Strategi SuperTrend Jaringan Saraf
Tanggal Pembuatan:
2023-09-14 16:49:38
Akhirnya memodifikasi:
2023-09-14 16:49:38
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ChaoZhang
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Pengikut
Prinsip Strategi
Strategi ini menggabungkan model jaringan saraf, indikator RSI, dan indikator supertrend untuk melakukan perdagangan.
Logika yang tepat adalah:
Membangun model jaringan saraf dengan input data multidimensi seperti perubahan volume transaksi, Brinks, RSI
Perkiraan jaringan untuk perubahan harga masa depan
Menghitung nilai RSI dan menggabungkan RSI dengan tingkat perubahan harga yang diprediksi
Generasi garis stop loss dinamis berdasarkan nilai indikator RSI
Ketika harga jatuh di bawah garis stop loss naik, maka Anda akan melakukan shorting; ketika harga melewati garis stop loss turun, maka Anda akan melakukan shorting.
Filter penilaian tren yang digabungkan dengan indikator tren super
Strategi ini memanfaatkan sepenuhnya kemampuan jaringan saraf untuk mensimulasikan data yang kompleks, dan didukung dengan validasi sinyal dengan indikator seperti RSI dan supertrend, untuk mengendalikan risiko perdagangan sambil meningkatkan akurasi penilaian.
Keunggulan Strategis
Jaringan saraf menilai tren pemodelan data multi-dimensi
RSI Stop loss melindungi keuntungan, super trend membantu penilaian
Verifikasi kombinasi multi-indikator untuk meningkatkan kualitas sinyal
Risiko Strategis
Untuk melatih jaringan saraf, dibutuhkan banyak data.
RSI dan parameter supertrend perlu disesuaikan secara optimal
Efek tergantung pada penilaian model, ada ketidakpastian
Meringkaskan
Strategi ini menggunakan teknologi pembelajaran mesin yang didukung dengan penilaian indikator tradisional untuk mengontrol risiko sambil mengejar efisiensi tinggi. Namun, penyesuaian parameter dan interpretasi modelnya masih perlu disempurnakan.
Kode Sumber Strategi
/*backtest
start: 2023-08-14 00:00:00
end: 2023-09-13 00:00:00
period: 2h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/
//@version=4
//ANN taken from https://www.tradingview.com/script/Eq4zZsTI-ANN-MACD-BTC/
//it only work for BTC as the ANN is trained for this data only
//super trend https://www.tradingview.com/script/VLWVV7tH-SuperTrend/
// Strategy version created for @che_trader
strategy ("ANN RSI SUPER TREND STRATEGY BY che_trader", overlay = true)
qty = input(10000, "Buy quantity")
testStartYear = input(2019, "Backtest Start Year")
testStartMonth = input(1, "Backtest Start Month")
testStartDay = input(1, "Backtest Start Day")
testStartHour = input(0, "Backtest Start Hour")
testStartMin = input(0, "Backtest Start Minute")
testPeriodStart = timestamp(testStartYear,testStartMonth,testStartDay,testStartHour,testStartMin)
testStopYear = input(2099, "Backtest Stop Year")
testStopMonth = input(1, "Backtest Stop Month")
testStopDay = input(30, "Backtest Stop Day")
testPeriodStop = timestamp(testStopYear,testStopMonth,testStopDay,0,0)
testPeriod() => true
max_bars_back = (21)
src = close[0]
// Essential Functions
// Highest - Lowest Functions ( All efforts goes to RicardoSantos )
f_highest(_src, _length)=>
_adjusted_length = _length < 1 ? 1 : _length
_value = _src
for _i = 0 to (_adjusted_length-1)
_value := _src[_i] >= _value ? _src[_i] : _value
_return = _value
f_lowest(_src, _length)=>
_adjusted_length = _length < 1 ? 1 : _length
_value = _src
for _i = 0 to (_adjusted_length-1)
_value := _src[_i] <= _value ? _src[_i] : _value
_return = _value
// Function Sum
f_sum(_src , _length) =>
_output = 0.00
_length_adjusted = _length < 1 ? 1 : _length
for i = 0 to _length_adjusted-1
_output := _output + _src[i]
// Unlocked Exponential Moving Average Function
f_ema(_src, _length)=>
_length_adjusted = _length < 1 ? 1 : _length
_multiplier = 2 / (_length_adjusted + 1)
_return = 0.00
_return := na(_return[1]) ? _src : ((_src - _return[1]) * _multiplier) + _return[1]
// Unlocked Moving Average Function
f_sma(_src, _length)=>
_output = 0.00
_length_adjusted = _length < 0 ? 0 : _length
w = cum(_src)
_output:= (w - w[_length_adjusted]) / _length_adjusted
_output
// Definition : Function Bollinger Bands
Multiplier = 2
_length_bb = 20
e_r = f_sma(src,_length_bb)
// Function Standard Deviation :
f_stdev(_src,_length) =>
float _output = na
_length_adjusted = _length < 2 ? 2 : _length
_avg = f_ema(_src , _length_adjusted)
evar = (_src - _avg) * (_src - _avg)
evar2 = ((f_sum(evar,_length_adjusted))/_length_adjusted)
_output := sqrt(evar2)
std_r = f_stdev(src , _length_bb )
upband = e_r + (Multiplier * std_r) // Upband
dnband = e_r - (Multiplier * std_r) // Lowband
basis = e_r // Midband
// Function : RSI
length = input(14, minval=1) //
f_rma(_src, _length) =>
_length_adjusted = _length < 1 ? 1 : _length
alpha = _length_adjusted
sum = 0.0
sum := (_src + (alpha - 1) * nz(sum[1])) / alpha
f_rsi(_src, _length) =>
_output = 0.00
_length_adjusted = _length < 0 ? 0 : _length
u = _length_adjusted < 1 ? max(_src - _src[_length_adjusted], 0) : max(_src - _src[1] , 0) // upward change
d = _length_adjusted < 1 ? max(_src[_length_adjusted] - _src, 0) : max(_src[1] - _src , 0) // downward change
rs = f_rma(u, _length) / f_rma(d, _length)
res = 100 - 100 / (1 + rs)
res
_rsi = f_rsi(src, length)
// MACD
_fastLength = input(12 , title = "MACD Fast Length")
_slowlength = input(26 , title = "MACD Slow Length")
_signalLength = input(9 , title = "MACD Signal Length")
_macd = f_ema(close, _fastLength) - f_ema(close, _slowlength)
_signal = f_ema(_macd, _signalLength)
_macdhist = _macd - _signal
// Inputs on Tangent Function :
tangentdiff(_src) => nz((_src - _src[1]) / _src[1] )
// Deep Learning Activation Function (Tanh) :
ActivationFunctionTanh(v) => (1 - exp(-2 * v))/( 1 + exp(-2 * v))
// DEEP LEARNING
// INPUTS :
input_1 = tangentdiff(volume)
input_2 = tangentdiff(dnband)
input_3 = tangentdiff(e_r)
input_4 = tangentdiff(upband)
input_5 = tangentdiff(_rsi)
input_6 = tangentdiff(_macdhist)
// LAYERS :
// Input Layers
n_0 = ActivationFunctionTanh(input_1 + 0)
n_1 = ActivationFunctionTanh(input_2 + 0)
n_2 = ActivationFunctionTanh(input_3 + 0)
n_3 = ActivationFunctionTanh(input_4 + 0)
n_4 = ActivationFunctionTanh(input_5 + 0)
n_5 = ActivationFunctionTanh(input_6 + 0)
// Hidden Layers
n_6 = ActivationFunctionTanh( -2.580743 * n_0 + -1.883627 * n_1 + -3.512462 * n_2 + -0.891063 * n_3 + -0.767728 * n_4 + -0.542699 * n_5 + 0.221093)
n_7 = ActivationFunctionTanh( -0.131977 * n_0 + -1.543499 * n_1 + 0.019450 * n_2 + 0.041301 * n_3 + -0.926690 * n_4 + -0.797512 * n_5 + -1.804061)
n_8 = ActivationFunctionTanh( -0.587905 * n_0 + -7.528007 * n_1 + -5.273207 * n_2 + 1.633836 * n_3 + 6.099666 * n_4 + 3.509443 * n_5 + -4.384254)
n_9 = ActivationFunctionTanh( -1.026331 * n_0 + -1.289491 * n_1 + -1.702887 * n_2 + -1.052681 * n_3 + -1.031452 * n_4 + -0.597999 * n_5 + -1.178839)
n_10 = ActivationFunctionTanh( -5.393730 * n_0 + -2.486204 * n_1 + 3.655614 * n_2 + 1.051512 * n_3 + -2.763198 * n_4 + 6.062295 * n_5 + -6.367982)
n_11 = ActivationFunctionTanh( 1.246882 * n_0 + -1.993206 * n_1 + 1.599518 * n_2 + 1.871801 * n_3 + 0.294797 * n_4 + -0.607512 * n_5 + -3.092821)
n_12 = ActivationFunctionTanh( -2.325161 * n_0 + -1.433500 * n_1 + -2.928094 * n_2 + -0.715416 * n_3 + -0.914663 * n_4 + -0.485397 * n_5 + -0.411227)
n_13 = ActivationFunctionTanh( -0.350585 * n_0 + -0.810108 * n_1 + -1.756149 * n_2 + -0.567176 * n_3 + -0.954021 * n_4 + -1.027830 * n_5 + -1.349766)
// Output Layer
_output = ActivationFunctionTanh(2.588784 * n_6 + 0.100819 * n_7 + -5.305373 * n_8 + 1.167093 * n_9 +
3.770143 * n_10 + 1.269190 * n_11 + 2.090862 * n_12 + 0.839791 * n_13 + -0.196165)
_chg_src = tangentdiff(src) * 100
_seed = (_output - _chg_src)
// BEGIN ACTUAL STRATEGY
length1 = input(title="RSI Period", type=input.integer, defval=21)
mult = input(title="RSI Multiplier", type=input.float, step=0.1, defval=4.0)
wicks = input(title="Take Wicks into Account ?", type=input.bool, defval=false)
showLabels = input(title="Show Buy/Sell Labels ?", type=input.bool, defval=true)
srsi = mult* rsi(_seed ,length1)
longStop = hl2 - srsi
longStopPrev = nz(longStop[1], longStop)
longStop := (wicks ? low[1] : close[1]) > longStopPrev ? max(longStop, longStopPrev) : longStop
shortStop = hl2 + srsi
shortStopPrev = nz(shortStop[1], shortStop)
shortStop := (wicks ? high[1] : close[1]) < shortStopPrev ? min(shortStop, shortStopPrev) : shortStop
dir = 1
dir := nz(dir[1], dir)
dir := dir == -1 and (wicks ? high : close) > shortStopPrev ? 1 : dir == 1 and (wicks ? low : close) < longStopPrev ? -1 : dir
longColor = color.green
shortColor = color.red
plot(dir == 1 ? longStop : na, title="Long Stop", style=plot.style_linebr, linewidth=2, color=longColor)
buySignal = dir == 1 and dir[1] == -1
plotshape(buySignal ? longStop : na, title="Long Stop Start", location=location.absolute, style=shape.circle, size=size.tiny, color=longColor, transp=0)
plotshape(buySignal and showLabels ? longStop : na, title="Buy Label", text="Buy", location=location.absolute, style=shape.labelup, size=size.tiny, color=longColor, textcolor=color.white, transp=0)
plot(dir == 1 ? na : shortStop, title="Short Stop", style=plot.style_linebr, linewidth=2, color=shortColor)
sellSignal = dir == -1 and dir[1] == 1
plotshape(sellSignal ? shortStop : na, title="Short Stop Start", location=location.absolute, style=shape.circle, size=size.tiny, color=shortColor, transp=0)
plotshape(sellSignal and showLabels ? shortStop : na, title="Sell Label", text="Sell", location=location.absolute, style=shape.labeldown, size=size.tiny, color=shortColor, textcolor=color.white, transp=0)
if testPeriod() and buySignal
strategy.entry("Long",strategy.long)
if testPeriod() and sellSignal
strategy.entry("Short",strategy.short)