The regression bands in this context are calculated using polynomial regression, a statistical technique used to model the relationship between a dependent variable (in this case, the logarithm of SOL prices) and one or more independent variables (in this case, time). The purpose of these bands is to visualize the trend in SOL prices over time and to identify potential areas of overvaluation or undervaluation.

Here's how the regression bands are calculated:

Polynomial Regression: A polynomial function is fitted to the SOL price data over time. The degree of the polynomial (in this case, 4) determines the flexibility of the curve.

Trendline: The polynomial function generates a trendline that represents the overall trend in SOL prices. This trendline serves as the central line for the regression bands.

Standard Deviation: The residuals, or the differences between the observed SOL prices and the predicted prices from the trendline, are calculated. The standard deviation of these residuals provides a measure of the variability of SOL prices around the trendline.

Upper and Lower Bands: The upper and lower bands are constructed by adding and subtracting multiples of the standard deviation from the trendline, respectively. These bands represent the boundaries within which SOL prices are expected to fluctuate if they follow the trend established by the polynomial regression.

The utility of regression bands lies in their ability to identify potential buying or selling opportunities based on deviations from the trendline. Traders often use these bands to determine entry and exit points for trading positions. When SOL prices approach or breach the upper or lower bands, it may signal overbought or oversold conditions, prompting traders to consider adjusting their positions accordingly. Additionally, the bands provide a visual representation of the historical volatility and trend direction of SOL prices, aiding in market analysis and decision-making.