回归分析指标 使用曲线分析的一种指标 具体使用起来还是不错的 主图指标 mt4指标类型：震荡指标 是否能用在mt4手机版上：否 是否含有未来函数： //+--------------------------------------------------------------------------------------------------+ //| b_Regression_Analysis_v2.mq4 | //| Copyright ? 2011, barmenteros | //| http://www.mql4.com/users/barmenteros | //+--------------------------------------------------------------------------------------------------+ //| This version: | //| -corrects error in calculating the standard deviation; | //| -makes more use of the indicators of MQL4 standard library; | //| -improves the functions of calculation of the regressions; | //| -frees a buffer. It only uses 3 buffers now; | //| -adds a Comment parameter to enable/disable comments on the screen. | //| The changes do not affect the indicator from the visual point of view, however, the values shown | //| on screen are corrected. | //+--------------------------------------------------------------------------------------------------+ #property copyright "barmenteros" #property link "[email protected]" #property indicator_chart_window #property indicator_buffers 3 #property indicator_color1 Magenta #property indicator_color2 Blue #property indicator_color3 Blue // ---- inputs // dp_limiter It should be >2 . If not it will be autoset to default value // endpos Last value position to the right. It should be >0. If not it will be autoset to // default value // record [true] - record on; [false] - record off extern int dp_limiter =100; // Number of data points extern int endpos =0; // Last value position extern double multStdDev =1.96; // Bands separation extern bool comments =true; // Comment on/off extern bool record =false; // Record info into a file text // ---- buffers double RegBfr; double BandUpBfr; double BandDwBfr; // ---- global variables int c_handle,p_handle, current_day,previous_day; double errorstddev[4], data[1], regression[1], regressionerror[1]; //+--------------------------------------------------------------------------------------------------+ //| Custom indicator initialization function | //+--------------------------------------------------------------------------------------------------+ int init() { // ---- checking inputs if(dp_limiter<3) {dp_limiter=100; Alert("dp_limiter readjusted");} if(endpos<0) {endpos=0; Alert("endpos readjusted");} // ---- drawing settings SetIndexStyle(0,DRAW_LINE); SetIndexStyle(1,DRAW_LINE); SetIndexLabel(1,"UpperBand"); SetIndexStyle(2,DRAW_LINE); SetIndexLabel(2,"LowerBand"); // ---- mapping SetIndexBuffer(0,RegBfr); SetIndexBuffer(1,BandUpBfr); SetIndexBuffer(2,BandDwBfr); // ---- resize arrays ArrayResize(data,dp_limiter); ArrayResize(regression,dp_limiter); ArrayResize(regressionerror,dp_limiter); // ---- done return(0); } //+--------------------------------------------------------------------------------------------------+ //| Custom indicator iteration function | //+--------------------------------------------------------------------------------------------------+ int start() { int i; // ---- file recording if(record) { previous_day=current_day; current_day=TimeDay(TimeCurrent()); if(current_day!=previous_day) { p_handle=c_handle; c_handle=FileOpen(StringConcatenate(TimeYear(TimeCurrent()),"-", TimeMonth(TimeCurrent()),"-",current_day,"-", Symbol(),"-b_Regression_Analysis.txt"), FILE_CSV|FILE_READ|FILE_WRITE); if(c_handle>0 && c_handle!=p_handle) { if(p_handle>0) FileClose(p_handle); FileWrite(c_handle,"time","error[LinReg]","error[QuadReg]", "error[LogReg]","error[ExpReg]"); } } } // ---- ArrayInitialize(data,EMPTY_VALUE); for(i=0;i0) FileWrite(c_handle,StringConcatenate(TimeHour(TimeCurrent()),":", TimeMinute(TimeCurrent()),":",TimeSeconds(TimeCurrent())), errorstddev[0],errorstddev[1],errorstddev[2],errorstddev[3]); // ---- BestReg(); ConfidenceBands(); // ---- return(0); } //+--------------------------------------------------------------------------------------------------+ //| Custom indicator deinitialization function | //+--------------------------------------------------------------------------------------------------+ int deinit() { if(comments) Comment(""); if(c_handle>0) FileClose(c_handle); // ---- return(0); } void iPolReg(int datapoints, int grade, double& array1, double& array2) { // y=c0+c1x+c2x^2+c3x^3...cnx^n // grade+1 n鷐ero de funciones y de coeficientes a calcular // 2*grade valor del mayor exponente // datapoints n鷐ero de valores a considerar // pos first value position (left to right direction) int pos,exp,k,row,col, initialrow,initialcol, loop,i,j; double sumx,sumyx,sum; double sumxvalues[1], sumyxvalues[1], matrix[1][10], constant[1]; pos=datapoints-1; ArrayResize(sumxvalues,2*grade+1); ArrayResize(sumyxvalues,grade+1); ArrayInitialize(sumxvalues,0.0); ArrayInitialize(sumyxvalues,0.0); sumxvalues[0]=datapoints; for(exp=1;exp<=2*grade;exp++) { sumx=0.0; sumyx=0.0; for(k=1;k<=datapoints;k++) { sumx+=MathPow(k,exp); if(exp==1) sumyx+=array1[pos-k+1]; else if(exp<=grade+1) sumyx+=array1[pos-k+1]*MathPow(k,exp-1); } sumxvalues[exp]=sumx; if(sumyx!=0.0) sumyxvalues[exp-1]=sumyx; } ArrayResize(matrix,grade+1); ArrayInitialize(matrix,0.0); for(row=0;row<=grade;row++) for(col=0;col<=grade;col++) matrix[row][col]=sumxvalues[row+col]; initialrow=1; initialcol=1; for(loop=1;loop<=grade;loop++) { for(row=initialrow;row<=grade;row++) { sumyxvalues[row]=sumyxvalues[row]-(matrix[row][loop-1]/matrix[loop-1][loop-1])*sumyxvalues[loop-1]; for(col=initialcol;col<=grade;col++) matrix[row][col]=matrix[row][col]-(matrix[row][loop-1]/matrix[loop-1][loop-1])*matrix[loop-1][col]; } initialrow++; initialcol++; } ArrayResize(constant,grade+1); ArrayInitialize(constant,0.0); j=0; for(i=grade;i>=0;i--) { if(j==0) constant=sumyxvalues/matrix; else { sum=0.0; for(k=j;k>0;k--) sum+=constant[i+k]*matrix[i+k]; constant=(sumyxvalues-sum)/matrix; } j++; } ArrayResize(array2,datapoints); ArrayInitialize(array2,EMPTY_VALUE); k=1; for(i=datapoints-1;i>=0;i--) { sum=0.0; for(j=0;j<=grade;j++) sum+=constant[j]*MathPow(k,j); array2=sum; k++; } } void iLogReg(int datapoints, double& array1, double& array2) { // y=c0*x^c1 // lny=lnc0+c1lnx <=> y=a+bx // c0=e^a // c1=b int pos,exp,k,row,col, initialrow,initialcol, loop,i,j; double sumx,sumyx, lnx,a; double sumxvalues[1], sumyxvalues[1], matrix[1][10], constant[1]; pos=datapoints-1; ArrayResize(sumxvalues,3); ArrayResize(sumyxvalues,2); ArrayInitialize(sumxvalues,0.0); ArrayInitialize(sumyxvalues,0.0); sumxvalues[0]=datapoints; for(exp=1;exp<=2;exp++) { sumx=0.0; sumyx=0.0; for(k=1;k<=datapoints;k++) { lnx=MathLog(k); sumx+=MathPow(lnx,exp); if(exp==1) sumyx+=MathLog(array1[pos-k+1]); else sumyx+=MathLog(array1[pos-k+1])*MathPow(lnx,exp-1); } sumxvalues[exp]=sumx; if(sumyx!=0.0) sumyxvalues[exp-1]=sumyx; } ArrayResize(matrix,2); ArrayInitialize(matrix,0.0); for(row=0;row<=1;row++) for(col=0;col<=1;col++) matrix[row][col]=sumxvalues[row+col]; sumyxvalues[1]=sumyxvalues[1]-(matrix[1][0]/matrix[0][0])*sumyxvalues[0]; matrix[1][1]=matrix[1][1]-(matrix[1][0]/matrix[0][0])*matrix[0][1]; ArrayResize(constant,2); ArrayInitialize(constant,0.0); constant[1]=sumyxvalues[1]/matrix[1][1]; a=(sumyxvalues[0]-(constant[1]*matrix[0][1]))/matrix[0][0]; constant[0]=MathExp(a); ArrayResize(array2,datapoints); ArrayInitialize(array2,EMPTY_VALUE); k=1; for(i=datapoints-1;i>=0;i--) { array2=constant[0]*MathPow(k,constant[1]); k++; } } void iExpReg(int datapoints, double& array1, double& array2) { // y=c0*e^(xc1) // lny=lnc0+c1x <=> y=a+bx // c0=e^a // c1=b int pos,exp,k,row,col, initialrow,initialcol, loop,i,j; double sumx,sumyx,a; double sumxvalues[1], sumyxvalues[1], matrix[1][10], constant[1]; pos=datapoints-1; ArrayResize(sumxvalues,3); ArrayResize(sumyxvalues,2); ArrayInitialize(sumxvalues,0.0); ArrayInitialize(sumyxvalues,0.0); sumxvalues[0]=datapoints; for(exp=1;exp<=2;exp++) { sumx=0.0; sumyx=0.0; for(k=1;k<=datapoints;k++) { sumx+=MathPow(k,exp); if(exp==1) sumyx+=MathLog(array1[pos-k+1]); else sumyx+=MathLog(array1[pos-k+1])*MathPow(k,exp-1); } sumxvalues[exp]=sumx; if(sumyx!=0.0) sumyxvalues[exp-1]=sumyx; } ArrayResize(matrix,2); ArrayInitialize(matrix,0.0); for(row=0;row<=1;row++) for(col=0;col<=1;col++) matrix[row][col]=sumxvalues[row+col]; sumyxvalues[1]=sumyxvalues[1]-(matrix[1][0]/matrix[0][0])*sumyxvalues[0]; matrix[1][1]=matrix[1][1]-(matrix[1][0]/matrix[0][0])*matrix[0][1]; ArrayResize(constant,2); ArrayInitialize(constant,0.0); constant[1]=sumyxvalues[1]/matrix[1][1]; a=(sumyxvalues[0]-(constant[1]*matrix[0][1]))/matrix[0][0]; constant[0]=MathExp(a); ArrayResize(array2,datapoints); ArrayInitialize(array2,EMPTY_VALUE); k=1; for(i=datapoints-1;i>=0;i--) { array2=constant[0]*MathExp(k*constant[1]); k++; } } void BestReg() { string short_name; double currentstddev; int c_stddevindex,i; if(comments) Comment("LinReg=",errorstddev[0]," || QuadReg=",errorstddev[1], " || LogReg=",errorstddev[2]," || ExpReg=",errorstddev[3]); currentstddev=errorstddev[0]; c_stddevindex=0; for(i=1;i<=3;i++) { if(errorstddev=0;i--) RegBfr[i+endpos]=regression; } void ConfidenceBands() { int i; double sum,variance; sum=0.0; for(i=dp_limiter-1;i>=0;i--) sum+=(data-RegBfr[i+endpos])*(data-RegBfr[i+endpos]); variance=MathSqrt(sum/dp_limiter); ArrayInitialize(BandUpBfr,EMPTY_VALUE); ArrayInitialize(BandDwBfr,EMPTY_VALUE); for(i=dp_limiter-1;i>=0;i--) { BandUpBfr[i+endpos]=RegBfr[i+endpos]+(multStdDev*variance); BandDwBfr[i+endpos]=RegBfr[i+endpos]-(multStdDev*variance); } }b_Regression_Analysis_v2.jpg

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