AI Trend Navigator

// 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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