Price Prediction By Nearest Neighbor Indicator For MT5

Price Prediction By Nearest Neighbor Indicator For MT5

Table Of Contents:

  1. Price Prediction By Nearest Neighbor Indicator For MT5
  2. Installazione della Price Prediction By Nearest Neighbor Indicator For MT5
  3. Parametri della Price Prediction By Nearest Neighbor Indicator For MT5
  4. Buffer della Price Prediction By Nearest Neighbor Indicator For MT5
  5. Parti principali del codice

La Price Prediction By Nearest Neighbor Indicator For MT5 disegna i movimenti di prezzo futuri previsti che sono calcolati da modelli di prezzo recenti. I recenti modelli di prezzo sono i cosiddetti vicini più vicini che hanno dato il nome a questo indicatore. I modelli di prezzo vengono utilizzati per calcolare una votazione ponderata. Da quel risultato i futuri movimenti di prezzo sono disegnati sul grafico.

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Installazione della Price Prediction By Nearest Neighbor Indicator For MT5

Dopo aver scaricato l'indicatore tramite il modulo sopra è necessario decomprimere il file zip. Quindi è necessario copiare il file nearest_neighbor.mq5 nella cartella MQL5Indicators dell'installazione di MT5 . Dopodiché, riavvia MT5 e sarai in grado di vedere l'indicatore nell'elenco degli indicatori.

Parametri della Price Prediction By Nearest Neighbor Indicator For MT5

Price Prediction By Nearest Neighbor Indicator For MT5 ha i parametri 2 da configurare.

input int    Npast   =300; // Past bars in a pattern input int    Nfut    =50;  // Future bars in a pattern (must be < Npast) 

Buffer della Price Prediction By Nearest Neighbor Indicator For MT5

Price Prediction By Nearest Neighbor Indicator For MT5 fornisce buffer 2 .

SetIndexBuffer(0,ynn,INDICATOR_DATA); SetIndexBuffer(1,xnn,INDICATOR_DATA); 

Parti principali del codice

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 lt 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 lt Npast;i++)      {       double y=Open[bars-Npast+i];       my +=y;       syy+=y*y;      }    double deny=syy*Npast-my*my;    if(deny lt =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 lt =bars-Npast-Nfut;k++)      {       if(k==0)         {          mx[0] =0.0;          sxx[0]=0.0;          for(int i=0;i lt 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 lt =bars-Npast-Nfut;k++)      {       //--- Compute sxy       sxy[k]=0.0;       for(int i=0;i lt Npast;i++) sxy[k]+=Open[k+i]*Open[bars-Npast+i];        //--- Compute corr coefficient       if(denx[k] lt =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 gt 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 lt Npast+Nfut;i++)      {       if(i lt =Npast-1) xnn[bars-Npast+i]=b*Open[knn+i]+delta;       if(i gt =Npast-1) ynn[bars-Npast-Nfut+i]=b*Open[knn+i]+delta;      }     return(rates_total);   } //+------------------------------------------------------------------+ 

 

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