Investment Risk and Return Analysis In Python

14 lessons0%

Resources

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 13 of 14

Premium lesson

Buy this course or get a membership to access all premium lessons.

One-time purchase - yours to keep forever

Access all premium courses and features with one membership

Already have access? Sign in here

AI Instructor

Hello, how can I help today?

Ask questions about this lesson and get instant answers.

Conducting Normality Tests in Python: Practical Applications

Normality tests like the Shapiro-Wilk test and the Anderson-Darling test determine whether a distribution follows a normal distribution. Python provides several libraries, including SciPy and Statsmodels, that offer functions for conducting normality tests.

Shapiro-Wilk Test for Normality

The Shapiro-Wilk test evaluates the null hypothesis that a sample comes from a normally distributed population. The results of the test are interpreted as follows:

Conducting Normality Tests in Python: Practical Applications

Premium membership required

Upgrade to premium to unlock all videos and courses