filtered_ma_crossover_test.r

#required libraries
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 test environment
quotes <- new.env()

#load data from local disc, modify this in accordance with your env.
tickers<-spl('GBPJPY')
file.path<- "G:\\TickData\\"
#parsing dates helper function
fun <- function(d, t) as.chron(paste(strptime(d, "%Y.%m.%d"), t))
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) 
}
# remove missing values
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 = 21)
EMA.slow<-EMA(prices,n = 89)
#macd
MACD <- MACD(prices)

#crossover stategy, checking fast and slow MA crosses
crossover.pure.strategy <- iif(cross.up(EMA.fast,EMA.slow),1,iif(cross.dn(EMA.fast, EMA.slow), -1 ,NA))
#crossover stategy, checking fast and slow MA crosses and filtered out noise by MACD
crossover.filtered.strategy <- iif(cross.up(EMA.fast,EMA.slow) & MACD$macd - MACD$signal > 0,1,
                          iif(cross.dn(EMA.fast, EMA.slow)  & MACD$macd - MACD$signal < 0, -1 ,NA))

#helper variable holding list of dates we will use later for plotting
effective_dates = '2015-08-15::2015-09-30'

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

#re-setting weights to neutral
quotes$weight[] = NA
#calculating weights MA crossover filtered by MACD
quotes$weight[] = crossover.filtered.strategy
#set model for crossover signals applying filtered crossover.strategy
models$apply.filtered.crossover = bt.run.share(quotes, clean.signal=T, trade.summary = TRUE) 

# TP and SL levels its R:R is 2:10!
Takeprofit = 10/100
Stoploss = 2/100

#this is simple method for TP/SL trade management
#it's taken from SIT web site
stop.loss.take.profit <- function(weight, price, tstart, tend, pstop, pprofit) {
  index = tstart : tend
  if(weight > 0) {
    temp = price[ index ] < (1 - pstop) * cummax(price[ index ])
    temp = temp | price[ index ] > (1 + pprofit) * price[ tstart ]
  } else {
    temp = price[ index ] > (1 + pstop) * cummin(price[ index ])
    temp = temp | price[ index ] < (1 - pprofit) * price[ tstart ]       
  }
  return( temp )  
}

#re-setting weights to neutral
quotes$weight[] = NA
#calculating weights of our MA crossover filtered by MACD strategy with Tp/SL levels
quotes$weight[] = custom.stop.fn(coredata(crossover.filtered.strategy), coredata(prices), stop.loss.take.profit,pstop = Stoploss, pprofit = Takeprofit)
#fill model with signals for our 3rd strategy
models$stop.loss.take.profit = 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.pure.crossover, dates = effective_dates, layout=F, main = 'Crossover', extra.plot.fn = extra.plot.fn, plotX = T)
#plot performance
strategy.performance.snapshoot(models, T) 

Hi Tomas! Trying to run your example... is there a way to get the example tick data? I was looking on your GitHub but couldn't find it.

Hi Tomas! Trying to run your example... is there a way to get the example tick data? I was looking on your GitHub but couldn't find it.

Hello.
I used to use tick data provided by Dukascopy. It's easy to download through out their JForex platform you will need just DEMO account. Another option is to download data through the web interface but it's much slower then former one.