutation() function / Bootstrapping with/without replacements
• pi (3.14159265) / The pi, e, log, and exponential functions
• PIN (Probability of informed trading) / Generating random numbers from a Poisson distribution
• pi value
  • estimating, simulation used / Using simulation to estimate the pi value
• plt.bar() function / Working with DuPont identity
• plus operation
  • performing / Performing plus and minus operations
• Poisson distribution
  • random numbers, generating from / Generating random numbers from a Poisson distribution
• portfolio diversification effect
  • graphical representation / Graphical representation of the portfolio diversification effect
  • number of stocks / Number of stocks and portfolio risk
  • portfolio risk / Number of stocks and portfolio risk
• power function
  • using / The power function, floor, and remainder
  • about / A true power function
• print() function / The print() function, Generating random numbers from a standard normal distribution
• Prof. French's Data Library
  • URL / Open data sources
• profit function
  • for call option / Payoff and profit/loss functions for the call and put options
• program
  • debugging, from Python editor / Debugging a program from a Python editor
  • debugging / Using and debugging other programs
• project directory
  • adding, to path / Adding our project directory to the path
• put-call parity
  • about / The put-call parity and its graphical representation
  • graphical representation / The put-call parity and its graphical representation
• put-call ratio
  • about / The put-call ratio
  • for shorter period / The put-call ratio for a short period with a trend
• put option
  • about / Payoff and profit/loss functions for the call and put options, Cash flows, types of options, a right, and an obligation
• pv() function / Launching Python from Anaconda
• pv_f() function / Defining functions from our Python editor, The second type of comment
  • calling / Two ways to call our pv_f() function
  • about / Finding information about our pv_f() function
• Python
  • about / Introduction to Python
  • benefits / Introduction to Python
  • modules / Introduction to Python
  • shortcoming / Introduction to Python
  • installing / Installing Python
  • launching, ways / Installing Python
  • versions / Different versions of Python
  • launching, with GUI / Launching Python with GUI
  • launching, from Python command line / Launching Python from the Python command line
  • launching, from own DOS window / Launching Python from our own DOS window
  • quiting, ways / Quitting Python, Python language is case sensitive
  • help window, finding / Finding the help window
  • version, finding / Finding the version of Python
  • addition operation / Basic math operations – addition, subtraction, multiplication, and division
  • subtraction operation / Basic math operations – addition, subtraction, multiplication, and division
  • multiplication operation / Basic math operations – addition, subtraction, multiplication, and division
  • division operation / Basic math operations – addition, subtraction, multiplication, and division
  • used, as financial calculator / Using Python as a financial calculator
  • launching, from Anaconda / Launching Python from Anaconda
  • launching, Anaconda command prompt used / Launching Python using the Anaconda command prompt
  • launching, DOS window used / Launching Python using the DOS window
  • launching, Spyder used / Launching Python using Spyder
• Python code
  • for down-and-in put option / Pricing barrier options using the Monte Carlo simulation
• Python command line
  • Python, launching from / Launching Python from the Python command line
• Python editor
  • functions, defining from / Defining functions from our Python editor
  • program, debugging from / Debugging a program from a Python editor
• Python function
  • writing / Writing a Python function without saving it
• Python home documents / Finding manuals and tutorials
• Python Manuals
  • finding / Finding manuals and tutorials
• Python Package Index
  • URL / Module dependency

Q

• quant / What is a module?
• Quant module / Module dependency

R

• r.sort() function / Retrieving historical price data from Yahoo! Finance
• randint() function / Selecting m stocks randomly from n given stocks
• random.rand() function / Generating random numbers with a seed
• random.seed() function / GJR_GARCH by Glosten, Jagannanthan, and Runkle (1993)
• random access
  • versus sequential access / Sequential versus random access
• random numbers
  • generating, with seed / Generating random numbers with a seed, Generating random numbers with a seed
  • generating, from standard normal distribution / Generating random numbers from a standard normal distribution
  • generating, from uniform distribution / Generating random numbers from a uniform distribution
  • generating, from Poisson distribution / Generating random numbers from a Poisson distribution
• random samples
  • drawing, from normal distribution / Drawing random samples from a normal (Gaussian) distribution
• randrange() function / Selecting m stocks randomly from n given stocks
• range() function / Understanding a for loop
• read_csv() function / Retrieving data from a web page
• read_table() function / Inputting data from a text file
• remainder / The power function, floor, and remainder
• remove() function / Selecting m stocks randomly from n given stocks
• return
  • versus volatility, comparing / Comparing return versus volatility for several stocks
• return distribution
  • displaying, histogram used / Histogram showing return distribution
• return estimation
  • about / Return estimation
  • daily returns, converting to monthly returns / Converting daily returns to monthly returns
  • daily returns, converting to annual returns / Converting daily returns to annual returns, Merging datasets by date
• Return on Equity (ROE) / Working with DuPont identity
• ret_f() function / Test of equivalency of volatility over two periods
• Roll's model to estimate spread (1984)
  • about / Roll's model to estimate spread (1984)
• rolling beta
  • estimating / Estimating rolling beta
• round() function / Choosing appropriate precision
• Runkle model / GJR_GARCH by Glosten, Jagannanthan, and Runkle (1993)

S

• SciPy
  • installing / Installation of NumPy and SciPy
  • functions, displaying in / Showing all functions in NumPy and SciPy
  • subpackages / A list of subpackages for SciPy
  • stats / Statistic submodule (stats) from SciPy
  • interpolating in / Interpolation in SciPy
  • used, for solving linear equations / Solving linear equations using SciPy
• SciPy, subpackages
  • Cluster / A list of subpackages for SciPy
  • Constants / A list of subpackages for SciPy
  • Fftpack / A list of subpackages for SciPy
  • Integrate / A list of subpackages for SciPy
  • Interpolate / A list of subpackages for SciPy
  • Io / A list of subpackages for SciPy
  • Linalg / A list of subpackages for SciPy
  • Ndimage / A list of subpackages for SciPy
  • Odr / A list of subpackages for SciPy
  • optimize / A list of subpackages for SciPy
  • signal / A list of subpackages for SciPy
  • sparse / A list of subpackages for SciPy
  • spatial / A list of subpackages for SciPy
  • special / A list of subpackages for SciPy
  • stats / A list of subpackages for SciPy
• SciPy, using
  • examples / Examples of using SciPy
• second type comment / The second type of comment
• Securities and Exchange Commission (SEC) / Introduction to Python
• seed
  • random numbers, generating with / Generating random numbers with a seed, Generating random numbers with a seed
• seed() function / Generating random numbers with a seed, Generating random numbers from a uniform distribution, Simulation of stock price movements
• sequential access
  • versus random access / Sequential versus random access
• Shapiro-Wilk test / Tests of normality
• sign() function / Estimating implied volatility by using a while loop
• signal / A list of subpackages for SciPy
• simple images
  • adding, to graph / Adding simple images to our graphs
• simple interest
  • defining / Understanding simple and compounded interest rates
• simulation
  • used, for pi value estimation / Using simulation to estimate the pi value
  • used, for pricing call / Pricing a call using simulation
• skewness / Estimating fat tails, Volatility smile and skewness
• Small minus Big (SMB) / A useful dataset
• SMB (Small Minus Big) / Selecting m stocks randomly from n given stocks
• smile / Volatility smile and skewness
• Sobol sequence
  • used, for improving efficiency / Using the Sobol sequence to improve the efficiency
  • URL / Using the Sobol sequence to improve the efficiency
• sp.fv() function / Examples of using SciPy
• sp.npv() function / Examples of using SciPy
• sp.pmt() function / Examples of using SciPy
• sp.prod() function / Examples of using SciPy
• sp.pv() function / Examples of using SciPy
• sp.stats.bartlet function
  • used, for testing equal variance / Tests of equal means and equal variances
• sparse / A list of subpackages for SciPy
• spatial / A list of subpackages for SciPy
• special / A list of subpackages for SciPy
• specific function
  • about / More information about a specific function
• specific subdirectory
  • certain files, displaying / Showing certain files in a specific subdirectory
• specific uninstalled module
  • finding / Finding a specific uninstalled module
• spread estimation
  • based on, high-frequency data / Spread estimated based on high-frequency data
• Spyder
  • used, for launching Python / Launching Python using Spyder
  • using / More on using Spyder
  • URL / More on using Spyder
• sqrt() function / Error messages, "import math" versus "from math import *", Importing a module, Deleting an imported module
• sqrt(3) command / Error messages
• standard normal distribution
  • about / Normal distribution, standard normal distribution, and cumulative standard normal distribution
  • random numbers, generating from / Generating random numbers from a standard normal distribution
• stats / A list of subpackages for SciPy, Statistic submodule (stats) from SciPy
• stats.anderson() function / Tests of normality
• stats.norm.cdf() function / Normal distribution, standard normal distribution, and cumulative standard normal distribution, The Black-Scholes-Merton option model on non-dividend paying stocks
• stats.norm.pdf() function / Normal distribution, standard normal distribution, and cumulative standard normal distribution
• Statsmodels / Different versions of Python
• statsmodels
  • installing / Installing Pandas and statsmodels
  • using, for statistical analysis / Examples from statsmodels
  • OLS regression method / Examples from statsmodels
• std() function / More information about a specific function
• stock
  • and market returns, comparing / Comparing stock and market returns
  • performance, comparing among / Performance comparisons among stocks
• stock price movements
  • simulating / Simulation of stock price movements
• straddle trading strategy / Various trading strategies, Straddle – buy a call and a put with the same exercise prices
• strangle trading strategy / Various trading strategies
• strap trading strategy / Various trading strategies
• string.replace() function / Python for high-frequency data
• strip() function / The upper() function
• strip trading strategy / Various trading strategies
• sys module / Finding the version of Python

T

• T-test
  • about / T-test and F-test
  • performing / T-test and F-test
  • equal variances test, performing / Tests of equal means and equal variances
  • equal means test, performing / Tests of equal means and equal variances
  • January effect, testing / Testing the January effect
• tail() function / Retrieving option data from Yahoo! Finance
• terminal stock prices
  • estimating / Graphical presentation of stock prices at options' maturity dates
• text file
  • data, inputting from / Inputting data from a text file
  • data, outputting to / Outputting data to a text file
• texts
  • adding, to graph / Adding texts to our graph
• time value, money
  • defining / Understanding the time value of money
• TORQ database
  • about / Python for high-frequency data
  • URL / Python for high-frequency data
• trading strategies
  • about / Various trading strategies
  • bull spread with calls / Various trading strategies
  • bull spread with puts / Various trading strategies
  • bear spread with puts / Various trading strategies
  • bear spread with calls / Various trading strategies
  • straddle / Various trading strategies, Straddle – buy a call and a put with the same exercise prices
  • strip / Various trading strategies
  • strap / Various trading strategies
  • strangle / Various trading strategies
  • butterfly with calls / Various trading strategies, Butterfly with calls
  • butterfly with puts / Various trading strategies
  • calendar spread / Various trading strategies, A calendar spread
  • covered call / Covered call – long a stock and short a call
• trading volume
  • and closing price, viewing / Presenting both closing price and trading volume
• trytond_account_statement module / Module dependency
• trytond_currency module / Module dependency
• trytond_project module / Module dependency
• trytond_stock_forecast module / Module dependency
• trytond_stock_split module / Module dependency
• ttest_1samp() function / Statistic submodule (stats) from SciPy
• tuple datatype / The tuple data type
• two-year price movement
  • graphical representation / Graphical representation of two-year price movement
• two strings
  • combining / Combining two strings
• type() function / The type() function

U

• U.S. Department of the Treasury
  • URL / Open data sources
• uniform distribution
  • random numbers, generating from / Generating random numbers from a uniform distribution
• unique() function / Spread estimated based on high-frequency data, Selecting m stocks randomly from n given stocks
• Up-and-in option / Pricing barrier options using the Monte Carlo simulation
• up-and-in parity
  • graphical representation / Graphical presentation of an up-and-out and up-and-in parity
• Up-and-out option / Pricing barrier options using the Monte Carlo simulation
• up-and-out parity
  • graphical representation / Graphical presentation of an up-and-out and up-and-in parity
• upper() function / The upper() function
• useful applications
  • 52-week high and low trading strategy / 52-week high and low trading strategy
  • Roll's model to estimate spread (1984) / Roll's model to estimate spread (1984)
  • Amihud's model for illiquidity (2002) / Amihud's model for illiquidity (2002), Pastor and Stambaugh (2003) liquidity measure
  • Pastor and Stambaugh (2003) liquidity measure / Pastor and Stambaugh (2003) liquidity measure

V

• values
  • assigning, to variables / Assigning values to variables
• vanilla options
  • about / Exotic options
• VaR
  • using / Understanding VaR
• variable
  • initializing / Initializing the variable
  • values, assigning to / Assigning values to variables
  • values, displaying / Displaying the value of a variable
  • unsigning / Deleting or unsigning a variable
  • deleting / Deleting or unsigning a variable
• variance-covariance matrix
  • estimating / Estimating a variance-covariance matrix
  • optimization / Optimization – minimization
• versions, Python
  • finding / Finding the version of Python
• Visual financial statements
  • URL / Finding manuals, examples, and videos
• volatility
  • versus return, comparing / Comparing return versus volatility for several stocks
  • about / Conventional volatility measure – standard deviation, Volatility smile and skewness
  • over two periods, equivalency testing / Test of equivalency of volatility over two periods
• volatility clustering
  • about / Graphical presentation of volatility clustering
• volatility skewness
  • about / Volatility smile and skewness
• volatility smile
  • about / Volatility smile and skewness

W

• 52-week high and low trading strategy
  • about / 52-week high and low trading strategy
• web page
  • data, retrieving from / Retrieving data from a web page
• web page examples
  • URL / Finding manuals, examples, and videos
• while loop
  • about / Understanding a while loop
  • used, for estimating implied volatility / Estimating implied volatility by using a while loop

X

• x.sum() dot function
  • about / The x.sum() dot function
• xlim() function / Understanding simple and compounded interest rates

Y

• Yahoo! Finance
  • URL / Open data sources, Retrieving option data from Yahoo! Finance
  • historical price data, retrieving from / Retrieving historical price data from Yahoo! Finance
  • option data, retrieving from / Retrieving option data from Yahoo! Finance, Retrieving option data from Yahoo! Finance
  • different expiring dates / Different expiring dates from Yahoo! Finance
  • current price, retrieving from / Retrieving the current price from Yahoo! Finance
• Yahoo Finance
  • historical price data, retrieving from / Retrieving historical price data from Yahoo! Finance
• yanMonthly.pickle
  • URL / Looping through an array/DataFrame
• ylim() function / Understanding simple and compounded interest rates
• Ystockquote / Module dependency