Financial analysis using Python training in delhi ncr

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)

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