R2 oscilator

R2 (R-squared) represents the square of the correlation coefficient between current prices and deducted from the linear regression. It is the statistical measure of how well the regression line is adjusted to the actual data, and therefore it measures the strength of the prevailing trend without distinguishing between ascending and descending one.

The R2 value varies between 0 and 1, therefore it is an oscillator of bands that can show signs of saturation (overbought / oversold). The more the value approaches zero, the weaker the trend and, conversely, the closer you approach to 1, the stronger the trend.

For example, an R2 of 0.7 calculated over a period of 5 days means that 70% of the price movements in that interval are explained by the regression line and the remaining 30% is unexplained noise.


Calculation

R2(p,t)= [ Cov(p,t) / (stdDev(t) x stdDev(p)) ] Cov= covariance; stdDev= standard deviation
p= price; t= period

The confidence of the predictions of strength of R2 depends on the period of its calculation. Statistically their critical values ​​for the 95% confidence are:

       period   R2 value (95% confidence)
         5        0.77 
         10       0.40 
         14       0.27 
         20       0.20 
         25       0.16 
         30       0.13 
         50       0.08 
         60       0.06 
         120      0.03

The usefulness of this indicator increases when used with the linear regression slope indicator that reports the direction of the trend, while R2 informs us of the strength of the trend.

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CCI normalized
Jose Miguel Soriano
Commodity Channel Index Technical Indicator (CCI) measures the deviation of the commodity price from its average statistical price. High values of the index point out that the price is unusually high being compared with the average one, and low values show that the price is too low. In spite of its name, the Commodity Channel Index can be applied for any financial instrument, and not only for the wares. There are two basic techniques of using Commodity Channel Index: Finding the divergences. The
If instead of giving the regression value indicator end of the regression line (LRMA), we give the value of its slope, we obtain LRS or Linear Regression Slope Indicator. Since the slope is positive when prices rise, zero when they are in range and negative when they are lowered, LRS provides us the data on the price trend. Calculation                       sum(XY, n) - avg(Y, n)*sum(X, n)   Y= a + mX;      m=  --------------------------------        a= avg(Y, n) - m*avg(X, n)                   
ATR channel all MAs
Jose Miguel Soriano
"ATR channel all MAs jm" is a indicator that allows displaying on a chart the ATR channel calculated according to the moving average selected. You can select 9 standard MAs available in MetaTrader 5 - SMA, EMA, SMMA, LWMA, DEMA, TEMA, Frama, VIDYA, AMA, TRIX, and 4 non-standard - LRMA, HMA, JMA, AFIRMA. General Parameters: Channel type - true: channel ATR, false: channel price. Method MA - select the type of moving average to show in the current graph. Period MA - the number of bars to calculat
"Bollinger Bands all MAs" is an indicator that allows drawing Bollinger Bands calculated according to the selected moving average. You can select 9 standard MAs available in MetaTrader 5 - SMA, EMA, SMMA, LWMA, DEMA, TEMA, Frama, VIDYA, AMA, TRIX, and 4 non-standard ones - LRMA, HMA, JMA, AFIRMA. General Parameters Method MA - select the type of moving average to be displayed in the current graph. Period MA - the number of bars to calculate the MA. Width bands - the width of the bands expressed
Normalized OBV
Jose Miguel Soriano
When looking at the information of On Balance Volume indicator, a question arises: is it a strong or weak movement? Should it be compared with previous days? These data should be normalized to always have a reference. This indicator presents the followings parameters: Normalization period (Max-Mins absolute) Smoothing period (-1 off) Tick/Real Volume Maximum number of bars to calculate (-1 off) Upper value normalization Lower value normalization Calculation: normOBV = (value - absMin) * (relMax
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