jaymon0703

#' calculate trade statistics for round turn trades.
#' 
#' One 'trade' is defined as a series of transactions which make up a 'round turn'.
#' It may contain many transactions.  This function reports statistics on these
#' round turn trades which may be used on their own or which are also used
#' by other functions, including \code{\link{tradeStats}} and \code{\link{tradeQuantiles}}
#' 
#' @details
#' Additional methods of determining 'round turns' are also supported.
#' 

jaymon0703

#' calculate flat to flat per-trade statistics
#'
#' One 'trade' is defined as the entire time the symbol is not flat.
#' It may contain many transactions.  From the initial transaction that
#' moves the position away from zero to the last transaction that flattens the
#' position is all one 'trade' for the purposes of this function.
#' 
#' This is sometimes referred to as 'flat to flat' analysis.
#' 
#' Note that a trade that is open at the end of the measured period will

jaymon0703

#' Monte Carlo simulate strategy results
#'
#' This function resamples the daily transaction, cash equity, or percent-return
#' P&L from a portfolio of trading results.  It may be applied to both real
#' (post-trade) and backtested transactions.
#'
#' The general argument here is that you can compare the performance of real
#' portfolio equity or a backtest equity curve to a sampled version of the same.
#'
#' We've chosen to use daily frequency for the resampling period. If your holding

jaymon0703

### Required returns due to testing multiplicity ------ Harvey and Liu
### (2014): "Backtesting", Duke University 

Profit_Hurdle <- function (num_tests, num_obs, alpha_sig, vol_annual, RHO){


###############################
####### Parameter inputs ######

### 'num_tests': No. of tests one allows for in multiple tests;

jaymon0703

require(MASS) # for mvrnorm()

### Generate empirical p-value distributions based on Harvey, Liu and Zhu (2014) ------ Harvey and Liu
### (2014): "Backtesting", Duke University 
sample_random_multests <- function(rho, m_tot, p_0, lambda, M_simu){ 
  
###Parameter input from Harvey, Liu and Zhu (2014) ############
###Default: para_vec = [0.2, 1377, 4.4589*0.1, 5.5508*0.001,M_simu]###########
  
p_0 <- p_0 ;  # probability for a random factor to have a zero mean   

jaymon0703

#' Monte Carlo simulate strategy results
#'
#' This function resamples the daily transaction, cash equity, or percent-return
#' P&L from a portfolio of trading results.  It may be applied to both real
#' (post-trade) and backtested transactions.
#'
#' The general argument here is that you can compare the performance of real
#' portfolio equity or a backtest equity curve to a sampled version of the same.
#'
#' We've chosen to use daily frequency for the resampling period. If your holding

jaymon0703

#' Monte Carlo simulate strategy results
#'
#' This function resamples the daily transaction, cash equity, or percent-return
#' P&L from a portfolio of trading results.  It may be applied to both real
#' (post-trade) and backtested transactions.
#'
#' The general argument here is that you can compare the performance of real
#' portfolio equity or a backtest equity curve to a sampled version of the same.
#'
#' We've chosen to use daily frequency for the resampling period. If your holding

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)