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指标 FAMA



FAMA指标图表效果如下:FAMA指标FAMA指标源码如下:

//+------------------------------------------------------------------+
//|                                                         fAMA.mq4 |
//|                                         Copyright @2005, Profi_R |
//+------------------------------------------------------------------+
#property copyright "Copyright @2005, Profi_R"
#property link      "rvm_fam@fromru.com"

#property indicator_chart_window
#property indicator_buffers 1
#property indicator_color1 Red
//---- input parameters
extern int Range=10;
extern int FastMA=2;
extern int SlowMA=30;
extern int filter=50;
extern int n=4;
extern int h=10;
//---- buffers
double fAMA[];
double mAMA[];
//+------------------------------------------------------------------+
//| Custom indicator initialization function                               |
//+------------------------------------------------------------------+
int init()
  {
   string short_name; 
//---- indicators
   IndicatorBuffers(2);
   SetIndexStyle(0,DRAW_LINE);
   SetIndexBuffer(0,fAMA);
   SetIndexBuffer(1,mAMA);
//---- name for DataWindow and indicator subwindow label
   
   short_name="fAMA("+Range+","+FastMA+","+SlowMA+","+filter+","+n+","+h+")";
  
   IndicatorShortName(short_name);
   SetIndexLabel(0,short_name);
//----
   SetIndexDrawBegin(0,Range); 
//----
   return(0);
  }
  
  void deinit() {
   Comment("www.125808047.com");
}
//+------------------------------------------------------------------+
//| Custom indicator iteration function                                    |
//+------------------------------------------------------------------+
int start()
  {
   int    counted_bars=IndicatorCounted();
//---- TODO: add your code here
   int cb,i;
   double  k1,k2,Noise,ER,SSC,AMA,sdAMA,dAMA;
   if(Bars<=Range) return(0);
   else
   {
      k1=2.0/(SlowMA+1);
      k2=2.0/(FastMA+1)-k1;
      AMA=Close[Bars-Range];
      mAMA[Bars-Range]=Close[Bars-Range+1];
      for(cb=Bars-counted_bars-Range-1;cb>=0;cb--)
      {
         Noise=0;
         for(i=cb;i<=cb+Range-1;i++)
         {
            Noise=Noise+MathAbs(Close[i]-Close[i+1]);
         }
         if(Noise!=0)
         {
            ER=MathAbs(Close[cb]-Close[cb+Range])/Noise;
         }
         else
         {
            ER=0;
         }
         SSC=(ER*k2+k1);
         AMA=AMA+NormalizeDouble(SSC*SSC*(Close[cb]-AMA),4);
         mAMA[cb]=AMA;
         if(filter<1)
         {
            fAMA[cb]=mAMA[cb];
         }
         else
         {
            for(i=cb;i<=cb+SlowMA-1;i++)
            {
               sdAMA=sdAMA+MathAbs(mAMA[i]-mAMA[i+1]);
            }
            dAMA=mAMA[cb]-mAMA[cb+1];
            if(dAMA>=0)
            {
               if(dAMA<NormalizeDouble(filter*sdAMA/(100*SlowMA),4)&&
                  High[cb]<=High[Highest(NULL,0,MODE_HIGH,n,cb)]+h*Point)
               {
                  fAMA[cb]=fAMA[cb+1];
               }
               else
               {
                  fAMA[cb]=mAMA[cb];
               }
            }
            else
            {
               if(MathAbs(dAMA)<NormalizeDouble(filter*sdAMA/(100*SlowMA),4)&&
                  Low[Lowest(NULL,0,MODE_LOW,n,cb)]-h*Point<Low[cb])
               {
                  fAMA[cb]=fAMA[cb+1];
               }
               else
               {
                  fAMA[cb]=mAMA[cb];
               }
            }
            sdAMA=0.0;
         }
      }
   }
//----
   return(0);
  }
//+------------------------------------------------------------------+
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