Price Prediction By Nearest Neighbor Indicator For MT5
Table Of Contents:
- Price Prediction By Nearest Neighbor Indicator For MT5
- Instalowanie Price Prediction By Nearest Neighbor Indicator For MT5
- Parametry Price Prediction By Nearest Neighbor Indicator For MT5
- Bufory słowa Price Prediction By Nearest Neighbor Indicator For MT5
- Główne części Kodeksu
Price Prediction By Nearest Neighbor Indicator For MT5 rysuje przewidywane przyszłe ruchy cen, które są obliczane na podstawie ostatnich modeli cen. Ostatnie wzorce cenowe to tak zwani najbliżsi sąsiedzi, którzy nadali nazwę temu wskaźnikowi. Wzorce cen są używane do obliczania ważonego głosowania. Na podstawie tego wyniku rysowane są przyszłe ruchy cen.
Partially Automated Trading Besides Your Day Job
Alerts In Real-Time When Divergences Occur
Instalowanie Price Prediction By Nearest Neighbor Indicator For MT5
Po pobraniu wskaźnika za pomocą powyższego formularza musisz rozpakować plik zip. Następnie musisz skopiować plik nearest_neighbor.mq5 do folderu MQL5Indicators instalacji MT5 . Następnie uruchom ponownie MT5, a wtedy będziesz mógł zobaczyć wskaźnik na liście wskaźników.
Parametry Price Prediction By Nearest Neighbor Indicator For MT5
Price Prediction By Nearest Neighbor Indicator For MT5 2 Price Prediction By Nearest Neighbor Indicator For MT5 ma parametry 2 do skonfigurowania.
input int Npast =300; // Past bars in a pattern input int Nfut =50; // Future bars in a pattern (must be < Npast) Bufory słowa Price Prediction By Nearest Neighbor Indicator For MT5
Price Prediction By Nearest Neighbor Indicator For MT5 zapewnia bufory 2 .
SetIndexBuffer(0,ynn,INDICATOR_DATA); SetIndexBuffer(1,xnn,INDICATOR_DATA); Główne części Kodeksu
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); } //+------------------------------------------------------------------+
