lm System on Nikkei with New Chart

I got a great idea from the zoo-overplot demo to make a very helpful visualization of system entry and exit.  Since the lm-based system presented in Unrequited lm Love is newest, I will use this system, but apply to the Nikkei 225 instead of the Russell 2000.

THIS IS STILL NOT INVESTMENT ADVICE, AND I TAKE NO RESPONSIBILITY FOR THE LOSSES THAT ARE VERY LIKELY IF YOU PURSUE THIS APPROACH.

Here is the new system visualization.

From TimelyPortfolio

From TimelyPortfolio

From TimelyPortfolio

From TimelyPortfolio

From TimelyPortfolio

R code (click to download):

#third version
#add another neat chart for visualization
#got idea from zoo-overplot demo   #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("N225",src="FRED")
getSymbols("^N225",from="1896-01-01",to=Sys.Date())   N225 <- to.weekly(N225)[,4]
N225mean <- runMean(N225,n=30)
#index(N225) <- as.Date(index(N225))      width = 10
for (i in (width+1):NROW(N225)) {
	linmod <- lm(N225[((i-width):i),1]~index(N225[((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$fitted.values,N225[((i-width):i),1]),
		signal3 <- rbind(signal3,cor(linmod$fitted.values,N225[((i-width):i),1])))
}   signal <- as.xts(signal,order.by=index(N225[(width+1):NROW(N225)]))
signal2 <- as.xts(signal2,order.by=index(N225[(width+1):NROW(N225)]))
signal3 <- as.xts(signal3,order.by=index(N225[(width+1):NROW(N225)]))
signal4 <- ifelse(N225 > N225mean,1,0)   price_ret_signal <- merge(N225,lag(signal,k=1),
	lag(signal2,k=1),
	lag(signal3,k=1),
	lag(signal4,k=1),
	lag(ROC(N225,type="discrete",n=15),k=1),
	ROC(N225,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((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="N225 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()   #eliminate NA at start of return series
retCompare[is.na(retCompare)] <- 0
price_system <- merge(N225,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 ),
	NA, 1),coredata(N225)[width+50]*cumprod(retCompare[,1]+1))
price_system[,2] <- price_system[,1]*price_system[,2]
colnames(price_system) <- c("In","Out","System")   #jpeg(filename="chartSeries with colored entry and exit.jpg",
#	quality=100,width=6.25, height = 6.25,  units="in",res=96)
chartSeries(price_system$System,theme="white",log=TRUE,up.col="black",
	yrange=c(min(price_system[,c(1,3)]),max(price_system[,c(1,3)])),
	TA="addTA(price_system$In,on=1,col=3);
	addTA(price_system$Out,on=1,col=2)",
	name="N225 Linear Model System")
#dev.off()

Created by Pretty R at inside-R.org