# System Failure-Maybe it Will Help

August 11, 2011
By

(This article was first published on Timely Portfolio, and kindly contributed to R-bloggers)

I hope everyone is enjoying the market.  After a crazy week personally and 6% intraday swings, I remember why I abandoned day trading.

I often wonder if I should share ideas that do not work as well as I would like.  In this case, I know I have generated an acceptable system in a previous life in Excel, but I cannot remember the details.  So far all the testing and various trails in R have not yielded anything exceptional, but I am sure capable readers can find the secret combination.  Please let me know what you discover.

This idea uses linear models to generate slope and correlation.  Then if slope is positive and correlation high, the system enters.  THIS IS NOT INVESTMENT ADVICE.  THIS CAN LOSE LOTS OF MONEY.

 From TimelyPortfolio

`require(PerformanceAnalytics)require(quantmod)   #set this up to get either FRED or Yahoo!Finance#getSymbols("GSPC",src="FRED")getSymbols("^GSPC",from="1896-01-01",to=Sys.Date())     GSPC <- to.weekly(GSPC)[,4]#GSPCmean <- runMean(GSPC,n=20)#index(GSPC) <- as.Date(index(GSPC))   width = 25for (i in (width+1):NROW(GSPC)) {	linmod <- lm(GSPC[((i-width):i),1]~index(GSPC[((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,GSPC[((i-width):i),1]),		signal3 <- rbind(signal3,cor(linmod\$fitted.values,GSPC[((i-width):i),1])))}signal <- as.xts(signal,order.by=index(GSPC[(width+1):NROW(GSPC)]))signal2 <- as.xts(signal2,order.by=index(GSPC[(width+1):NROW(GSPC)]))signal3 <- as.xts(signal3,order.by=index(GSPC[(width+1):NROW(GSPC)]))   price_ret_signal <- merge(GSPC,lag(signal,k=1),	lag(signal2,k=1),lag(signal3,k=1),	ROC(GSPC,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((runMean(price_ret_signal[,3],n=5) > 0 & 	runMean(price_ret_signal[,4],n=5) > 0.25),	1, 0) * price_ret_signal[,5]   retCompare <- merge(ret, price_ret_signal[,5])colnames(retCompare) <- c("Linear System", "BuyHold")charts.PerformanceSummary(retCompare,ylog=TRUE,cex.legend=1.2,	colorset=c("black","gray70"),main="GSPC System Return Comparison")`

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