ma_crossover_test.r

library(quantmod)
library(zoo)
library(chron)

#following section loads "Systematic Investor Toolbox"
setInternet2(TRUE)
con <- gzcon(url('https://github.com/systematicinvestor/SIT/raw/master/sit.gz', 'rb'))
source(con)
close(con)

#creates new environemnt
quotes <- new.env()

#load data from local disc:
tickers<-spl('GBPJPY')
file.path<- "G:\\TickData\\"
fun <- function(d, t) as.chron(paste(strptime(d, "%Y.%m.%d"), t))#parsing dates helper function
for(n in tickers) { 
  quotes[[n]] =   as.xts(read.zoo(read.csv(
    file=paste(file.path, n, '_H1_UTC+0_00_noweekends_fxmtf.csv', sep=''), 
    header=F, col.names=c("Date", "Time", "Open","High","Low","Close","Volume")), header=T, index=1:2, FUN=fun)) 
  # fill missing values
  quotes[[n]] = na.locf(quotes[[n]], fromLast=TRUE) 
}
bt.prep(quotes, align='remove.na')

#create prices and prepare models  
prices <- quotes$prices
models <- list()

#technical indicators, exp. moving averages
EMA.fast<-EMA(prices,n = 8)
EMA.slow<-EMA(prices,n = 89)

#crossover stategy, checking fast and slow MA crosses
crossover.strategy <- iif(cross.up(EMA.fast,EMA.slow),1,iif(cross.dn(EMA.fast, EMA.slow), -1 ,NA))

#helper variable holding list of dates we will use later for plotting
effective_dates = '2015-08-15::2015-10-30'

#setting weights to neutral
quotes$weight[] = NA
#claculating weights which are effectively trades triggers
quotes$weight[] = crossover.strategy
#set model for crossover signals and apply our crossover.strategy
models$apply.crossover = bt.run.share(quotes, clean.signal=T, trade.summary = TRUE) 

#we'd like to see our MAs plotted later
extra.plot.fn <- function() {
  plota.lines(EMA.fast, col='red')
  plota.lines(EMA.slow, col='blue')
}
#plot strategy, using dates range defines above and extra plots
bt.stop.strategy.plot(quotes, models$apply.crossover, dates = effective_dates, layout=F, main = 'Crossover', extra.plot.fn = extra.plot.fn, plotX = T)
#plot performance
strategy.performance.snapshoot(models, T)