tlove777

Combo Strategy

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This is a combination of multiple coders work. It involves RSI , ADX , awesome oscillator , ANN strategy, etc. The long and short signals are given only at or after hour 11 (this works best in an hour window). You may also have to change the screen under functions should it show a really tiny graph.

I'm not a coder so the code is very messy. In fact, some variables are named after Star Wars characters. However, it does seem to do reliably well in long positions.

Let me know what improvements you guys make. Good Luck!
Mã nguồn mở

Với tinh thần của TradingView, tác giả đã xuất bản tập lệnh theo mã nguồn mở, vì thế trader có thể dễ dàng hiểu và tùy chỉnh được. Bạn có thể sử dụng miễn phí, hoặc tùy chỉnh lại mã đã được cấp phép bởi Quy tắc Chung. Bạn có thể sử dụng nó trên biểu đồ.

Bạn muốn sử dụng tập lệnh này trên biểu đồ?
//@version=2
//This script utilizes codes from: LazyBear, Vdubus, the creator of the ANN indicator, and others.
//I am in no way accepting credit for the majority of work here, nor will I copyright it in my own name
//I'm not a coder, so this is going to seem extremely messy to those who are. 
//@version=2
strategy("Combo Strategy", overlay=true)

multKC = 1.5
mult = 2.0
length = 20
lengthKC  =20
useTrueRange = input(true, title="Use TrueRange (KC)", type=bool)

// Calculate BB
source = close
basis = sma(source, 20)
dev = multKC * stdev(source, 20)
upperBB = basis + dev
lowerBB = basis - dev

// Calculate KC
ma = sma(source, 20)
range = useTrueRange ? tr : (high - low)
rangema = sma(range, 20)
upperKC = ma + rangema * multKC
lowerKC = ma - rangema * multKC

sqzOn  = (lowerBB > lowerKC) and (upperBB < upperKC)
sqzOff = (lowerBB < lowerKC) and (upperBB > upperKC)
noSqz  = (sqzOn == false) and (sqzOff == false)

val = linreg(source  -  avg(avg(highest(high, 20), lowest(low, 20)),sma(close,20)),20,0)

scolor = noSqz ? blue : sqzOn ? black : gray 

fastLength = 65
slowLength = 90
signalLength=12


fastMA = ema(close, fastLength)
slowMA = ema(close, slowLength)
macd = fastMA - slowMA
signal = sma(macd, signalLength)

pos = signal < macd? 1 : -1

//setups
h = high
l = low
c = close

coloring = input(true,type=bool,title="3 colors?")

g= 0.75
ob = 0.80
os = 0.20
smooth = 1

//calc
p = c
L0 = ((1 - g)*p)+(g*nz(L0[1]))
L1 = (-g*L0)+nz(L0[1])+(g*nz(L1[1]))
L2 = (-g*L1)+nz(L1[1])+(g*nz(L2[1]))
L3 = (-g*L2)+nz(L2[1])+(g*nz(L3[1]))
cu = (L0>L1?L0-L1:0)+(L1>L2?L1-L2:0)+(L2>L3?L2-L3:0)
cd = (L0<L1?L1-L0:0)+(L1<L2?L2-L1:0)+(L2<L3?L3-L2:0)
//plots
lrsi=ema((cu+cd==0?-1:cu+cd)==-1?0:(cu/(cu+cd==0?-1:cu+cd)), smooth)

col1 =  lrsi > lrsi[1] and lrsi > os ? #00FF7B : lrsi < lrsi[1] and lrsi < ob ? #FF3571 : lrsi == lrsi ? #ECA700 : #ECA700
col2 =  lrsi > lrsi[1] and lrsi > os ? #00FF7B : lrsi < lrsi[1] and lrsi < ob ? #FF3571 : lrsi < os ? #FF3571 : lrsi > ob ? #00FF7B : lrsi == lrsi ? #ECA700 : #ECA700
col = coloring ? col1 : col2


RSIindi = lrsi > lrsi[1] and lrsi > os and lrsi < 0.20? 1 : 0
RSIindi1 = lrsi < lrsi[1] and lrsi < ob and lrsi > 0.80? -1 : 0

nLengthSlow = 34
nLengthFast = 5

xSMA1_hl2 = sma(hl2, nLengthFast)
xSMA2_hl2 = sma(hl2, nLengthSlow)
xSMA1_SMA2 = xSMA1_hl2 - xSMA2_hl2
xSMA_hl2 = sma(xSMA1_SMA2, nLengthFast)
nRes =  xSMA1_SMA2 - xSMA_hl2
cClr = nRes > nRes[1] ? blue : red

osc1 = nRes > nRes[1]? 0.1 : -0.4
	    
Estar=(close[2] > open[2] and min(open[1], close[1]) > close[2] and open < min(open[1], close[1]) and close < open )

Mstar=(close[2] < open[2] and max(open[1], close[1]) < close[2] and open > max(open[1], close[1]) and close > open )

candlesig = Estar > Mstar? 1 : 0
HBear=(close[1] > open[1] and open > close and open <= close[1] and open[1] <= close and open - close < close[1] - open[1] )

HBull=(open[1] > close[1] and close > open and close <= open[1] and close[1] <= open and close - open < open[1] - close[1] )

GBear=(close[1] > open[1] and open > close and open >= close[1] and open[1] >= close and open - close > close[1] - open[1] )

GBull=(open[1] > close[1] and close > open and close >= open[1] and close[1] >= open and close - open > open[1] - close[1] )

Plinegreen=(close[1] < open[1] and  open < low[1] and close > close[1] + ((open[1] - close[1])/2) and close < open[1])

KBull=(open[1]>close[1] and open>=open[1] and close>open)

KBear=(open[1]<close[1] and open<=open[1] and close<=open)

plineindi = Plinegreen > 0? 1 : 0

kbullindi = KBull > KBear? 1 : 0

threshold = 0.0014
 
getDiff() =>
    yesterday=security(tickerid, 'D', ohlc4[1])
    today=security(tickerid, 'D', ohlc4)
    delta=today-yesterday
    percentage=delta/yesterday
 
PineActivationFunctionLinear(v) => v
PineActivationFunctionTanh(v) =>
    (exp(v) - exp(-v))/(exp(v) + exp(-v))
 
l0_0 = PineActivationFunctionLinear(getDiff())
l0_1 = PineActivationFunctionLinear(getDiff())
l0_2 = PineActivationFunctionLinear(getDiff())
l0_3 = PineActivationFunctionLinear(getDiff())
l0_4 = PineActivationFunctionLinear(getDiff())
l0_5 = PineActivationFunctionLinear(getDiff())
l0_6 = PineActivationFunctionLinear(getDiff())
l0_7 = PineActivationFunctionLinear(getDiff())
l0_8 = PineActivationFunctionLinear(getDiff())
l0_9 = PineActivationFunctionLinear(getDiff())
l0_10 = PineActivationFunctionLinear(getDiff())
l0_11 = PineActivationFunctionLinear(getDiff())
l0_12 = PineActivationFunctionLinear(getDiff())
l0_13 = PineActivationFunctionLinear(getDiff())
l0_14 = PineActivationFunctionLinear(getDiff())
 
l1_0 = PineActivationFunctionTanh(l0_0*5.040340774 + l0_1*-1.3025994088 + l0_2*19.4225543981 + l0_3*1.1796960423 + l0_4*2.4299395823 + l0_5*3.159003445 + l0_6*4.6844527551 + l0_7*-6.1079267196 + l0_8*-2.4952869198 + l0_9*-4.0966081154 + l0_10*-2.2432843111 + l0_11*-0.6105764807 + l0_12*-0.0775684605 + l0_13*-0.7984753138 + l0_14*3.4495907342)
l1_1 = PineActivationFunctionTanh(l0_0*5.9559031982 + l0_1*-3.1781960056 + l0_2*-1.6337491061 + l0_3*-4.3623166512 + l0_4*0.9061990402 + l0_5*-0.731285093 + l0_6*-6.2500232251 + l0_7*0.1356087758 + l0_8*-0.8570572885 + l0_9*-4.0161353298 + l0_10*1.5095552083 + l0_11*1.324789197 + l0_12*-0.1011973878 + l0_13*-2.3642090162 + l0_14*-0.7160862442)
l1_2 = PineActivationFunctionTanh(l0_0*4.4350881378 + l0_1*-2.8956461034 + l0_2*1.4199762607 + l0_3*-0.6436844261 + l0_4*1.1124274281 + l0_5*-4.0976954985 + l0_6*2.9317456342 + l0_7*0.0798318393 + l0_8*-5.5718144311 + l0_9*-0.6623352208 +l0_10*3.2405203222 + l0_11*-10.6253384513 + l0_12*4.7132919253 + l0_13*-5.7378151597 + l0_14*0.3164836695)
l1_3 = PineActivationFunctionTanh(l0_0*-6.1194605467 + l0_1*7.7935605604 + l0_2*-0.7587522153 + l0_3*9.8382495905 + l0_4*0.3274314734 + l0_5*1.8424796541 + l0_6*-1.2256355427 + l0_7*-1.5968600758 + l0_8*1.9937700922 + l0_9*5.0417809111 + l0_10*-1.9369944654 + l0_11*6.1013201778 + l0_12*1.5832910747 + l0_13*-2.148403244 + l0_14*1.5449437366)
l1_4 = PineActivationFunctionTanh(l0_0*3.5700040028 + l0_1*-4.4755892733 + l0_2*0.1526702072 + l0_3*-0.3553664401 + l0_4*-2.3777962662 + l0_5*-1.8098849587 + l0_6*-3.5198449134 + l0_7*-0.4369370497 + l0_8*2.3350169623 + l0_9*1.9328960346 + l0_10*1.1824141812 + l0_11*3.0565148049 + l0_12*-9.3253401534 + l0_13*1.6778555498 + l0_14*-3.045794332)
l1_5 = PineActivationFunctionTanh(l0_0*3.6784907623 + l0_1*1.1623683715 + l0_2*7.1366362145 + l0_3*-5.6756546585 + l0_4*12.7019884334 + l0_5*-1.2347823331 + l0_6*2.3656619827 + l0_7*-8.7191778213 + l0_8*-13.8089238753 + l0_9*5.4335943836 + l0_10*-8.1441181338 + l0_11*-10.5688113287 + l0_12*6.3964140758 + l0_13*-8.9714236223 + l0_14*-34.0255456929)
l1_6 = PineActivationFunctionTanh(l0_0*-0.4344517548 + l0_1*-3.8262167437 + l0_2*-0.2051098003 + l0_3*0.6844201221 + l0_4*1.1615893422 + l0_5*-0.404465314 + l0_6*-0.1465747632 + l0_7*-0.006282458 + l0_8*0.1585655487 + l0_9*1.1994484991 + l0_10*-0.9879081404 + l0_11*-0.3564970612 + l0_12*1.5814717823 + l0_13*-0.9614804676 + l0_14*0.9204822346)
l1_7 = PineActivationFunctionTanh(l0_0*-4.2700957175 + l0_1*9.4328591157 + l0_2*-4.3045548 + l0_3*5.0616868842 + l0_4*3.3388781058 + l0_5*-2.1885073225 + l0_6*-6.506301518 + l0_7*3.8429000108 + l0_8*-1.6872237349 + l0_9*2.4107095799 + l0_10*-3.0873985314 + l0_11*-2.8358325447 + l0_12*2.4044366491 + l0_13*0.636779082 + l0_14*-13.2173215035)
l1_8 = PineActivationFunctionTanh(l0_0*-8.3224697492 + l0_1*-9.4825530183 + l0_2*3.5294389835 + l0_3*0.1538618049 + l0_4*-13.5388631898 + l0_5*-0.1187936017 + l0_6*-8.4582741139 + l0_7*5.1566299292 + l0_8*10.345519938 + l0_9*2.9211759333 + l0_10*-5.0471804233 + l0_11*4.9255989983 + l0_12*-9.9626142544 + l0_13*23.0043143258 + l0_14*20.9391809343)
l1_9 = PineActivationFunctionTanh(l0_0*-0.9120518654 + l0_1*0.4991807488 + l0_2*-1.877244586 + l0_3*3.1416466525 + l0_4*1.063709676 + l0_5*0.5210126835 + l0_6*-4.9755780108 + l0_7*2.0336532347 + l0_8*-1.1793121093 + l0_9*-0.730664855 + l0_10*-2.3515987428 + l0_11*-0.1916546514 + l0_12*-2.2530340504 + l0_13*-0.2331829119 + l0_14*0.7216218149)
l1_10 = PineActivationFunctionTanh(l0_0*-5.2139618683 + l0_1*1.0663790028 + l0_2*1.8340834959 + l0_3*1.6248173447 + l0_4*-0.7663740145 + l0_5*0.1062788171 + l0_6*2.5288021501 + l0_7*-3.4066549066 + l0_8*-4.9497988755 + l0_9*-2.3060668143 + l0_10*-1.3962486274 + l0_11*0.6185583427 + l0_12*0.2625299576 + l0_13*2.0270246444 + l0_14*0.6372015811)
l1_11 = PineActivationFunctionTanh(l0_0*0.2020072665 + l0_1*0.3885852709 + l0_2*-0.1830248843 + l0_3*-1.2408598444 + l0_4*-0.6365798088 + l0_5*1.8736534268 + l0_6*0.656206442 + l0_7*-0.2987482678 + l0_8*-0.2017485963 + l0_9*-1.0604095303 + l0_10*0.239793356 + l0_11*-0.3614172938 + l0_12*0.2614678044 + l0_13*1.0083551762 + l0_14*-0.5473833797)
l1_12 = PineActivationFunctionTanh(l0_0*-0.4367517149 + l0_1*-10.0601304934 + l0_2*1.9240604838 + l0_3*-1.3192184047 + l0_4*-0.4564760159 + l0_5*-0.2965270368 + l0_6*-1.1407423613 + l0_7*2.0949647291 + l0_8*-5.8212599297 + l0_9*-1.3393321939 + l0_10*7.6624548265 + l0_11*1.1309391851 + l0_12*-0.141798054 + l0_13*5.1416736187 + l0_14*-1.8142503125)
l1_13 = PineActivationFunctionTanh(l0_0*1.103948336 + l0_1*-1.4592033032 + l0_2*0.6146278432 + l0_3*0.5040966421 + l0_4*-2.4276090772 + l0_5*-0.0432902426 + l0_6*-0.0044259999 + l0_7*-0.5961347308 + l0_8*0.3821026107 + l0_9*0.6169102373 +l0_10*-0.1469847611 + l0_11*-0.0717167683 + l0_12*-0.0352403695 + l0_13*1.2481310788 + l0_14*0.1339628411)
l1_14 = PineActivationFunctionTanh(l0_0*-9.8049980534 + l0_1*13.5481068519 + l0_2*-17.1362809025 + l0_3*0.7142100864 + l0_4*4.4759163422 + l0_5*4.5716161777 + l0_6*1.4290884628 + l0_7*8.3952862712 + l0_8*-7.1613700432 + l0_9*-3.3249489518+ l0_10*-0.7789587912 + l0_11*-1.7987628873 + l0_12*13.364752545 + l0_13*5.3947219678 + l0_14*12.5267547127)
l1_15 = PineActivationFunctionTanh(l0_0*0.9869461803 + l0_1*1.9473351905 + l0_2*2.032925759 + l0_3*7.4092080633 + l0_4*-1.9257741399 + l0_5*1.8153585328 + l0_6*1.1427866392 + l0_7*-0.3723167449 + l0_8*5.0009927384 + l0_9*-0.2275103411 + l0_10*2.8823012914 + l0_11*-3.0633141934 + l0_12*-2.785334815 + l0_13*2.727981E-4 + l0_14*-0.1253009512)
l1_16 = PineActivationFunctionTanh(l0_0*4.9418118585 + l0_1*-2.7538199876 + l0_2*-16.9887588104 + l0_3*8.8734475297 + l0_4*-16.3022734814 + l0_5*-4.562496601 + l0_6*-1.2944373699 + l0_7*-9.6022946986 + l0_8*-1.018393866 + l0_9*-11.4094515429 + l0_10*24.8483091382 + l0_11*-3.0031522277 + l0_12*0.1513114555 + l0_13*-6.7170487021 + l0_14*-14.7759227576)
l1_17 = PineActivationFunctionTanh(l0_0*5.5931454656 + l0_1*2.22272078 + l0_2*2.603416897 + l0_3*1.2661196599 + l0_4*-2.842826446 + l0_5*-7.9386099121 + l0_6*2.8278849111 + l0_7*-1.2289445238 + l0_8*4.571484248 + l0_9*0.9447425595 + l0_10*4.2890688351 + l0_11*-3.3228258483 + l0_12*4.8866215526 + l0_13*1.0693412194 + l0_14*-1.963203112)
l1_18 = PineActivationFunctionTanh(l0_0*0.2705520264 + l0_1*0.4002328199 + l0_2*0.1592515845 + l0_3*0.371893552 + l0_4*-1.6639467871 + l0_5*2.2887318884 + l0_6*-0.148633664 + l0_7*-0.6517792263 + l0_8*-0.0993032992 + l0_9*-0.964940376 + l0_10*0.1286342935 + l0_11*0.4869943595 + l0_12*1.4498648166 + l0_13*-0.3257333384 + l0_14*-1.3496419812)
l1_19 = PineActivationFunctionTanh(l0_0*-1.3223200798 + l0_1*-2.2505204324 + l0_2*0.8142804525 + l0_3*-0.848348177 + l0_4*0.7208860589 + l0_5*1.2033423756 + l0_6*-0.1403005786 + l0_7*0.2995941644 + l0_8*-1.1440473062 + l0_9*1.067752916 + l0_10*-1.2990534679 + l0_11*1.2588583869 + l0_12*0.7670409455 + l0_13*2.7895972983 + l0_14*-0.5376152512)
l1_20 = PineActivationFunctionTanh(l0_0*0.7382351572 + l0_1*-0.8778865631 + l0_2*1.0950766363 + l0_3*0.7312146997 + l0_4*2.844781386 + l0_5*2.4526730903 + l0_6*-1.9175165077 + l0_7*-0.7443755288 + l0_8*-3.1591419438 + l0_9*0.8441602697 + l0_10*1.1979484448 + l0_11*2.138098544 + l0_12*0.9274159536 + l0_13*-2.1573448803 + l0_14*-3.7698356464)
l1_21 = PineActivationFunctionTanh(l0_0*5.187120117 + l0_1*-7.7525670576 + l0_2*1.9008346975 + l0_3*-1.2031603996 + l0_4*5.917669142 + l0_5*-3.1878682719 + l0_6*1.0311747828 + l0_7*-2.7529484612 + l0_8*-1.1165884578 + l0_9*2.5524942323 + l0_10*-0.38623241 + l0_11*3.7961317445 + l0_12*-6.128820883 + l0_13*-2.1470707709 + l0_14*2.0173792965)
l1_22 = PineActivationFunctionTanh(l0_0*-6.0241676562 + l0_1*0.7474455584 + l0_2*1.7435724844 + l0_3*0.8619835076 + l0_4*-0.1138406797 + l0_5*6.5979359352 + l0_6*1.6554154348 + l0_7*-3.7969458806 + l0_8*1.1139097376 + l0_9*-1.9588417 + l0_10*3.5123392221 + l0_11*9.4443103128 + l0_12*-7.4779291395 + l0_13*3.6975940671 + l0_14*8.5134262747)
l1_23 = PineActivationFunctionTanh(l0_0*-7.5486576471 + l0_1*-0.0281420865 + l0_2*-3.8586839454 + l0_3*-0.5648792233 + l0_4*-7.3927282026 + l0_5*-0.3857538046 + l0_6*-2.9779885698 + l0_7*4.0482279965 + l0_8*-1.1522499578 + l0_9*-4.1562500212 + l0_10*0.7813134307 + l0_11*-1.7582667612 + l0_12*1.7071109988 + l0_13*6.9270873208 + l0_14*-4.5871357362)
l1_24 = PineActivationFunctionTanh(l0_0*-5.3603442228 + l0_1*-9.5350611629 + l0_2*1.6749984422 + l0_3*-0.6511065892 + l0_4*-0.8424823239 + l0_5*1.9946675213 + l0_6*-1.1264361638 + l0_7*0.3228676616 + l0_8*5.3562230396 + l0_9*-1.6678168952+ l0_10*1.2612580068 + l0_11*-3.5362671399 + l0_12*-9.3895191366 + l0_13*2.0169228673 + l0_14*-3.3813191557)
l1_25 = PineActivationFunctionTanh(l0_0*1.1362866429 + l0_1*-1.8960071702 + l0_2*5.7047307243 + l0_3*-1.6049785053 + l0_4*-4.8353898931 + l0_5*-1.4865381145 + l0_6*-0.2846893475 + l0_7*2.2322095997 + l0_8*2.0930488668 + l0_9*1.7141411002 + l0_10*-3.4106032176 + l0_11*3.0593289612 + l0_12*-5.0894813904 + l0_13*-0.5316299133 + l0_14*0.4705265416)
l1_26 = PineActivationFunctionTanh(l0_0*-0.9401400975 + l0_1*-0.9136086957 + l0_2*-3.3808688582 + l0_3*4.7200776773 + l0_4*3.686296919 + l0_5*14.2133723935 + l0_6*1.5652940954 + l0_7*-0.2921139433 + l0_8*1.0244504511 + l0_9*-7.6918299134 + l0_10*-0.594936135 + l0_11*-1.4559914156 + l0_12*2.8056435224 + l0_13*2.6103905733 + l0_14*2.3412348872)
l1_27 = PineActivationFunctionTanh(l0_0*1.1573980186 + l0_1*2.9593661909 + l0_2*0.4512594325 + l0_3*-0.9357210858 + l0_4*-1.2445804495 + l0_5*4.2716471631 + l0_6*1.5167912375 + l0_7*1.5026853293 + l0_8*1.3574772038 + l0_9*-1.9754386842 + l0_10*6.727671436 + l0_11*8.0145772889 + l0_12*7.3108970663 + l0_13*-2.5005627841 + l0_14*8.9604502277)
l1_28 = PineActivationFunctionTanh(l0_0*6.3576350212 + l0_1*-2.9731672725 + l0_2*-2.7763558082 + l0_3*-3.7902984555 + l0_4*-1.0065574585 + l0_5*-0.7011836061 + l0_6*-1.0298068578 + l0_7*1.201007784 + l0_8*-0.7835862254 + l0_9*-3.9863597435 + l0_10*6.7851825502 + l0_11*1.1120256721 + l0_12*-2.263287351 + l0_13*1.8314374104 + l0_14*-2.279102097)
l1_29 = PineActivationFunctionTanh(l0_0*-7.8741911036 + l0_1*-5.3370618518 + l0_2*11.9153868964 + l0_3*-4.1237170553 + l0_4*2.9491152758 + l0_5*1.0317132502 + l0_6*2.2992199883 + l0_7*-2.0250502364 + l0_8*-11.0785995839 + l0_9*-6.3615588554 + l0_10*-1.1687644976 + l0_11*6.3323478015 + l0_12*6.0195076962 + l0_13*-2.8972208702 + l0_14*3.6107747183)
 
l2_0 = PineActivationFunctionTanh(l1_0*-0.590546797 + l1_1*0.6608304658 + l1_2*-0.3358268839 + l1_3*-0.748530283 + l1_4*-0.333460383 + l1_5*-0.3409307681 + l1_6*0.1916558198 + l1_7*-0.1200399453 + l1_8*-0.5166151854 + l1_9*-0.8537164676 +l1_10*-0.0214448647 + l1_11*-0.553290271 + l1_12*-1.2333302892 + l1_13*-0.8321813811 + l1_14*-0.4527761741 + l1_15*0.9012545631 + l1_16*0.415853215 + l1_17*0.1270548319 + l1_18*0.2000460279 + l1_19*-0.1741942671 + l1_20*0.419830522 + l1_21*-0.059839291 + l1_22*-0.3383001769 + l1_23*0.1617814073 + l1_24*0.3071848006 + l1_25*-0.3191182045 + l1_26*-0.4981831822 + l1_27*-1.467478375 + l1_28*-0.1676432563 + l1_29*1.2574849126)
l2_1 = PineActivationFunctionTanh(l1_0*-0.5514235841 + l1_1*0.4759190049 + l1_2*0.2103576983 + l1_3*-0.4754377924 + l1_4*-0.2362941295 + l1_5*0.1155082119 + l1_6*0.7424215794 + l1_7*-0.3674198672 + l1_8*0.8401574461 + l1_9*0.6096563193 + l1_10*0.7437935674 + l1_11*-0.4898638101 + l1_12*-0.4168668092 + l1_13*-0.0365111095 + l1_14*-0.342675224 + l1_15*0.1870268765 + l1_16*-0.5843050987 + l1_17*-0.4596547471 + l1_18*0.452188522 + l1_19*-0.6737126684 + l1_20*0.6876072741 + l1_21*-0.8067776704 + l1_22*0.7592979467 + l1_23*-0.0768239468 + l1_24*0.370536097 + l1_25*-0.4363884671 + l1_26*-0.419285676 + l1_27*0.4380251141 + l1_28*0.0822528948 + l1_29*-0.2333910809)
l2_2 = PineActivationFunctionTanh(l1_0*-0.3306539521 + l1_1*-0.9382247194 + l1_2*0.0746711276 + l1_3*-0.3383838985 + l1_4*-0.0683232217 + l1_5*-0.2112358049 + l1_6*-0.9079234054 + l1_7*0.4898595603 + l1_8*-0.2039825863 + l1_9*1.0870698641+ l1_10*-1.1752901237 + l1_11*1.1406403923 + l1_12*-0.6779626786 + l1_13*0.4281048906 + l1_14*-0.6327670055 + l1_15*-0.1477678844 + l1_16*0.2693637584 + l1_17*0.7250738509 + l1_18*0.7905904504 + l1_19*-1.6417250883 + l1_20*-0.2108095534 +l1_21*-0.2698557472 + l1_22*-0.2433656685 + l1_23*-0.6289943273 + l1_24*0.436428207 + l1_25*-0.8243825184 + l1_26*-0.8583496686 + l1_27*0.0983131026 + l1_28*-0.4107462518 + l1_29*0.5641683087)
l2_3 = PineActivationFunctionTanh(l1_0*1.7036869992 + l1_1*-0.6683507666 + l1_2*0.2589197112 + l1_3*0.032841148 + l1_4*-0.4454796342 + l1_5*-0.6196149423 + l1_6*-0.1073622976 + l1_7*-0.1926393101 + l1_8*1.5280232458 + l1_9*-0.6136527036 +l1_10*-1.2722934357 + l1_11*0.2888655811 + l1_12*-1.4338638512 + l1_13*-1.1903556863 + l1_14*-1.7659663905 + l1_15*0.3703086867 + l1_16*1.0409140889 + l1_17*0.0167382209 + l1_18*0.6045646461 + l1_19*4.2388788116 + l1_20*1.4399738234 + l1_21*0.3308571935 + l1_22*1.4501137667 + l1_23*0.0426123724 + l1_24*-0.708479795 + l1_25*-1.2100800732 + l1_26*-0.5536278651 + l1_27*1.3547250573 + l1_28*1.2906250286 + l1_29*0.0596007114)
l2_4 = PineActivationFunctionTanh(l1_0*-0.462165126 + l1_1*-1.0996742176 + l1_2*1.0928262999 + l1_3*1.806407067 + l1_4*0.9289147669 + l1_5*0.8069022793 + l1_6*0.2374237802 + l1_7*-2.7143979019 + l1_8*-2.7779203877 + l1_9*0.214383903 + l1_10*-1.3111536623 + l1_11*-2.3148813568 + l1_12*-2.4755355804 + l1_13*-0.6819733236 + l1_14*0.4425615226 + l1_15*-0.1298218043 + l1_16*-1.1744832824 + l1_17*-0.395194848 + l1_18*-0.2803397703 + l1_19*-0.4505071197 + l1_20*-0.8934956598 + l1_21*3.3232916348 + l1_22*-1.7359534851 + l1_23*3.8540421743 + l1_24*1.4424032523 + l1_25*0.2639823693 + l1_26*0.3597053634 + l1_27*-1.0470693728 + l1_28*1.4133480357 + l1_29*0.6248098695)
l2_5 = PineActivationFunctionTanh(l1_0*0.2215807411 + l1_1*-0.5628295071 + l1_2*-0.8795982905 + l1_3*0.9101585104 + l1_4*-1.0176831976 + l1_5*-0.0728884401 + l1_6*0.6676331658 + l1_7*-0.7342174108 + l1_8*9.4428E-4 + l1_9*0.6439774272 + l1_10*-0.0345236026 + l1_11*0.5830977027 + l1_12*-0.4058921837 + l1_13*-0.3991888077 + l1_14*-1.0090426973 + l1_15*-0.9324780698 + l1_16*-0.0888749165 + l1_17*0.2466351736 + l1_18*0.4993304601 + l1_19*-1.115408696 + l1_20*0.9914246705 + l1_21*0.9687743445 + l1_22*0.1117130875 + l1_23*0.7825109733 + l1_24*0.2217023612 + l1_25*0.3081256411 + l1_26*-0.1778007966 + l1_27*-0.3333287743 + l1_28*1.0156352461 + l1_29*-0.1456257813)
l2_6 = PineActivationFunctionTanh(l1_0*-0.5461783383 + l1_1*0.3246015999 + l1_2*0.1450605434 + l1_3*-1.3179944349 + l1_4*-1.5481775261 + l1_5*-0.679685633 + l1_6*-0.9462335139 + l1_7*-0.6462399371 + l1_8*0.0991658683 + l1_9*0.1612892194 +l1_10*-1.037660602 + l1_11*-0.1044778824 + l1_12*0.8309203243 + l1_13*0.7714766458 + l1_14*0.2566767663 + l1_15*0.8649416329 + l1_16*-0.5847461285 + l1_17*-0.6393969272 + l1_18*0.8014049359 + l1_19*0.2279568228 + l1_20*1.0565217821 + l1_21*0.134738029 + l1_22*0.3420395576 + l1_23*-0.2417397219 + l1_24*0.3083072038 + l1_25*0.6761739059 + l1_26*-0.4653817053 + l1_27*-1.0634057566 + l1_28*-0.5658892281 + l1_29*-0.6947283681)
l2_7 = PineActivationFunctionTanh(l1_0*-0.5450410944 + l1_1*0.3912849372 + l1_2*-0.4118641117 + l1_3*0.7124695074 + l1_4*-0.7510266122 + l1_5*1.4065673913 + l1_6*0.9870731545 + l1_7*-0.2609363107 + l1_8*-0.3583639958 + l1_9*0.5436375706 +l1_10*0.4572450099 + l1_11*-0.4651538878 + l1_12*-0.2180218212 + l1_13*0.5241262959 + l1_14*-0.8529323253 + l1_15*-0.4200378937 + l1_16*0.4997885721 + l1_17*-1.1121528189 + l1_18*0.5992411048 + l1_19*-1.0263270781 + l1_20*-1.725160642 + l1_21*-0.2653995722 + l1_22*0.6996703032 + l1_23*0.348549086 + l1_24*0.6522482482 + l1_25*-0.7931928436 + l1_26*-0.5107994359 + l1_27*0.0509642698 + l1_28*0.8711187423 + l1_29*0.8999449627)
l2_8 = PineActivationFunctionTanh(l1_0*-0.7111081522 + l1_1*0.4296245062 + l1_2*-2.0720732038 + l1_3*-0.4071818684 + l1_4*1.0632721681 + l1_5*0.8463224325 + l1_6*-0.6083948423 + l1_7*1.1827669608 + l1_8*-0.9572307844 + l1_9*-0.9080517673 + l1_10*-0.0479029057 + l1_11*-1.1452853213 + l1_12*0.2884352688 + l1_13*0.1767851586 + l1_14*-1.089314461 + l1_15*1.2991763966 + l1_16*1.6236630806 + l1_17*-0.7720263697 + l1_18*-0.5011541755 + l1_19*-2.3919413568 + l1_20*0.0084018338 + l1_21*0.9975216139 + l1_22*0.4193541029 + l1_23*1.4623834571 + l1_24*-0.6253069691 + l1_25*0.6119677341 + l1_26*0.5423948388 + l1_27*1.0022450377 + l1_28*-1.2392984069 + l1_29*1.5021529822)
 
l3_0 = PineActivationFunctionTanh(l2_0*0.3385061186 + l2_1*0.6218531956 + l2_2*-0.7790340983 + l2_3*0.1413078332 + l2_4*0.1857010624 + l2_5*-0.1769456351 + l2_6*-0.3242337911 + l2_7*-0.503944883 + l2_8*0.1540568869)
 
buying = l3_0 > threshold ? true : l3_0 < -threshold ? false : buying[1]

vol_spike1 = l3_0
vol_spike2 = 0

bbbuying = l3_0 > threshold ? true : l3_0 < -threshold ? false : bbbuying[1]

rubert = (macd-signal)

rupsig = rubert > 0? 1 : -1.5
rupsig2 = rubert > rubert[1] and rubert > 0? 1 : 0
rupsig3 = rubert < rubert[1] and rubert[1] < rubert[2] and rubert < 0? -1 : 1

AvgLen = 7
xClose = close
xOpen = open
xVolume = volume
nVolAccum = sum(iff(xClose > xOpen, xVolume, iff(xClose < xOpen, -xVolume, 0))  ,AvgLen)
volly = nVolAccum / AvgLen


volly1 = volly < -100000? -1 : 0
volly2 = volly > 0? 0.0001 : 0
rupsig3value = input(title="R3", defval=3, step=1)
rupsig2value = input(title="R2", defval=0, step=1)
rupsig1value = input(title="R1", defval=3, step=1)
MacSig = input(title="MACD/SIG", defval=100, step=1)
//RSIinput = input(title="RSI", defval=15, step=1)
kit1 = input(title="RSI2", defval=5, step=1)
kit2 = input(title="RSI2", defval=1, step=1)
Oscar = input(title="Ozzy", defval=1, step=1)
Oscar2 = input(title="Ozzy2", defval=1, step=0.01)
areok = input(title="Annie", defval=5, step=0.01)
volly1vol = input(title="Volume", defval=0, step=0.01)

comboline = ((macd-signal)*MacSig) + (val*0) + (RSIindi*kit1) + (RSIindi1*kit2) + (vol_spike1*areok) + ((volly1+volly2)*volly1vol) + (nRes*Oscar) + (osc1*Oscar2) +(rupsig*rupsig1value) + (rupsig2*rupsig2value) + (rupsig3*rupsig3value)


buysig = (comboline <= -30.0)

shortTerm = 5
longTerm = 20

src=close
vpc = vwma(src, longTerm) - sma(src, longTerm)
vpr = vwma(src, shortTerm)/sma(src, shortTerm)
vm  = sma(volume, shortTerm)/sma(volume, longTerm)

vpci = vpc*vpr*vm

DrawMA = input(true, type=bool, title="Draw MA on VPCI?")

lengthMA = 8

s=sma(vpci, lengthMA)

DrawBands = input(false, type=bool)
HighlightBreaches = input(true, type=bool)

lengths = 20
mults = 2.5

bb_s = vpci
basiss = sma(bb_s, lengths)
devs = (mults * stdev(bb_s, lengths))
upper = (basiss + devs)
lower = (basiss - devs)


p1 = plot(DrawBands?upper:na, color=gray)
p2 = plot(DrawBands?lower:na , color=gray)
fill(p1, p2, blue)

b_color = (bb_s > upper) ? red : (bb_s < lower) ? green : na
offs_v = 0.3
breach_pos = (bb_s >= upper) ? (bb_s+offs_v) : (bb_s <= lower ? (bb_s - offs_v) : 0)
Breached=(bb_s >= upper) or (bb_s <= lower)

srcs = close
lengthss=input(20)
obv(srcs) => cum(change(srcs) > 0 ? volume : change(srcs) < 0 ? -volume : 0*volume)
oss=obv(srcs)
obv_osc = (oss - ema(oss,lengthss))

luke = obv_osc <= 0 ? 1 : 0

thz = 20

lenz = 14

TrueRange = max(max(high-low, abs(high-nz(close[1]))), abs(low-nz(close[1])))
DirectionalMovementPlus = high-nz(high[1]) > nz(low[1])-low ? max(high-nz(high[1]), 0): 0
DirectionalMovementMinus = nz(low[1])-low > high-nz(high[1]) ? max(nz(low[1])-low, 0): 0


SmoothedTrueRange = nz(SmoothedTrueRange[1]) - (nz(SmoothedTrueRange[1])/lenz) + TrueRange
SmoothedDirectionalMovementPlus = nz(SmoothedDirectionalMovementPlus[1]) - (nz(SmoothedDirectionalMovementPlus[1])/lenz) + DirectionalMovementPlus
SmoothedDirectionalMovementMinus = nz(SmoothedDirectionalMovementMinus[1]) - (nz(SmoothedDirectionalMovementMinus[1])/lenz) + DirectionalMovementMinus

DIPlusz = SmoothedDirectionalMovementPlus / SmoothedTrueRange * 100
DIMinusz = SmoothedDirectionalMovementMinus / SmoothedTrueRange * 100
DXz = abs(DIPlusz-DIMinusz) / (DIPlusz+DIMinusz)*100
ADXz = sma(DXz, lenz)

vader = ADXz > 45? 1 : 0
vaders = DIPlusz < 20? 1 : 0

lengthe = 14
obr = 80
osr = 20

WiMA(src, lengthr) => 
    MA_s=(src + nz(MA_s[1] * (lengthr-1)))/lengthr
    MA_s

calc_rsi_volume(fv, length) =>	
	up=iff(fv>fv[1],abs(fv-fv[1])*volume,0)
	dn=iff(fv<fv[1],abs(fv-fv[1])*volume,0)
	upt=WiMA(up,length)
	dnt=WiMA(dn,length)
	100*(upt/(upt+dnt))

rsi_v = calc_rsi_volume(close, length)

u=plot(ob)
lrr=plot(os)

leia = rsi_v > obr? 1 : 0

han = rsi_v < osr? 1 : 0
    
chewy = ohlc4 < ohlc4[1]? 1 : 0

falcon = han+luke+vader > 2? 1 : 0
redbull = (abs(open-close)/close <= 0.0015)and comboline < 0 and falcon >= 1? 1 : 0
qual = comboline < comboline[1]? 0 : na
qual1 = comboline-comboline[1]
qual2 = qual1/comboline
qual3 = qual2 <= 0.006? 1 : 0
RB = redbull+redbull[1]+redbull[3] >=1? 1 : 0
Falc = falcon+falcon[1]+falcon[2] >=1? 1 : 0
GBindi = (GBull+GBull[1]+GBull[2]+GBull[3]+GBull[4]) >=1
Breachey = Breached and bb_s < lower and comboline < 0 and (comboline[2] > comboline[1] and comboline[1] > comboline)? 1 : 0
Breach = Breached and bb_s > lower and comboline > 0 and (comboline[2] < comboline[1] and comboline[1] < comboline)? 1 : 0
Breacher = Breachey+Breachey[1]+Breachey[2]+Breachey[3]+Breachey[4]+Breachey[5]+Breachey[6]+Breachey[7]+Breachey[8]+Breachey[9]+Breachey[10] >=1? 1 : 0

perfectstorm = (RB + Falc + qual3 + Breacher + GBindi) > 4 ? 1 : 0

tttester = buying and hour >=11.00 and hour < 14 and comboline < 0? true : false
selling = (buying != true and hour >=11.00 and hour < 14) and comboline > 0 and comboline < comboline[1]? true : false

longCondition = tttester
if (longCondition)
    strategy.entry("Long", strategy.long)

profit = strategy.openprofit
yesterday = dayofweek-1

longpos = strategy.position_size
if year > 2001
    strategy.entry("sell", strategy.short, when=profit>4 and hour<=11)
shortCondition = selling
if (shortCondition)
    strategy.entry("Short", strategy.short)



Bình luận

This strategy rely on future data (repaints) and can not be used in real life. Please vote up for fixing this issue: https://getsatisfaction.com/tradingview/topics/strategies-and-indicators-are-repainting
+4 Phản hồi
Also, I've altered things to best suit long positions. So overall numbers may not look great but long position profit should
+1 Phản hồi