Momentum Adaptive Gaussian Channel Multi-Pried Strategy

/*backtest
start: 2024-02-08 00:00:00
end: 2025-02-06 08:00:00
period: 4h
basePeriod: 4h
exchanges: [{"eid":"Futures_Binance","currency":"DIA_USDT"}]
*/

//@version=6
strategy("Demo GPT - Gold Gaussian Strategy", overlay=true, commission_type=strategy.commission.percent, commission_value=0.1)

// ====== INPUTS ======
// Gaussian Channel
lengthGC = input.int(144, "Gaussian Period", minval=20)
poles = input.int(4, "Poles", minval=1, maxval=9)
multiplier = input.float(1.414, "Volatility Multiplier", minval=1)

// Stochastic RSI
smoothK = input.int(3, "Stoch K", minval=1)
lengthRSI = input.int(14, "RSI Length", minval=1)
lengthStoch = input.int(14, "Stoch Length", minval=1)
overbought = input.int(80, "Overbought Level", minval=50)

// Seasonal Filter (corrected)
startMonth = input.int(9, "Start Month (1-12)", minval=1, maxval=12)
endMonth = input.int(2, "End Month (1-12)", minval=1, maxval=12)

// Volatility Management
atrLength = input.int(22, "ATR Length", minval=5)
riskPercent = input.float(0.5, "Risk per Trade (%)", minval=0.1, step=0.1)

// ====== GAUSSIAN CHANNEL ======
f_filt9x(alpha, source, iterations) =>
    float f = 0.0
    float x = 1 - alpha
    int m2 = iterations == 9 ? 36 : iterations == 8 ? 28 : iterations == 7 ? 21 : 
           iterations == 6 ? 15 : iterations == 5 ? 10 : iterations == 4 ? 6 : 
           iterations == 3 ? 3 : iterations == 2 ? 1 : 0
    
    int m3 = iterations == 9 ? 84 : iterations == 8 ? 56 : iterations == 7 ? 35 : 
           iterations == 6 ? 20 : iterations == 5 ? 10 : iterations == 4 ? 4 : 
           iterations == 3 ? 1 : 0
    
    int m4 = iterations == 9 ? 126 : iterations == 8 ? 70 : iterations == 7 ? 35 : 
           iterations == 6 ? 15 : iterations == 5 ? 5 : iterations == 4 ? 1 : 0
    
    int m5 = iterations == 9 ? 126 : iterations == 8 ? 56 : iterations == 7 ? 21 : 
           iterations == 6 ? 6 : iterations == 5 ? 1 : 0
    
    int m6 = iterations == 9 ? 84 : iterations == 8 ? 28 : iterations == 7 ? 7 : 
           iterations == 6 ? 1 : 0
    
    int m7 = iterations == 9 ? 36 : iterations == 8 ? 8 : iterations == 7 ? 1 : 0
    
    int m8 = iterations == 9 ? 9 : iterations == 8 ? 1 : 0
    int m9 = iterations == 9 ? 1 : 0
    
    f := math.pow(alpha, iterations) * nz(source) +
      iterations * x * nz(f[1]) -
      (iterations >= 2 ? m2 * math.pow(x, 2) * nz(f[2]) : 0) +
      (iterations >= 3 ? m3 * math.pow(x, 3) * nz(f[3]) : 0) -
      (iterations >= 4 ? m4 * math.pow(x, 4) * nz(f[4]) : 0) +
      (iterations >= 5 ? m5 * math.pow(x, 5) * nz(f[5]) : 0) -
      (iterations >= 6 ? m6 * math.pow(x, 6) * nz(f[6]) : 0) +
      (iterations >= 7 ? m7 * math.pow(x, 7) * nz(f[7]) : 0) -
      (iterations >= 8 ? m8 * math.pow(x, 8) * nz(f[8]) : 0) +
      (iterations == 9 ? m9 * math.pow(x, 9) * nz(f[9]) : 0)
    f

f_pole(alpha, source, iterations) =>
    float fn = na
    float f1 = f_filt9x(alpha, source, 1)
    float f2 = iterations >= 2 ? f_filt9x(alpha, source, 2) : na
    float f3 = iterations >= 3 ? f_filt9x(alpha, source, 3) : na
    float f4 = iterations >= 4 ? f_filt9x(alpha, source, 4) : na
    float f5 = iterations >= 5 ? f_filt9x(alpha, source, 5) : na
    float f6 = iterations >= 6 ? f_filt9x(alpha, source, 6) : na
    float f7 = iterations >= 7 ? f_filt9x(alpha, source, 7) : na
    float f8 = iterations >= 8 ? f_filt9x(alpha, source, 8) : na
    float f9 = iterations == 9 ? f_filt9x(alpha, source, 9) : na
    
    fn := iterations == 1 ? f1 : 
         iterations == 2 ? f2 : 
         iterations == 3 ? f3 : 
         iterations == 4 ? f4 : 
         iterations == 5 ? f5 : 
         iterations == 6 ? f6 : 
         iterations == 7 ? f7 : 
         iterations == 8 ? f8 : 
         iterations == 9 ? f9 : na
    [fn, f1]

beta = (1 - math.cos(4 * math.asin(1) / lengthGC)) / (math.pow(1.414, 2 / poles) - 1)
alpha = -beta + math.sqrt(math.pow(beta, 2) + 2 * beta)
lag = int((lengthGC - 1) / (2 * poles))

srcAdjusted = hlc3 + (hlc3 - hlc3[lag])
[mainFilter, filt1] = f_pole(alpha, srcAdjusted, poles)
[trFilter, tr1] = f_pole(alpha, ta.tr(true), poles)

upperBand = mainFilter + trFilter * multiplier
lowerBand = mainFilter - trFilter * multiplier

// ====== STOCHASTIC RSI ======
rsiValue = ta.rsi(close, lengthRSI)
k = ta.sma(ta.stoch(rsiValue, rsiValue, rsiValue, lengthStoch), smoothK)
stochSignal = k >= overbought

// ====== SEASONAL FILTER (FIXED) ======
currentMonth = month(time)
inSeason = (currentMonth >= startMonth and currentMonth <= 12) or 
         (currentMonth >= 1 and currentMonth <= endMonth)

// ====== VOLATILITY MANAGEMENT ======
atr = ta.atr(atrLength)
positionSize = math.min((strategy.equity * riskPercent/100) / atr, strategy.equity * 0.5 / close)

// ====== TRADING LOGIC ======
trendUp = mainFilter > mainFilter[1]
priceAbove = close > upperBand
longCondition = trendUp and priceAbove and stochSignal and inSeason

exitCondition = ta.crossunder(close, lowerBand)

// ====== EXECUTION ======
if longCondition
    strategy.entry("Long", strategy.long, qty=positionSize)
    
if exitCondition
    strategy.close("Long")

// ====== VISUALIZATION ======
plot(upperBand, "Upper Band", color=color.new(#00FF00, 0))
plot(lowerBand, "Lower Band", color=color.new(#FF0000, 0))
bgcolor(inSeason ? color.new(color.blue, 90) : na, title="Season Filter")