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Discussion of article "Exploring Seasonal Patterns of Financial Time Series with Boxplot"

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New article Exploring Seasonal Patterns of Financial Time Series with Boxplot has been published:

In this article we will view seasonal characteristics of financial time series using Boxplot diagrams. Each separate boxplot (or box-and-whiskey diagram) provides a good visualization of how values are distributed along the dataset. Boxplots should not be confused with the candlestick charts, although they can be visually similar.

Prices have a shift in the average value over time and form trends, therefore the statistical analysis is not applicable to such raw series. Percentage price changes (price increments) are usually used in econometrics to ensure they all lie in the same value range. The percentage changes can be received using the pd.DataFrame(rates['close'].pct_change(1)) method.

We need average monthly price ranges. Let us arrange the table so as to receive the average values of monthly increments by years and display them on the boxplot diagram.


Fig. 1. Average price increment ranges by month, over 10 years.

Author: Maxim Dmitrievsky

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