## Financial analysis using Python training in delhi gurgaon noida.

###
Introduction and Installation of Python

• Introduction to Python

• Installing Python

• Different versions of Python

• Ways to launch Python

• Launching Python with GUI

• Launching Python from the Python command line

• Launching Python from our own DOS window

• Quitting Python

• Error messages

• Python language is case sensitive

• Initializing the variable

• Finding the help window

• Finding manuals and tutorials

• Finding the version of Python

Using Python as an Ordinary Calculator

• Assigning values to variables

• Displaying the value of a variable

• Error messages

• Can’t call a variable without assignment

• Choosing meaningful names

• Using dir() to find variables and functions

• Deleting or unsigning a variable

• Basic math operations – addition, subtraction,

multiplication, and division

• The power function, floor, and remainder

• A true power function

• Choosing appropriate precision

• Finding out more information about a specific built-in function

• Listing all built-in functions

• Importing the math module

• The pi, e, log, and exponential functions

• “import math” versus “from math import *”

• A few frequently used functions

• The print() function

• The type() function

• Last expression _ (underscore)

• Combining two strings

• The upper() function

• The tuple data type

Using Python as a Financial Calculator

• Writing a Python function without saving it

• Default input values for a function

• Indentation is critical in Python

• Checking the existence of our functions

• Defining functions from our Python editor

• Activating our function using the import function

• Debugging a program from a Python editor

• Two ways to call our pv_f() function

• Generating our own module

• Types of comments

• The first type of comment

• The second type of comment

• Finding information about our pv_f() function

• The if() function

• Annuity estimation

• Converting the interest rates

• Continuously compounded interest rate

• A data type – list

• Net present value and the NPV rule

• Defining the payback period and the payback period rule

• Defining IRR and the IRR rule

• Showing certain files in a specific subdirectory

• Using Python as a financial calculator

• Adding our project directory to the path

13 Lines of Python to Price a Call Option

• Writing a program – the empty shell method

• Writing a program – the comment-all-out method

• Using and debugging other programs

Introduction to Modules

• What is a module?

• Importing a module

• Adopting a short name for an imported module

• Showing all functions in an imported module

• Comparing “import math” and “from math import *”

• Deleting an imported module

• Importing only a few needed functions

• Finding out all built-in modules

• Finding out all the available modules

• Finding the location of an imported module

• More information about modules

• Finding a specific uninstalled module

• Module dependency

Introduction to NumPy and SciPy

• Installation of NumPy and SciPy

• Launching Python from Anaconda

• Examples of using NumPy

• Examples of using SciPy

• Showing all functions in NumPy and SciPy

• More information about a specific function

• Understanding the list data type

• Working with arrays of ones, zeros, and the identity matrix

• Performing array manipulations

• Performing array operations with +, -, *, /

• Performing plus and minus operations

• Performing a matrix multiplication operation

• Performing an item-by-item multiplication operation

• The x.sum() dot function

• Looping through an array

• Using the help function related to modules

• A list of subpackages for SciPy

• Cumulative standard normal distribution

• Logic relationships related to an array

• Statistic submodule (stats) from SciPy

• Interpolation in SciPy

• Solving linear equations using SciPy

• Generating random numbers with a seed

• Finding a function from an imported module

• Understanding optimization

• Linear regression and Capital Assets Pricing Model (CAPM)

• Retrieving data from an external text file

• The loadtxt() and getfromtxt() functions

• Installing NumPy independently

• Understanding the data types

Visual Finance via Matplotlib

• Installing matplotlib via ActivePython

• Alternative installation via Anaconda

• Understanding how to use matplotlib

• Understanding simple and compounded interest rates

• Adding texts to our graph

• Working with DuPont identity

• Understanding the Net Present Value (NPV) profile

• Using colors effectively

• Using different shapes

• Graphical representation of the portfolio diversification effect

• Number of stocks and portfolio risk

• Retrieving historical price data from Yahoo! Finance

• Histogram showing return distribution

• Comparing stock and market returns

• Understanding the time value of money

• Candlesticks representation of IBM’s daily price

• Graphical representation of two-year price movement

• IBM’s intra-day graphical representations

• Presenting both closing price and trading volume

• Adding mathematical formulae to our graph

• Adding simple images to our graphs

• Saving our figure to a file

• Performance comparisons among stocks

• Comparing return versus volatility for several stocks

• Finding manuals, examples, and videos

• Installing the matplotlib module independently

Statistical Analysis of Time Series

• Installing Pandas and statsmodels

• Launching Python using the Anaconda command prompt

• Launching Python using the DOS window

• Launching Python using Spyder

• Using Pandas and statsmodels

• Using Pandas

• Examples from statsmodels

• Open data sources

• Retrieving data to our programs

• Inputting data from the clipboard

• Retrieving historical price data from Yahoo! Finance

• Inputting data from a text file

• Inputting data from an Excel file

• Inputting data from a CSV file

• Retrieving data from a web page

• Inputting data from a MATLAB dataset

• Several important functionalities

• Using pd.Series() to generate one-dimensional time series

• Using date variables

• Using the DataFrame

• Return estimation

• Converting daily returns to monthly returns

• Converting daily returns to annual returns

• Merging datasets by date

• Forming an n-stock portfolio

• T-test and F-test

• Tests of equal means and equal variances

• Testing the January effect

• Many useful applications

• 52-week high and low trading strategy

• Roll’s model to estimate spread (1984)

• Amihud’s model for illiquidity (2002)

• Pastor and Stambaugh (2003) liquidity measure

• Fama-French three-factor model

• Fama-MacBeth regression

• Estimating rolling beta

• Understanding VaR

• Constructing an efficient frontier

• Estimating a variance-covariance matrix

• Optimization – minimization

• Constructing an optimal portfolio

• Constructing an efficient frontier with n stocks

• Understanding the interpolation technique

• Outputting data to external files

• Outputting data to a text file

• Saving our data to a binary file

• Reading data from a binary file

• Python for high-frequency data

• Spread estimated based on high-frequency data

• More on using Spyder

• A useful dataset

The Black-Scholes-Merton Option Model

• Payoff and profit/loss functions for the call and put options

• European versus American options

• Cash flows, types of options, a right, and an obligation

• Normal distribution, standard normal distribution, and cumulative standard normal distribution

• The Black-Scholes-Merton option model on non-dividend

paying stocks

• The p4f module for options

• European options with known dividends

• Various trading strategies

• Covered call – long a stock and short a call

• Straddle – buy a call and a put with the same exercise prices

• A calendar spread

• Butterfly with calls

• Relationship between input values and option values

• Greek letters for options

• The put-call parity and its graphical representation

• Binomial tree (the CRR method) and its graphical representation

• The binomial tree method for European options

• The binomial tree method for American options

• Hedging strategies

Python Loops and Implied Volatility

• Definition of an implied volatility

• Understanding a for loop

• Estimating the implied volatility by using a for loop

• Implied volatility function based on a European call

• Implied volatility based on a put option model

• The enumerate() function

• Estimation of IRR via a for loop

• Estimation of multiple IRRs

• Understanding a while loop

• Using keyboard commands to stop an infinitive loop

• Estimating implied volatility by using a while loop

• Nested (multiple) for loops

• Estimating implied volatility by using an American call

• Measuring efficiency by time spent in finishing a program

• The mechanism of a binary search

• Sequential versus random access

• Looping through an array/DataFrame

• Assignment through a for loop

• Looping through a dictionary

• Retrieving option data from CBOE

• Retrieving option data from Yahoo! Finance

• Different expiring dates from Yahoo! Finance

• Retrieving the current price from Yahoo! Finance

• The put-call ratio

• The put-call ratio for a short period with a trend

Monte Carlo Simulation and Options

• Generating random numbers from a standard normal distribution

• Drawing random samples from a normal (Gaussian) distribution

• Generating random numbers with a seed

• Generating n random numbers from a normal distribution

• Histogram for a normal distribution

• Graphical presentation of a lognormal distribution

• Generating random numbers from a uniform distribution

• Using simulation to estimate the pi value

• Generating random numbers from a Poisson distribution

• Selecting m stocks randomly from n given stocks

• Bootstrapping with/without replacements

• Distribution of annual returns

• Simulation of stock price movements

• Graphical presentation of stock prices at options’ maturity dates

• Finding an efficient portfolio and frontier

• Finding an efficient frontier based on two stocks

• Impact of different correlations

• Constructing an efficient frontier with n stocks

• Geometric versus arithmetic mean

• Long-term return forecasting

• Pricing a call using simulation

• Exotic options

• Using the Monte Carlo simulation to price average options

• Pricing barrier options using the Monte Carlo simulation

• Barrier in-and-out parity

• Graphical presentation of an up-and-out and up-and-in parity

• Pricing lookback options with floating strikes

• Using the Sobol sequence to improve the efficiency

Volatility Measures and GARCH

• Conventional volatility measure – standard deviation

• Tests of normality

• Estimating fat tails

• Lower partial standard deviation

• Test of equivalency of volatility over two periods

• Test of heteroskedasticity, Breusch, and Pagan (1979)

• Retrieving option data from Yahoo! Finance

• Volatility smile and skewness

• Graphical presentation of volatility clustering

• The ARCH model

• Simulating an ARCH (1) process

• The GARCH (Generalized ARCH) model

• Simulating a GARCH process

• Simulating a GARCH (p,q) process using modified garchSim()

• GJR_GARCH by Glosten, Jagannanthan, and Runkle (1993)