Price Prediction By Nearest Neighbor Indicator For MT5
Table Of Contents:
- Price Prediction By Nearest Neighbor Indicator For MT5
- Installation des Price Prediction By Nearest Neighbor Indicator For MT5
- Parameter des Price Prediction By Nearest Neighbor Indicator For MT5
- Puffer des Price Prediction By Nearest Neighbor Indicator For MT5
- Hauptteile des Codes
Die Price Prediction By Nearest Neighbor Indicator For MT5 zeichnet die erwarteten zukünftigen Preisbewegungen auf, die aus den jüngsten Preismustern berechnet werden. Die jüngsten Preismuster sind sogenannte nächste Nachbarn, die den Namen für diesen Indikator gaben. Die Preismuster werden verwendet, um eine gewichtete Abstimmung zu berechnen. Aus diesem Ergebnis werden die zukünftigen Kursbewegungen auf dem Chart gezogen.
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Installation des Price Prediction By Nearest Neighbor Indicator For MT5
Nachdem Sie den Indikator über das obige Formular heruntergeladen haben, müssen Sie die Zip-Datei entpacken. Anschließend müssen Sie die Datei nearest_neighbor.mq5 in den Ordner MQL5Indicators Ihrer MT5 Installation kopieren. Starten Sie danach MT5 neu und Sie können den Indikator in der Liste der Indikatoren sehen.
Parameter des Price Prediction By Nearest Neighbor Indicator For MT5
Der Price Prediction By Nearest Neighbor Indicator For MT5 verfügt über 2 zu konfigurierende Parameter.
input int Npast =300; // Past bars in a pattern input int Nfut =50; // Future bars in a pattern (must be < Npast) Puffer des Price Prediction By Nearest Neighbor Indicator For MT5
Der Price Prediction By Nearest Neighbor Indicator For MT5 stellt 2 Puffer bereit.
SetIndexBuffer(0,ynn,INDICATOR_DATA); SetIndexBuffer(1,xnn,INDICATOR_DATA); Hauptteile des Codes
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); } //+------------------------------------------------------------------+
