Monte Carlo Simulation in R – Part III

Articles From: RStudio
Website: RStudio


Director of Financial Services Practice at RStudio

In a previous post, we reviewed how to set up and run a Monte Carlo (MC) simulation of future portfolio returns and growth of a dollar. Today, we will run that simulation many times and then visualize the results.

Our ultimate goal is to build a Shiny app that enables an end user to build a custom portfolio, simulate returns and visualize the results. If you just can’t wait, a link to that final Shiny app is available here.

This post builds off the work we did previously. I won’t go through the logic again, but the code for building a portfolio, calculating returns, mean and standard deviation of returns and using them for a simulation is here:

# These are the package we need for today's post.


symbols <- c("SPY","EFA", "IJS", "EEM","AGG")

prices <- 
  getSymbols(symbols, src = 'yahoo', 
             from = "2012-12-31",
             to = "2017-12-31",
             auto.assign = TRUE, warnings = FALSE) %>% 
  map(~Ad(get(.))) %>%
  reduce(merge) %>% 

w <- c(0.25, 0.25, 0.20, 0.20, 0.10)

asset_returns_long <-  
  prices %>% 
  to.monthly(indexAt = "lastof", OHLC = FALSE) %>% 
  tk_tbl(preserve_index = TRUE, rename_index = "date") %>%
  gather(asset, returns, -date) %>% 
  group_by(asset) %>%  
  mutate(returns = (log(returns) - log(lag(returns)))) %>% 

portfolio_returns_tq_rebalanced_monthly <- 
  asset_returns_long %>%
  tq_portfolio(assets_col  = asset, 
               returns_col = returns,
               weights     = w,
               col_rename  = "returns",
               rebalance_on = "months")

mean_port_return <- 

stddev_port_return <- 

simulation_accum_1 <- function(init_value, N, mean, stdev) {
    tibble(c(init_value, 1 + rnorm(N, mean, stdev))) %>% 
    `colnames<-`("returns") %>%
    mutate(growth = 
                        function(x, y) x * y)) %>% 

That code allows us to run one simulation of the growth of a dollar over the next 10 years, with the simulation_accum_1() that we build for that purpose. Today, we will review how to run 51 simulations, though we could choose any number (and our Shiny applications allows an end user to do us that).

In the next article,, the author will code an empty matrix with 51 columns, an initial value of $1 and intuitive column names .

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