Author Message:
The Regularized MA Oscillator Suite calculates the moving average (MA) based on user-defined parameters such as length, moving average type, and custom smoothing factors. It then derives the mean and standard deviation of the MA using a normalized period. Finally, it computes the Z-Score by subtracting the mean from the MA and dividing it by the standard deviation.
CODE:
CSS:
#//https://www.tradingview.com/v/LAFDETZX/
#// This work is licensed under a Attribution-NonCommercial-ShareAlike 4.0 International
#// © EliCobra
#indicator("Regularized-MA Oscillator Suite", "{?} - MA Osc. Suite", false)
# Converted by Sam4Cok@Samer800 - 12/2023
declare lower;
input matype = {"SMA", default "EMA", "DEMA", "TEMA", "HMA", "EHMA", "THMA", "RMA", "WMA", "VWMA", "T3", "KAMA", "ALMA", "LSMA"};
input Source = close; # "Source"
input Length = 14; # "Length"
input KaufmanFast = 0.666; # "Kaufman Fast"
input KaufmanSlow = 0.0645; # "Kaufman Slow"
input almaOffset = 0.85; # "ALMA Offset"
input almaSigma = 6; # "ALMA Sigma"
input RegularizeLength = 30; # "Regularize Length"
input ReversionThreshold = {"1", "2", default "3"}; # "Reversion Threshold"
input ShowReversalSignals = yes; # "Show Reversal Signals"
input BarColoring = {default "None", "Trend", "Extremities", "Reversions", "Slope"}; # "Bar Coloring"
def na = Double.NaN;
def last = isNaN(Close);
DefineGlobalColor("up", CreateColor(0,179,80));
DefineGlobalColor("dn", CreateColor(187,0,16));
def revt;
Switch (ReversionThreshold) {
Case "3" : revt = 3;
Case "2" : revt = 2;
Default : revt = 1;
}
#f_kama(src, len, kamaf, kamas) =>
script f_kama {
input src = close;
input len = 14;
input kamaf = 0.666;
input kamas = 0.0645;
def white = AbsValue(src - src[1]);
def nsignal = AbsValue(src - src[len]);
def nnoise = Sum(white, len);
def nefratio = if nnoise != 0 then nsignal / nnoise else 0;
def nsmooth = Power(nefratio * (kamaf - kamas) + kamas, 2) ;
def ama = CompoundValue(1, ama[1] + nsmooth * (src - ama[1]), 0);
plot out = ama;
}
#f_t3(src, len) =>
script f_t3 {
input src = close;
input len = 14;
def x1 = ExpAverage(src, len);
def x2 = ExpAverage(x1, len);
def x3 = ExpAverage(x2, len);
def x4 = ExpAverage(x3, len);
def x5 = ExpAverage(x4, len);
def x6 = ExpAverage(x5, len);
def b = 0.7;
def c1 = - Power(b, 3);
def c2 = 3 * Power(b, 2) + 3 * Power(b, 3);
def c3 = -6 * Power(b, 2) - 3 * b - 3 * Power(b, 3);
def c4 = 1 + 3 * b + Power(b, 3) + 3 * Power(b, 2);
def f_t3 = c1 * x6 + c2 * x5 + c3 * x4 + c4 * x3;
plot out = f_t3;
}
#f_ehma(src, length) =>
script f_ehma {
input src = close;
input length = 14;
def ema1 = ExpAverage(src, length);
def ema2 = 2 * ExpAverage(src, length / 2);
def SqrtLen = Round(Sqrt(length), 0);
def f_ehma = ExpAverage(ema2 - ema1, SqrtLen);
plot out = f_ehma;
}
#f_thma(src, length) =>
script f_thma {
input src = close;
input length = 14;
def wma1 = WMA(src, length);
def wma2 = WMA(src, length / 2);
def wma3 = 3 * WMA(src, length / 3);
def f_thma = WMA(wma3 - wma2 - wma1, length);
plot out = f_thma;
}
script f_alma {
input series = close;
input windowsize = 9;
input offset = 0.85;
input sigma = 6;
def m = offset * (windowsize - 1);
def s = windowsize / sigma;
def norm = fold i = 0 to windowsize with p do
p + Exp(-1 * Sqr(i - m) / (2 * Sqr(s)));
def sum = fold j = 0 to windowsize with q do
q + GetValue(series, windowsize - j - 1) * Exp(-1 * Sqr(j - m) / (2 * Sqr(s)));
def f_alma = sum / norm;
plot out = f_alma;
}
#vwma(source, length)
script VWMA {
input src = close;
input len = 14;
input vol = volume;
def nom = Average(src * vol, len);
def den = Average(vol, len);
def VWMA = nom / den;
plot result = VWMA;
}
#f_ma(src, len, type, kamaf, kamas, offset, sigma) =>
script f_ma {
input src = close;
input len = 14;
input type = "EMA";
input kamaf = 0.666;
input kamas = 0.0645;
input offset = 0.85;
input sigma = 6;
def x =
if type == "SMA" then Average(src, len) else
if type == "EMA" then ExpAverage(src, len) else
if type == "HMA" then HullMovingAvg(src, len) else
if type == "RMA" then WildersAverage(src, len) else
if type == "WMA" then WMA(src, len) else
if type == "VWMA" then vwma(src, len) else
if type == "ALMA" then f_alma(src, len, offset, sigma) else
if type == "DEMA" then DEMA(src, len) else
if type == "TEMA" then TEMA(src, len) else
if type == "EHMA" then f_ehma(src, len) else
if type == "THMA" then f_thma(src, len) else
if type == "T3" then f_t3(src, len) else
if type == "KAMA" then f_kama(src, len, kamaf, kamas) else
if type == "LSMA" then Inertia(src, len) else ExpAverage(src, len);
def f_ma = x;
plot out = f_ma;
}
def ma = f_ma(Source, Length, matype, KaufmanFast, KaufmanSlow, almaOffset, almaSigma);
def mean = Average(ma, RegularizeLength);
def dev = stdev(ma, RegularizeLength);
def zmean = (ma - mean) / dev;
def max = if last then na else 4;
def hh = if last then na else 3;
def lh = if last then na else 2;
plot mid = if last then na else 0;#, "Mid Line"
def min = if last then na else -4;
def ll = if last then na else -3;
def hl = if last then na else -2;
mid.SetStyle(Curve.SHORT_DASH);
mid.SetDefaultColor(Color.GRAY);
AddCloud(max, hh, CreateColor(88, 4, 48));
AddCloud(max, lh, CreateColor(88, 4, 48));
AddCloud(ll, min, CreateColor(2, 75, 48));
AddCloud(hl, min, CreateColor(2, 75, 48));
plot z = zmean; # "Z"
z.AssignValueColor(if zmean > 0 then GlobalColor("up") else GlobalColor("dn"));
z.SetLineWeight(2);
def zh = z / 2;
AddCloud(z, zh, Color.DARK_GREEN, Color.DARK_RED);
def col;
Switch (BarColoring) {
Case "Trend" : col = if zmean > 0 then 1 else -1;#? #00b350 : #bb0010
Case "Extremities" :
col = if zmean > 2 then 1 else
if zmean < -2 then -1 else 0;
Case "Reversions":
col = if (zmean > revt and zmean < zmean[1]) and !(zmean[1] < zmean[2]) then -1 else
if (zmean < -revt and zmean > zmean[1]) and !(zmean[1] > zmean[2]) then 1 else 0;
Case "Slope" :
col = if zmean > zmean[1] then 1 else -1;
Default : col = na;
}
def color = col;
AssignPriceColor(if isNaN(color) then Color.CURRENT else
if color > 0 then GlobalColor("up") else
if color < 0 then GlobalColor("dn") else Color.GRAY);
def os = ShowReversalSignals and zmean < -revt and zmean > zmean[1] and !(zmean[1] > zmean[2]);
def ob = ShowReversalSignals and zmean > revt and zmean < zmean[1] and !(zmean[1] < zmean[2]);
plot OverSold = if os then z - 0.5 else na;
plot OverBought = if ob then z + 0.5 else na;
OverSold.SetPaintingStrategy(PaintingStrategy.POINTS);
OverBought.SetPaintingStrategy(PaintingStrategy.POINTS);
OverSold.SetDefaultColor(Color.GREEN);
OverBought.SetDefaultColor(Color.RED);
OverSold.SetLineWeight(2);
OverBought.SetLineWeight(2);
#-- END OF CODE