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
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
