Cosine Kernel Regressions For ThinkOrSwim

apdusp

Active member
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Very interesting reversals' indicator. It looks promising for covered calls, among other options strategies that can benefit from it

Author states:
The Cosine Kernel Regressions indicator (CKR) uses mathematical concepts to offer a unique approach to market analysis. This indicator employs Kernel Regressions using bespoke tunable Cosine functions in order to smoothly interpret a variety of market data, providing traders with incredibly clean insights into market trends.

The CKR is particularly useful for traders looking to understand underlying trends without the 'noise' typical in raw price movements. It can serve as a standalone trend analysis tool or be combined with other indicators for more robust trading strategies.
ClECCuZ.png


hope our script stars can implement this indicator and maybe adding scanning possibilities
thanks in advance

https://www.tradingview.com/script/wgTxuL34-Cosine-Kernel-Regressions-QuantraSystems/
 
Last edited by a moderator:
very interesting reversals indicator. i am looking as promising for covered calls, among other options strategies that can benefit from it
hope our script stars can implement this indicator and maybe adding scanning possibilities
thanks in advance

https://www.tradingview.com/script/wgTxuL34-Cosine-Kernel-Regressions-QuantraSystems/
check the below.

CSS:
#// This Pine Script™ code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
#// © QuantraSystems
#indicator("Cosine Kernel Regressions [QuantraSystems]", "CKR [QuantraSystems]" , overlay = false, max_labels_count = 500)
# Converted by Sam4Cok@Samer800    - 06/2024
declare lower;

#// Indicator Calibrations
input ColorType = {"None", default "Fast Trend", "Slow Trend"};    # "Choose Mode"
input CosineKernelRegressionType = {default "Tuneable", "Stepped"};    # "Cosine Kernel Regression Type"
input Source     = close;        # "Source"
input RegressionLookback = 60;     # "Regression Lookback"
input TuningCoefficient    = 15.0; #   "Tuning Coefficient"
input useStoch = yes;    # "STOCH"
input StochasticLength  = 14;     # "Stochastic Length"
input useRsi   = yes;    # "RSI"
input rsiLength = 14;     # "RSI Length"
input useBollinger = yes;    # "BBPCT"
input bbLength = 20;     # "BBPCT Length"
input useCmo   = yes;    # "CMO"
input ChandeMomentumLength    = 14;     # "Chande Momentum Length"
input useCci   = yes;    # "CCI"
input cciLength = 20;     # "CCI Length"
input useFisher = yes;    # "FISH"
input FisherTransformLength   = 9;      # "Fisher Transform Length"
input useVzo    = yes;    # "VZO"
input vzoLength = 21;     # "VZO Length"

def na = Double.NaN;
def last = IsNaN(close);
def tune = CosineKernelRegressionType == CosineKernelRegressionType."Tuneable";
#// Define a kernel that utilizes the cosine function
#// Kernel Regression //
script kernelRegression {
    input src = close;
    input lookback = 60;
    input tuning = 15;
#    def bar_index = BarNumber();
    def pi = Double.Pi;
    def toFold = lookback - 1; #Min(lookback, bar_index);
    def currentWeight = fold i = 0 to toFold with p do
        p + GetValue(src, i) * (if AbsValue(i / lookback) <= pi / (2 * tuning) then AbsValue(Cos((i / lookback) * tuning)) else 0);
    def totalWeight   = fold j = 0 to toFold with q do
        q + (if AbsValue(j / lookback) <= pi / (2 * tuning) then AbsValue(Cos((j / lookback) * tuning)) else 0);
    def kernelRegression = currentWeight / totalWeight;
    plot out = if !IsNaN(close) and !isNaN(kernelRegression) then kernelRegression else Double.NaN;
}
#// Multi Cosine
script multicosine {
    input src = close;
    input lookback = 60;
    input steps = 15;
    def pi = Double.Pi;
    def toFold = lookback - 1;
    def regression = fold k = 1 to steps-1 with r do
        r + ((fold i = 0 to toFold with p do
        p + (if AbsValue(i / lookback) <= pi / (2 * k) then AbsValue(Cos((i / lookback) * k)) else 0) * src[i]) /
             (fold j = 0 to toFold with q do
        q + (if AbsValue(j / lookback) <= pi / (2 * k) then AbsValue(Cos((j / lookback) * k)) else 0)));
    def multicosine = regression / steps;
    plot out = if !IsNaN(close) and !isNaN(multicosine) then multicosine else Double.NaN;
}
script ALMA {
    input src = close;
    input len = 9;
    input offset = 0;
    input sigma = 6;
    def m = Floor(offset * (len - 1));
    def s = len / sigma;
    def alm = fold i = 0 to len with p do
        if !IsNaN(src[i]) then p + src[i] * Exp(-1 * Power(i - m, 2) / (2 * Power(s, 2))) else p;
    def norm = fold j = 0 to len with q do
        if !IsNaN(src[j]) then  q + Exp(-1 * Power(j - m, 2) / (2 * Power(s, 2))) else q;
    def ALMA = if norm > 0 then alm / norm else Double.NaN;
    plot out = if !IsNaN(close) and !isNaN(ALMA) then ALMA else Double.NaN;
}

#//  ║     RELATIVE STRENGTH INDEX    ║  //
script DynamicRSI {
    input src = close;
    input length = 14;
    def nRSI = RSI(Price = src, Length = length);
    def RSI_ReScale = (nRSI - 50) * 2.8;
    plot out = RSI_ReScale;
}
#/  ║     STOCHASTIC OSCILLATOR      ║  //
script DynamicSTOCH {
    input source = close;
    input hi = high;
    input lo = low;
    input length = 14;
    def lowestLow   = Lowest(lo,  length);
    def highestHigh = Highest(hi, length);
    def stochastic  = 100 * (source - lowestLow) / (highestHigh - lowestLow);
    def STOCH_ReScale = (stochastic - 50) * 2;
    plot out = STOCH_ReScale;
}
#//  ║     BOLLINGER BAND PERCENT     ║  //
script DynamicBBPCT {
    input src = close;
    input length = 20;
    input multi = 1;
    def basis =   Average(src, length);
    def dev   =   multi * StDev(src, length);
    def upper =   basis + dev;
    def lower =   basis - dev;
    def bbpct =  (src   - lower) / (upper - lower);
    def BBPCT_ReScale = (bbpct - 0.5) * 120;
    plot out = BBPCT_ReScale;
}
#//  ║        CHANDE MOMENTUM         ║  //
script DynamicCMO {
    input src = close;
    input length = 20;
    def momm = (src - src[1]);
    def m1   = if momm >= 0 then  momm else 0.0;
    def m2   = if momm <  0 then -momm else 0.0;
    def sm1  = Sum(m1, length);
    def sm2  = Sum(m2, length);
    def div  = sm1 + sm2;
    def chandeMO = if div != 0 then 100 * (sm1 - sm2) / div else 0;
    def CMO_ReScale = (chandeMO * 1.15);
    plot out = CMO_ReScale;
}
#//  ║    COMMODITY CHANNEL INDEX     ║  //
script DynamicCCI {
    input src = close;
    input len = 20;
    def ma  = Average(src, len);
    def Dev = LinDev(src, len);
    def cci = (src - ma) / (0.015 * Dev);
    def CCI_ReScale = if Dev == 0 then 0 else (cci / 2);
    plot out = CCI_ReScale;
}
#//  ║        FISHER TRANSFORM        ║  //
script DynamicFisher {
    input len = 10;
    def fish = FisherTransform(Length = len);
    def FISH_ReScale = (fish * 30);
    plot out = FISH_ReScale;
}
#//  ║     VOLUME ZONE OSCILLATOR     ║  //
script DynamicVZO {
    input length = 14;
    def mom = hlc3 - hlc3[1];
    def sig = Sign(mom);
    def VP = ExpAverage(sig * volume, length / 3);
    def TV = ExpAverage(volume, length / 3);
    def VZO_ReScale = (VP / TV) * 110;
    plot out = VZO_ReScale;
}
#//║    CORE CALCULATIONS   
#// Function to count active indicators
script countCondition {
    input condition = close;
    def count = if !IsNaN(condition) then 1 else 0;
    plot out = count;
}
script nz {
    input val = close;
    def count = if !IsNaN(val) then val else 0;
    plot out = count;
}
def f_rsi = DynamicRSI(Source, rsiLength);
def f_sto = DynamicSTOCH(Source, high, low, StochasticLength);
def f_bbc = DynamicBBPCT(Source, bbLength, 2);
def f_cmo = DynamicCMO(Source, ChandeMomentumLength);
def f_cci = DynamicCCI(Source, cciLength);
def f_fis = DynamicFisher(FisherTransformLength);
def f_vzo = DynamicVZO(vzoLength);

#// Pull all standardized base indicator values
def val_RSI   = if useRsi then f_rsi else na;
def val_STOCH = if useStoch then f_sto else na;
def val_BBPCT = if useBollinger then f_bbc else na;
def val_CMO   = if useCmo then f_cmo else na;
def val_CCI   = if useCci then f_cci else na;
def val_FISH  = if useFisher then f_fis else na;
def val_VZO   = if useVzo then f_vzo else na;

#// Count the number of active indicators
def activeIndicators =
          countCondition(useRsi  ) +
          countCondition(useStoch) +
          countCondition(useBollinger) +
          countCondition(useCmo  ) +
          countCondition(useCci  ) +
          countCondition(useFisher ) +
          countCondition(useVzo  );

#// Calculate the average only with active indicators
def value1 = if activeIndicators > 0 then (
             nz(val_RSI) +
             nz(val_STOCH) +
             nz(val_BBPCT) +
             nz(val_CMO) +
             nz(val_CCI) +
             nz(val_FISH) +
             nz(val_VZO)) / activeIndicators else na;
#// Gentle ALMA smoothing
def value = ALMA(value1, 9, 0, 6);

#// Calulate the Output - Depending on the method of Cosine Regression Selected
def rLen = Round(TuningCoefficient / 5, 0);
def kernelReg1 = kernelRegression(value, RegressionLookback, TuningCoefficient);
def multCosin1 = multicosine(value, RegressionLookback, TuningCoefficient);
def kernelReg2 = kernelRegression(value, RegressionLookback, rLen);
def multCosin2 = multicosine(value, RegressionLookback, rLen);

def out1 = if tune then kernelReg1 else multCosin1;
def out2 = if tune then kernelReg2 else multCosin2;
#// Define Alert Conditions
def fastTrend_up  = out1 > out1[1]  and !(out1[1] > out1[2]);
def fastTrend_dn  = out1 < out1[1]  and !(out1[1] < out1[2]);

#//║         VISUALIZATION        ║//
def col;
switch (ColorType) {
case "None" :
    col = 0;
case "Slow Trend" :
    col = if out2 > 0 then 1 else -1;
default           :
    col = if out1 > out1[1] then 1 else -1;
}
def col1 = if !col then if out1 > out1[1] then 1 else -1 else col;
def col2 = if out2 > 0 then 1 else 0;

#-- plots
plot shapeDn = if fastTrend_dn then out1 else na;
plot shapeUp = if fastTrend_up then out1 else na;
plot sig = out1; #,  "Fast Signal"
plot sig2 = out2;

shapeDn.SetLineWeight(3);
shapeUp.SetLineWeight(3);
shapeDn.SetPaintingStrategy(PaintingStrategy.SQUARES);
shapeUp.SetPaintingStrategy(PaintingStrategy.SQUARES);
shapeDn.SetDefaultColor(Color.RED);
shapeUp.SetDefaultColor(Color.GREEN);
sig.SetLineWeight(2);
sig.AssignValueColor(if col1>0 then Color.CYAN else if col1<0 then Color.MAGENTA else Color.LIGHT_GRAY);
sig2.AssignValueColor(if col2 then Color.DARK_GREEN else Color.DARK_RED);

AssignPriceColor(if !col then Color.CURRENT else if col>0 then Color.CYAN else Color.MAGENTA);

AddCloud(sig2,  0,  Color.DARK_GREEN,  Color.DARK_RED);
AddCloud(if last then na else 100, 50, Color.PLUM);
AddCloud(if last then na else 100, 75, Color.PLUM);
AddCloud(if last then na else -50, -100, CreateColor(0, 118, 118));
AddCloud(if last then na else -75, -100, CreateColor(0, 118, 118));

#-- END of CODE
Code:
 
check the below.

CSS:
#// This Pine Script™ code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
#// © QuantraSystems
#indicator("Cosine Kernel Regressions [QuantraSystems]", "CKR [QuantraSystems]" , overlay = false, max_labels_count = 500)
# Converted by Sam4Cok@Samer800    - 06/2024
declare lower;

#// Indicator Calibrations
input ColorType = {"None", default "Fast Trend", "Slow Trend"};    # "Choose Mode"
input CosineKernelRegressionType = {default "Tuneable", "Stepped"};    # "Cosine Kernel Regression Type"
input Source     = close;        # "Source"
input RegressionLookback = 60;     # "Regression Lookback"
input TuningCoefficient    = 15.0; #   "Tuning Coefficient"
input useStoch = yes;    # "STOCH"
input StochasticLength  = 14;     # "Stochastic Length"
input useRsi   = yes;    # "RSI"
input rsiLength = 14;     # "RSI Length"
input useBollinger = yes;    # "BBPCT"
input bbLength = 20;     # "BBPCT Length"
input useCmo   = yes;    # "CMO"
input ChandeMomentumLength    = 14;     # "Chande Momentum Length"
input useCci   = yes;    # "CCI"
input cciLength = 20;     # "CCI Length"
input useFisher = yes;    # "FISH"
input FisherTransformLength   = 9;      # "Fisher Transform Length"
input useVzo    = yes;    # "VZO"
input vzoLength = 21;     # "VZO Length"

def na = Double.NaN;
def last = IsNaN(close);
def tune = CosineKernelRegressionType == CosineKernelRegressionType."Tuneable";
#// Define a kernel that utilizes the cosine function
#// Kernel Regression //
script kernelRegression {
    input src = close;
    input lookback = 60;
    input tuning = 15;
#    def bar_index = BarNumber();
    def pi = Double.Pi;
    def toFold = lookback - 1; #Min(lookback, bar_index);
    def currentWeight = fold i = 0 to toFold with p do
        p + GetValue(src, i) * (if AbsValue(i / lookback) <= pi / (2 * tuning) then AbsValue(Cos((i / lookback) * tuning)) else 0);
    def totalWeight   = fold j = 0 to toFold with q do
        q + (if AbsValue(j / lookback) <= pi / (2 * tuning) then AbsValue(Cos((j / lookback) * tuning)) else 0);
    def kernelRegression = currentWeight / totalWeight;
    plot out = if !IsNaN(close) and !isNaN(kernelRegression) then kernelRegression else Double.NaN;
}
#// Multi Cosine
script multicosine {
    input src = close;
    input lookback = 60;
    input steps = 15;
    def pi = Double.Pi;
    def toFold = lookback - 1;
    def regression = fold k = 1 to steps-1 with r do
        r + ((fold i = 0 to toFold with p do
        p + (if AbsValue(i / lookback) <= pi / (2 * k) then AbsValue(Cos((i / lookback) * k)) else 0) * src[i]) /
             (fold j = 0 to toFold with q do
        q + (if AbsValue(j / lookback) <= pi / (2 * k) then AbsValue(Cos((j / lookback) * k)) else 0)));
    def multicosine = regression / steps;
    plot out = if !IsNaN(close) and !isNaN(multicosine) then multicosine else Double.NaN;
}
script ALMA {
    input src = close;
    input len = 9;
    input offset = 0;
    input sigma = 6;
    def m = Floor(offset * (len - 1));
    def s = len / sigma;
    def alm = fold i = 0 to len with p do
        if !IsNaN(src[i]) then p + src[i] * Exp(-1 * Power(i - m, 2) / (2 * Power(s, 2))) else p;
    def norm = fold j = 0 to len with q do
        if !IsNaN(src[j]) then  q + Exp(-1 * Power(j - m, 2) / (2 * Power(s, 2))) else q;
    def ALMA = if norm > 0 then alm / norm else Double.NaN;
    plot out = if !IsNaN(close) and !isNaN(ALMA) then ALMA else Double.NaN;
}

#//  ║     RELATIVE STRENGTH INDEX    ║  //
script DynamicRSI {
    input src = close;
    input length = 14;
    def nRSI = RSI(Price = src, Length = length);
    def RSI_ReScale = (nRSI - 50) * 2.8;
    plot out = RSI_ReScale;
}
#/  ║     STOCHASTIC OSCILLATOR      ║  //
script DynamicSTOCH {
    input source = close;
    input hi = high;
    input lo = low;
    input length = 14;
    def lowestLow   = Lowest(lo,  length);
    def highestHigh = Highest(hi, length);
    def stochastic  = 100 * (source - lowestLow) / (highestHigh - lowestLow);
    def STOCH_ReScale = (stochastic - 50) * 2;
    plot out = STOCH_ReScale;
}
#//  ║     BOLLINGER BAND PERCENT     ║  //
script DynamicBBPCT {
    input src = close;
    input length = 20;
    input multi = 1;
    def basis =   Average(src, length);
    def dev   =   multi * StDev(src, length);
    def upper =   basis + dev;
    def lower =   basis - dev;
    def bbpct =  (src   - lower) / (upper - lower);
    def BBPCT_ReScale = (bbpct - 0.5) * 120;
    plot out = BBPCT_ReScale;
}
#//  ║        CHANDE MOMENTUM         ║  //
script DynamicCMO {
    input src = close;
    input length = 20;
    def momm = (src - src[1]);
    def m1   = if momm >= 0 then  momm else 0.0;
    def m2   = if momm <  0 then -momm else 0.0;
    def sm1  = Sum(m1, length);
    def sm2  = Sum(m2, length);
    def div  = sm1 + sm2;
    def chandeMO = if div != 0 then 100 * (sm1 - sm2) / div else 0;
    def CMO_ReScale = (chandeMO * 1.15);
    plot out = CMO_ReScale;
}
#//  ║    COMMODITY CHANNEL INDEX     ║  //
script DynamicCCI {
    input src = close;
    input len = 20;
    def ma  = Average(src, len);
    def Dev = LinDev(src, len);
    def cci = (src - ma) / (0.015 * Dev);
    def CCI_ReScale = if Dev == 0 then 0 else (cci / 2);
    plot out = CCI_ReScale;
}
#//  ║        FISHER TRANSFORM        ║  //
script DynamicFisher {
    input len = 10;
    def fish = FisherTransform(Length = len);
    def FISH_ReScale = (fish * 30);
    plot out = FISH_ReScale;
}
#//  ║     VOLUME ZONE OSCILLATOR     ║  //
script DynamicVZO {
    input length = 14;
    def mom = hlc3 - hlc3[1];
    def sig = Sign(mom);
    def VP = ExpAverage(sig * volume, length / 3);
    def TV = ExpAverage(volume, length / 3);
    def VZO_ReScale = (VP / TV) * 110;
    plot out = VZO_ReScale;
}
#//║    CORE CALCULATIONS  
#// Function to count active indicators
script countCondition {
    input condition = close;
    def count = if !IsNaN(condition) then 1 else 0;
    plot out = count;
}
script nz {
    input val = close;
    def count = if !IsNaN(val) then val else 0;
    plot out = count;
}
def f_rsi = DynamicRSI(Source, rsiLength);
def f_sto = DynamicSTOCH(Source, high, low, StochasticLength);
def f_bbc = DynamicBBPCT(Source, bbLength, 2);
def f_cmo = DynamicCMO(Source, ChandeMomentumLength);
def f_cci = DynamicCCI(Source, cciLength);
def f_fis = DynamicFisher(FisherTransformLength);
def f_vzo = DynamicVZO(vzoLength);

#// Pull all standardized base indicator values
def val_RSI   = if useRsi then f_rsi else na;
def val_STOCH = if useStoch then f_sto else na;
def val_BBPCT = if useBollinger then f_bbc else na;
def val_CMO   = if useCmo then f_cmo else na;
def val_CCI   = if useCci then f_cci else na;
def val_FISH  = if useFisher then f_fis else na;
def val_VZO   = if useVzo then f_vzo else na;

#// Count the number of active indicators
def activeIndicators =
          countCondition(useRsi  ) +
          countCondition(useStoch) +
          countCondition(useBollinger) +
          countCondition(useCmo  ) +
          countCondition(useCci  ) +
          countCondition(useFisher ) +
          countCondition(useVzo  );

#// Calculate the average only with active indicators
def value1 = if activeIndicators > 0 then (
             nz(val_RSI) +
             nz(val_STOCH) +
             nz(val_BBPCT) +
             nz(val_CMO) +
             nz(val_CCI) +
             nz(val_FISH) +
             nz(val_VZO)) / activeIndicators else na;
#// Gentle ALMA smoothing
def value = ALMA(value1, 9, 0, 6);

#// Calulate the Output - Depending on the method of Cosine Regression Selected
def rLen = Round(TuningCoefficient / 5, 0);
def kernelReg1 = kernelRegression(value, RegressionLookback, TuningCoefficient);
def multCosin1 = multicosine(value, RegressionLookback, TuningCoefficient);
def kernelReg2 = kernelRegression(value, RegressionLookback, rLen);
def multCosin2 = multicosine(value, RegressionLookback, rLen);

def out1 = if tune then kernelReg1 else multCosin1;
def out2 = if tune then kernelReg2 else multCosin2;
#// Define Alert Conditions
def fastTrend_up  = out1 > out1[1]  and !(out1[1] > out1[2]);
def fastTrend_dn  = out1 < out1[1]  and !(out1[1] < out1[2]);

#//║         VISUALIZATION        ║//
def col;
switch (ColorType) {
case "None" :
    col = 0;
case "Slow Trend" :
    col = if out2 > 0 then 1 else -1;
default           :
    col = if out1 > out1[1] then 1 else -1;
}
def col1 = if !col then if out1 > out1[1] then 1 else -1 else col;
def col2 = if out2 > 0 then 1 else 0;

#-- plots
plot shapeDn = if fastTrend_dn then out1 else na;
plot shapeUp = if fastTrend_up then out1 else na;
plot sig = out1; #,  "Fast Signal"
plot sig2 = out2;

shapeDn.SetLineWeight(3);
shapeUp.SetLineWeight(3);
shapeDn.SetPaintingStrategy(PaintingStrategy.SQUARES);
shapeUp.SetPaintingStrategy(PaintingStrategy.SQUARES);
shapeDn.SetDefaultColor(Color.RED);
shapeUp.SetDefaultColor(Color.GREEN);
sig.SetLineWeight(2);
sig.AssignValueColor(if col1>0 then Color.CYAN else if col1<0 then Color.MAGENTA else Color.LIGHT_GRAY);
sig2.AssignValueColor(if col2 then Color.DARK_GREEN else Color.DARK_RED);

AssignPriceColor(if !col then Color.CURRENT else if col>0 then Color.CYAN else Color.MAGENTA);

AddCloud(sig2,  0,  Color.DARK_GREEN,  Color.DARK_RED);
AddCloud(if last then na else 100, 50, Color.PLUM);
AddCloud(if last then na else 100, 75, Color.PLUM);
AddCloud(if last then na else -50, -100, CreateColor(0, 118, 118));
AddCloud(if last then na else -75, -100, CreateColor(0, 118, 118));

#-- END of CODE
Code:
@samer800 , thank you!! great work, as usual. 👋 👋👋
 
Have you experimented with the default settings in order to make CKR slightly faster?
If so, can you mention which price bar 'source' or what other variable shows the most promise ?
Many Thanks
peregrine
 

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