Price Prediction By Nearest Neighbor Indicator For MT5
Table Of Contents:
- Price Prediction By Nearest Neighbor Indicator For MT5
- Installazione della Price Prediction By Nearest Neighbor Indicator For MT5
- Parametri della Price Prediction By Nearest Neighbor Indicator For MT5
- Buffer della Price Prediction By Nearest Neighbor Indicator For MT5
- 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.
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); } //+------------------------------------------------------------------+