Index
A
- ActivatePython installation
- 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)
- 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
- 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
- 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
- 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
- Bondsonline
- URL / Open data sources
- bootstrapping
- without replacements / Bootstrapping with/without replacements
- with replacements / Bootstrapping with/without replacements
- Breusch
- 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
- call option
- 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
- 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
- comments types
- first type comment / The first type of comment
- second type comment / The second type of comment
- compounded interest
- Constants / A list of subpackages for SciPy
- Consumer Price Index (CPI) / A useful dataset
- continuously compounded interest rate
- Cook Pine Capital
- URL / Estimating fat tails
- CPI (consumer price index) / Choosing meaningful names
- CQ dataset
- CSV file
- data, inputting from / Inputting data from a CSV file
- CT dataset
- 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
- datasets
- merging, by date / Merging datasets by date
- n-stock portfolio, forming / Forming an n-stock portfolio, T-test and F-test
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- intra-day graphical representations
- intra-day pattern
- 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
- 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
- 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
- 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
- Pandas
- installing / Installing Pandas and statsmodels
- used, for data manipulation / Using Pandas, Examples from statsmodels
- Pandas module / Introduction to Python
- Pandas pickle format
- 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
- 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
- pv() function / Launching Python from Anaconda
- pv_f() function / Defining functions from our Python editor, The second type of comment
- 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
- 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)
- 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
- 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
- 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
- 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
- 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
- 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
- volatility skewness
- about / Volatility smile and skewness
- volatility smile
- about / Volatility smile and skewness
W
- 52-week high and low trading strategy
- web page
- data, retrieving from / Retrieving data from a web page
- web page examples
- 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
- ylim() function / Understanding simple and compounded interest rates
- Ystockquote / Module dependency