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#second version
#this one actually has an additional mean reverting element
#for markets that have moved down so long entry is quicker   require(PerformanceAnalytics)
require(quantmod)   #set this up to get either FRED or Yahoo!Finance
#getSymbols("RUT",src="FRED")
getSymbols("^RUT",from="1896-01-01",to=Sys.Date())     RUT <- to.weekly(RUT)[,4]
RUTmean <- runMean(RUT,n=30)
#index(RUT) <- as.Date(index(RUT))   width = 10
for (i in (width+1):NROW(RUT)) {
	linmod <- lm(RUT[((i-width):i),1]~index(RUT[((i-width):i)]))
	ifelse(i==width+1,signal <- coredata(linmod$residuals[length(linmod$residuals)]),
		signal <- rbind(signal,coredata(linmod$residuals[length(linmod$residuals)])))
	ifelse(i==width+1,signal2 <- coredata(linmod$coefficients[2]),
		signal2 <- rbind(signal2,coredata(linmod$coefficients[2])))
	ifelse(i==width+1,signal3 <- cor(linmod$fitted.values,RUT[((i-width):i),1]),
		signal3 <- rbind(signal3,cor(linmod$fitted.values,RUT[((i-width):i),1])))
}
signal <- as.xts(signal,order.by=index(RUT[(width+1):NROW(RUT)]))
signal2 <- as.xts(signal2,order.by=index(RUT[(width+1):NROW(RUT)]))
signal3 <- as.xts(signal3,order.by=index(RUT[(width+1):NROW(RUT)]))
signal4 <- ifelse(RUT > RUTmean,1,0)   price_ret_signal <- merge(RUT,lag(signal,k=1),
	lag(signal2,k=1),lag(signal3,k=1),lag(signal4,k=1),lag(ROC(RUT,type="discrete",n=15),k=1),
	ROC(RUT,type="discrete",n=1))
price_ret_signal[,2] <- price_ret_signal[,2]/price_ret_signal[,1]
price_ret_signal[,3] <- price_ret_signal[,3]/price_ret_signal[,1]   #ret <- ifelse((runMin(price_ret_signal[,3],n=10) >= 0  &
#	runSum(price_ret_signal[,2],n=30) >= 0.0) | 
#	(runMin(price_ret_signal[,3],30) < 0 & 
#	runSum(price_ret_signal[,2],n=50) >= 0.02),
#	 1, 0) * price_ret_signal[,5]
#ret <- ifelse(runSum(price_ret_signal[,3],n=10) >= 0, 1, 0) * price_ret_signal[,5]   ret <- ifelse((price_ret_signal[,5] == 1) | (price_ret_signal[,5] == 0  & 
	runMean(price_ret_signal[,3],n=50) > 0 & runMean(price_ret_signal[,2],n=10) < 0 ),
	1, 0) * price_ret_signal[,7]   retCompare <- merge(ret, price_ret_signal[,7])
colnames(retCompare) <- c("Linear System", "BuyHold")
#jpeg(filename="performance summary.jpg",
#	quality=100,width=6.25, height = 8,  units="in",res=96)
charts.PerformanceSummary(retCompare,ylog=TRUE,cex.legend=1.2,
	colorset=c("black","gray70"),main="RUT System Return Comparison")
#dev.off()   require(ggplot2)
df <- as.data.frame(na.omit(merge(price_ret_signal[,5],price_ret_signal[,7])))
colnames(df) <- c("signal_avg","return")
#jpeg(filename="boxplot by average.jpg",
#	quality=100,width=6.25, height = 8,  units="in",res=96)
ggplot(df,aes(x=factor(signal_avg),y=return)) + geom_boxplot()
#dev.off()   df2 <- as.data.frame(na.omit(merge(ifelse((price_ret_signal[,5] == 0  & 
	runMean(price_ret_signal[,3],n=50) > 0 & runSum(price_ret_signal[,2],n=10) < 0 ),
	1, 0),price_ret_signal[,7])))
colnames(df2) <- c("signal_other","return")
#jpeg(filename="boxplot by other signal.jpg",
#	quality=100,width=6.25, height = 8,  units="in",res=96)
ggplot(df2,aes(x=factor(signal_other),y=return)) + geom_boxplot()
#dev.off()   df3 <- as.data.frame(na.omit(merge(ifelse((price_ret_signal[,5] == 1) |
	(price_ret_signal[,5] == 0  & 
	runMean(price_ret_signal[,3],n=50) > 0 & runMean(price_ret_signal[,2],n=10) < 0 ),
	1, 0),price_ret_signal[,7])))
colnames(df3) <- c("signals_all","return")
#jpeg(filename="boxplot by long signal.jpg",
#	quality=100,width=6.25, height = 8,  units="in",res=96)
ggplot(df3,aes(x=factor(signals_all),y=return)) + geom_boxplot()
#dev.off()   #jpeg(filename="text plot of return and risk.jpg",
#	quality=100,width=6.25, height = 6.25,  units="in",res=96)
textplot(rbind(table.AnnualizedReturns(retCompare),
	table.DownsideRisk(retCompare)[c(1:3,7,11),]))
#dev.off()