Help for the translation from trading view

This topic has 8 replies, 3 voices, and was last updated 2 weeks ago by Annchow8052014.

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04/24/2024 at 4:32 AM #231868

Annchow8052014

Hi All,

I would like to ask for help to translate a script from tradingview as below with thanks in advance

AI Trend Navigator by Zeiierma

Annchow8052014

Participant

Topics: 3

Replies: 17

Been thanked: 2 times

04/24/2024 at 10:50 AM #231882

GraHal

You need to post the script on here so as to tempt one of our wizard coders to help you.

Click on Insert PRT Code buttom (at left hand end of the top row of tools above) before inserting the code for AI Trend Navigator.

GraHal

Participant

Topics: 214

Replies: 8829

Been thanked: 2200 times

04/24/2024 at 11:49 AM #231885

Annchow8052014

// This work is licensed under a Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) https://creativecommons.org/licenses/by-nc-sa/4.0/
// © Zeiierman {

//@version=5
indicator('AI Trend Navigator', overlay=true)

// ~~ Tooltips {
t1 ="PriceValue selects the method of price computation. \n\nSets the smoothing period for the PriceValue. \n\nAdjusting these settings will change the input values for the K-Nearest Neighbors algorithm, influencing how the trend is calculated."
t2 = "TargetValue specifies the target to evaluate. \n\nSets the smoothing period for the TargetValue."
t3 ="numberOfClosestValues sets the number of closest values that are considered when calculating the KNN Moving Average. Adjusting this number will affect the sensitivity of the trend line, with a higher value leading to a smoother line and a lower value resulting in a line that is more responsive to recent price changes."
t4 ="smoothingPeriod sets the period for the moving average applied to the KNN classifier. Adjusting the smoothing period will affect how rapidly the trend line responds to price changes, with a larger smoothing period leading to a smoother line that may lag recent price movements, and a smaller smoothing period resulting in a line that more closely tracks recent changes."
t5 ="This option controls the background color for the trend prediction. Enabling it will change the background color based on the prediction, providing visual cues on the direction of the trend. A green color indicates a positive prediction, while red indicates a negative prediction."
//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~}

// ~~ Inputs {
PriceValue   = input.string("hl2", options = ["hl2","VWAP", "sma", "wma", "ema", "hma"], group="", inline="Value")
maLen        = input.int(5, minval=2, maxval=200, title="", group="", inline="Value", tooltip=t1)
TargetValue  = input.string("Price Action", options = ["Price Action","VWAP", "Volatility", "sma", "wma", "ema", "hma"], group="", inline="Target")
maLen_       = input.int(5, minval=2, maxval=200, title="", group="", inline="Target", tooltip=t2)
// Input parameters for the KNN Moving Average
numberOfClosestValues = input.int(3, "Number of Closest Values", 2, 200, tooltip=t3) 
smoothingPeriod       = input.int(50, "Smoothing Period", 2, 500, tooltip=t4) 
windowSize            = math.max(numberOfClosestValues, 30) 

// knn Color
Upknn_col   = input.color(color.lime, title="", group="KNN Color", inline="knn col")
Dnknn_col   = input.color(color.red, title="", group="KNN Color", inline="knn col")
Neuknn_col  = input.color(color.orange, title="", group="KNN Color", inline="knn col")
// MA knn Color
Maknn_col   = input.color(color.teal, title="", group="MA KNN Color", inline="MA knn col")
// BG Color
bgcolor = input.bool(false, title="Trend Prediction Color", group="BG Color", inline="bg", tooltip=t5)
Up_col  = input.color(color.lime, title="", group="BG Color", inline="bg")
Dn_col  = input.color(color.red, title="", group="BG Color", inline="bg")
//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~}

// ~~ kNN Classifier {
value_in = switch PriceValue
    "hl2"  => ta.sma(hl2,maLen)
    "VWAP" => ta.vwap(close[maLen])
    "sma" => ta.sma(close,maLen)
    "wma" => ta.wma(close,maLen)
    "ema" => ta.ema(close,maLen)
    "hma" => ta.hma(close,maLen)

meanOfKClosest(value_,target_) => 
    closestDistances = array.new_float(numberOfClosestValues, 1e10) 
    closestValues    = array.new_float(numberOfClosestValues, 0.0) 
    for i = 1 to windowSize 
        value = value_[i] 
        distance = math.abs(target_ - value) 
        maxDistIndex = 0 
        maxDistValue = closestDistances.get(0) 
        for j = 1 to numberOfClosestValues - 1 
            if closestDistances.get(j) > maxDistValue
                maxDistIndex := j
                maxDistValue := closestDistances.get(j)
        if distance < maxDistValue 
            closestDistances.set(maxDistIndex, distance)
            closestValues.set(maxDistIndex, value)
    closestValues.sum() / numberOfClosestValues 

// Choose the target input based on user selection
target_in = switch TargetValue
    "Price Action"  => ta.rma(close,maLen_) 
    "VWAP"          => ta.vwap(close[maLen_])
    "Volatility"    => ta.atr(14)
    "sma" => ta.sma(close,maLen_)
    "wma" => ta.wma(close,maLen_)
    "ema" => ta.ema(close,maLen_)
    "hma" => ta.hma(close,maLen_)

knnMA = meanOfKClosest(value_in,target_in)
//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~}

// ~~ kNN Prediction {
// Function to calculate KNN Classifier
price = math.avg(knnMA, close)
c     = ta.rma(knnMA[1], smoothingPeriod) 
o     = ta.rma(knnMA, smoothingPeriod)

// Defines KNN function to perform classification
knn(price) => 
    Pos_count = 0 
    Neg_count = 0 
    min_distance = 10e10 
    nearest_index = 0 
    for j = 1 to 10 
        distance = math.sqrt(math.pow(price[j] - price, 2)) 
        if distance < min_distance 
            min_distance := distance
            nearest_index := j
            Neg = c[nearest_index] > o[nearest_index] 
            Pos = c[nearest_index] < o[nearest_index] 
            if Pos 
                Pos_count += 1
            if Neg 
                Neg_count += 1
    output = Pos_count>Neg_count?1:-1 

// Calls KNN function and smooths the prediction
knn_prediction_raw = knn(price) 
knn_prediction     = ta.wma(knn_prediction_raw, 3)
//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~}

// ~~ Plots {
// Plots for display on the chart
knnMA_          = ta.wma(knnMA,5)
knnMA_col       = knnMA_>knnMA_[1]?Upknn_col:knnMA_<knnMA_[1]?Dnknn_col:Neuknn_col
Classifier_Line = plot(knnMA_,"Knn Classifier Line", knnMA_col)
MAknn_          = ta.rma(knnMA, smoothingPeriod)
plot(MAknn_,"Average Knn Classifier Line" ,Maknn_col) 
green = knn_prediction < 0.5
red   = knn_prediction > -0.5 
bgcolor( green and bgcolor? color.new(Dn_col,80) : 
 red and bgcolor ? color.new(Up_col,80) : na) 
//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~}

// ~~ Alerts {
knnMA_cross_Over_Ma      = ta.crossover(knnMA_,MAknn_)
knnMA_cross_Under_Ma     = ta.crossunder(knnMA_,MAknn_)
knnMA_cross_Over_Close   = ta.crossover(knnMA_,close)
knnMA_cross_Under_Close  = ta.crossunder(knnMA_,close)
knnMA_Switch_Up          = knnMA_[1]<knnMA_ and knnMA_[1]<=knnMA_[2]
knnMA_Switch_Dn          = knnMA_[1]>knnMA_ and knnMA_[1]>=knnMA_[2]
knnMA_Neutral            = knnMA_col==Neuknn_col and knnMA_col[1]!=Neuknn_col
greenBG                  = green and not green[1]
redBG                    = red and not red[1]

alertcondition(knnMA_cross_Over_Ma,  title = "Knn Crossover Average Knn",  message = "Knn Crossover Average Knn")
alertcondition(knnMA_cross_Under_Ma, title = "Knn Crossunder Average Knn", message = "Knn Crossunder Average Knn")
alertcondition(knnMA_cross_Over_Close,  title = "Knn Crossover Close",  message = "Knn Crossover Close")
alertcondition(knnMA_cross_Under_Close, title = "Knn Crossunder Close", message = "Knn Crossunder Close")
alertcondition(knnMA_Switch_Up,  title = "Knn Switch Up",  message = "Knn Switch Up")
alertcondition(knnMA_Switch_Dn, title = "Knn Switch Dn", message = "Knn Switch Dn")
alertcondition(knnMA_Neutral, title = "Knn is Neutral", message = "Knn is Neutral")
alertcondition(greenBG, title = "Positive Prediction", message = "Positive Prediction")
alertcondition(redBG, title = "Negative Prediction", message = "Negative Prediction")
//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~}

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// This work is licensed under a Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) https://creativecommons.org/licenses/by-nc-sa/4.0/

// © Zeiierman {

//@version=5

indicator('AI Trend Navigator', overlay=true)

// ~~ Tooltips {

t1 ="PriceValue selects the method of price computation. \n\nSets the smoothing period for the PriceValue. \n\nAdjusting these settings will change the input values for the K-Nearest Neighbors algorithm, influencing how the trend is calculated."

t2 = "TargetValue specifies the target to evaluate. \n\nSets the smoothing period for the TargetValue."

t3 ="numberOfClosestValues sets the number of closest values that are considered when calculating the KNN Moving Average. Adjusting this number will affect the sensitivity of the trend line, with a higher value leading to a smoother line and a lower value resulting in a line that is more responsive to recent price changes."

t4 ="smoothingPeriod sets the period for the moving average applied to the KNN classifier. Adjusting the smoothing period will affect how rapidly the trend line responds to price changes, with a larger smoothing period leading to a smoother line that may lag recent price movements, and a smaller smoothing period resulting in a line that more closely tracks recent changes."

t5 ="This option controls the background color for the trend prediction. Enabling it will change the background color based on the prediction, providing visual cues on the direction of the trend. A green color indicates a positive prediction, while red indicates a negative prediction."

//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~}

// ~~ Inputs {

PriceValue = input.string("hl2", options = ["hl2","VWAP", "sma", "wma", "ema", "hma"], group="", inline="Value")

maLen = input.int(5, minval=2, maxval=200, title="", group="", inline="Value", tooltip=t1)

TargetValue = input.string("Price Action", options = ["Price Action","VWAP", "Volatility", "sma", "wma", "ema", "hma"], group="", inline="Target")

maLen_ = input.int(5, minval=2, maxval=200, title="", group="", inline="Target", tooltip=t2)

// Input parameters for the KNN Moving Average

numberOfClosestValues = input.int(3, "Number of Closest Values", 2, 200, tooltip=t3)

smoothingPeriod = input.int(50, "Smoothing Period", 2, 500, tooltip=t4)

windowSize = math.max(numberOfClosestValues, 30)

// knn Color

Upknn_col = input.color(color.lime, title="", group="KNN Color", inline="knn col")

Dnknn_col = input.color(color.red, title="", group="KNN Color", inline="knn col")

Neuknn_col = input.color(color.orange, title="", group="KNN Color", inline="knn col")

// MA knn Color

Maknn_col = input.color(color.teal, title="", group="MA KNN Color", inline="MA knn col")

// BG Color

bgcolor = input.bool(false, title="Trend Prediction Color", group="BG Color", inline="bg", tooltip=t5)

Up_col = input.color(color.lime, title="", group="BG Color", inline="bg")

Dn_col = input.color(color.red, title="", group="BG Color", inline="bg")

//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~}

// ~~ kNN Classifier {

value_in = switch PriceValue

"hl2" => ta.sma(hl2,maLen)

"VWAP" => ta.vwap(close[maLen])

"sma" => ta.sma(close,maLen)

"wma" => ta.wma(close,maLen)

"ema" => ta.ema(close,maLen)

"hma" => ta.hma(close,maLen)

meanOfKClosest(value_,target_) =>

closestDistances = array.new_float(numberOfClosestValues, 1e10)

closestValues = array.new_float(numberOfClosestValues, 0.0)

for i = 1 to windowSize

value = value_[i]

distance = math.abs(target_ - value)

maxDistIndex = 0

maxDistValue = closestDistances.get(0)

for j = 1 to numberOfClosestValues - 1

if closestDistances.get(j) > maxDistValue

maxDistIndex := j

maxDistValue := closestDistances.get(j)

if distance < maxDistValue

closestDistances.set(maxDistIndex, distance)

closestValues.set(maxDistIndex, value)

closestValues.sum() / numberOfClosestValues

// Choose the target input based on user selection

target_in = switch TargetValue

"Price Action" => ta.rma(close,maLen_)

"VWAP" => ta.vwap(close[maLen_])

"Volatility" => ta.atr(14)

"sma" => ta.sma(close,maLen_)

"wma" => ta.wma(close,maLen_)

"ema" => ta.ema(close,maLen_)

"hma" => ta.hma(close,maLen_)

knnMA = meanOfKClosest(value_in,target_in)

//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~}

// ~~ kNN Prediction {

// Function to calculate KNN Classifier

price = math.avg(knnMA, close)

c = ta.rma(knnMA[1], smoothingPeriod)

o = ta.rma(knnMA, smoothingPeriod)

// Defines KNN function to perform classification

knn(price) =>

Pos_count = 0

Neg_count = 0

min_distance = 10e10

nearest_index = 0

for j = 1 to 10

distance = math.sqrt(math.pow(price[j] - price, 2))

if distance < min_distance

min_distance := distance

nearest_index := j

Neg = c[nearest_index] > o[nearest_index]

Pos = c[nearest_index] < o[nearest_index]

if Pos

Pos_count += 1

if Neg

Neg_count += 1

output = Pos_count>Neg_count?1:-1

// Calls KNN function and smooths the prediction

knn_prediction_raw = knn(price)

knn_prediction = ta.wma(knn_prediction_raw, 3)

//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~}

// ~~ Plots {

// Plots for display on the chart

knnMA_ = ta.wma(knnMA,5)

knnMA_col = knnMA_>knnMA_[1]?Upknn_col:knnMA_<knnMA_[1]?Dnknn_col:Neuknn_col

Classifier_Line = plot(knnMA_,"Knn Classifier Line", knnMA_col)

MAknn_ = ta.rma(knnMA, smoothingPeriod)

plot(MAknn_,"Average Knn Classifier Line" ,Maknn_col)

green = knn_prediction < 0.5

red = knn_prediction > -0.5

bgcolor( green and bgcolor? color.new(Dn_col,80) :

red and bgcolor ? color.new(Up_col,80) : na)

//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~}

// ~~ Alerts {

knnMA_cross_Over_Ma = ta.crossover(knnMA_,MAknn_)

knnMA_cross_Under_Ma = ta.crossunder(knnMA_,MAknn_)

knnMA_cross_Over_Close = ta.crossover(knnMA_,close)

knnMA_cross_Under_Close = ta.crossunder(knnMA_,close)

knnMA_Switch_Up = knnMA_[1]<knnMA_ and knnMA_[1]<=knnMA_[2]

knnMA_Switch_Dn = knnMA_[1]>knnMA_ and knnMA_[1]>=knnMA_[2]

knnMA_Neutral = knnMA_col==Neuknn_col and knnMA_col[1]!=Neuknn_col

greenBG = green and not green[1]

redBG = red and not red[1]

alertcondition(knnMA_cross_Over_Ma, title = "Knn Crossover Average Knn", message = "Knn Crossover Average Knn")

alertcondition(knnMA_cross_Under_Ma, title = "Knn Crossunder Average Knn", message = "Knn Crossunder Average Knn")

alertcondition(knnMA_cross_Over_Close, title = "Knn Crossover Close", message = "Knn Crossover Close")

alertcondition(knnMA_cross_Under_Close, title = "Knn Crossunder Close", message = "Knn Crossunder Close")

alertcondition(knnMA_Switch_Up, title = "Knn Switch Up", message = "Knn Switch Up")

alertcondition(knnMA_Switch_Dn, title = "Knn Switch Dn", message = "Knn Switch Dn")

alertcondition(knnMA_Neutral, title = "Knn is Neutral", message = "Knn is Neutral")

alertcondition(greenBG, title = "Positive Prediction", message = "Positive Prediction")

alertcondition(redBG, title = "Negative Prediction", message = "Negative Prediction")

//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~}

Hi GraHal,

Noted with thanks for your reminding.

Here above is the code, please help me for the translation in your convenience with thanks.

Annchow8052014

Participant

Topics: 3

Replies: 17

Been thanked: 2 times

05/03/2024 at 5:33 PM #232290

Annchow8052014

Hi All,

Can anyone help me to convert the above indicator with thanks.

Annchow8052014

Participant

Topics: 3

Replies: 17

Been thanked: 2 times

05/22/2024 at 1:49 PM #232925

Iván

Hi
Here you have the indicator:

//-----------------------------------------------------------//
//PRC_AI Trend Navigator
//version = 0
//24.04.24
//Iván González @ www.prorealcode.com
//Sharing ProRealTime knowledge
//-----------------------------------------------------------//
//-----Inputs------------------------------------------------//
PriceValue=0//0=hl2,1=vwap,2=sma,3=wma,4=ema,5=hma
maLen=5
TargetValue=0//0=Price Action, 1=vwap,2=volatility,3=sma,4=wma,5=ema,6=hma
maLen1=5
//---Input parameters for the KNN Moving Average
numberOfClosestValues=3//Number of Closest Values
smoothingPeriod=50
windowSize=max(numberOfClosestValues,30)
//---Background Colour
bgcolour=0//Boolean//Backgroundcolor
//-----------------------------------------------------------//
//-----PriceValue calculation--------------------------------//
if PriceValue=0 then
ValueIn=average[maLen]((high+low)/2)
elsif PriceValue=1 then
ValueIn=VolumeAdjustedAverage[maLen](close)
elsif PriceValue=2 then
ValueIn=average[maLen](close)
elsif PriceValue=3 then
ValueIn=weightedaverage[maLen](close)
elsif PriceValue=4 then
ValueIn=exponentialaverage[maLen](close)
elsif PriceValue=5 then
ValueIn=hullaverage[maLen](close)
endif
//-----------------------------------------------------------//
//-----Target input calculation------------------------------//
if TargetValue=0 then
alpha = 1/maLen1
if barindex <= maLen1 then
TargetIn=close
else
if barindex = maLen1 then
TargetIn = average[maLen1](close)
else
TargetIn = alpha*close + (1-alpha)*TargetIn[1]
endif
endif
elsif TargetValue=1 then
TargetIn=VolumeAdjustedAverage[maLen1](close)
elsif TargetValue=2 then
TargetIn=averagetruerange[14](close)
elsif TargetValue=3 then
TargetIn=average[maLen1](close)
elsif TargetValue=4 then
TargetIn=weightedaverage[maLen1](close)
elsif TargetValue=5 then
TargetIn=exponentialaverage[maLen1](close)
elsif TargetValue=6 then
TargetIn=hullaverage[maLen1](close)
endif
//-----------------------------------------------------------//
//-----KNN Classifier----------------------------------------//
//knnMA = meanOfKClosest(value_in,target_in)
$closestDistances[0]=exp(10)
$closestValues[0]=0
for i=1 to windowSize do
myvalue=valueIn[i]
distance=abs(targetIn-myvalue)
maxDistIndex=0
maxDistValue=$closestDistances[0]
for j=0 to numberOfClosestValues-1 do
if $closestDistances[j] > maxDistValue then
maxDistIndex=j
maxDistValue=$closestDistances[j]
endif
next
if distance < maxDistValue then
$closestDistances[maxDistIndex]=distance
$closestValues[maxDistIndex]=myvalue
endif
next

sumclosestValues=summation[numberOfClosestValues]($closestValues[lastset($closestValues)])

knnMA = sumclosestValues/numberOfClosestValues
//-----------------------------------------------------------//
price1=(KnnMA+close)/2

alpha1 = 1/smoothingPeriod
alpha2 = 1/smoothingPeriod

if barindex <= smoothingPeriod then
c = knnMA[1]
o = knnMA
else
if barindex = smoothingPeriod then
c = average[smoothingPeriod](knnMA[1])
else
c = alpha1*knnMA[1] + (1-alpha1)*c[1]
endif

if barindex = smoothingPeriod then
o = average[smoothingPeriod](knnMA)
else
o = alpha2*knnMA + (1-alpha2)*o[1]
endif
endif
//-----------------------------------------------------------//
poscount=0
negcount=0
mindistance=10*exp(10)
nearestindex=0
for j=1 to 10 do
distance1=sqrt(pow(price1[j]-price1,2))
if distance1 < mindistance then
mindistance=distance1
nearestindex=j
neg=c[nearestindex]>o[nearestindex]
pos=c[nearestindex]<o[nearestindex]
if pos then
poscount=1+poscount
elsif neg then
negcount=1+negcount
endif
endif
next
if poscount>negcount then
knnPredictionRaw=1
else
knnPredictionRaw=-1
endif
knnPrediction=weightedaverage[3](knnPredictionRaw)
//-----------------------------------------------------------//
KnnMA1 = weightedaverage[5](KnnMA)

if KnnMA1>KnnMA1[1] then
r=0
g=230
b=118
elsif KnnMA1<KnnMA1[1] then
r=255
g=82
b=82
else
r=255
g=152
b=0
endif
//-----------------------------------------------------------//
alpha2 = 1/smoothingPeriod
if barindex <= smoothingPeriod then
MAknn=knnMA
else
if barindex = smoothingPeriod then
MAknn = average[smoothingPeriod](knnMA)
else
MAknn = alpha2*knnMA + (1-alpha2)*MAknn[1]
endif
endif
//-----------------------------------------------------------//

if bgcolour then
if knnPrediction > -0.5 then
rbars=0
gbars=230
bbars=118
elsif knnPrediction < 0.5 then
rbars=255
gbars=82
bbars=82
endif
backgroundcolor(rbars,gbars,bbars,35)
endif
//-----------------------------------------------------------//
return knnMA1 as "Knn Classifier Line"coloured(r,g,b)style(line,2), MAknn as "Average Knn Classifier Line"coloured(1,137,123)

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//-----------------------------------------------------------//

//PRC_AI Trend Navigator

//version = 0

//24.04.24

//Iván González @ www.prorealcode.com

//Sharing ProRealTime knowledge

//-----------------------------------------------------------//

//-----Inputs------------------------------------------------//

PriceValue=0//0=hl2,1=vwap,2=sma,3=wma,4=ema,5=hma

maLen=5

TargetValue=0//0=Price Action, 1=vwap,2=volatility,3=sma,4=wma,5=ema,6=hma

maLen1=5

//---Input parameters for the KNN Moving Average

numberOfClosestValues=3//Number of Closest Values

smoothingPeriod=50

windowSize=max(numberOfClosestValues,30)

//---Background Colour

bgcolour=0//Boolean//Backgroundcolor

//-----------------------------------------------------------//

//-----PriceValue calculation--------------------------------//

if PriceValue=0 then

ValueIn=average[maLen]((high+low)/2)

elsif PriceValue=1 then

ValueIn=VolumeAdjustedAverage[maLen](close)

elsif PriceValue=2 then

ValueIn=average[maLen](close)

elsif PriceValue=3 then

ValueIn=weightedaverage[maLen](close)

elsif PriceValue=4 then

ValueIn=exponentialaverage[maLen](close)

elsif PriceValue=5 then

ValueIn=hullaverage[maLen](close)

endif

//-----------------------------------------------------------//

//-----Target input calculation------------------------------//

if TargetValue=0 then

alpha = 1/maLen1

if barindex <= maLen1 then

TargetIn=close

else

if barindex = maLen1 then

TargetIn = average[maLen1](close)

else

TargetIn = alpha*close + (1-alpha)*TargetIn[1]

endif

elsif TargetValue=1 then

TargetIn=VolumeAdjustedAverage[maLen1](close)

elsif TargetValue=2 then

TargetIn=averagetruerange[14](close)

elsif TargetValue=3 then

TargetIn=average[maLen1](close)

elsif TargetValue=4 then

TargetIn=weightedaverage[maLen1](close)

elsif TargetValue=5 then

TargetIn=exponentialaverage[maLen1](close)

elsif TargetValue=6 then

TargetIn=hullaverage[maLen1](close)

endif

//-----------------------------------------------------------//

//-----KNN Classifier----------------------------------------//

//knnMA = meanOfKClosest(value_in,target_in)

$closestDistances[0]=exp(10)

$closestValues[0]=0

for i=1 to windowSize do

myvalue=valueIn[i]

distance=abs(targetIn-myvalue)

maxDistIndex=0

maxDistValue=$closestDistances[0]

for j=0 to numberOfClosestValues-1 do

if $closestDistances[j] > maxDistValue then

maxDistIndex=j

maxDistValue=$closestDistances[j]

endif

if distance < maxDistValue then

$closestDistances[maxDistIndex]=distance

$closestValues[maxDistIndex]=myvalue

endif

sumclosestValues=summation[numberOfClosestValues]($closestValues[lastset($closestValues)])

knnMA = sumclosestValues/numberOfClosestValues

//-----------------------------------------------------------//

price1=(KnnMA+close)/2

alpha1 = 1/smoothingPeriod

alpha2 = 1/smoothingPeriod

if barindex <= smoothingPeriod then

c = knnMA[1]

o = knnMA

else

if barindex = smoothingPeriod then

c = average[smoothingPeriod](knnMA[1])

else

c = alpha1*knnMA[1] + (1-alpha1)*c[1]

endif

if barindex = smoothingPeriod then

o = average[smoothingPeriod](knnMA)

else

o = alpha2*knnMA + (1-alpha2)*o[1]

endif

//-----------------------------------------------------------//

poscount=0

negcount=0

mindistance=10*exp(10)

nearestindex=0

for j=1 to 10 do

distance1=sqrt(pow(price1[j]-price1,2))

if distance1 < mindistance then

mindistance=distance1

nearestindex=j

neg=c[nearestindex]>o[nearestindex]

pos=c[nearestindex]<o[nearestindex]

if pos then

poscount=1+poscount

elsif neg then

negcount=1+negcount

endif

if poscount>negcount then

knnPredictionRaw=1

else

knnPredictionRaw=-1

endif

knnPrediction=weightedaverage[3](knnPredictionRaw)

//-----------------------------------------------------------//

KnnMA1 = weightedaverage[5](KnnMA)

if KnnMA1>KnnMA1[1] then

r=0

g=230

b=118

elsif KnnMA1<KnnMA1[1] then

r=255

g=82

b=82

else

r=255

g=152

b=0

endif

//-----------------------------------------------------------//

alpha2 = 1/smoothingPeriod

if barindex <= smoothingPeriod then

MAknn=knnMA

else

if barindex = smoothingPeriod then

MAknn = average[smoothingPeriod](knnMA)

else

MAknn = alpha2*knnMA + (1-alpha2)*MAknn[1]

endif

//-----------------------------------------------------------//

if bgcolour then

if knnPrediction > -0.5 then

rbars=0

gbars=230

bbars=118

elsif knnPrediction < 0.5 then

rbars=255

gbars=82

bbars=82

endif

backgroundcolor(rbars,gbars,bbars,35)

endif

//-----------------------------------------------------------//

return knnMA1 as "Knn Classifier Line"coloured(r,g,b)style(line,2), MAknn as "Average Knn Classifier Line"coloured(1,137,123)

1 user thanked author for this post.

Annchow8052014

Iván

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05/22/2024 at 3:18 PM #232928

GraHal

I copy and pasted the above code and the Indicator appears to work fine.

I then used the Algo Simplified Creation tool on the Indicator, but I get the attached error message when I try to run the Algo … any ideas why?

GraHal

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05/22/2024 at 4:16 PM #232932

GraHal

I get exact same error message as I posted above when I make an Algo using the full Indicator code in the Algo (i.e. NOT using Simplified Creation).

Indicator works fine … see attached

GraHal

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12/02/2024 at 6:59 AM #240960

Annchow8052014

Hi Ivan,

Thanks for your help. I also find some error, please check and update for us. Thanks in advance.

Annchow8052014

Participant

Topics: 3

Replies: 17

Been thanked: 2 times

12/02/2024 at 7:27 AM #240961

Annchow8052014

Hi Ivan,

Further to my study, the indicator is in good condition (picture 1), but on last Friday, the Knn line go to “0” after around 22:10 (picture 2) , can you check and update for us with thanks.

Annchow8052014

Participant

Topics: 3

Replies: 17

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