May

17

Dr. Rafter discussed smoothing some time ago. One of his techniques was to take a regression and project the regression line forward at each point in the time series. I have been interested in trying this technique, but have found it very time-consuming to implement in E****, in which one must go to a dialog window to run a single regression. I looked for a better way.

Below is a rough piece of R code that calculates a "moving regression projection" for a daily price series (in this case a 20-period regression projected 2 periods forward). For input, I use a CSV file with date, open, high, low, and close prices, plus a sequence number (1 for the first row, 2 for the second row, etc.) to be used as the independent variable in the regression.

require(gregmisc)

dailyPrices<-read.csv('cotton.csv',header=TRUE,sep=",",quote="\"") arrSize<-dim(dailyPrices) numrows<-arrSize[1] regressCol<-array(0,c(numrows,1)) combined<-cbind(dailyPrices, regressCol)

for (i in 20:numrows)
{
# Get a regression of the last 20 closes last20<-subset(dailyPrices[(i-19):i,]) regression<-lm(Close ~ X, last20) # Extend the regression line by 2 additional points predictedValues<-predict(regression) combined[i,7]<-(2*predictedValues[20])-predictedValues[18]
}

write.csv(combined,file="cottonRegression.csv")

 The output looks like:
   Date         Open    High      Low      Close     X   RegressCol
 5/3/2007     48.59   48.95    48.35    48.4     84  47.98565
 5/4/2007     48.4     48.65    48.21    48.28   85  47.73247
 5/7/2007     48.58   48.75    48.45    48.6     86  47.50965
 5/8/2007     48.6     49.19    48.45    48.7     87  47.37444
 5/9/2007     49.1     49.24    48.65    48.82   88  47.36022
5/10/2007    48.9     49.15    48.9      49.03   89  47.37723
5/11/2007    48.35    48.7     47.58    48.11   90  47.29259
5/14/2007    48.15    48.2     46.9      46.92   91  46.91736
5/15/2007    47.1      47.9     47.1      47.75   92  46.75521
5/16/2007    48.8      50.2     48.4      49.19   93  47.05242
 

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