Investment Risk and Return Analysis In Python

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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 4 of 14

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Step 1: Load the Data

First, we'll use pandas to load the CSV file. Ensure your CSV file has at least two columns: Date and Close, where Date represents the trading date and Close represents the closing price of the stock on that day. We also need to ensure that the data is correctly sorted by date, especially when working with time series data like stock prices. This step is essential to maintain the chronological order of the data, which affects calculations of returns, moving averages, and other time-series analyses.

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