Trying to program trading signals in R - r

Question

I new new to R and am trying to program a pair trading strategy in R.
I have already written the code for downloading the data. And have created additional columns and prepared the data. Now i need to calculate the trading signals.
My signal rules are as follows.
- If Z-Score is greater than 2.25 , Sell the pair; Buy back when Z-Score is less than 0.25.
- If Z-Score is less than -2.25 , Buy the pair; sell (Exit) when z-score is above -0.25.
- close any open position if there is a change in signal.
When we sell a pair, we sell the first stock and buy the second stock. In this case, we sell ACC and Buy Ambujacem.
When we buy a pair, we buy the first stock and sell the second stock. In this case, we buy ACC and Sell Ambujacem.
Could anyone help me with the coding for the trading signals.
Enclosing the code.
Regards,
Subash
# Trading Code
library(quantmod)
getSymbols("ACC.NS", from=as.Date('2007-01-01'), to=as.Date('2015-07-24'))
getSymbols("AMBUJACEM.NS", from=as.Date('2007-01-01'), to=as.Date('2015-07-24'))
acc=ACC.NS[,6]
amb=AMBUJACEM.NS[,6]
t.zoo <- merge(acc, amb, all=TRUE)
t.zoo=as.data.frame(t.zoo)
typeof(t.zoo)
t.zoo=na.omit(t.zoo)
#adding columns
t.zoo$spread <- 0
t.zoo$adfTest <- 0
t.zoo$mean <- 0
t.zoo$stdev <- 0
t.zoo$zScore <- 0
t.zoo$signal <- 0
t.zoo$BuyPrice <- 0
t.zoo$SellPrice <- 0
t.zoo$LongReturn <- 0
t.zoo$ShortReturn <- 0
t.zoo$Slippage <- 0
t.zoo$TotalReturn <- 0
#preparing the data
#Calculating the pair ratio
t.zoo$pairRatio <- t.zoo$ACC.NS.Adjusted/t.zoo$AMBUJACEM.NS.Adjusted
#Calculate the log prices of the two time series
t.zoo$LogA <- log10(t.zoo$ACC.NS.Adjusted)
t.zoo$LogB <- log10(t.zoo$AMBUJACEM.NS.Adjusted)
#Calculating the spread
t.zoo$spread <- t.zoo$ACC.NS.Adjusted/t.zoo$AMBUJACEM.NS.Adjusted
#Calculating the mean
# Computes the mean using the SMA function
# choose the number of days for calculating the mean
SMAdays = 20
t.zoo$mean <- SMA(t.zoo$spread,SMAdays)
#Calculating the Std Deviation
t.zoo$stdev <- rollapply(t.zoo$spread,20,sd, fill=NA, align='right')
#Calculating the Z Score
t.zoo$zScore <- (t.zoo$pairRatio - t.zoo$mean)/t.zoo$spread
View(t.zoo)
#Calculation of trading signals and trading prices
#Trigger sell or buy signal if Z Score moves above 2.25 or below -2.25.
# Close position if Z Score reaches 0.2 or -0.2.
# close any open position if there is a change in signal.

Answer 1

I think the main issue was to come up with trading signals for a strategy that depends not only on the current level of indicator but also on the direction from which the indicator is crossed.
There were a number of problems with the code posted in comments, including use of single = for comparisons . So I've worked it afresh
Here's my attempt at solving this. It seems to be fine. I've added some plotting code to eyeball the results. I suggest you check the result over different periods.
This code comes after the one in the original question . Only difference is that I have kept t.zoo as an xts/zoo object and not converted it to data.frame. Also, I've multiplied zScores with 100
It generates trigger dates and also a column depicting the state of strategy. Calculating returns would be easy from there
colnames(t.zoo)
#t.zoo must be an xts object
#working on a separate xts object
sigs<- t.zoo[, c("ACC.NS.Adjusted", "AMBUJACEM.NS.Adjusted" , "zScore")]
# creating my own triggers as there are not enough good values
# buyTrig<- mean(t.zoo$zScore ,na.rm = T) - 1*sd(t.zoo$zScore ,na.rm = T)
# sellTrig<- (-1) * buyTrig
# sqOffTrig<- mean(t.zoo$zScore ,na.rm = T) - 0.5*sd(t.zoo$zScore ,na.rm = T)
# Another approach: scaling tz.zoo to fit your criterion
sigs$zScore<- sigs$zScore*100
buyTrig<- (-2.25)
sellTrig<- (-1) * buyTrig
sqOffTrig<- 0.25
cat ( buyTrig, sellTrig , sqOffTrig)
hist(sigs$zScore, breaks = 40)
abline(v=c(buyTrig,sellTrig), col="red")
abline(v=c(-sqOffTrig, sqOffTrig), col="green")
sum(sigs$zScore >= -sqOffTrig & sigs$zScore<= sqOffTrig , na.rm = T) # 139
sigs$action<- 0
sigs$mode <- NA
sigs$zLag<- lag.xts(sigs$zScore,1)
sigs[19:22,]
#these are not the real trigger dates, but they will serve our purpose
# along with na.locf
buyTrigDays<- time(sigs[sigs$zScore<= buyTrig & sigs$zLag > buyTrig, ])
sellTrigDays<- time(sigs[sigs$zScore>= sellTrig & sigs$zLag < sellTrig, ])
#square offs
buySqOffDays<- time( sigs[sigs$zScore>= (-1*sqOffTrig) & sigs$zLag < (-1*sqOffTrig), ] )
buySqOffDays
sellSqOffDays<- time( sigs[sigs$zScore<= (sqOffTrig) & sigs$zLag > (sqOffTrig), ] )
sellSqOffDays
sigs$mode[buyTrigDays]=1 ; sigs$mode[sellTrigDays]= -1;
sigs$mode[buySqOffDays]=0 ; sigs$mode[sellSqOffDays]= 0;
sigs$mode
# use local fill to repeat these triggered position into future
# till you meet another non NA value
sigs$mode<- na.locf(sigs$mode, fromLast = F)
plot((sigs$zScore["2015"] ))
points(sigs$zScore[sigs$mode==1], col="red", on=1, pch = 19)
points(sigs$zScore[sigs$mode==-1], col="green", on=1 , pch = 19)
points(sigs$zScore[sigs$mode==0], col="blue", on=1)
sum(is.na(sigs$mode))
#now to get the real dates when square off is triggered
trigdays<- time( sigs[diff(sigs$mode,1) != 0, ] ) #when the value changes
squareOffTrigger_real<- time(sigs[sigs$mode==0][trigdays])
buyTrigger_real<- time(sigs[sigs$mode==1] [trigdays])
sellTrigger_real<- time(sigs[sigs$mode==-1][trigdays])
#check
length(sellTrigger_real) + length(buyTrigger_real) == length(squareOffTrigger_real)
plot(sigs$zScore["2015"])
points(sigs$zScore[buyTrigger_real] , col="blue", pch = 19, on=1)
points(sigs$zScore[sellTrigger_real] , col="red", pch = 19, on=1)
points(sigs$zScore[squareOffTrigger_real] , col="green", pch = 19, on=1)
abline(h=c(-sqOffTrig, sqOffTrig) , col= "green" )
# further calculations can be easily made using either the mode
# column or the trigger dates computed at the end