# R package Blotter

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How many times have you been disappointed by nice trading system, because neither trading cost or slippage or bid/ask spread were included into back-test results? Did you find difficult to back-test a portfolio in R or many portfolios with different stocks? Blotter package is supposed to solve these problems.

In really – it is complicated. I spent a couple of days to start using it. There was one bug in the demo example, but the rest was in my code. You should remember:

1. Time zone must be specified:

Sys.setenv(TZ="GMT")

2. xts object must be created with explicit index class:

getSymbols('^GSPC',from='2000-01-01',<strong>index.class=c("POSIXt","POSIXct")strong>)

3. When the test is conducted all blotter related values are written into .blotter environment. If you want repeat the same test, then you need to get rid of all .blotter values. So, you have to run something like this:

rm(list=ls(envir=.blotter),envir=.blotter)

You can find the author’s comment about this issue here: http://n4.nabble.com/Blotter-package-problem-with-example-tp1018634p1572911.html

4. It is slow. If you want to find the fittest rule from a bunch of them – try avoid using blotter, instead, use pure cumulative return and later on, include blotter.

Let’s test this rule with blotter as an example:

short SPY if today’s low is higher than yesterday’s close and close next day.

require(xts) require(quantmod) Sys.setenv(TZ="GMT") initDate='2000-01-01' getSymbols('SPY',from=initDate,index.class=c("POSIXt","POSIXct")) SPY<-adjustOHLC(SPY,use.Adjusted=T) #yesterday's price tmp<-lag(SPY,1) # if today's low is higher than yesterday's close 1, else 0 signal<-ifelse(Lo(SPY)>Cl(tmp),1,0) signal[1]<-0 #let's plot cumulitative return to make sure, that we are on the right path tmp<-lag(Delt(Cl(SPY)),-1) tmp[length(tmp)]<-0 plot(cumprod(signal*tmp+1)) SPY<-Cl(SPY) symbols<-c('SPY') rm(list=ls(envir=.blotter),envir=.blotter) initPortf(ltportfolio,symbols, initDate=initDate) initAcct(ltaccount,portfolios=c(ltportfolio), initDate=initDate,initEq=initEq) signal<-signal['2000-02-01::'] for(i in 1:length(signal)) { currentDate= time(signal)[i] equity = getEndEq(ltaccount, currentDate) position = getPosQty(ltportfolio, Symbol=symbols[1], Date=currentDate) if(position==0) { #open a new position if signal is >0 if(signal[i]>0) { closePrice<-as.double(Cl(SPY[currentDate])) unitSize = as.numeric(trunc((equity/closePrice))) # 5$ per transaction addTxn(ltportfolio, Symbol=symbols[1], TxnDate=currentDate, TxnPrice=closePrice, TxnQty = -unitSize , TxnFees=5, verbose=T) } } else { #position is open. If signal is 0 - close it. if(as.double(signal[i])==0 && position<0) { position = getPosQty(ltportfolio, Symbol=symbols[1], Date=currentDate) closePrice<-as.double((Cl(SPY[currentDate])))#as.double(get(symbols[1])[i+100]) # 5$ per transaction addTxn(ltportfolio, Symbol=symbols[1], TxnDate=currentDate, TxnPrice=closePrice, TxnQty = -position , TxnFees=5, verbose=T) } } updatePortf(ltportfolio, Dates = currentDate) updateAcct(ltaccount, Dates = currentDate) updateEndEq(ltaccount, Dates = currentDate) equity = getEndEq(ltaccount, currentDate) } plot(getAccount(ltaccount)[["TOTAL"]]$End.Eq)

The result (it will take time to get it…):

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**Quantitative thoughts » EN**.

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