232 lines
18 KiB
MQL5
232 lines
18 KiB
MQL5
//+------------------------------------------------------------------+
|
|
//| teste-regressao.mq5 |
|
|
//| Copyright 2020, OS Corp. |
|
|
//| http://www.os.org |
|
|
//+------------------------------------------------------------------+
|
|
#property copyright "Copyright 2020, OS Corp."
|
|
#property link "http://www.os.org"
|
|
#property version "1.001"
|
|
|
|
#include <Math\Alglib\dataanalysis.mqh>
|
|
#include <Math\Stat\Math.mqh>
|
|
#include <Graphics\Graphic.mqh>
|
|
//+------------------------------------------------------------------+
|
|
//| Script program start function |
|
|
//+------------------------------------------------------------------+
|
|
|
|
void OnStart(){
|
|
|
|
datetime to;
|
|
while(true){
|
|
to = TimeCurrent();
|
|
prepAndAnalizeRegres( to-300, to );
|
|
Sleep(50);
|
|
}
|
|
|
|
//prepAndAnalizeRegres( D'2020.05.15 10:00:00', D'2020.05.15 10:00:21' );
|
|
//prepAndAnalizeRegres( D'2020.05.15 10:00:21', D'2020.05.15 10:00:42' );
|
|
//prepAndAnalizeRegres( D'2020.05.15 10:00:42', D'2020.05.15 10:01:03' );
|
|
//prepAndAnalizeRegres( D'2020.05.15 10:00:00', D'2020.05.15 10:01:03' );
|
|
//prepAndAnalizeRegres( D'2020.05.15 10:31:00', D'2020.05.15 10:32:00' );
|
|
//prepAndAnalizeRegres( D'2020.05.15 10:32:00', D'2020.05.15 10:33:00' );
|
|
|
|
|
|
//prepAndAnalizeRegres( D'2020.05.15 10:00:00', D'2020.05.15 10:11:00' );
|
|
//prepAndAnalizeRegres( D'2020.05.15 10:01:00', D'2020.05.15 10:02:00' );
|
|
//prepAndAnalizeRegres( D'2020.05.15 10:02:00', D'2020.05.15 10:03:00' );
|
|
//prepAndAnalizeRegres( D'2020.05.15 10:03:00', D'2020.05.15 10:04:00' );
|
|
//prepAndAnalizeRegres( D'2020.05.15 10:04:00', D'2020.05.15 10:05:00' );
|
|
//prepAndAnalizeRegres( D'2020.05.15 10:05:00', D'2020.05.15 10:06:00' );
|
|
//prepAndAnalizeRegres( D'2020.05.15 10:06:00', D'2020.05.15 10:07:00' );
|
|
//prepAndAnalizeRegres( D'2020.05.15 10:07:00', D'2020.05.15 10:08:00' );
|
|
//prepAndAnalizeRegres( D'2020.05.15 10:08:00', D'2020.05.15 10:09:00' );
|
|
//prepAndAnalizeRegres( D'2020.05.15 10:09:00', D'2020.05.15 10:10:00' );
|
|
//prepAndAnalizeRegres( D'2020.05.15 10:10:00', D'2020.05.15 10:11:00' );
|
|
|
|
//prepAndAnalizeRegres( D'2020.05.15 10:00:00', D'2020.05.15 10:20:00' );
|
|
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
|
|
void prepAndAnalizeRegres(datetime dtFrom, datetime dtTo){
|
|
MqlTick ticks[];
|
|
int qtdTicks = 0;
|
|
|
|
MqlDateTime dtFrom_, dtTo_;
|
|
TimeToStruct(dtFrom,dtFrom_);
|
|
TimeToStruct(dtTo ,dtTo_ );
|
|
|
|
string desdeAte = dtFrom_.hour+":"+dtFrom_.min+":"+dtFrom_.sec+" a "+
|
|
dtTo_ .hour+":"+dtTo_ .min+":"+dtTo_ .sec ;
|
|
|
|
|
|
qtdTicks = CopyTicksRange(Symbol(),ticks,COPY_TICKS_INFO ,dtFrom*1000, dtTo*1000 ); analisarRegressao(ticks,qtdTicks,5.0, "INF:"+desdeAte);
|
|
//qtdTicks = CopyTicksRange(Symbol(),ticks,COPY_TICKS_TRADE,dtFrom*1000, dtTo*1000 ); analisarRegressao(ticks,qtdTicks,5.0, "TRA:"+desdeAte);
|
|
//qtdTicks = CopyTicksRange(Symbol(),ticks,COPY_TICKS_ALL ,dtFrom*1000, dtTo*1000 ); analisarRegressao(ticks,qtdTicks,5.0, "ALL:"+desdeAte);
|
|
}
|
|
|
|
void analisarRegressao(MqlTick &ticks[], int qtdTicks, double tickSize, string comment){
|
|
|
|
//MqlTick ticks[];
|
|
//int qtdMinutos = 5;
|
|
|
|
//datetime dtFrom=D'2020.05.15 10:00:00';
|
|
//datetime dtTo =D'2020.05.15 10:00:01';
|
|
|
|
|
|
|
|
//int qtdTicks = CopyTicksRange(Symbol(),ticks,COPY_TICKS_INFO,dtFrom*1000, dtTo*1000 );
|
|
double rAtu = 0 ; // run atual
|
|
double rAnt = 0 ; // run anterior
|
|
double ret = 0 ; // retorno
|
|
double rets[] ; // vetor de retornos
|
|
double retacum[]; // vetor de retornos acumulados
|
|
double is[] ; // vetor de I's
|
|
ArrayResize(rets ,qtdTicks);
|
|
ArrayResize(retacum,qtdTicks);
|
|
ArrayResize(is ,qtdTicks);
|
|
rets [0] = 0.0;
|
|
retacum[0] = 0.0;
|
|
int runs = 0;
|
|
|
|
|
|
// obtendo o vetor de runs...
|
|
for(int i = 1; i < qtdTicks; i++){
|
|
|
|
rAnt = rAtu;
|
|
|
|
//ret = (ticks[i].last/ticks[i-1].last)-1.0;
|
|
rAtu = (ticks[i].ask-ticks[i-1].ask) + rAnt ;
|
|
|
|
//if( (rAtu*rAnt)<0 ){ rAtu=0;}
|
|
|
|
if( ( rAtu >= tickSize*2.0 ||
|
|
rAtu <= -tickSize*2.0 ) ){
|
|
//ret = (ticks[i].ask/(ticks[i].ask -(rAtu) ) ) -1.0; // retorno normal...
|
|
//ret = MathLog( ticks[i].ask/(ticks[i].ask-rAtu) ); // log retorno
|
|
ret = rAtu ; // retorno em pontos
|
|
|
|
rets [runs] = ret;
|
|
retacum[runs] = (runs>0)?(retacum[runs-1]+ret):(ret);
|
|
|
|
rAtu = 0.0;
|
|
runs++;
|
|
}else{
|
|
ret = 0.0;
|
|
}
|
|
}
|
|
|
|
|
|
//-- Preenche a matriz: Y - precos, X - numero ordinal do tick
|
|
CMatrixDouble xy(qtdTicks, 2);
|
|
for(int i = 1; i < runs; i++){
|
|
|
|
rAnt = rAtu;
|
|
|
|
//ret = (ticks[i].last/ticks[i-1].last)-1.0;
|
|
rAtu = (ticks[i].ask-ticks[i-1].ask) + rAnt ;
|
|
|
|
//if( (rAtu*rAnt)<0 ){ rAtu=0;}
|
|
|
|
if( ( rAtu >= tickSize*2.0 ||
|
|
rAtu <= -tickSize*2.0 ) ){
|
|
//ret = (ticks[i].ask/(ticks[i].ask -(rAtu) ) ) -1.0; // retorno normal...
|
|
//ret = MathLog( ticks[i].ask/(ticks[i].ask-rAtu) ); // log retorno
|
|
ret = rAtu ; // retorno em pontos
|
|
rAtu = 0.0;
|
|
runs++;
|
|
}else{
|
|
ret = 0.0;
|
|
}
|
|
|
|
is[i] = i;
|
|
//xy[i].Set(0, i );
|
|
xy[i].Set(0, rets[i-1] );
|
|
xy[i].Set(1, ret );
|
|
rets [i] = ret;
|
|
retacum[i] = retacum[i-1]+ret;
|
|
/*
|
|
Print( " last:" , ticks[i].last ,
|
|
" ask:" , ticks[i].ask ,
|
|
" bid:" , ticks[i].bid ,
|
|
" spd:" , ticks[i].ask - ticks[i].bid,
|
|
" time:" , ticks[i].time ,
|
|
" time_msc:", ticks[i].time_msc ,
|
|
" rAnt:" , rAnt ,
|
|
" rAtu:" , rAtu ,
|
|
" i:" , i ,
|
|
" ret:" , ret );
|
|
*/
|
|
}
|
|
|
|
|
|
|
|
//-- Encontre os coeficientes a e b do modelo linear y = a*x + b;
|
|
int retcode = 0;
|
|
double a, b;
|
|
//CLinReg::LRLine(xy, qtdTicks, retcode, a, b);
|
|
|
|
double vara =0;
|
|
double varb =0;
|
|
double covab =0;
|
|
double corrab=0;
|
|
double p =0;
|
|
|
|
//--- create array
|
|
double s[];
|
|
ArrayResize(s,qtdTicks);
|
|
for(int i=0;i<=qtdTicks-1;i++){ s[i]=1; }
|
|
|
|
CLinReg::LRLines(xy, s, qtdTicks, retcode, a, b, vara, varb, covab, corrab, p);
|
|
|
|
//-- Gerar os valores de regressão linear para cada X;
|
|
double estimate [];
|
|
double estimateAr [];
|
|
double estimateArAcum[];
|
|
//ArrayResize(rets , qtdTicks);
|
|
ArrayResize(estimate , qtdTicks);
|
|
ArrayResize(estimateAr , qtdTicks);
|
|
ArrayResize(estimateArAcum, qtdTicks);
|
|
for(int x = 1; x < qtdTicks; x++) {
|
|
estimate [x] = x*a+b;
|
|
estimateAr [x] = rets [x-1]*a+b;
|
|
estimateArAcum[x] = estimateArAcum[x-1]+estimateAr[x];
|
|
//rets [x] = ticks[x].ask;
|
|
}
|
|
|
|
//-- Encontra o coeficiente de correlacao dos valores com sua regressão linear
|
|
double corr = 0.0;
|
|
//corr = CAlglib::PearsonCorr2(ticks, estimate);
|
|
//corr = CAlglib::SpearmanCorr2(equity, estimate);
|
|
MathCorrelationPearson(rets, estimate, corr);
|
|
|
|
//-- Encontra R²
|
|
double r2 = MathPow(corr, 2.0);
|
|
|
|
//-- Printando os resultados
|
|
Print( comment ,
|
|
" a=" , DoubleToString(a ,5),
|
|
" b=" , DoubleToString(b ,5),
|
|
" corrab:" , DoubleToString(corrab,5),
|
|
//" corrR=" , DoubleToString(corr ,5),
|
|
//" corrR2=" , DoubleToString(r2 ,5),
|
|
" tks=" , qtdTicks ,
|
|
" runs:" , runs ,
|
|
" retcod:" , retcode ,
|
|
" vara:" , DoubleToString(vara ,5),
|
|
" varb:" , DoubleToString(varb ,5),
|
|
" covab:" , DoubleToString(covab ,5),
|
|
" p:" , DoubleToString(p ,5));
|
|
|
|
//GraphPlot( is, estimate, is, rets, CURVE_POINTS,"nome_teste");
|
|
//GraphPlot( is, estimate, is, rets, CURVE_POINTS,"nome_teste");
|
|
//GraphPlot( estimate, CURVE_POINTS,"nome_teste");
|
|
//GraphPlot( rets , CURVE_LINES,"nome_teste");
|
|
//GraphPlot( retacum , CURVE_LINES,"nome_teste");
|
|
//GraphPlot( is, retacum , CURVE_LINES,"nome_teste");
|
|
//GraphPlot( is, retacum , is, estimate , CURVE_LINES,"nome_teste");
|
|
//GraphPlot( is, retacum , is, estimateAr , CURVE_LINES,"nome_teste");
|
|
GraphPlot( is, retacum , is, estimateArAcum, CURVE_LINES,"nome_teste");
|
|
//GraphPlot( estimateArAcum, CURVE_LINES,"nome_teste");
|
|
//GraphPlot( is, estimateAr, is, estimateArAcum, CURVE_LINES,"nome_teste");
|
|
}
|