Quadratic RegressionA quadratic regression is the process of finding the equation that best fits a set of data.This form of regression is mainly used for smoothing data shaped like a parabola.
Because we can use short/midterm/longterm periods we can say that we use a Quadratic Least Squares Moving Average or a Moving Quadratic Regression.
Like the Linear Regression (LSMA) a Quadratic regression attempt to minimize the sum of squares (sum of the squared difference between a set of data and an estimator), this is why
those kinds of filters have low lag .
Here the difference between a Least Squared Moving Average ( green ) and a Quadratic Regression ( red ) of both period 500
Here it look like the Quadratic Regression have a best fit than the LSMA
Zerolag
PRICE SATURATION INDEX / FİYAT YOĞUNLUK ENDEKSİEN: PRICE SATURATION INDEX is a momentum algorithm that measures price intensity. It helps us to determine the times when the price reaches intensity and calculates the latency in those moving averages. Moving averages have lag. The lag is necessary because the smoothing is done using past data. It shows you how to filtered a selected amount of lag from an exponential moving average (ema) and price movements. Removing all the lag is not necessarily a good thing, because with no lag, the indicator would just track out the price we were filtering, just as it is the moving average of 1 period; the amount of lag removed is a tradeoff with the amount of smoothing we are willing to forgo with golden ratio and multiline function. We show you the effects of lag removal in an indicator and then use the filter in an effective trading strategy with multiline function. The multiline function is inspired by Jhon Ehlers' zero lag formule, smooth moving average strategy and Schrödinger equation. The Schrödinger equation is a wave function based on quantum mechanics
TR: FİYAT YOĞUNLUK ENDEKSİ, fiyat yoğunluğunu ölçen bir momentum algoritmasıdır. Fiyatın yoğunluğa ulaştığı zamanları belirlememize ve hareketli ortalamalardaki gecikmeyi hesaplamamıza yardımcı olur. Hareketli ortalamalar daima gecikir. Gecikme gereklidir çünkü yumuşatma geçmiş veriler kullanılarak yapılır. Bu algoritma hem fiyat hareketlerindeki hemde üstel hareketli ortalamadaki gecikme miktarının nasıl filtreleneceğini gösterir. Tüm gecikmenin kaldırılması iyi bir şey değildir, çünkü gecikme olmadığında gösterge sadece 1 periyodun hareketli ortalaması gibi davranacağı için filtrelediğimiz fiyatı izleyecektir; filtrelenen gecikme miktarı, terk etmek istediğimiz yumuşatma miktarına alternatif bir multiline fonksiyon ve altın orana uyarlanan frekans değirinden oluşur. Bu göstergede gecikmenin ortadan kaldırılmasının etkilerini gösteriyoruz ve daha sonra filtreyi multiline fonksiyona sahip etkili bir trading stratejisi olarak kullanıyoruz. Multiline fonksiyon, Jhon Ehler'in zero lag formülü, smooth hareketli ortalama stratejisi ve Schrödinger denkleminden esinlenmiştir. Schrödinger denklemi ise kuantum mekaniğini temel alan bir dalga fonksiyonudur.
Double Exponential SmoothingSingle Exponential Smoothing ( ema ) does not excel in following the data when there is a trend. This situation can be improved by the introduction of a second equation with a second constant gamma .
The gamma constant cant be lower than 0 and cant be greater than 1, higher values of gamma create less lag while preserving smoothness.Higher values of length must be followed by higher values of gamma in order to keep the lag low.
The first smoothing part consist of a classic ema but we add s-s1 to the previous smoothed value, this will help decrease lag.The second smoothing part then updates the trend, which is expressed as the difference between the last two values.
Finite Impulse Response (FIR) FilterFinite Impulse Response (FIR) Filter indicator script.
This indicator was originally developed by John F. Ehlers (Stocks & Commodities V. 20:7 (26-31): Zero-Lag Data Smoothers).
NOTE: Ehlers' favorite FIR filter had 1, 2, 3, 3, 2, 1, 0 coefficients.
Ahrens Moving AverageAhrens Moving Average indicator script.
This indicator was originally developed by Richard D. Ahrens (Stocks & Commodities V.31:11 (26-30): Build A Better Moving Average).
Infinite Impulse Response (IIR) FilterInfinite Impulse Response (IIR) Filter indicator script.
This indicator was originally developed by John Ehlers (Stocks & Commodities V. 20:7 (26-31): Zero-Lag Data Smoothers).
Zero Lag Exponential Moving AverageZero Lag Exponential Moving Average indicator script based on the original version by John Ehlers and Ric Way
Forex MA Racer - SMA Performance /w ZeroLag EMA TriggerThis strategy uses 5 Simple Moving Averages and 2 ZeroLag Exponential Moving Averages, to determine possible entries and exits.
- Pretuned for Forex on 15m period
- Uses SMA(10/20/50/100/200) and EMA(9/21) by default
- Be cautios in sideward markets!
Zero Lag EMA v2 by KIVANÇ fr3762A different version of ZERO LAG EMA indicator by John Ehlers and Ric Way...
In this cover, Zero Lag EMA is calculated without using the PREV function.
The main purpose is that to provide BUY/SELL signals earlier than classical EMA's.
You can see the difference of conventional and Zero Lag EMA in the chart.
The red line is classical EMA and the blue colored line is ZEMA ( Zero Lag Ema ).
Turkish Explanation:
Ehlers ve Way'in ZERO LAG ,ndikatörünün Prev (previous value) kullanılmadan yorumlanarak hesaplanmış hali.
Amaç klasik Üssel Ortalamaya göre daha hızlı tepki verip, Al/Sat sinyallerini daha erken alabilmek.
Grafikte kırmızı renkle görülen normal Üssel HO ve mavi renkli olan Zero Lag (gecikmesiz) Üssel HO
Zero-Lag Average Directional Index with DI+ and DI-This average directional index follows the Nyquist Sampling Criterion making showing even better behaviour in high volatility environments than the Ehlers & Ric's "Zero Lag Moving Average".
Applies the same formulae as the moving average at Zero-lag Dürschner Moving Average
From the paper abstract: "The well-known Moving Averages (MA), namely the Simple Moving Average ( SMA ), the Exponential Moving Average ( EMA ) and the Weighted Moving Average ( WMA ), are modified in this paper with the help of the Nyquist Criterion. These modified Moving Averages 3.0 show good smoothing characteristics, illustrate relevant trends and trend reversals in price series without a time lag as far as calculated. With regard to smoothing, trend patterns and time lag bring about a significant improvement on conventional SMA (Moving Averages 1.0: SMA, EMA and WMA ). In addition to this, the efficiency of the Moving Averages 3.0 is demonstrated by applying several tests and a simple trading system."
The Dürschner Moving Average was published at the IFTA 2012 (International Federation of Technical Analysts) Journal, page 27.
