Bonjour, serait il possible de convertir un script trading view? Merci
// © Julien_Eche
indicator(“Smart Trend Envelope”, overlay=true, max_bars_back=1000, max_lines_count=500)
group1 = “Channel Settings”
h = input.float(8.0, title=’Bandwidth’, group=group1)
multnw = input.float(3.0, title=’Multiplier’)
log(x) => math.log(x) / math.log(math.e)
exp(x) => math.pow(math.e, x)
src = log(input(close, title=’Source’))
up_col = input(color.teal, title=’Upper Line Color’)
dn_col = input(color.red, title=’Lower Line Color’)
line_widthnw = input.int(2, title=’Line Width’)
group2 = “Table Settings”
tablePositionInput = input.string(“Top Center”, “Table Position”, options=[“Bottom Right”, “Bottom Left”, “Middle Right”, “Middle Left”, “Top Right”, “Top Left”, “Top Center”, “Bottom Center”], group=group2)
getTablePosition(pos) =>
// Function to convert table position input string to the corresponding position value
if pos == “Bottom Right”
position.bottom_right
else if pos == “Bottom Left”
position.bottom_left
else if pos == “Middle Right”
position.middle_right
else if pos == “Middle Left”
position.middle_left
else if pos == “Top Right”
position.top_right
else if pos == “Top Left”
position.top_left
else if pos == “Top Center”
position.top_center
else if pos == “Bottom Center”
position.bottom_center
else
position.bottom_right
tablePos = getTablePosition(tablePositionInput)
rightTableTextColorInput = input.color(color.silver, “Projection Confidence Text Color”)
showPearsonInput = input.bool(false, “Show Pearson’s R instead of Projection Confidence Level”)
lengthInput1 = 50
lengthInput2 = 100
lengthInput3 = 150
lengthInput4 = 200
lengthInput5 = 250
lengthInput6 = 300
lengthInput7 = 350
lengthInput8 = 400
lengthInput9 = 450
lengthInput10 = 500
calcSlope(src, length) =>
// Function to calculate slope, average, and intercept
max_bars_back(src, 5000)
if not barstate.islast or length <= 1
[float(na), float(na), float(na)]
else
sumX = 0.0
sumY = 0.0
sumXSqr = 0.0
sumXY = 0.0
for i = 0 to length – 1 by 1
val = math.log(src[i])
per = i + 1.0
sumX += per
sumY += val
sumXSqr += per * per
sumXY += val * per
slope = (length * sumXY – sumX * sumY) / (length * sumXSqr – sumX * sumX)
average = sumY / length
intercept = average – slope * sumX / length + slope
[slope, average, intercept]
calcDev(src, length, slope, average, intercept) =>
// Function to calculate standard deviation, Pearson’s R, up deviation, and down deviation
upDev = 0.0
dnDev = 0.0
stdDevAcc = 0.0
dsxx = 0.0
dsyy = 0.0
dsxy = 0.0
periods = length – 1
daY = intercept + slope * periods / 2
val = intercept
for j = 0 to periods by 1
price = math.log(high[j]) – val
if price > upDev
upDev := price
price := val – math.log(low[j])
if price > dnDev
dnDev := price
price := math.log(src[j])
dxt = price – average
dyt = val – daY
price -= val
stdDevAcc += price * price
dsxx += dxt * dxt
dsyy += dyt * dyt
dsxy += dxt * dyt
val += slope
stdDev = math.sqrt(stdDevAcc / (periods == 0 ? 1 : periods))
pearsonR = dsxx == 0 or dsyy == 0 ? 0 : dsxy / math.sqrt(dsxx * dsyy)
[stdDev, pearsonR, upDev, dnDev]
// Calculate slope, average, and intercept for each length
[s1, a1, i1] = calcSlope(src, lengthInput1)
[s2, a2, i2] = calcSlope(src, lengthInput2)
[s3, a3, i3] = calcSlope(src, lengthInput3)
[s4, a4, i4] = calcSlope(src, lengthInput4)
[s5, a5, i5] = calcSlope(src, lengthInput5)
[s6, a6, i6] = calcSlope(src, lengthInput6)
[s7, a7, i7] = calcSlope(src, lengthInput7)
[s8, a8, i8] = calcSlope(src, lengthInput8)
[s9, a9, i9] = calcSlope(src, lengthInput9)
[s10, a10, i10] = calcSlope(src, lengthInput10)
// Calculate standard deviation, Pearson’s R, up deviation, and down deviation for each length
[stdDev1, pearsonR1, upDev1, dnDev1] = calcDev(src, lengthInput1, s1, a1, i1)
[stdDev2, pearsonR2, upDev2, dnDev2] = calcDev(src, lengthInput2, s2, a2, i2)
[stdDev3, pearsonR3, upDev3, dnDev3] = calcDev(src, lengthInput3, s3, a3, i3)
[stdDev4, pearsonR4, upDev4, dnDev4] = calcDev(src, lengthInput4, s4, a4, i4)
[stdDev5, pearsonR5, upDev5, dnDev5] = calcDev(src, lengthInput5, s5, a5, i5)
[stdDev6, pearsonR6, upDev6, dnDev6] = calcDev(src, lengthInput6, s6, a6, i6)
[stdDev7, pearsonR7, upDev7, dnDev7] = calcDev(src, lengthInput7, s7, a7, i7)
[stdDev8, pearsonR8, upDev8, dnDev8] = calcDev(src, lengthInput8, s8, a8, i8)
[stdDev9, pearsonR9, upDev9, dnDev9] = calcDev(src, lengthInput9, s9, a9, i9)
[stdDev10, pearsonR10, upDev10, dnDev10] = calcDev(src, lengthInput10, s10, a10, i10)
// Find the highest Pearson’s R among all lengths
highestPearsonR = math.max(pearsonR1, pearsonR2, pearsonR3, pearsonR4, pearsonR5, pearsonR6, pearsonR7, pearsonR8, pearsonR9, pearsonR10)
// Select the length, slope, intercept, and standard deviation based on the highest Pearson’s R
selectedLength = highestPearsonR == pearsonR1 ? lengthInput1 : (highestPearsonR == pearsonR2 ? lengthInput2 : (highestPearsonR == pearsonR3 ? lengthInput3 : (highestPearsonR == pearsonR4 ? lengthInput4 : (highestPearsonR == pearsonR5 ? lengthInput5 : (highestPearsonR == pearsonR6 ? lengthInput6 : (highestPearsonR == pearsonR7 ? lengthInput7 : (highestPearsonR == pearsonR8 ? lengthInput8 : (highestPearsonR == pearsonR9 ? lengthInput9 : lengthInput10))))))))
selectedS = highestPearsonR == pearsonR1 ? s1 : (highestPearsonR == pearsonR2 ? s2 : (highestPearsonR == pearsonR3 ? s3 : (highestPearsonR == pearsonR4 ? s4 : (highestPearsonR == pearsonR5 ? s5 : (highestPearsonR == pearsonR6 ? s6 : (highestPearsonR == pearsonR7 ? s7 : (highestPearsonR == pearsonR8 ? s8 : (highestPearsonR == pearsonR9 ? s9 : s10))))))))
selectedI = highestPearsonR == pearsonR1 ? i1 : (highestPearsonR == pearsonR2 ? i2 : (highestPearsonR == pearsonR3 ? i3 : (highestPearsonR == pearsonR4 ? i4 : (highestPearsonR == pearsonR5 ? i5 : (highestPearsonR == pearsonR6 ? i6 : (highestPearsonR == pearsonR7 ? i7 : (highestPearsonR == pearsonR8 ? i8 : (highestPearsonR == pearsonR9 ? i9 : i10))))))))
selectedStdDev = highestPearsonR == pearsonR1 ? stdDev1 : (highestPearsonR == pearsonR2 ? stdDev2 : (highestPearsonR == pearsonR3 ? stdDev3 : (highestPearsonR == pearsonR4 ? stdDev4 : (highestPearsonR == pearsonR5 ? stdDev5 : (highestPearsonR == pearsonR6 ? stdDev6 : (highestPearsonR == pearsonR7 ? stdDev7 : (highestPearsonR == pearsonR8 ? stdDev8 : (highestPearsonR == pearsonR9 ? stdDev9 : stdDev10))))))))
startPrice = math.exp(selectedI + selectedS * (selectedLength – 1))
endPrice = math.exp(selectedI)
StartPrice = startPrice
EndPrice = endPrice
var string confidenceLevel = “”
var color lineColor = na
trendDirection = startPrice > endPrice ? -1 : 1
if trendDirection == 1
lineColor := color.teal
else
lineColor := color.red
if barstate.islast
var table t = table.new(position = tablePos, columns = 2, rows = 2)
text1 = “”
if selectedLength == lengthInput1
text1 := “Auto-Selected Length: 50”
if selectedLength == lengthInput2
text1 := “Auto-Selected Length: 100”
if selectedLength == lengthInput3
text1 := “Auto-Selected Length: 150”
if selectedLength == lengthInput4
text1 := “Auto-Selected Length: 200”
if selectedLength == lengthInput5
text1 := “Auto-Selected Length: 250”
if selectedLength == lengthInput6
text1 := “Auto-Selected Length: 300”
if selectedLength == lengthInput7
text1 := “Auto-Selected Length: 350”
if selectedLength == lengthInput8
text1 := “Auto-Selected Length: 400”
if selectedLength == lengthInput9
text1 := “Auto-Selected Length: 450”
if selectedLength == lengthInput10
text1 := “Auto-Selected Length: 500”
confidenceLevel := “”
if highestPearsonR < 0.3
confidenceLevel := “Ultra Weak”
else if highestPearsonR < 0.5
confidenceLevel := “Very Weak”
else if highestPearsonR < 0.6
confidenceLevel := “Weak”
else if highestPearsonR < 0.7
confidenceLevel := “Moderately Weak”
else if highestPearsonR < 0.8
confidenceLevel := “Slightly Weak”
else if highestPearsonR < 0.9
confidenceLevel := “Moderate”
else if highestPearsonR < 0.92
confidenceLevel := “Slightly Strong”
else if highestPearsonR < 0.94
confidenceLevel := “Moderately Strong”
else if highestPearsonR < 0.96
confidenceLevel := “Strong”
else if highestPearsonR < 0.98
confidenceLevel := “Very Strong”
else
confidenceLevel := “Ultra Strong”
table.cell(t, 0, 0, text1, text_color=lineColor)
if showPearsonInput
table.cell(t, 1, 0, “Pearson’s R: ” + str.tostring(highestPearsonR, “#.##”), text_color=rightTableTextColorInput)
else
table.cell(t, 1, 0, “Projection Confidence: ” + confidenceLevel, text_color=rightTableTextColorInput)
lengthnw = selectedLength
n = bar_index
var k = 2
var uppernw = array.new_line(0)
var lowernw = array.new_line(0)
lset(l, x1, y1, x2, y2, col) =>
line.set_xy1(l, x1, exp(y1))
line.set_xy2(l, x2, exp(y2))
line.set_color(l, col)
line.set_width(l, line_widthnw)
line.set_style(l, line.style_solid)
if barstate.isfirst
for i = 0 to lengthnw/k-1
array.push(uppernw, line.new(na, na, na, na, color=up_col, width=line_widthnw, style=line.style_solid))
array.push(lowernw, line.new(na, na, na, na, color=dn_col, width=line_widthnw, style=line.style_solid))
line up = na
line dn = na
cross_up = 0.0
cross_dn = 0.0
if barstate.islast
y = array.new_float(0)
sum_e = 0.0
for i = 0 to lengthnw-1
sum = 0.0
sumw = 0.0
for j = 0 to lengthnw-1
w = math.exp(-(math.pow(i-j, 2)/(h*h*2)))
sum += src[j] * w
sumw += w
y2 = sum / sumw
sum_e += math.abs(src[i] – y2)
array.push(y, y2)
mae = sum_e / lengthnw * multnw
for i = 1 to lengthnw-1
y2 = array.get(y, i)
y1 = array.get(y, i-1)
up := array.get(uppernw, i/k)
dn := array.get(lowernw, i/k)
lset(up, n-i+1, y1 + mae, n-i, y2 + mae, up_col)
lset(dn, n-i+1, y1 – mae, n-i, y2 – mae, dn_col)
.
