/Applied-Finance-with-Python

Applied-Finance-with-Python

This repository contains Financial concepts in Python programming language.

Contents

  • Theoretical Concepts:

    • Intro to Python for Finance: In this jupyter notebook, I have built some basic visualizations from Stocks data of 2 Companies. I have used Matplotlib package for creating line plots, and histograms.
    • Python Datetime() function for Finance: In this jupyter notebook, I have explained Python Datetime() function. How to create and manipulate Python datetime objects to help identify key financial events.
    • Exploratory Data Analysis for Financial Data: In this jupyter notebook, I have performed Exploratory Data Analysis (EDA) on Stock Prices dataset of a company (any XYZ).
    • Financial Concepts in Python: In this jupyter notebook, I have explained the basic principles of finance. How these are essential in making data-driven financial decisions.
    • Working with Time Series in pandas: In this jupyter notebook, I have explained how to create, manipulate and do calculations on time series data.
    • Time Series Metrics and Resampling: In this jupyter notebook, I have explained how to compare different time series, changing time series frequency (resampling) and its types.
    • Resampling and Interpolation: In this jupyter notebook, I have explained how to resample and interpolate time series data using pandas in Python. What is upsampling and use of different interpolation techniques. Also, What is downsampling and grouping of data.
    • Window Functions with pandas: In this jupyter notebook, I have explained what are window functions, how to calculate time series metrics for both rolling and expanding window functions.
    • Importing Financial Data from Excel: In this jupyter notebook, I have explained how to import, clean and combine data from Excel Workbook Sheets to Pandas DataFrame.
    • Importing Financial Data from Web: In this jupyter notebook, I have explained how to online access financial data through the Pandas DataReader package.
    • Summarizing & Visualizing Financial Data: In this jupyter notebook, I have explained how to capture key characterstics of variables and to understand distribution of variables in the dataset.
    • Aggregating & Describing Financial Data: In this jupyter notebook, I have explained how to group data by one or more categorical variables, and to calculate and visualize summary statistics for each category.

  • Case Studies:

    • S&P 100 Companies: The S&P 100 is a stock market index made up of one hundred major companies in the United States that span multiple industries. In this case study, I have analyzed all the S&P 100 companies as well as sector specific companies.

  • Projects:

    • Predicting Credit Card Approvals: In this project, I have built a machine learning model to predict if a credit card application will get approved or not. I have used Credit Card Approval Dataset from UCI Machine Learning Repository.