Seasonal Chart

The Seasonality Chart script displays seasonal variations of price changes that are best used on the daily timeframe . Users have the option to select the calculation lookback (in years) as well as show the cumulative sum of the seasonal indexes.

Settings

• Lookback (Years): Number of years to use for the calculation of the seasonality chart.
  
• Cumulative Sum: Displays the cumulative sum of seasonal indexes.
  
• Use Percent Change: Uses relative price changes (as a percentage) instead of absolute changes.
  
• Linear Regression: Fits a line on the seasonality chart results using the method of least squares.

Usage

Seasonality refers to the recurrent tendencies in a time series to increase or decrease at specific times of the year. The proposed tool can highlight the seasonal variation of price changes.

It is common for certain analysts to use a cumulative sum of these indexes to display the results, highlighting months with the most significant bullish / bearish progressions.

The above chart allows us to highlight which months prices tended to have their worst performances over the selected number of years.

Note

Daily price changes are required for the construction of the seasonal chart. Thus, charts using a low timeframe might lack data compared to higher ones. We recommend using the daily timeframe for the best user experience.

Details

To construct our seasonal chart, we obtain the average price changes for specific days on a specific month over a user-set number of years from January to December. These individual averages form "seasonal indexes."

This is a common method in classical time series decomposition.

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Example:

To obtain the seasonal index of price changes on January first we record every price change occurring on January first over the years of interest, we then average the result. This operation is done for all days in each month to construct our seasonal chart. This operation is done for all days in each month to construct our seasonal chart.

Seasonal variations are often highlighted if the underlying time series is affected by seasonal factors. For market prices, it is difficult to assess if there are stable seasonal variations on all securities.

The consideration of seasonality by market practitioners has often been highlighted through strategies or observations. One of the most common is expressed by the adage "Sell in May and Go Away" for the US market. We can also mention:

• January Effect
  
• Santa Claus Rally
  
• Mark Twain Effect
  
• ...etc.
  

These are commonly known as calendar effects and appear from the study of seasonal variations over certain years.