rr=NA
rr=rollingSum(r, data.frame(r$date, lam=(coeff*abs(r$high2-
r$high3)/r$close2)/sw ), sw )
rr$lam=ifelse(rr$lam==0, 0.001, rr$lam)
x=cbind(x,
x6=(rr$high2-rr$high3)/(rr$close2*rr$lam)
)
rr=NA
rr=rollingSum(r, data.frame(r$date, lam=(coeff*abs(r$low2-r$low3)/r$close2)/sw
), sw )
rr$lam=ifelse(rr$lam==0, 0.001, rr$lam)
x=cbind(x,
x7=(rr$low2-rr$low3)/(rr$close2*rr$lam)
)
rr=NA
rr=rollingSum(r, data.frame(r$date, lam=(coeff*abs(r$close3-
r$close4)/r$close3)/sw ),sw )
rr$lam=ifelse(rr$lam==0, 0.001, rr$lam)
x=cbind(x,
x8=(rr$close3-rr$close4)/(rr$close3*rr$lam)
)
rr=NA
rr=rollingSum(r, data.frame(r$date, lam=(coeff*abs(r$high3-
r$high4)/r$close3)/sw ), sw )
rr$lam=ifelse(rr$lam==0, 0.001, rr$lam)
x=cbind(x,
x9=(rr$high3-rr$high4)/(rr$close3*rr$lam)
)
rr=NA
rr=rollingSum(r, data.frame(r$date, lam=(coeff*abs(r$low3-r$low4)/r$close3)/sw
), sw )
rr$lam=ifelse(rr$lam==0, 0.001, rr$lam)
x=cbind(x,
x10=(rr$low3-rr$low4)/(rr$close3*rr$lam)
)
rr=NA
rr=rollingSum(r, data.frame(r$date, lam=(coeff*abs(r$high-
r$close)/r$close1)/sw ),sw )
rr$lam=ifelse(rr$lam==0, 0.001, rr$lam)
x=cbind(x,
d1=(rr$high-rr$close)/(rr$close1*rr$lam)
)
rr=NA
rr=rollingSum(r, data.frame(r$date, lam=(coeff*abs(r$close-
r$close1)/r$close1)/sw ), sw )
rr$lam=ifelse(rr$lam==0, 0.001, rr$lam)
x=cbind(x,
d2=(rr$close-rr$close1)/(rr$close1*rr$lam)
)
rr=NA
rr=rollingSum(r, data.frame(r$date, lam=(coeff*abs(r$close-r$low)/r$close1)/sw
), sw )
rr$lam=ifelse(rr$lam==0, 0.001, rr$lam)
x=cbind(x,
d3=(rr$close-rr$low)/(rr$close1*rr$lam)
)
require ("neuralnet")
net.out <- neuralnet(d1+d2+d3~x1+x2+x3+x4+x5+x6+x7+x8+x9+x10, data=x,
hidden=18, threshold=0.001,
stepmax=1e+6)
rollingSum=function(x, y, sw)
{
# c(date, colman=forumula)
y=data.frame(y)
y=y[order( y[,1], decreasing=T),]
y_colnames= colnames(y[2])
for (i in 1:(length(y[,1])-(sw-1)))
