This indicator uses a Lorentzian distance k-nearest neighbors classification algorithm to predict market direction based on up to five technical features, such as RSI, WaveTrend, CCI, and ADX. It compares current feature values against historical bar patterns
Indicator · Pine Script v5 · On the chart
Tsh Arrow
An overlay that draws a SuperTrend-style trend line built from a volume-weighted moving average, selectable from SMA, EMA, WMA, RMA or VWMA types, with a fixed ATR factor.
GitHub stars1on the repository
Pine versionv5runs as is
Lines of code174
Settings9inputs
LicenceMIT
Last updateyesterday28 Oct 2026
Add It to Your TradingView Chart
- 1Copy the scriptPress Copy script above, or the copy button on the code below.
- 2Open the Pine EditorOpen a TradingView chart and choose Pine Editor at the bottom of the screen.
- 3Paste over the templateCreate a new indicator, select all of the template text and paste. Press Save and give it a name.
- 4Add it to the chartPress Add to chart, then adjust the settings with the gear icon next to its name.
Source Code
174 lines of Pine Script v5 from src/indicators/custom/tshArrow.pine, published under MIT. Keep the author's credit and licence lines when you reuse it.
// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © PresentTrading
//@version=5
indicator("Tsh_Arrow", overlay = true,max_boxes_count = 1000)
// ~~ Input settings for K and N values
k = 3
n_ = 12
n = math.max(3,12)
//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~}
// ~~ Input settings for prediction values
KNN_PriceLen = 20
KNN_STLen = 80
aisignals = input.bool(true,title="Trend Signals",inline="signal", group="Super Trend Settings")
voir_fleche=input.bool(true, title="Arrow",inline="signal")
Bullish_col = input.color(color.lime,"",inline="signal")
Bearish_col = input.color(color.red,"",inline="signal")
//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~}
// ~~ Define SuperTrend parameters
maSrc = input.string("WMA","Moving Average Source",["SMA","EMA","WMA","RMA","VWMA"],inline="")
len = input.int(20, "Length", minval=1,inline="SuperTrend")
factor = 4
upCol = input.color( color.new(color.lime,80),"Up",inline="col")
dnCol = input.color(color.new(color.red,80),"Down",inline="col")
neCol = input.color(color.new(color.blue,80),"Neutral",inline="col")
//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~}
// ~~ Calculate the SuperTrend based on the user's choice
vwma = switch maSrc
"SMA" => ta.sma(close*volume, len) / ta.sma(volume, len)
"EMA" => ta.ema(close*volume, len) / ta.ema(volume, len)
"WMA" => ta.wma(close*volume, len) / ta.wma(volume, len)
"RMA" => ta.rma(close*volume, len) / ta.rma(volume, len)
"VWMA" => ta.vwma(close*volume, len) / ta.vwma(volume, len)
atr = ta.atr(len)
upperBand = vwma + factor * atr
lowerBand = vwma - factor * atr
prevLowerBand = nz(lowerBand[1])
prevUpperBand = nz(upperBand[1])
lowerBand := lowerBand > prevLowerBand or close[1] < prevLowerBand ? lowerBand : prevLowerBand
upperBand := upperBand < prevUpperBand or close[1] > prevUpperBand ? upperBand : prevUpperBand
int direction = na
float superTrend = na
prevSuperTrend = superTrend[1]
if na(atr[1])
direction := 1
else if prevSuperTrend == prevUpperBand
direction := close > upperBand ? -1 : 1
else
direction := close < lowerBand ? 1 : -1
superTrend := direction == -1 ? lowerBand : upperBand
//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~}
// ~~ Collect data points and their corresponding labels
price = ta.wma(close,KNN_PriceLen)
sT = ta.wma(superTrend,KNN_STLen)
data = array.new_float(n)
labels = array.new_int(n)
for i = 0 to n - 1
data.set(i, superTrend[i])
label_i = price[i] > sT[i] ? 1 : 0
labels.set(i, label_i)
//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~}
// ~~ Define a function to compute distance between two data points
distance(x1, x2) =>
math.abs(x1 - x2)
//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~}
// ~~ Define the weighted k-nearest neighbors (KNN) function
knn_weighted(data, labels, k, x) =>
n1 = data.size()
distances = array.new_float(n1)
indices = array.new_int(n1)
// Compute distances from the current point to all other points
for i = 0 to n1 - 1
x_i = data.get(i)
dist = distance(x, x_i)
distances.set(i, dist)
indices.set(i, i)
// Sort distances and corresponding indices in ascending order
// Bubble sort method
for i = 0 to n1 - 2
for j = 0 to n1 - i - 2
if distances.get(j) > distances.get(j + 1)
tempDist = distances.get(j)
distances.set(j, distances.get(j + 1))
distances.set(j + 1, tempDist)
tempIndex = indices.get(j)
indices.set(j, indices.get(j + 1))
indices.set(j + 1, tempIndex)
// Compute weighted sum of labels of the k nearest neighbors
weighted_sum = 0.
total_weight = 0.
for i = 0 to k - 1
index = indices.get(i)
label_i = labels.get(index)
weight_i = 1 / (distances.get(i) + 1e-6)
weighted_sum += weight_i * label_i
total_weight += weight_i
weighted_sum / total_weight
//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~}
// ~~ Classify the current data point
current_superTrend = superTrend
label_ = knn_weighted(data, labels, k, current_superTrend)
label_upTrend = label_ == 1 ? 1:0
label_downTrend = label_ == 0 ? -1:0
//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~}
// ~~ Plot
col = label_ == 1?upCol:label_ == 0?dnCol:neCol
plot(current_superTrend, color=col, title="Volume Super Trend AI")
upTrend = plot(superTrend==lowerBand?current_superTrend:na, title="Up Volume Super Trend AI", color=col, style=plot.style_linebr)
Middle = plot((open + close) / 2, display=display.none, editable=false)
downTrend = plot(superTrend==upperBand?current_superTrend:na, title="Down Volume Super Trend AI", color=col, style=plot.style_linebr)
fill_col = color.new(col,95)
fill(Middle, upTrend, fill_col, fillgaps=false,title="Up Volume Super Trend AI")
fill(Middle, downTrend, fill_col, fillgaps=false, title="Down Volume Super Trend AI")
// ~~ Ai Super Trend Signals
Start_TrendUp = col==upCol and (col[1]!=upCol or col[1]==neCol) and aisignals
Start_TrendDn = col==dnCol and (col[1]!=dnCol or col[1]==neCol) and aisignals
TrendUp = direction == -1 and direction[1] == 1 and label_ == 1 and aisignals
TrendDn = direction == 1 and direction[1] ==-1 and label_ == 0 and aisignals
plotshape(Start_TrendUp?superTrend:na, location=location.absolute, style= shape.circle, size=size.tiny, color=Bullish_col, title="AI Bullish Trend Start")
plotshape(Start_TrendDn?superTrend:na, location=location.absolute, style= shape.circle,size=size.tiny, color=Bearish_col, title="AI Bearish Trend Start")
plotshape(TrendUp?superTrend:na, location=location.absolute, style= shape.triangleup, size=size.tiny, color=Bullish_col, title="AI Bullish Trend Signal")
plotshape(TrendDn?superTrend:na, location=location.absolute, style= shape.triangledown,size=size.tiny, color=Bearish_col, title="AI Bearish Trend Signal")
// Définir la fonction DEMA MACD
dema(src, length) =>
ema1 = ta.ema(src, length)
ema2 = ta.ema(ema1, length)
2 * ema1 - ema2
// Paramètres pour DEMA
p = 12
q = 26
r = 9
// Calculer DEMA pour les différentes périodes
z1 = dema(close, p)
z2 = dema(close, q)
e = z1 - z2
z3 = dema(e, r)
f = z3
g = e - f
// Condition pour afficher une flèche verte
bullishCross = ta.crossover(e, f)
Inf0= e < 0 and f <0
// Condition pour afficher une flèche rouge
bearishCross = ta.crossunder(e, f)
Sup0= e > 0 and f>0
if (bullishCross and Inf0 and col==upCol and voir_fleche)
label.new(bar_index, low, "▲", color=color.new(color.white,100), textcolor=Bullish_col, style=label.style_label_up, size=size.small)
if (bearishCross and Sup0 and col==dnCol and voir_fleche)
label.new(bar_index, high, "▼", color=color.new(color.white,100), textcolor=Bearish_col, style=label.style_label_down, size=size.small)
Overview
A k-nearest neighbours routine compares recent prices with the smoothed trend line to produce trend signals, with arrows and background colours for up, down and neutral states. Length and colours are adjustable. The method uses a small data window, so its signals are sensitive to settings.
- Volume-weighted SuperTrend-style line
- K-nearest neighbours trend classification
- Selectable moving average type
- Arrow markers and trend colours
Details
Published by
teshmi9z-beep on GitHub
Code credit
PresentTrading, as written in the script's header
Type
Draws
On the price chart
Category
Markets
Any symbol
Timeframes
Any timeframe
Risk Notes
No repainting, lookahead or stacked entries were found in its code or README. That says nothing about how well it works: test it first.
Settings
The inputs you can change from the gear icon on the chart. Defaults are the author's.
| Setting | Type | Default | Variable |
|---|---|---|---|
| Trend Signals | bool | true | aisignals |
| Arrow | bool | true | voir_fleche |
| Bullish_col | color | color.lime | Bullish_col |
| Bearish_col | color | color.red | Bearish_col |
| Moving Average Source | string | WMA | maSrc |
| Length | int | 20 | len |
| Up | color | color.new(color.lime,80) | upCol |
| Down | color | color.new(color.red,80) | dnCol |
| Neutral | color | color.new(color.blue,80) | neCol |
Third-party code. ForexR did not write, test or endorse this script, and the summary above was generated automatically from its code and README. Indicators describe the past, and a strategy's backtest can look better than live trading, especially when a script repaints. Check it in the Strategy Tester and on a demo account before you rely on it.
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