OPEN-SOURCE SCRIPT
Atualizado Polynomial-Regression-Fitted Oscillator [Loxx]

Polynomial-Regression-Fitted Oscillator [Loxx] is an oscillator that is calculated using Polynomial Regression Analysis. This is an extremely accurate and processor intensive oscillator.
What is Polynomial Regression?
In statistics, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modeled as an nth degree polynomial in x. Polynomial regression fits a nonlinear relationship between the value of x and the corresponding conditional mean of y, denoted E(y |x). Although polynomial regression fits a nonlinear model to the data, as a statistical estimation problem it is linear, in the sense that the regression function E(y | x) is linear in the unknown parameters that are estimated from the data. For this reason, polynomial regression is considered to be a special case of multiple linear regression .
Things to know
Included
Other indicators in this series using Polynomial Regression Analysis.
Poly Cycle
![Poly Cycle [Loxx]](https://s3.tradingview.com/y/ywjaMkyI_mid.png)
PA-Adaptive Polynomial Regression Fitted Moving Average [Loxx]
![PA-Adaptive Polynomial Regression Fitted Moving Average [Loxx]](https://s3.tradingview.com/x/xj9dbGma_mid.png)
What is Polynomial Regression?
In statistics, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modeled as an nth degree polynomial in x. Polynomial regression fits a nonlinear relationship between the value of x and the corresponding conditional mean of y, denoted E(y |x). Although polynomial regression fits a nonlinear model to the data, as a statistical estimation problem it is linear, in the sense that the regression function E(y | x) is linear in the unknown parameters that are estimated from the data. For this reason, polynomial regression is considered to be a special case of multiple linear regression .
Things to know
- You can select from 33 source types
- The source is smoothed before being injected into the Polynomial fitting algorithm, there are 35+ moving averages to choose from for smoothing
- This indicator is very processor heavy. so it will take some time load on the chart. Ideally the period input should allow for values from 1 to 200 or more, but due to processing restraints on Trading View, the max value is 80.
Included
- Alerts
- Signals
- Bar coloring
Other indicators in this series using Polynomial Regression Analysis.
Poly Cycle
![Poly Cycle [Loxx]](https://s3.tradingview.com/y/ywjaMkyI_mid.png)
PA-Adaptive Polynomial Regression Fitted Moving Average [Loxx]
![PA-Adaptive Polynomial Regression Fitted Moving Average [Loxx]](https://s3.tradingview.com/x/xj9dbGma_mid.png)
Notas de Lançamento
Small update on Alerts.Script de código aberto
No verdadeiro espirito do TradingView, o autor desse script o publicou como código aberto, para que os traders possam entendê-lo e verificá-lo. Parabéns ao autor Você pode usá-lo gratuitamente, mas a reutilização desse código em publicações e regida pelas Regras da Casa.
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Aviso legal
As informações e publicações não devem ser e não constituem conselhos ou recomendações financeiras, de investimento, de negociação ou de qualquer outro tipo, fornecidas ou endossadas pela TradingView. Leia mais em Termos de uso.
Script de código aberto
No verdadeiro espirito do TradingView, o autor desse script o publicou como código aberto, para que os traders possam entendê-lo e verificá-lo. Parabéns ao autor Você pode usá-lo gratuitamente, mas a reutilização desse código em publicações e regida pelas Regras da Casa.
Public Telegram Group, t.me/algxtrading_public
VIP Membership Info: patreon.com/algxtrading/membership
VIP Membership Info: patreon.com/algxtrading/membership
Aviso legal
As informações e publicações não devem ser e não constituem conselhos ou recomendações financeiras, de investimento, de negociação ou de qualquer outro tipo, fornecidas ou endossadas pela TradingView. Leia mais em Termos de uso.