Investment Risk and Return Analysis In Python

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Lessons

Investment Risk and Returns

Discreet Vs Logarithmic Returns – Which One to Use?

Analyzing Financial Time Series Data with Python

Step 1: Load the Data

Step 2: Calculate Returns

Step 3: Visualize the Distribution of Returns

Statistical Foundations of Return Distributions

Moments of a Distribution

Mean, Variance, and Normal Distribution

Calculating the First and Second Moments (Mu and Variance) in Python

Higher Moments of Distribution

Calculate Skewness and Kurtosis in Python

Conducting Normality Tests in Python: Practical Applications

Conclusion

Lesson 3 of 14

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Analyzing Financial Time Series Data with Python

Python is a popular programming language for financial data analysis due to its extensive libraries and tools. Libraries such as Pandas for data manipulation, NumPy for numerical calculations, provide efficient data structures and functions for handling and manipulating financial data. In addition, libraries like Matplotlib and Seaborn allow for visualization of financial data. Analyzing financial timeseries data involves steps like data cleaning, visualization, and statistical analysis to understand the historical performance of investments and predict future trends.

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