A Monte Carlo Simulation function for your back-test results - in R

mcsimr.R

# Record script start time ----------------------------------------
    t1 <- Sys.time()

# Load required packages ------------------------------------------
    library(quantmod)
    library(TTR)
    library(PerformanceAnalytics)
    library(ggplot2)
    library(timeSeries)

# Build the function ----------------------------------------------
    mcsimr <- function(n, b = TRUE){
# Read price data and build xts object
    data <- read.csv("yourdirectory/simSample.csv", header = TRUE, stringsAsFactors=F)
    s1.dates <- as.Date(data[,2], format="%d-%m-%Y") #beware the formatting may need adjusting for your excel settings
    s1 <- xts(data[,3], s1.dates)

# Calculate ROC
    ret <- ROC(s1[,1])
# Chart cum returns
    chart.CumReturns(ret)

# Set up for Sample() and Replicate()
    ret_sample <- replicate(n,sample(as.vector(ret[-1,]), replace=b)) #use ret[-1] so we exclude 1st NA value from ROC calc
    ret_cum_sample <- apply(ret_sample, 2, function(x) cumsum(x))
    ret_cum_samplexts <- xts(ret_cum_sample, s1.dates[-1]) #use s1.dates[-1] so that length of dates is identical to length of ret_sample

# Build the 5% and 95% quantile datasets
    ret_5 <- apply(ret_cum_samplexts, 1, function(x) quantile(x, .05))
    ret_5 <- as.xts(ret_5)

    ret_95 <- apply(ret_cum_samplexts, 1, function(x) quantile(x, .95))
    ret_95 <- as.xts(ret_95)

    ret_25 <- apply(ret_cum_samplexts, 1, function(x) quantile(x, .25))
    ret_25 <- as.xts(ret_25)

    ret_75 <- apply(ret_cum_samplexts, 1, function(x) quantile(x, .75))
    ret_75 <- as.xts(ret_75)

    charts <- merge(ret_5, ret_95, ret_25, ret_75)

# Draw the graph with a ribbon
    h <- ggplot(charts, aes(x = index(charts))) +
      geom_ribbon(aes(ymin = ret_25, ymax = ret_75, colour = "50%"), alpha = 0.3, fill = "red3") +
      geom_ribbon(aes(ymin = ret_5, ymax = ret_95, colour = "90%"), alpha = 0.3, fill = "cornflowerblue") +
      theme(axis.text.x = element_text(angle=0, hjust = 0),
            axis.title = element_text(face = 'bold', size = 14),
            title = element_text(face = 'bold', size = 16),
            legend.position = 'bottom',
            legend.title = element_blank(),
            legend.text = element_text(size = 12),
            legend.key.width = unit(2, 'cm'))
      h <- h + geom_line(aes(y = cumsum(ret[-1,])), colour = "black", linetype = 1) +
      ylab(label="Cumulative Returns") +
      xlab(label="Time") +
      ggtitle("Returns Distribution")
    h

    return(h)
}
# End of function -------------------------------------------------

# Run function ----------------------------------------------------
    mcsimr(1000)

# Record and print time to run script -----------------------------
    t2 <- Sys.time()
    difftime(t2,t1)