使用quantmod进行滚动窗口回归的回归模型回溯测试精度

Question

我一直试图通过实施滚动窗口回归并计算和记录过去每一天的估计值和最后一天之间的差异来回溯测试回归的可预测性(试图获得一步预测) ,在一个专栏中.

我尝试在滚动回归中应用Christoph_J的答案返回多个对象

代码中没有语法错误.但是,我不确定是否存在语义错误.是行的值i的"预言"一栏,该行的事前预测i的值OpCl列？

library(zoo) 
library(dynlm)
library(quantmod)
sp <- getSymbols("^GSPC", auto.assign=FALSE)
sp$GSPC.Adjusted <- NULL
colnames(sp) <- gsub("^GSPC\\.","",colnames(sp))

sp$Number<-NA
sp$Number<-1:nrow(sp)

sp$OpCl <- OpCl(sp) 
sp$ClHi <- HiCl(sp) 
sp$LoCl <- LoCl(sp) 
sp$LoHi <- LoHi(sp) 

#### LAG

spLag <- lag(sp)
colnames(spLag) <- paste(colnames(sp),"lag",sep="")
sp <- na.omit(merge(sp, spLag))

### REGRESSION

f <- OpCl ~ Openlag + Highlag + OpCllag + ClHilag 

OpClLM <- lm(f, data=sp)

#sp$OpClForecast <- NA
#sp$OpClForecast <- tail(fitted(OpClLM),1)

#####################################################

rolling.regression <- function(series) {
mod <- dynlm(formula = OpCl ~ L(Open) + L(High) + L(OpCl) + L(ClHi), 
data = as.zoo(series))    

nextOb <- min(series[,6])+1 # To get the first row that follows the window
if (nextOb<=nrow(sp)) {   # You won't predict the last one

# 1) Make Predictions
predicted=predict(mod,newdata=data.frame(OpCl=sp[nextOb,'OpCl'],
Open=sp[nextOb,'Open'],High=sp[nextOb,'High'],


OpCl=sp[nextOb,'OpCl'], ClHi=sp[nextOb,'ClHi']))
attributes(predicted)<-NULL

#Solution ; Get column names right
c(predicted=predicted, 
AdjR = summary(mod)$adj.r.squared)
}
}

rolling.window <- 300
results.sp <-  rollapply(sp, width=rolling.window, 
FUN=rolling.regression,  by.column=F, align='right')
sp<-cbind(sp,results.sp)

View(sp)