Polynomial Regression Extrapolation

Discover a new way to look at trading trends with a Polynomial Regression Indicator. This advanced tool fits a polynomial curve to the price data using a user-defined degree, enabling traders to forecast potential price movements by extrapolating the result. Whether you're trading Bitcoin or any other asset, this indicator can help you anticipate future trends based on historical data patterns.

Configuration Settings

Optimize your use of this indicator by tweaking these essential settings:

• Length: Defines how many recent price observations are taken to fit the model.
• Extrapolate: Sets the horizon for how far you want to forecast or predict the price.
• Degree: Allows you to select the degree of the polynomial fit. It's crucial for the accuracy and reliability of your analysis.
• Src: The source input for the price to be analyzed.
• Lock Fit: Ensures the polynomial does not adjust to new prices, making the model extend to the most recent price bar without re-fitting.

How to Trade with Polynomial Regression Indicator?

Polynomial regression is an effective tool for identifying trends where relationships between two variables can be explained with a polynomial equation. It is a powerful method in technical analysis to capture underlying trends and establish support or resistance levels on your charts.

A well-known application is fitting a linear regression (polynomial of degree 1) to chart trends. This indicates not only the current movement direction but can also be adapted for nuanced longer-term analyses such as those relating to cryptocurrencies like Bitcoin.

Select the polynomial degree based on your market observations. Typically, going higher than a degree of 3 can introduce noise rather than clarity. It's important to align the degree with the trend you're examining:

• Degree 1: Returns a straight line, best for linear trends.
• Degree 2: Offers a parabolic fit, perfect for assets like stocks which show accelerating trends.
• Degree 6: Captures intricate variations but may forecast potential turning points, which should be validated with caution.

In practice, a lower degree indicates broader trends whereas higher degrees offer detailed trend mapping, albeit with increased noise.

Technical Details

The polynomial regression model is mathematically expressed as:

y(t) = β(0) + β(1)x(t) + β(2)x(t)^2 + ... + β(p)x(t)^p

Here, the coefficient vector β minimizes the squared error between the observed market prices and the polynomial model output y(t). Solve these equations by either iterative algorithms or straightforward numerical solutions:

β(0) + β(1)x(0) + β(2)x(0)^2 + ... + β(p)x(0)^p = y(0)
β(0) + β(1)x(1) + β(2)x(1)^2 + ... + β(p)x(1)^p = y(1)
...
β(0) + β(1)x(t-1) + β(2)x(t-1)^2 + ... + β(p)x(t-1)^p = y(t-1)

To enhance accuracy when dealing with higher degrees, subtract the mean from prices to stabilize the x values.

FAQs: Getting Started with Polynomial Regression Indicator

1. How can I access the Polynomial Regression Indicator?

You can get access to this indicator on the LuxAlgo Library for charting platforms like TradingView, MetaTrader (MT4/MT5), and NinjaTrader for free.

2. Can polynomial regression predict accurate turning points?

It has potential, but there's no guarantee for exact reversals. Higher degrees may highlight potential areas, which should be confirmed with other analysis methods.