Strategi Pelacakan Momentum Adaptif Multi-faktor

Penulis:ChaoZhang, Tanggal: 2023-12-12 12:02:13
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Gambaran umum

Strategi pelacakan momentum adaptif multi-faktor mewujudkan perdagangan otomatis aset yang sangat fluktuatif seperti cryptocurrency dengan mengidentifikasi tren pasar dan tingkat dukungan / resistensi utama melalui integrasi beberapa indikator teknis. Strategi ini menggabungkan indikator seperti RSI, MACD, Stochastic untuk menentukan waktu masuk dan keluar, sementara juga menggabungkan perubahan persentase harga untuk memungkinkan pengenalan pola yang lebih akurat.

Prinsip Strategi

Inti dari strategi pelacakan momentum adaptif multifaktor terletak pada integrasi dari beberapa indikator teknis.

  1. RSI untuk menilai kondisi overbought/oversold. Parameter yang berbeda dapat digunakan untuk mengidentifikasi sinyal RSI reguler atau sinyal RSI Connors yang disesuaikan untuk menentukan apakah ada peluang pembalikan.

  2. Sinyal beli dan jual dihasilkan ketika garis MACD melintasi di atas atau di bawah garis sinyal.

  3. Stokastik untuk spot zona overbought/oversold. kombinasi golden cross dan death cross dari garis K dan D menunjukkan apakah pembalikan dapat terjadi.

  4. Perubahan persentase harga untuk memeriksa apakah breakout nyata. Menghitung perubahan persentase harga tertinggi, harga terendah, harga tutup dll selama periode tertentu untuk menentukan apakah breakout yang sebenarnya telah terjadi.

  5. EMA untuk menilai tren keseluruhan. Upcrossing EMA cepat di atas EMA lambat memberikan sinyal bullish sementara downcrossing memberikan sinyal bearish.

Strategi ini memilih untuk pergi panjang atau pendek berdasarkan kondisi pasar, dan menetapkan stop loss dan mengambil keuntungan setelah memasuki posisi untuk mengontrol risiko secara efektif. Keluar ketika sinyal pembalikan terjadi. Seluruh proses keputusan mengintegrasikan penilaian dari beberapa faktor untuk mewujudkan hasil yang lebih akurat.

Analisis Keuntungan

Keuntungan dari strategi ini meliputi:

  1. Berbagai faktor mendorong penilaian yang lebih baik. Dibandingkan dengan indikator tunggal, menggabungkan beberapa indikator memungkinkan verifikasi timbal balik dan hasil yang lebih dapat diandalkan, menghemat biaya perdagangan yang tidak perlu.

  2. Kondisi yang ketat mencegah perdagangan buruk. Strategi menetapkan persyaratan yang ketat untuk sinyal beli / jual, yang membutuhkan beberapa sinyal bersamaan untuk menyaring kebisingan dan menghindari perdagangan buruk.

  3. Parameter adaptif mengurangi gangguan manual. Kemampuan bawaan untuk menghitung parameter indikator secara dinamis menghindari subjektivitas pemilihan parameter manual, membuat parameter lebih ilmiah dan objektif.

  4. Stop loss/take profit mengontrol risiko. Strategi terus menghitung dan plot stop loss/take profit level setelah membuka posisi, secara efektif membatasi per trade loss dan mencegah margin call.

Analisis Risiko

Risiko yang harus dicegah meliputi:

  1. Kemungkinan sinyal yang salah dari indikator. Meskipun proses verifikasi ganda sangat mengurangi sinyal yang salah, beberapa kemungkinan tetap ada. Ini dapat menyebabkan kerugian yang tidak perlu.

  2. Risiko penetrasi stop loss. Dalam kondisi pasar yang ekstrim, harga dapat terjun tebing dan menembus stop loss yang awalnya ditetapkan dengan mudah, menyebabkan kerugian di atas rata-rata.

  3. Meskipun parameter dinamis mengurangi subjektivitas, mereka juga dapat menyebabkan over-fit dan kehilangan generalisasi.

Solusi:

  1. Tingkatkan ketatnya penyaringan sinyal untuk mengurangi sinyal yang salah.
  2. Mengadopsi entri bertahap untuk menghindari kehilangan stop tunggal yang terlalu besar.
  3. Meningkatkan pengujian sampel untuk secara ketat mengevaluasi stabilitas parameter.

Arahan Optimasi

Strategi ini dapat dioptimalkan lebih lanjut melalui:

  1. Meningkatkan faktor penilaian: Menggabungkan sinyal dari lebih banyak indikator dari berbagai jenis, misalnya volatilitas, volume, dll untuk membantu penilaian.

  2. Mengoptimalkan algoritma stop loss. Memperkenalkan algoritma stop loss yang lebih maju seperti trailing stop loss, volatility stop loss dll untuk lebih mengurangi kemungkinan stop loss terkena.

  3. Memperkenalkan model pembelajaran mesin. Memodelkan data historis menggunakan RNN, LSTM dll untuk membantu keputusan pembelian/penjualan.

  4. Mengintegrasikan strategi Mengadopsi beberapa sub-strategi dan menggunakan metode ensemble untuk mengintegrasikan kinerja keseluruhan yang lebih kuat.

Kesimpulan

Strategi pelacakan momentum adaptif multi-faktor mengintegrasikan beberapa indikator teknis untuk mengidentifikasi peluang perdagangan. Dibandingkan dengan indikator tunggal, strategi ini memiliki penilaian yang lebih akurat, ditambah dengan adaptasi parameter bawaan dan mekanisme stop loss untuk mengendalikan risiko. Langkah selanjutnya termasuk memperkenalkan lebih banyak faktor penilaian bantu, algoritma stop loss lanjutan, pembelajaran mesin dll untuk meningkatkan kinerja strategi.


/*backtest
start: 2023-12-04 00:00:00
end: 2023-12-11 00:00:00
period: 3m
basePeriod: 1m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
//@version=4

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//╚══════╝ ╚═════╝ ╚══════╝ ╚═════╝    ╚═╝   ╚═╝ ╚═════╝ ╚═╝  ╚═══╝╚══════╝ ╚═╝ ╚════╝    ╚═╝   ╚════╝ 

strategy(shorttitle='Ain1 No Label',title='All in One Strategy no RSI Label', overlay=true, scale=scale.left, initial_capital = 1000, process_orders_on_close=true, default_qty_type = strategy.percent_of_equity, default_qty_value = 100, commission_type=strategy.commission.percent, commission_value=0.18, calc_on_every_tick=true)

kcolor = color.new(#0094FF, 60)
dcolor = color.new(#FF6A00, 60)



// -----------------  Strategy Inputs -------------------------------------------------------------
//Backtest dates with auto finish date of today
start = input(defval = timestamp("01 April 2021 00:00 -0500"), title = "Start Time", type = input.time)
finish = input(defval = timestamp("31 December 2021 00:00 -0600"), title = "End Time", type = input.time)
window()  => true       // create function "within window of time"


// Strategy Selection - Long, Short, or Both
stratinfo = input(true, "Long/Short for Mixed Market, Long for Bull, Short for Bear")
strat = input(title="Trade Types", defval="Long/Short", options=["Long Only", "Long/Short", "Short Only"])
strat_val = strat == "Long Only" ? 1 : strat == "Long/Short" ? 0 : -1

// Risk Management Inputs
sl= input(10.0, "Stop Loss %", minval = 0, maxval = 100, step = 0.01)
stoploss = sl/100
tp = input(20.0, "Target Profit %", minval = 0, maxval = 100, step = 0.01)
TargetProfit = tp/100


useXRSI = input(false, "Use RSI crossing back, select only one strategy")
useCRSI = input(false, "Use Tweaked Connors RSI, select only one")
RSIInfo = input(true, "These are the RSI Strategy Inputs, RSI Length applies to MACD, set OB and OS to 45 for using Stoch and EMA strategies.")
length = input(14, "RSI Length", minval=1)
overbought= input(62, "Overbought")
oversold= input(35, "Oversold")
cl1 = input(3, "Connor's MA Length 1", minval=1, step=1)
cl2 = input(20, "Connor's MA Lenght 2", minval=1, step=1)
cl3 = input(50, "Connor's MA Lenght 3", minval=1, step=1)

// MACD and EMA Inputs
useMACD = input(false, "Use MACD Only, select only one strategy")
useEMA  = input(false, "Use EMA Only, select only one strategy (EMA uses Stochastic inputs too)")
MACDInfo=input(true, "These are the MACD strategy variables")
fastLength = input(5, minval=1, title="EMA Fast Length")
slowLength = input(10, minval=1, title="EMA Slow Length")
ob_min = input(52, "Overbought Lookback Minimum Value", minval=0, maxval=200)
ob_lb = input(25, "Overbought Lookback Bars", minval=0, maxval=100)
os_min = input(50, "Oversold Lookback Minimum Value", minval=0, maxval=200)
os_lb = input(35, "Oversold Lookback Bars", minval=0, maxval=100)
source = input(title="Source", type=input.source, defval=close)
RSI = rsi(source, length)


// Price Movement Inputs
PriceInfo = input(true, "Price Change Percentage Cross Check Inputs for all Strategies, added logic to avoid early sell")
lkbk = input(5,"Max Lookback Period")

// EMA and SMA Background Inputs
useStoch    = input(false, "Use Stochastic Strategy, choose only one")
StochInfo   = input(true, "Stochastic Strategy Inputs")
smoothK     = input(3, "K", minval=1)
smoothD     = input(3, "D", minval=1)
k_mode      = input("SMA", "K Mode", options=["SMA", "EMA", "WMA"])
high_source = input(high,"High Source")
low_source= input(low,"Low Source")
HTF = input("","Curernt or Higher time frame only", type=input.resolution)

// Selections to show or hide the overlays
showZones = input(true, title="Show Bullish/Bearish Zones")
showStoch = input(true, title="Show Stochastic Overlays")
showRSIBS = input(true, title="Show RSI Buy Sell Zones")
showMACD = input(true, title="Show MACD")
color_bars=input(true, "Color Bars")



// ------------------ Dynamic RSI Calculation ----------------------------------------

AvgHigh(src,cnt,val) =>
    total = 0.0
    count = 0
    for i = 0 to cnt
        if src[i] > val
            count := count + 1
            total := total + src[i]
    round(total / count)
    
RSI_high = AvgHigh(RSI, ob_lb, ob_min)

AvgLow(src,cnt,val) =>
    total = 0.0
    count = 0
    for i = 0 to cnt
        if src[i] < val
            count := count + 1
            total := total + src[i]
    round(total / count)

RSI_low = AvgLow(RSI, os_lb, os_min)




// ------------------ Price Percentage Change Calculation -----------------------------------------
perc_change(lkbk) =>
    overall_change = ((close[0] - open[lkbk]) / open[lkbk]) * 100
    highest_high = 0.0
    lowest_low = 0.0
    for i = lkbk to 0
        highest_high := i == lkbk ? high : high[i] > high[(i + 1)] ? high[i] : highest_high[1]
        lowest_low := i == lkbk ? low : low[i] < low[(i + 1)] ? low[i] : lowest_low[1]
    
    start_to_high = ((highest_high - open[lkbk]) / open[lkbk]) * 100
    start_to_low = ((lowest_low - open[lkbk]) / open[lkbk]) * 100
    previous_to_high = ((highest_high - open[1])/open[1])*100
    previous_to_low = ((lowest_low-open[1])/open[1])*100
    previous_bar = ((close[1]-open[1])/open[1])*100
    
    [overall_change, start_to_high, start_to_low, previous_to_high, previous_to_low, previous_bar]
    
// Call the function    
[overall, to_high, to_low, last_high, last_low, last_bar] = perc_change(lkbk)

// Plot the function
//plot(overall*50, color=color.white, title='Overall Percentage Change', linewidth=3)
//plot(to_high*50, color=color.green,title='Percentage Change from Start to High', linewidth=2)
//plot(to_low*50, color=color.red, title='Percentage Change from Start to Low', linewidth=2)
//plot(last_high*100, color=color.teal, title="Previous to High", linewidth=2)
//plot(last_low*100, color=color.maroon, title="Previous to Close", linewidth=2)
//plot(last_bar*100, color=color.orange, title="Previous Bar", linewidth=2)
//hline(0, title='Center Line', color=color.orange, linewidth=2)

true_dip = overall < 0 and to_high > 0 and to_low < 0 and last_high > 0 and last_low < 0 and last_bar < 0
true_peak = overall > 0 and to_high > 0 and to_low > 0 and last_high > 0 and last_low < 0 and last_bar > 0

alertcondition(true_dip, title='True Dip', message='Dip')
alertcondition(true_peak, title='True Peak', message='Peak')

// ------------------ Background Colors based on EMA Indicators -----------------------------------
// Uses standard lengths of 9 and 21, if you want control delete the constant definition and uncomment the inputs
haClose(gap) => (open[gap] + high[gap] + low[gap] + close[gap]) / 4
rsi_ema = rsi(haClose(0), length)
v2 = ema(rsi_ema, length)                                                
v3 = 2 * v2 - ema(v2, length)  
emaA = ema(rsi_ema, fastLength)                                     
emaFast = 2 * emaA - ema(emaA, fastLength)
emaB = ema(rsi_ema, slowLength)                                     
emaSlow = 2 * emaB - ema(emaB, slowLength) 

//plot(rsi_ema, color=color.white, title='RSI EMA', linewidth=3)
//plot(v2, color=color.green,title='v2', linewidth=2)
//plot(v3, color=color.red, title='v3', linewidth=2)
//plot(emaFast, color=color.teal, title="EMA Fast", linewidth=2)
//plot(emaSlow, color=color.maroon, title="EMA Slow", linewidth=2)

EMABuy = crossunder(emaFast, v2) and window()
EMASell = crossover(emaFast, emaSlow) and window()


alertcondition(EMABuy, title='EMA Buy', message='EMA Buy Condition')
alertcondition(EMASell, title='EMA Sell', message='EMA Sell Condition')



// bullish signal rule: 
bullishRule =emaFast > emaSlow
// bearish signal rule: 
bearishRule =emaFast < emaSlow

// current trading State
ruleState = 0
ruleState := bullishRule ? 1 : bearishRule ? -1 : nz(ruleState[1])
ruleColor = ruleState==1 ? color.new(color.blue, 90) : ruleState == -1 ? color.new(color.red, 90) : ruleState == 0 ? color.new(color.gray, 90) : na
bgcolor(showZones ? ruleColor : na, title="Bullish/Bearish Zones")


// ------------------  Stochastic Indicator Overlay -----------------------------------------------

// Calculation
// Use highest highs and lowest lows
h_high = highest(high_source ,lkbk)
l_low = lowest(low_source ,lkbk)

stoch = stoch(RSI, RSI_high, RSI_low, length)
k =
 k_mode=="EMA" ? ema(stoch, smoothK) :
 k_mode=="WMA" ? wma(stoch, smoothK) :
 sma(stoch, smoothK)
d = sma(k, smoothD)
k_c = change(k)
d_c = change(d)
kd = k - d

// Plot
signalColor = k>oversold and d<overbought and k>d and k_c>0 and d_c>0 ? kcolor : 
 k<overbought and d>oversold and k<d and k_c<0 and d_c<0 ? dcolor : na
kp = plot(showStoch ? k : na, "K", color=kcolor)
dp = plot(showStoch ? d : na, "D", color=dcolor)
fill(kp, dp, color = signalColor, title="K-D")
signalUp = showStoch ? not na(signalColor) and kd>0 : na
signalDown = showStoch ? not na(signalColor) and kd<0 : na
//plot(signalUp ? kd : na, "Signal Up", color=kcolor, transp=90, style=plot.style_columns)
//plot(signalDown ? (kd+100) : na , "Signal Down", color=dcolor, transp=90, style=plot.style_columns, histbase=100)

//StochBuy = crossover(k, d) and kd>0 and to_low<0 and window()
//StochSell = crossunder(k,d) and kd<0 and to_high>0 and window()

StochBuy = crossover(k, d) and window()
StochSell = crossunder(k, d) and window()

alertcondition(StochBuy, title='Stoch Buy', message='K Crossing D')
alertcondition(StochSell, title='Stoch Sell', message='D Crossing K')


// -------------- Add Price Movement -------------------------
// Calculations
h1 = vwma(high, length)
l1 = vwma(low, length)
hp = h_high[1]
lp = l_low[1]

// Plot
var plot_color=#353535
var sig = 0
if (h1 >hp)
    sig:=1
    plot_color:=color.lime
else if (l1 <lp)
    sig:=-1
    plot_color:=color.maroon
//plot(1,title = "Price Movement Bars", style=plot.style_columns,color=plot_color)
//plot(sig,title="Signal 1 or -1",display=display.none)



// --------------------------------------- RSI Plot ----------------------------------------------
// Plot Oversold and Overbought Lines
over = hline(oversold, title="Oversold", color=color.green)
under = hline(overbought, title="Overbought", color=color.red)
fillcolor = color.new(#9915FF, 90)
fill(over, under, fillcolor, title="Band Background")


// Show RSI and EMA crosses with arrows and RSI Color (tweaked Connors RSI)
// Improves strategy setting ease by showing where EMA 5 crosses EMA 10 from above to confirm overbought conditions or trend reversals
// This shows where you should enter shorts or exit longs

// Tweaked Connors RSI Calculation
connor_ob = overbought
connor_os = oversold
ma1 = sma(close,cl1)
ma2 = sma(close, cl2)
ma3 = sma(close, cl3)

// Buy Sell Zones using tweaked Connors RSI (RSI values of 80 and 20 for Crypto as well as ma3, ma20, and ma50 are the tweaks)
RSI_SELL = ma1 > ma2 and open > ma3 and RSI >= connor_ob and true_peak and window()
RSI_BUY = ma2 < ma3 and ma3 > close and RSI <= connor_os and true_dip and window()

alertcondition(RSI_BUY, title='Connors Buy', message='Connors RSI Buy')
alertcondition(RSI_SELL, title='Connors Sell', message='Connors RSI Sell')

// Color Definition
col = useCRSI ? (close > ma2 and close < ma3 and RSI <= connor_os ? color.lime : close < ma2 and close > ma3 and RSI <= connor_ob ? color.red : color.yellow ) : color.yellow

// Plot colored RSI Line
plot(RSI, title="RSI", linewidth=3, color=col)


//------------------- MACD Strategy -------------------------------------------------
[macdLine, signalLine, _] = macd(close, fastLength, slowLength, length)

bartrendcolor = macdLine > signalLine and k > 50 and RSI > 50 ? color.teal : macdLine < signalLine and k < 50 and RSI < 50 ? color.maroon : macdLine < signalLine ? color.yellow : color.gray
barcolor(color = color_bars ? bartrendcolor : na)


MACDBuy = macdLine>signalLine and RSI<RSI_low and overall<0 and window()
MACDSell = macdLine<signalLine and RSI>RSI_high and overall>0 and window()

//plotshape(showMACD ? MACDBuy: na, title = "MACD Buy", style = shape.arrowup, text = "MACD Buy", color=color.green, textcolor=color.green, size=size.small)
//plotshape(showMACD ? MACDSell: na, title = "MACD Sell", style = shape.arrowdown, text = "MACD Sell", color=color.red, textcolor=color.red, size=size.small)
MACColor = MACDBuy ? color.new(color.teal, 50) : MACDSell ? color.new(color.maroon, 50) : na
bgcolor(showMACD ? MACColor : na, title ="MACD Signals")


// -------------------------------- Entry and Exit Logic ------------------------------------


// Entry Logic
XRSI_OB = crossunder(RSI, overbought) and overall<0 and window()
RSI_OB = RSI>overbought and true_peak and window()
XRSI_OS = crossover(RSI, oversold) and overall>0 and window()
RSI_OS = RSI<oversold and true_dip and window()

alertcondition(XRSI_OB, title='Reverse RSI Sell', message='RSI Crossing back under OB')
alertcondition(XRSI_OS, title='Reverse RSI Buy', message='RSI Crossing back over OS')

alertcondition(RSI_OS, title='RSI Buy', message='RSI Crossover OS')
alertcondition(RSI_SELL, title='RSI Sell', message='RSI Crossunder OB')


// Strategy Entry and Exit with built in Risk Management
GoLong = strategy.position_size==0 and strat_val > -1 and rsi_ema > RSI and k < d ? (useXRSI ? XRSI_OS : useMACD ? MACDBuy : useCRSI ? RSI_BUY : useStoch ? StochBuy : RSI_OS) : false

GoShort = strategy.position_size==0 and strat_val < 1 and rsi_ema < RSI and d < k ? (useXRSI ? XRSI_OB : useMACD ? MACDSell : useCRSI ? RSI_SELL : useStoch ? StochSell : RSI_OB) : false

if (GoLong)
    strategy.entry("LONG", strategy.long)

if (GoShort) 
    strategy.entry("SHORT", strategy.short)


longStopPrice  = strategy.position_avg_price * (1 - stoploss)
longTakePrice  = strategy.position_avg_price * (1 + TargetProfit)
shortStopPrice = strategy.position_avg_price * (1 + stoploss)
shortTakePrice = strategy.position_avg_price * (1 - TargetProfit)

//plot(series=(strategy.position_size > 0) ? longTakePrice : na, color=color.green, style=plot.style_circles, linewidth=3, title="Long Take Profit")
//plot(series=(strategy.position_size < 0) ? shortTakePrice : na, color=color.green, style=plot.style_circles, linewidth=3, title="Short Take Profit")
//plot(series=(strategy.position_size > 0) ? longStopPrice : na, color=color.red, style=plot.style_cross, linewidth=2, title="Long Stop Loss")
//plot(series=(strategy.position_size < 0) ? shortStopPrice : na, color=color.red, style=plot.style_cross, linewidth=2, title="Short Stop Loss")

if (strategy.position_size > 0)
    strategy.exit(id="Exit Long", from_entry = "LONG", stop = longStopPrice, limit = longTakePrice)
    
if (strategy.position_size < 0)
    strategy.exit(id="Exit Short", from_entry = "SHORT", stop = shortStopPrice, limit = shortTakePrice)


CloseLong = strat_val > -1 and strategy.position_size > 0 and rsi_ema > RSI and d > k ? (useXRSI ? XRSI_OB : useMACD ? MACDSell : useCRSI ? RSI_SELL : RSI_OB) : false

if(CloseLong)
    strategy.close("LONG")
        
CloseShort = strat_val < 1 and strategy.position_size < 0 and rsi_ema < RSI and k > d ? (useXRSI ? XRSI_OS : useMACD ? MACDBuy : useCRSI ? RSI_BUY : RSI_OS) : false

if(CloseShort)
    strategy.close("SHORT")




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