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Linear Regression Slope
The basic "programming" in MetaStock of the LRS-ROC indicator is like this: NIO is the
_basic_ number of points taken to calculate the ROCs. The four ROCs _per day_ used for interpolation are:

rll:=ROC(O,nio-1,%)/(nio-1);
rl:=ROC(O,nio,%)/nio;
rh:=ROC(O,nio+1,%)/(nio+1);
rhh:=ROC(O,nio+2,%)/(nio+2);

Now, interpolation is done at a point XIO (0<=xio<=1), i.e., between the points NIO and NIO+1, using a simple interpolation formula weightingwith the _distances_ of the four ROCs from the interpolation point XIO:

rit:=(rll/(1+xio)+rl/(xio+.0001)+rh/(1-xio)+rhh/(2-xio))/(1/(1+xio)+1/(xio+.0001)+1/(1-xio)+1/(2-xio));

Then the LinearRegressionSlope is taken _twice_ (for numerical reasons) from the interpolated ROC, using the basic number of points; i.e., NIO. That is where we get our LRS-ROC indicator, named RO here:

ro:=LinRegSlope(LinRegSlope(rit,nio),nio);

NIO+XIO together represent the _real_ (not integer!) number of points ordays, the LRS-ROC indicator is calculated for, e.g. 4.57 days. Unfortunately,MetaStock is not able to use _one_ real number (e.g., 4.57) as input to be splitted into NIO and XIO and to be used in the formulas above. So NIO and XIO have to be provided separately, because of the restrictions in MetaStock, and both are subjected to some "optimization" in accordance to the historical prices.

To be clear: This LRS-ROC indicator triggers _only the timing_ for entering/closing a position, using an appropriate criterion. Additional (also ROC-based) criterions are used to stay out/in during extreme bearish/bullish situations.

Additionally: This "TA" is only one basic element of my option trading system, primarily to catch some "special reality effects" that cannot be modeledby example-based know-how recycling from historical data. But probably thisTA system may also be used as a "stand-alone" system.

Rudolf Stricker


Linear Regression Slope

nio:=Input("Number of points taken to calculate the ROCs",3,1000,14);

rll:=ROC(O,nio-1,%)/(nio-1);
rl:=ROC(O,nio,%)/nio;
rh:=ROC(O,nio+1,%)/(nio+1);
rhh:=ROC(O,nio+2,%)/(nio+2);

xio:=Input("Distances of ROCs from interpolation point XIO ",0.01,1,0.5);

rit:=(rll / (1+xio) +
rl / (xio+.0001)+
rh / (1-xio) +
rhh / (2-xio)) /
( 1 / (1+xio) + 1 / (xio+.0001) + 1 / (1-xio) + 1 / (2-xio));

ro:=LinRegSlope(LinRegSlope(rit,nio),nio);
ro

 

e-w-Linear_Regression_Slope.gif
Source / From: TOP
http://www.guppytraders.com

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