The package comes equipped with a large amount of stock market data for testing purposes. The data is obtained from Datastream. The file contains the daily closing prices of the DAX30, Dow Jones Industrials and the Nikkei 225.
dji <- sample.pre(data[[3]])
#> [1] "cut input by" "259"
plot(dji,type="l",main= "Dow Jones Industrials",xlab="Trading Days",ylab="Closing Price",col="blue")
abline(v=400, col="red")
abline(v=1250,col="red")
abline(v=1500, col="green")
abline(v=2100,col="green")
abline(v=2300, col="yellow")
abline(v=2800,col="yellow")
plot(dji[400:1250],type="l",main= "Inverse HS",xlab="Trading Days",ylab="Closing Price",col="blue")
plot(dji[1500:2100],type="l",main= "Dow Jones Industrials",xlab="Trading Days",ylab="Closing Price",col="blue")
plot(dji[2300:2800],type="l",main= "Dow Jones Industrials",xlab="Trading Days",ylab="Closing Price",col="blue")
We are looking for long-term pattern, so we set the smoother to a high value:
a <- kernel(dji,30)
plot(a,type="l",main= "Dow Jones Industrials",xlab="Trading Days",ylab="Closing Price",col="blue")
abline(v=400, col="red")
abline(v=1250,col="red")
abline(v=1500, col="green")
abline(v=2100,col="green")
abline(v=2300, col="yellow")
abline(v=2800,col="yellow")
slicer(a,750,150,btpiq=FALSE,rtpiq=FALSE,dtpiq=FALSE)
#> [1] "Patterns were found in "
#> [2] "29"
#> [3] "% of windows analysed. Refer to function value for details"
#> [[1]]
#> [1] 0 0 0 1 0 0 0 0 0 1 1 0 0 0 1 1 1 1 0 0 0 0 0 0
#>
#> [[2]]
#> [1] 450 1350 1500 2100 2250 2400 2550
plot(a[450:1200],type="l",main= "Dow Jones Industrials",xlab="Trading Days",ylab="Closing Price",col="blue")
plot(a[1350:2350],type="l",main= "Dow Jones Industrials",xlab="Trading Days",ylab="Closing Price",col="blue")
plot(a[2100:3300],type="l",main= "Different windows",xlab="Trading Days",ylab="Closing Price",col="blue")
abline(v=0)
abline(v=750)
abline(v=150, col="red")
abline(v=900, col="red")
abline(v=300, col="green")
abline(v=1050, col="green")
abline(v=450,col = "yellow")
abline(v=1200,col = "yellow")
interpret(a[2100:2850])
#> $EXT
#> [1] 0 1 0 1 0 1
#>
#> $EXV
#> [1] 8155.584 10117.672 10082.070 10555.243 9442.936 10240.981
#>
#> $EXP
#> [1] 145 290 320 413 523 620
#>
#> $HSP
#> $HSP$HS
#> [1] 10117.672 10082.070 10555.243 9442.936 10240.981
#>
#>
#> $BTPorTTP
#> [1] NA
#>
#> $RTP
#> [1] NA
#>
#> $DTP
#> $DTP$DTOP
#> [1] 10117.67 10082.07 10555.24
#>
#>
#> $RESULT
#> [1] TRUE
interpret(a[2250:3000])
#> $EXT
#> [1] 1 0 1 0 1 0
#>
#> $EXV
#> [1] 10117.672 10082.070 10555.243 9442.936 10240.981 8649.195
#>
#> $EXP
#> [1] 140 170 263 373 470 673
#>
#> $HSP
#> $HSP$HS
#> [1] 10117.672 10082.070 10555.243 9442.936 10240.981
#>
#>
#> $BTPorTTP
#> [1] NA
#>
#> $RTP
#> [1] NA
#>
#> $DTP
#> $DTP$DTOP
#> [1] 10117.67 10082.07 10555.24
#>
#>
#> $RESULT
#> [1] TRUE