Polynomial Regression Extrapolation

Polynomial Regression Extrapolation fits a polynomial curve of user-defined degree to recent prices and extends the result into the future, turning a statistical fit into a visible forecast on the chart. Degree 1 recovers the familiar straight regression line; higher degrees let the curve bend with accelerating trends and hint at where they might turn.

How to Trade the Polynomial Regression Extrapolation?

• Degree 1: a straight line, the cleanest read on linear trend direction.
• Degree 2: a parabolic fit suited to steadily accelerating moves.
• Higher degrees (such as 6): capture intricate variation and can flag potential turning points. Validate these with caution before acting.
• Extrapolated segment: the forward portion of the curve is the model's projection; watch how price behaves against it rather than trading it blindly.

As a rule, lower degrees describe the broad trend while higher degrees map detail at the cost of noise. Going beyond degree 3 usually adds more of the latter than the former. The fitted curve can also serve as a reference for support or resistance within the trend it describes.

Polynomial Regression Extrapolation Settings

• Length: how many recent price observations feed the fit.
• Extrapolate: the forecast horizon, how far forward the curve is projected.
• Degree: the polynomial degree, the central trade-off between smoothness and detail.
• Src: the price source being modeled.
• Lock Fit: freezes the fit so the curve extends to the most recent bar without re-fitting to new prices.

Frequently Asked Questions

Can it predict exact turning points?

How does it relate to Linear Regression?

How can I start using Polynomial Regression Extrapolation?