Monte Carlo Simulation with Block Bootstrapping

mcblocksim.R

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

# Load required packages ------------------------------------------
    library(quantmod)

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

# Declare and assign values to other variables
    n <- length(s1) #length of backtest results
    #l <- 5 #block length for sampling (user-defined)
    n.sim <- n
    #R <- 5000 #number of samples required (user-defined)
    endpt <- n - l + 1
    nn <- ceiling(n.sim/l) #number of blocks
    lens <- c(rep(l, nn - 1), 1 + (n.sim - 1)%%l) #lengths of each block
    st <- matrix(sample.int(endpt, nn * R, replace = TRUE), R) #start points of each block
    i.a <- list(starts = st, lengths = lens) #list of start points and lengths of each block

# Get sample indices - store in "inds" matrix
    block <- NULL
    indsim <- NULL
    inds <- NULL
    for(r in 1:nrow(i.a$starts)){
      for(c in 1:ncol(i.a$starts)){
        block <- seq.int(i.a$starts[r, c], i.a$starts[r, c] + i.a$lengths[c] - 1, length.out = i.a$lengths[c])
        indsim <- append(indsim, block)
      }
      inds <- cbind(inds, indsim)
      indsim <- NULL
    }

# Declare and assign values to other variables
    k <- NULL
    tsbootARR <- NULL
    tsbootxts <- NULL
    tmp <- NULL
    ret <- data.frame(ROC(s1[,1]))
    ret[is.na(ret)] <- 0

# Subset ret dataframe with inds and build "tsbootARR" using apply with cumsum()
    for(k in 1:ncol(inds)){
      tmp <- cbind(tmp, ret[inds[,k],])
    }
    tsbootARR <- apply(tmp, 2, function(x) cumsum(x))
    which(is.na(tsbootARR)) #double-check no NAs in ret as a result ROC() call above

# Build xts object for use in plot.zoo
    tsbootxts <- xts(tsbootARR, s1.dates)
    dev.new()
    p <- plot.zoo(tsbootxts, main=paste(R,"sims of simSample", sep = " "), plot.type = "single", col = "lightgray", ylab = "Cumulative Return Distributions")
    p <- lines(cumsum(ROC(s1)[-1,]), col = "red")

    p
    
    return(p)
    }
# End of function -------------------------------------------------
    
# Run function ----------------------------------------------------
    mcblocksim(1000, 5)
    
# Record and print time to run script -----------------------------
    t2 <- Sys.time()
    difftime(t2,t1)