【SOURCE FILE】Nearest_Neighbor.mq5

【解説】【MQL5 community】 Price prediction by Nearest Neighbor : 過去のk個の価格の列と現在の価格の列の最も似たものを探し、 将来の価格を予測するというもの。 ここでのアルゴリズムはPearson correlation coefficientを使用し、データの類似性を調べる。

//+--------------------------------------------------------------------------------------+
//|                                                                 Nearest_Neighbor.mq5 |
//|                                                                 Copyright 2010, gpwr |
//+--------------------------------------------------------------------------------------+
#property copyright "gpwr"
#property version   "1.00"
#property description "Prediction of future based on the nearest neighbor in the past"
#property indicator_chart_window
#property indicator_buffers 2
#property indicator_plots   2
//--- future model outputs
#property indicator_label1  "NN future"
#property indicator_type1   DRAW_LINE
#property indicator_color1  Red
#property indicator_style1  STYLE_SOLID
#property indicator_width1  1
//--- past model outputs
#property indicator_label2  "NN past"
#property indicator_type2   DRAW_LINE
#property indicator_color2  Blue
#property indicator_style2  STYLE_SOLID
#property indicator_width2  1

//Global constants
#define pi 3.141592653589793238462643383279502884197169399375105820974944592

//===================================== INPUTS ===========================================
input int    Npast   =300; // # of past bars in a pattern
input int    Nfut    =50;  // # of future bars in a pattern (must be ＜ Npast)

// Global variables
int bars,PrevBars;
double mx[],sxx[],denx[];
bool FirstTime;

// Indicator buffers
double ynn[],xnn[];

// Custom indicator initialization function ---------------------------------------------+
void OnInit()
{
// Initialize global variables
   PrevBars=Bars(_Symbol,_Period)-1;
   FirstTime=true;

// Map indicator buffers
   SetIndexBuffer(0,ynn,INDICATOR_DATA);
   SetIndexBuffer(1,xnn,INDICATOR_DATA);
   IndicatorSetInteger(INDICATOR_DIGITS,_Digits);
   IndicatorSetString(INDICATOR_SHORTNAME,"1NN("+string(Npast)+")");
   PlotIndexSetInteger(0,PLOT_SHIFT,Nfut);
}

//====================================== MAIN ============================================
int OnCalculate(const int rates_total,
                const int prev_calculated,
                const datetime& Time[],
                const double& Open[],
                const double& High[],
                const double& Low[],
                const double& Close[],
                const long& tick_volume[],
                const long& volume[],
                const int& spread[])
{
// Check for insufficient data and new bar
   int bars=rates_total;
   if(bars＜Npast+Nfut)
   {
      Print("Error: not enough bars in history!");
      return(0);
   }
   if(PrevBars==bars) return(rates_total);
   PrevBars=bars;

// Initialize indicator buffers to EMPTY_VALUE
   ArrayInitialize(xnn,EMPTY_VALUE);
   ArrayInitialize(ynn,EMPTY_VALUE);

// Main cycle ---------------------------------------------------------------------------+
   // Compute correlation sums for current pattern
   // Current pattern starts at i=bars-Npast and ends at i=bars-1
   double my=0.0;
   double syy=0.0;
   for(int i=0;i＜Npast;i++)
   {
      double y=Open[bars-Npast+i];
      my +=y;
      syy+=y*y;
   }
   double deny=syy*Npast-my*my;
   if(deny＜=0)
   {
      Print("Zero or negative syy*Npast-my*my = ",deny);
      return(0);
   }
   deny=MathSqrt(deny);
   
   // Compute correlation sums for past patterns
   // Past patterns start at k=0 and end at k=bars-Npast-Nfut
   ArrayResize(mx,bars-Npast-Nfut+1);
   ArrayResize(sxx,bars-Npast-Nfut+1);
   ArrayResize(denx,bars-Npast-Nfut+1);
   int kstart;
   if(FirstTime) kstart=0;
   else kstart=bars-Npast-Nfut;
   FirstTime=false;
   for(int k=kstart;k＜=bars-Npast-Nfut;k++)
   {
      if(k==0)
      {
         mx[0] =0.0;
         sxx[0]=0.0;
         for(int i=0;i＜Npast;i++)
         {
            double x =Open[i];
            mx[0] +=x;
            sxx[0]+=x*x;
         }
      }
      else
      {
         double xnew=Open[k+Npast-1];
         double xold=Open[k-1];
         mx[k] =mx[k-1]+xnew-xold;
         sxx[k]=sxx[k-1]+xnew*xnew-xold*xold;
      }
      denx[k]=sxx[k]*Npast-mx[k]*mx[k];
   }
   
   // Compute cross-correlation sums and correlation coefficients and find NN
   double sxy[];
   ArrayResize(sxy,bars-Npast-Nfut+1);
   double b,corrMax=0;
   int knn=0;
   for(int k=0;k＜=bars-Npast-Nfut;k++)
   {
      // Compute sxy
      sxy[k]=0.0;
      for(int i=0;i＜Npast;i++) sxy[k]+=Open[k+i]*Open[bars-Npast+i];
      
      // Compute corr coefficient
      if(denx[k]＜=0)
      {
         Print("Zero or negative sxx[k]*Npast-mx[k]*mx[k]. Skipping pattern # ",k);
         continue;
      }
      double num=sxy[k]*Npast-mx[k]*my;
      double corr=num/MathSqrt(denx[k])/deny;
      if(corr>corrMax)
      {
         corrMax=corr;
         knn=k;
         b=num/denx[k];
      }
   }
   Print("Nearest neighbor is dated ",Time[knn]," and has correlation with current pattern of ",corrMax);

   // Compute xm[] and ym[] by scaling the nearest neighbor
   double delta=Open[bars-1]-b*Open[knn+Npast-1];
   for(int i=0;i＜Npast+Nfut;i++)
   {
      if(i＜=Npast-1) xnn[bars-Npast+i]=b*Open[knn+i]+delta;
      if(i>=Npast-1) ynn[bars-Npast-Nfut+i]=b*Open[knn+i]+delta;
   }
   
   return(rates_total);
}



【表示結果】















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