jaymon0703

#' Monte Carlo analysis of transactions
#'
#' Running simulations with similar properties as the backtest or production 
#' portfolio may allow the analyst to evaluate the distribution of returns 
#' possible with similat trading approaches and evaluate skill versus luck or
#' overfitting. 
#' 
#' @details 
#' 
#' If \code{update=TRUE} (the default), the user may wish to pass \code{Interval}

jaymon0703

#' Monte Carlo simulate strategy results
#'
#' Return bands of returns based on Monte Carlo simulations of back-test results
#' @param Portfolio string identifier of portfolio name
#' @param Account string identifier of account name
#' @param n number of simulations, default = 1000
#' @param \dots any other passthrough parameters
#' @param l block length, default = 20
#' @param use determines whether to use 'daily' or 'txn' PL, default = "equity" ie. daily
#' @param gap numeric number of periods from start of series to start on, to eliminate leading NA's

jaymon0703

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

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

jaymon0703

# 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