Asset Classes

Free investment financial education

More Campus Resources

Useful Tools and Information

Language

Multilingual content from IBKR

Learn more about IBKR accounts

# Monte Carlo Simulation in R – Part III

###### Posted June 21, 2019 at 9:11 am
Jonathan Regenstein
##### RStudio
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.

library(tidyquant)
library(tidyverse)
library(timetk)
library(broom)
library(highcharter)

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) %>%
`colnames<-`(symbols)

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)))) %>%
na.omit()

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 <-
mean(portfolio_returns_tq_rebalanced_monthly\$returns)

stddev_port_return <-
sd(portfolio_returns_tq_rebalanced_monthly\$returns)

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

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 .

###### Disclosure: Interactive Brokers

Information posted on IBKR Campus that is provided by third-parties does NOT constitute a recommendation that you should contract for the services of that third party. Third-party participants who contribute to IBKR Campus are independent of Interactive Brokers and Interactive Brokers does not make any representations or warranties concerning the services offered, their past or future performance, or the accuracy of the information provided by the third party. Past performance is no guarantee of future results.

This material is from RStudio and is being posted with its permission. The views expressed in this material are solely those of the author and/or RStudio and Interactive Brokers is not endorsing or recommending any investment or trading discussed in the material. This material is not and should not be construed as an offer to buy or sell any security. It should not be construed as research or investment advice or a recommendation to buy, sell or hold any security or commodity. This material does not and is not intended to take into account the particular financial conditions, investment objectives or requirements of individual customers. Before acting on this material, you should consider whether it is suitable for your particular circumstances and, as necessary, seek professional advice.

This website uses cookies to collect usage information in order to offer a better browsing experience. By browsing this site or by clicking on the "ACCEPT COOKIES" button you accept our Cookie Policy.