Help converting “Trend Following MAs 3D” Indicator from Trading View

This topic has 5 replies, 2 voices, and was last updated 2 years ago by denmar.

Viewing 6 posts - 1 through 6 (of 6 total)

04/12/2021 at 2:44 PM #166864

denmar

I would be most grateful if somebody could please convert this indicator.

It seems much more intuitive than the standard heatmap/spectrogram attempts to display changes occurring across multiple moving averages.

Thank you

Denmar

study("Trend Following MAs 3D", precision=0)
matype= input('EMA', title = "MA Type", options =['EMA', 'SMA', 'RMA', 'WMA', 'VWMA'])
ulinreg = input(true, title = "Use Linear Regression")
linprd = input(10, title = "Linear Regression Period", minval = 2)

getma(len)=>
    _ret = matype == 'EMA' ? ema(close, len) : matype == 'RMA' ? rma(close, len) : matype == 'VWMA' ? vwma(close, len) : matype == 'WMA' ? wma(close, len) : sma(close, len)
    _ret := ulinreg ? linreg(_ret, linprd, 0) : _ret
    _ret

p1 = getma(5) - getma(10)
p2 = p1 + getma(10) - getma(15)
p3 = p2 + getma(15) - getma(20)
p4 = p3 + getma(20) - getma(25)
p5 = p4 + getma(25) - getma(30)
p6 = p5 + getma(30) - getma(35)
p7 = p6 + getma(35) - getma(40)
p8 = p7 + getma(40) - getma(45)
p9 = p8 + getma(45) - getma(50)
p10 = p9 + getma(50) - getma(55)
p11 = p10 + getma(55) - getma(60)
p12 = p11 + getma(60) - getma(65)
p13 = p12 + getma(65) - getma(70)
p14 = p13 + getma(70) - getma(75)
p15 = p14 + getma(75) - getma(80)
p16 = p15 + getma(80) - getma(85)
p17 = p16 + getma(85) - getma(90)
p18 = p17 + getma(90) - getma(95)
p19 = p18 + getma(95) - getma(100)

l1 = plot(p1, color = na)
l2 = plot(p2, color = na)
l3 = plot(p3, color = na)
l4 = plot(p4, color = na)
l5 = plot(p5, color = na)
l6 = plot(p6, color = na)
l7 = plot(p7, color = na)
l8 = plot(p8, color = na)
l9 = plot(p9, color = na)
l10 = plot(p10, color = na)
l11 = plot(p11, color = na)
l12 = plot(p12, color = na)
l13 = plot(p13, color = na)
l14 = plot(p14, color = na)
l15 = plot(p15, color = na)
l16 = plot(p16, color = na)
l17 = plot(p17, color = na)
l18 = plot(p18, color = na)
l19 = plot(p19, color = na)

getwidth(a, b)=>
    t = abs(a - b)
    w = t * 10 / (highest(t, 200) - lowest(t, 200))
    w := max(w, 4)
    m = p1 > 0 ? p1 > nz(p1[1]) ? w > nz(w[1]) ? w + 1 : w - 1 : w > nz(w[1]) ? w - 3 : w - 2 : p1 < nz(p1[1]) ? w > nz(w[1]) ? -(w + 1) : -(w - 1) : w > nz(w[1]) ? -(w - 3) : -(w - 2)
    m

getcolmult(trend)=>
    _ret = trend >= 10.0 ? #00FF00ff : trend >= 9.0 ? #00FF00ef : trend >= 8.0 ? #00FF00df : trend >= 7.0 ? #00FF00cf : trend >= 6.0 ? #00FF00bf :
       trend >= 5.0 ? #00FF00af : trend >= 4.0 ? #00FF009f : trend >= 3.0 ? #00FF008f : trend >= 2.0 ? #00FF007f : trend >= 1.0 ? #00FF006f :  
       trend <= -10.0 ? #FF0000ff : trend <= -9.0 ?  #FF0000ef : trend <= -8.0 ?  #FF0000df : trend <= -7.0 ?  #FF0000cf : trend <= -6.0 ?  #FF0000bf :
       trend <= -5.0 ?  #FF0000af : trend <= -4.0 ?  #FF00009f : trend <= -3.0 ?  #FF00008f : trend <= -2.0 ?  #FF00007f : trend <= -1.0 ?  #FF00006f :
       na

getcol(p1, p2)=>
    m = getwidth(p1, p2)
   
    _ret = getcolmult(m)
    
fill(l18, l19, color = getcol(p18, p19), transp = 0)
fill(l17, l18, color = getcol(p17, p18), transp = 0)
fill(l16, l17, color = getcol(p16, p17), transp = 0)
fill(l15, l16, color = getcol(p15, p16), transp = 0)
fill(l14, l15, color = getcol(p14, p15), transp = 0)
fill(l13, l14, color = getcol(p13, p14), transp = 0)
fill(l12, l13, color = getcol(p12, p13), transp = 0)
fill(l11, l12, color = getcol(p11, p12), transp = 0)
fill(l10, l11, color = getcol(p10, p11), transp = 0)
fill(l9, l10, color = getcol(p9, p10), transp = 0)
fill(l8, l9, color = getcol(p8, p9), transp = 0)
fill(l7, l8, color = getcol(p7, p8), transp = 0)
fill(l6, l7, color = getcol(p6, p7), transp = 0)
fill(l5, l6, color = getcol(p5, p6), transp = 0)
fill(l4, l5, color = getcol(p4, p5), transp = 0)
fill(l3, l4, color = getcol(p3, p4), transp = 0)
fill(l2, l3, color = getcol(p2, p3), transp = 0)
fill(l1, l2, color = getcol(p1, p2), transp = 0)

study("Trend Following MAs 3D", precision=0)

matype= input('EMA', title = "MA Type", options =['EMA', 'SMA', 'RMA', 'WMA', 'VWMA'])

ulinreg = input(true, title = "Use Linear Regression")

linprd = input(10, title = "Linear Regression Period", minval = 2)

getma(len)=>

_ret = matype == 'EMA' ? ema(close, len) : matype == 'RMA' ? rma(close, len) : matype == 'VWMA' ? vwma(close, len) : matype == 'WMA' ? wma(close, len) : sma(close, len)

_ret := ulinreg ? linreg(_ret, linprd, 0) : _ret

_ret

p1 = getma(5) - getma(10)

p2 = p1 + getma(10) - getma(15)

p3 = p2 + getma(15) - getma(20)

p4 = p3 + getma(20) - getma(25)

p5 = p4 + getma(25) - getma(30)

p6 = p5 + getma(30) - getma(35)

p7 = p6 + getma(35) - getma(40)

p8 = p7 + getma(40) - getma(45)

p9 = p8 + getma(45) - getma(50)

p10 = p9 + getma(50) - getma(55)

p11 = p10 + getma(55) - getma(60)

p12 = p11 + getma(60) - getma(65)

p13 = p12 + getma(65) - getma(70)

p14 = p13 + getma(70) - getma(75)

p15 = p14 + getma(75) - getma(80)

p16 = p15 + getma(80) - getma(85)

p17 = p16 + getma(85) - getma(90)

p18 = p17 + getma(90) - getma(95)

p19 = p18 + getma(95) - getma(100)

l1 = plot(p1, color = na)

l2 = plot(p2, color = na)

l3 = plot(p3, color = na)

l4 = plot(p4, color = na)

l5 = plot(p5, color = na)

l6 = plot(p6, color = na)

l7 = plot(p7, color = na)

l8 = plot(p8, color = na)

l9 = plot(p9, color = na)

l10 = plot(p10, color = na)

l11 = plot(p11, color = na)

l12 = plot(p12, color = na)

l13 = plot(p13, color = na)

l14 = plot(p14, color = na)

l15 = plot(p15, color = na)

l16 = plot(p16, color = na)

l17 = plot(p17, color = na)

l18 = plot(p18, color = na)

l19 = plot(p19, color = na)

getwidth(a, b)=>

t = abs(a - b)

w = t * 10 / (highest(t, 200) - lowest(t, 200))

w := max(w, 4)

m = p1 > 0 ? p1 > nz(p1[1]) ? w > nz(w[1]) ? w + 1 : w - 1 : w > nz(w[1]) ? w - 3 : w - 2 : p1 < nz(p1[1]) ? w > nz(w[1]) ? -(w + 1) : -(w - 1) : w > nz(w[1]) ? -(w - 3) : -(w - 2)

getcolmult(trend)=>

_ret = trend >= 10.0 ? #00FF00ff : trend >= 9.0 ? #00FF00ef : trend >= 8.0 ? #00FF00df : trend >= 7.0 ? #00FF00cf : trend >= 6.0 ? #00FF00bf :

trend >= 5.0 ? #00FF00af : trend >= 4.0 ? #00FF009f : trend >= 3.0 ? #00FF008f : trend >= 2.0 ? #00FF007f : trend >= 1.0 ? #00FF006f :

trend <= -10.0 ? #FF0000ff : trend <= -9.0 ? #FF0000ef : trend <= -8.0 ? #FF0000df : trend <= -7.0 ? #FF0000cf : trend <= -6.0 ? #FF0000bf :

trend <= -5.0 ? #FF0000af : trend <= -4.0 ? #FF00009f : trend <= -3.0 ? #FF00008f : trend <= -2.0 ? #FF00007f : trend <= -1.0 ? #FF00006f :

getcol(p1, p2)=>

m = getwidth(p1, p2)

_ret = getcolmult(m)

fill(l18, l19, color = getcol(p18, p19), transp = 0)

fill(l17, l18, color = getcol(p17, p18), transp = 0)

fill(l16, l17, color = getcol(p16, p17), transp = 0)

fill(l15, l16, color = getcol(p15, p16), transp = 0)

fill(l14, l15, color = getcol(p14, p15), transp = 0)

fill(l13, l14, color = getcol(p13, p14), transp = 0)

fill(l12, l13, color = getcol(p12, p13), transp = 0)

fill(l11, l12, color = getcol(p11, p12), transp = 0)

fill(l10, l11, color = getcol(p10, p11), transp = 0)

fill(l9, l10, color = getcol(p9, p10), transp = 0)

fill(l8, l9, color = getcol(p8, p9), transp = 0)

fill(l7, l8, color = getcol(p7, p8), transp = 0)

fill(l6, l7, color = getcol(p6, p7), transp = 0)

fill(l5, l6, color = getcol(p5, p6), transp = 0)

fill(l4, l5, color = getcol(p4, p5), transp = 0)

fill(l3, l4, color = getcol(p3, p4), transp = 0)

fill(l2, l3, color = getcol(p2, p3), transp = 0)

fill(l1, l2, color = getcol(p1, p2), transp = 0)

denmar

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Replies: 59

Been thanked: 4 times

04/12/2021 at 3:54 PM #166871

Nicolas

Added to my list.

Nicolas

Keymaster

Topics: 81

Replies: 20828

Been thanked: 4739 times

04/21/2021 at 11:51 AM #167693

denmar

Hi Nicolas,

If this is proving to difficult I would willingly settle for just the lines without the colouration – just a suggestion.

Thanks.

denmar

Participant

Topics: 14

Replies: 59

Been thanked: 4 times

04/27/2021 at 9:36 AM #168157

denmar

I would really appreciate it if somebody could have a look at the code and please transcribe the logic into english or (preferably) write the code for plotting the first line – I should be able to take it from there. Thanks in advance.

denmar

Participant

Topics: 14

Replies: 59

Been thanked: 4 times

04/29/2021 at 9:42 AM #168352

Nicolas

easy part first, without the dynamic color between the curves:

matype= 1 //0 = SMA 1 = EMA 2 = WMA 3 = Wilder 4 = Triangular 5 = End point 6 = Time series 7 = Hull (PRT v11 only) 8 = ZeroLag  (PRT v11 only)
//ulinreg = 1 // Use Linear Regression
linprd = 10 //Linear Regression Period

p1 = linearregression[linprd](average[5,matype]) - linearregression[linprd](average[10,matype])
p2 = p1+(linearregression[linprd](average[10,matype]) - linearregression[linprd](average[15,matype]))
p3 = p2+(linearregression[linprd](average[15,matype]) - linearregression[linprd](average[20,matype]))
p4 = p3+(linearregression[linprd](average[20,matype]) - linearregression[linprd](average[25,matype]))
p5 = p4+(linearregression[linprd](average[25,matype]) - linearregression[linprd](average[30,matype]))
p6 = p5+(linearregression[linprd](average[30,matype]) - linearregression[linprd](average[35,matype]))
p7 = p6+(linearregression[linprd](average[35,matype]) - linearregression[linprd](average[40,matype]))
p8 = p7+(linearregression[linprd](average[40,matype]) - linearregression[linprd](average[45,matype]))
p9 = p8+(linearregression[linprd](average[45,matype]) - linearregression[linprd](average[50,matype]))
p10 = p9+(linearregression[linprd](average[50,matype]) - linearregression[linprd](average[55,matype]))
p11 = p10+(linearregression[linprd](average[55,matype]) - linearregression[linprd](average[60,matype]))
p12 = p11+(linearregression[linprd](average[60,matype]) - linearregression[linprd](average[65,matype]))
p13 = p12+(linearregression[linprd](average[65,matype]) - linearregression[linprd](average[70,matype]))
p14 = p13+(linearregression[linprd](average[70,matype]) - linearregression[linprd](average[75,matype]))
p15 = p14+(linearregression[linprd](average[75,matype]) - linearregression[linprd](average[80,matype]))
p16 = p15+(linearregression[linprd](average[80,matype]) - linearregression[linprd](average[85,matype]))
p17 = p16+(linearregression[linprd](average[85,matype]) - linearregression[linprd](average[90,matype]))
p18 = p17+(linearregression[linprd](average[90,matype]) - linearregression[linprd](average[95,matype]))
p19 = p18+(linearregression[linprd](average[95,matype]) - linearregression[linprd](average[100,matype]))

return p1,p2,p3,p4,p5,p6,p7,p8,p9,p10,p11,p12,p13,p14,p15,p16,p17,p18,p19

matype= 1 //0 = SMA 1 = EMA 2 = WMA 3 = Wilder 4 = Triangular 5 = End point 6 = Time series 7 = Hull (PRT v11 only) 8 = ZeroLag (PRT v11 only)

//ulinreg = 1 // Use Linear Regression

linprd = 10 //Linear Regression Period

p1 = linearregression[linprd](average[5,matype]) - linearregression[linprd](average[10,matype])

p2 = p1+(linearregression[linprd](average[10,matype]) - linearregression[linprd](average[15,matype]))

p3 = p2+(linearregression[linprd](average[15,matype]) - linearregression[linprd](average[20,matype]))

p4 = p3+(linearregression[linprd](average[20,matype]) - linearregression[linprd](average[25,matype]))

p5 = p4+(linearregression[linprd](average[25,matype]) - linearregression[linprd](average[30,matype]))

p6 = p5+(linearregression[linprd](average[30,matype]) - linearregression[linprd](average[35,matype]))

p7 = p6+(linearregression[linprd](average[35,matype]) - linearregression[linprd](average[40,matype]))

p8 = p7+(linearregression[linprd](average[40,matype]) - linearregression[linprd](average[45,matype]))

p9 = p8+(linearregression[linprd](average[45,matype]) - linearregression[linprd](average[50,matype]))

p10 = p9+(linearregression[linprd](average[50,matype]) - linearregression[linprd](average[55,matype]))

p11 = p10+(linearregression[linprd](average[55,matype]) - linearregression[linprd](average[60,matype]))

p12 = p11+(linearregression[linprd](average[60,matype]) - linearregression[linprd](average[65,matype]))

p13 = p12+(linearregression[linprd](average[65,matype]) - linearregression[linprd](average[70,matype]))

p14 = p13+(linearregression[linprd](average[70,matype]) - linearregression[linprd](average[75,matype]))

p15 = p14+(linearregression[linprd](average[75,matype]) - linearregression[linprd](average[80,matype]))

p16 = p15+(linearregression[linprd](average[80,matype]) - linearregression[linprd](average[85,matype]))

p17 = p16+(linearregression[linprd](average[85,matype]) - linearregression[linprd](average[90,matype]))

p18 = p17+(linearregression[linprd](average[90,matype]) - linearregression[linprd](average[95,matype]))

p19 = p18+(linearregression[linprd](average[95,matype]) - linearregression[linprd](average[100,matype]))

return p1,p2,p3,p4,p5,p6,p7,p8,p9,p10,p11,p12,p13,p14,p15,p16,p17,p18,p19

Note that due to lack of function capability of ProBuilder, I have set of the curve to use linear regression, while it was an option in the original code.

Nicolas

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Topics: 81

Replies: 20828

Been thanked: 4739 times

04/29/2021 at 10:22 AM #168361

denmar

Thank you

denmar

Participant

Topics: 14

Replies: 59

Been thanked: 4 times

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