Adaptive Stochastic Overbought Oversold Levels - aflcode.com | Latest collection of Amibroker afl 2023

Adaptive Stochastic Overbought Oversold Levels
//www.aflcode.com


function Fisher(array)
// Figure 1.7 on p. 7
{
  F = array;
  F = .25 * log((1+ array)/(1 - array)) + .5 * Ref(F, -1);
  return F;
};

function CyberCycle( array, alpha )
{
  smooth = ( array + 2 * Ref( array, -1 ) +
             2 * Ref( array, -2 ) + Ref( array, -3 ) ) / 6;
  // init value
  Cycle = ( array[ 2 ] - 2 * array[ 1 ] + array[ 0 ] )/4;
  for( i = 6; i < BarCount; i++ )
  {
     Cycle[ i ] = ( ( 1 - 0.5 * alpha) ^ 2 ) *
                  ( smooth[ i ] - 2 * smooth[ i - 1 ] + smooth[ i - 2] ) +
                  2 * ( 1 - alpha ) * Cycle[ i - 1 ] -
                  ( ( 1 - alpha) ^ 2 ) * Cycle[ i - 2 ];
  }

   MaxCycle = HHV(Cycle, 14);
   MinCycle = LLV(Cycle, 14);
   for (i = 0; i < BarCount; i++) {
  if (MaxCycle[i] != MinCycle[i]) {
   Value1[i] = (Cycle[i]-MinCycle[i])/(MaxCycle[i] - MinCycle[i]);
   Value2[i] = (4*Value1[i] + 3*Value1[i-1] + 2*Value1[i-2] + Value1[i-3])/10;
   Value2[i] = 2*(Value2[i] -.5);
  };
 }
   return Value2;
}

// get log price
logprice = ln(Close);
OA1 = (logprice - Ref(logprice, -1))/sqrt(1);

// get change in bar and multiply it by the change in square root of time between yesterday and today
n = 0;
totalprice[0]= 0;
for (i = BarCount-1; i>=1; i--){
 n = n + 1;
   deltaPrice[i] = OA1[i]*(sqrt(n) - sqrt(n-1));
 totalprice[i]  = totalprice[i] + OA1[i]*(sqrt(n));
};

// get NMR
natCoef = Sum(deltaPrice, 40)/Sum(totalprice, 40);


Cycle = CyberCycle( (H+L)/2, .07);
Plot( Cycle, "Stoc CyberCycle", colorRed, styleThick );
Plot( Ref(Cycle, -1), "Trigger", colorPink, styleDashed);

PlotGrid(-0.80, colorLightGrey);
PlotGrid(0.80, colorLightGrey);