Python is most powerful tool for Financial Analysis and Data Analysis. You can use python for Machine Learning and Artificial Intelligence.
Syllabus Content
Subject Name: Python for Financial Analysis
| Unit | Title | Details of Topic |
| Unit I | Getting Started with Python | This unit will prepare the learner to use basic python for data analytics- This foundational unit will equip the learner to use the basic syntax of Python |
| Unit II | Numpy, General Overview of Pandas and Matplotlib | Numpy for financial analysis, General overview of pandas and visualization with matplotlib and pandas. Pandas with timeseries data. Capstone stock market analysis project. |
| Unit III | Time series analysis | Time Series basic, Introduction to basic Stat Model, ETS theory, EWMA Theory, ARIMA theory, ACF and PACF theory, ARIMA with stat model. |
| Unit IV | Python finance fundamentals, Calculating and Comparing Rate of Returns, Measuring Investment Risk | Introduction to python finance fundamental, Shape ratio, Simple and moving average, Calculating rate of return of individual share and portfolio. Calculating rate of return of Index. Calculating risk of individual share and portfolio Calculating index risk. |
| Unit V | Using regression in python, Markowitz portfolio optimization, Capital Assets Pricing Model, Multivariate regression analysis, Monte Carlo Simulations. | Calculation of simple regression. Calculation of Alpha, Beta and R squire in python. Markowitz portfolio optimization. Calculation of Multivariate regression. |
Lesson Plan
| U. No | Title | Details of Topic | Duration |
| Unit 1 | Getting Started with Python | Installing Python, Installing Jupyter Notebook, Introduction of Jupyter Notebook and Google colab | |
| Python variables, Data types, Basic Python Syntax, Python Operators. | |||
| Conditional statements, Python Functions, Python sequence, Iteration in Python | |||
| Object oriented programming, Modules and Package, Standard Library, Importing modules. | |||
| Unit 2 | Numpy, General Overview of Pandas and Matplotlib | Introduction to Numpy, Numpy Array, Numpy operation, Numpy indexing | |
| Introduction to Pandas, Series, DataFrames, Missing Data, Groupby with Pandas, Mearging, Joining, Concatenating DataFrame, Pandas common operations, Data input and output | |||
| Introduction to Visualization in Python, Matplotlib | |||
| Pandas visualization overview, Pandas Timeseries visualization | |||
| Unit 3 | Time Series Analysis | Introduction ot data source, Note on PandasDatareader, quandl, Introduction to time Series with Pandas. Datetime Index, Time resampline, Time Shift, Pandas Rolling and Expanding. | |
| Introduction to time series, Time series basic, Introduction to stat models, | |||
| ETS theory, EWMA Theory, EWMA Code along, ETS Code along. | |||
| ARIMA theory, ACF and PACF, ARIMA with Statmodels, Discussion choosing PDQ. | |||
| Unit 4 | Python Finance Fundamentals, Calculating and Computing Rate of Returns. Measuring Investment Risk | Welcome o the Finance Fundamentals, Sharp Ratio, Portfolio Allocation Code Along, Considering Both Risk and Retrun, Calculating Security Rate of Retrun (Simple retrun and logarithmic return), | |
| What is a portfolio of securities and how to calculate its rate of return, Popular stock indices that can help us understand financial markets, Calculating the Indices’ Rate of Return | |||
| Calculating the Indices’ Rate of Return, Calculating a Security’s Risk in Python, The benefits of portfolio diversification, Calculating the covariance between securities, Measuring the correlation between stocks | |||
| Calculating Covariance and Correlation, Considering the risk of multiple securities in a portfolio, Calculating Portfolio Risk, Understanding Systematic vs. Idiosyncratic risk, Calculating Diversifiable and Non-Diversifiable Risk of a Portfolio | |||
| Unit 5 | Using regression in python, Markowitz portfolio optimization, Capital Assets Pricing Model, Multivariate regression analysis, Monte Carlo Simulations | The fundamentals of simple regression analysis, Computing Alpha, Beta, and R Squared in Python | |
| Finance -Markowitz Portfolio Optimization, Obtaining the Efficient Frontier in Python | |||
| The intuition behind the Capital Asset Pricing Model, Understanding and calculating a security’s Beta, Calculating the Expected Return of a Stock. | |||
| Multivariate regression analysis – a valuable tool for finance practitioners, The essence of Monte Carlo simulations |
Batch 1: The course started on the 15th and was completed on the 20th of June 2022 and on the 11th of August 2022.
| S.No. | Name | Certificate Link |
| 1. | Mohamed Hareesudeen email: mohamedharees007@yahoo.com | Link |
| 2. | Dr. Mohammed Mujahed Ali email: mubarak_mujahed@yahoo.co.in | Link |
| 3. | Md Abrar Alam email: mdabrar1994@gmail.com | Link |
| 4. | Abuzar Nomani email: abuzarnomani88@gmail.com | Link |
| 5. | Rithi S R email: rithi.satheesan@gmail.com | Link |
| 6. | HARISH R email: krharish2727@gmail.com | Link |
| 7. | Monika Mishra email: monikabarsha200@gmail.com | Link |
| 8. | Ali Thabit Yahya Al Qaser email: ali.ust77@gmail.com | Link |
| 9. | Tamer Elsheikh email: tamer.elshiekh@com.kfs.adu.eg | Link |
| 10. | Faozi Abduljalil Gazem Al-Maqtari email: faozi@umt.edu.my | Link |
| 11. | Waleed Mutahar Al-ahdal email: wm.alahdal2011@gmail.com | Link |
| 12. | Ayesha Siddiqui email: ayesha.siddiqui91@gmail.com | Link |
| 13. | Najib Hamood Saif Farhan email: Najib720000@gmil.com | Link |