Python and Statistics for Financial Analysis

Course by: The Hong Kong University of Science and Technology

About the Course

Why do investment banks and consumer banks use Python to build quantitative models to predict returns and evaluate risks? What makes Python one of the most popular tools for financial analysis?

In this module, you are going to learn basic Python to import, manipulate, and visualize stock data. As Python is highly readable and simple enough, you can build one of the most popular trading models — Trend Following Strategy — by the end of this module!

Learning Objectives

By the end of this repository/course, you will be able to:

• Explain the 2 major reasons why Python is one of the most popular languages for doing financial analysis.
• Summarize the 4 major Python packages that are essential for financial data analysis.
• Show the overall procedure of importing CSV files into a DataFrame.
• Select the important columns of a DataFrame and create new features to further enhance the analysis.
• Build a simple trading strategy (Trend Following Strategy) based on the concepts and coding provided.

Technologies & Tools

• Python 3
• Pandas (Data manipulation)
• NumPy (Numerical computing)
• Matplotlib / Seaborn (Data visualization)
• Jupyter Notebooks

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Disclaimer: The code provided in this repository is for educational purposes only and does not constitute financial advice.