I am enjoying the journey of discovering more of the functionality of Amibroker!

Generally, it is useful for me to visualize results. In the below code, I attempt to plot the asymmetrical characteristic of implied volatility in index options. I am attempting to use the XY scatter charts before moving on to learn gfx (which may be the appropriate solution).

The black dots represent pre-processed binned data representing the average relationship between log returns and implied volatility in an index (SPY) for 2017. The orange dots represent a continuous linear regression line for all data plotted (both positive and negative log returns). The green dots represent the regression line for only positive log returns, and the red dots represent the regression line for only negative log returns.

It is common to graph a regression line(s) in XY scatter plots. But, after much research on this forum and google, I cannot discern a way to do this other than with the coordinate dots (as outlined above). That is, I cannot connect the orange dots separately, the red dots separately, the green dots separately and the black dots separately. If I style the XYChartAxis() with styleLine, the connection is programmed to connect all the dots in aggregate and I cannot discern a way to allow separation.

Is it possible to plot separate regression lines (or any lines) in the XY scatter plot? @Tomasz mentioned in this post that he was considering adding a similar feature in a future development. Does anyone know if this has been implemented? Is my requirement something that would be more appropriately handled in gfx?

I truly appreciate the experience and professionalism offered on this forum and look forward to learning more.

Kind regards.

```
// exploration
posA = posB = posSumX = posSumY = posSumXY = posSumX2 = 0; // regression line for positive logReturns
negA = negB = negSumX = negSumY = negSumXY = negSumX2 = 0; // regression line for negative logReturns
a = b = sumX = sumY = sumXY = sumX2 = 0; // regression line for continuous logReturns
// pre-processed data, binned at $1 wide strikes ( 1/spot0 )
midBin[0] = -0.034091112;
midBin[1] = -0.030502574;
midBin[2] = -0.026914036;
midBin[3] = -0.023325498;
midBin[4] = -0.01973696;
midBin[5] = -0.016148421;
midBin[6] = -0.012559883;
midBin[7] = -0.008971345;
midBin[8] = -0.005382807;
midBin[9] = -0.001794269;
midBin[10] = 0.001794269;
midBin[11] = 0.005382807;
midBin[12] = 0.008971345;
midBin[13] = 0.012559883;
midBin[14] = 0.016148421;
midBin[15] = 0.01973696;
midBin[16] = 0.023325498;
midBin[17] = 0.026914036;
avgVolDiff[0] = 0.100732148;
avgVolDiff[1] = 0.117457709;
avgVolDiff[2] = 0.06869593;
avgVolDiff[3] = 0.05300329;
avgVolDiff[4] = 0.045185022;
avgVolDiff[5] = 0.032521213;
avgVolDiff[6] = 0.026830977;
avgVolDiff[7] = 0.023176704;
avgVolDiff[8] = 0.010523093;
avgVolDiff[9] = 0.002684039;
avgVolDiff[10] = -0.00346206;
avgVolDiff[11] = -0.008633991;
avgVolDiff[12] = -0.020071165;
avgVolDiff[13] = -0.024138759;
avgVolDiff[14] = -0.028698314;
avgVolDiff[15] = -0.043775271;
avgVolDiff[16] = -0.063140221;
avgVolDiff[17] = -0.054909837;
count = 18;
for( i = 0; i < count; i++ )
{
if( midBin[i] < 0 )
{ // calculate regression for negative logReturns
negSumX = negSumX + midBin[i];
negSumY = negSumY + avgVolDiff[i];
negSumXY = negSumXY + midBin[i] * avgVolDiff[i];
negSumX2 = negSumX2 + midBin[i] * midBin[i];
}
else
{ // calculate regression for positive logReturns
posSumX = posSumX + midBin[i];
posSumY = posSumY + avgVolDiff[i];
posSumXY = posSumXY + midBin[i] * avgVolDiff[i];
posSumX2 = posSumX2 + midBin[i] * midBin[i];
}
// calculate continuous regression for all logReturns
sumX = sumX + midBin[i];
sumY = sumY + avgVolDiff[i];
sumXY = sumXY + midBin[i] * avgVolDiff[i];
sumX2 = sumX2 + midBin[i] * midBin[i];
}
posA = ( posSumY * posSumX2 - posSumX * posSumXY ) / ( count * posSumX2 - posSumX * posSumX );
posB = ( count * posSumXY - posSumX * posSumY ) / ( count * posSumX2 - posSumX * posSumX );
negA = ( negSumY * negSumX2 - negSumX * negSumXY ) / ( count * negSumX2 - negSumX * negSumX );
negB = ( count * negSumXY - negSumX * negSumY ) / ( count * negSumX2 - negSumX * negSumX );
a = (sumY * sumX2 - sumX * sumXY ) / ( count * sumX2 - sumX * sumX );
b = ( count * sumXY - sumX * sumY ) / ( count * sumX2 - sumX * sumX );
changePer1PctPos = .01 * posB + posA;
changePer1PctNeg = -.01 * negB + negA;
j = 0;
for( j = 0; j < count; j++ )
{
XYChartAddPoint( "pvSlope", "", midBin[j], avgVolDiff[j], colorBlack, colorBlack ); // avgVolDiff by $1 bins
if( midBin[j] <=0 )
{
XYChartAddPoint( "pvSlope", "", midBin[j], midBin[j] * negB + negA, colorRed, colorRed ); // discontinuous regression line identifying negative logReturn
}
else
{
XYChartAddPoint( "pvSlope", "", midBin[j], midBin[j] * posB + posA, colorGreen, colorGreen ); // discontinuous regression line identifying positive logReturn
}
XYChartAddPoint( "pvSlope", "", midBin[j], midBin[j] * b + a, colorOrange, colorOrange ); // continuous regression line
}
XYChartSetAxis( "pvSlope", "[logReturn]", "[IVchange]" );
```