NET (Noise Elimination Technology) on Variety Moving Averages is a moving average indicator that applies John Ehlers' NET (Noise Elimination Technology) to your choice of 36 different moving averages.
█ What is NET (Noise Elimination Technology)?
Noise Elimination Technology (NET) is a method introduced by John Ehlers to enhance the clarity of technical indicators by removing noise without resorting to filtering. Here's a more detailed explanation:
Purpose of Technical Indicators: Technical indicators aim to provide insights into market inefficiencies, assisting traders in making informed decisions. However, many indicators are inherently noisy due to their reliance on a limited amount of data.
Traditional Noise Removal: Noise in indicators is typically removed using smoothing filters. While these filters can reduce noise, they introduce lag, leading to potentially delayed trading decisions which can be costly.
NET's Approach: NET offers a solution to this problem by using the nonlinearity of a rank-ordered Kendall correlation. Instead of filtering, NET clarifies indicators by focusing on their main direction and stripping out noise components.
Kendall Correlation: This is a statistical method that compares the ranked order of two sets of random variables. These pairs of ranked variables can be either concordant or discordant. In the context of NET:
The "y" variable represents a straight line with a positive slope.
The "x" variable is the output of the technical indicator.
When applied, the Kendall correlation in this configuration removes noise components that don't align with the primary direction of the indicator.
NET's Mechanism:
The "y" variable (a straight line with a positive slope) and the "x" variable (indicator output) are used in the Kendall correlation.
This correlation essentially removes noise components not aligned with the main direction of the indicator in a nonlinear manner.
The effectiveness of NET lies in its ability to reduce noise without introducing lag.
Flexibility: NET is designed to be versatile and can be applied to various technical indicators. It doesn't necessarily replace traditional smoothing filters but can complement them to provide a clearer visual representation of the indicator's behavior.
In essence, NET offers a novel approach to refining technical indicators by removing noise using the principles of Kendall correlation, without the drawbacks associated with traditional smoothing filters.
█ Moving Average Types
ADXvma - Average Directional Volatility Moving Average Ahrens Moving Average Alexander Moving Average - ALXMA Double Exponential Moving Average - DEMA Double Smoothed Exponential Moving Average - DSEMA Exponential Moving Average - EMA Fast Exponential Moving Average - FEMA Fractal Adaptive Moving Average - FRAMA Hull Moving Average - HMA IE/2 - Early T3 by Tim Tilson Integral of Linear Regression Slope - ILRS Instantaneous Trendline Laguerre Filter Leader Exponential Moving Average Linear Regression Value - LSMA (Least Squares Moving Average) Linear Weighted Moving Average - LWMA McGinley Dynamic McNicholl EMA Non-Lag Moving Average Parabolic Weighted Moving Average Recursive Moving Trendline Simple Moving Average - SMA Sine Weighted Moving Average Smoothed Moving Average - SMMA Smoother Super Smoother Three-pole Ehlers Butterworth Three-pole Ehlers Smoother Triangular Moving Average - TMA Triple Exponential Moving Average - TEMA Two-pole Ehlers Butterworth Two-pole Ehlers smoother Volume Weighted EMA - VEMA Zero-Lag DEMA - Zero Lag Double Exponential Moving Average Zero-Lag Moving Average Zero Lag TEMA - Zero Lag Triple Exponential Moving Average
█ Included
Bar coloring
Alerts
Channels fill
Loxx's Expanded Source Types
█ Libraries included
loxxmas - moving averages used in Loxx's indis & strats
Skrip ini dipublikasikan secara closed-source dan anda dapat menggunakannya dengan bebas. Anda dapat memfavoritkannya untuk digunakan pada grafik. Anda tidak dapat melihat atau mengubah kode sumbernya.
Informasi dan publikasi tidak dimaksudkan untuk menjadi, dan bukan merupakan saran keuangan, investasi, perdagangan, atau rekomendasi lainnya yang diberikan atau didukung oleh TradingView. Baca selengkapnya di Persyaratan Penggunaan.