Price Prediction By Nearest Neighbor Indicator For MT5
Table Of Contents:
- Price Prediction By Nearest Neighbor Indicator For MT5
- Instaliranje riječi Price Prediction By Nearest Neighbor Indicator For MT5
- Parametri Price Prediction By Nearest Neighbor Indicator For MT5
- Puferi Price Prediction By Nearest Neighbor Indicator For MT5
- Glavni dijelovi kodeksa
Price Prediction By Nearest Neighbor Indicator For MT5 crta očekivane buduće poteze cijena koji se izračunavaju iz nedavnih obrazaca cijena. Nedavni uzorak cijena takozvani su najbliži susjedi koji su dali ime ovom pokazatelju. Obrasci cijena koriste se za izračunavanje ponderiranog glasanja. Iz tog rezultata prikazani su budući pomaci cijena na grafikonu.
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Instaliranje riječi Price Prediction By Nearest Neighbor Indicator For MT5
Nakon što preuzmete indikator preko gornjeg obrasca, morate raspakirati zip datoteku. Tada morate kopirati datoteku nearest_neighbor.mq5 u mapu MQL5Indicators vaše MT5 instalacije. Nakon toga ponovno pokrenite MT5 i tada ćete indikator moći vidjeti na popisu pokazatelja.
Parametri Price Prediction By Nearest Neighbor Indicator For MT5
Price Prediction By Nearest Neighbor Indicator For MT5 ima 2 parametre za konfiguriranje.
input int Npast =300; // Past bars in a pattern input int Nfut =50; // Future bars in a pattern (must be < Npast) Puferi Price Prediction By Nearest Neighbor Indicator For MT5
Price Prediction By Nearest Neighbor Indicator For MT5 osigurava 2 međuspremnike.
SetIndexBuffer(0,ynn,INDICATOR_DATA); SetIndexBuffer(1,xnn,INDICATOR_DATA); Glavni dijelovi kodeksa
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); } //+------------------------------------------------------------------+
