## Retirement : simulating wealth with random returns, inflation and withdrawals – Shiny web application

Today, I want to share the Retirement : simulating wealth with random returns, inflation and withdrawals – Shiny web application (code at GitHub).

This application was developed and contributed by Pierre Chretien, I only made minor updates. This is application is a great example of how easy it is to convert your R script into interactive Shiny web application.

Please see below the sample script to simulate wealth random returns, inflation and withdrawals:

#------------------------------------- # Inputs #------------------------------------- # Initial capital start.capital = 2000000 # Investment annual.mean.return = 5 / 100 annual.ret.std.dev = 7 / 100 # Inflation annual.inflation = 2.5 / 100 annual.inf.std.dev = 1.5 / 100 # Withdrawals monthly.withdrawals = 10000 # Number of observations (in Years) n.obs = 20 # Number of simulations n.sim = 200 #------------------------------------- # Simulation #------------------------------------- # number of months to simulate n.obs = 12 * n.obs # monthly Investment and Inflation assumptions monthly.mean.return = annual.mean.return / 12 monthly.ret.std.dev = annual.ret.std.dev / sqrt(12) monthly.inflation = annual.inflation / 12 monthly.inf.std.dev = annual.inf.std.dev / sqrt(12) # simulate Returns monthly.invest.returns = matrix(0, n.obs, n.sim) monthly.inflation.returns = matrix(0, n.obs, n.sim) monthly.invest.returns[] = rnorm(n.obs * n.sim, mean = monthly.mean.return, sd = monthly.ret.std.dev) monthly.inflation.returns[] = rnorm(n.obs * n.sim, mean = monthly.inflation, sd = monthly.inf.std.dev) # simulate Withdrawals nav = matrix(start.capital, n.obs + 1, n.sim) for (j in 1:n.obs) { nav[j + 1, ] = nav[j, ] * (1 + monthly.invest.returns[j, ] - monthly.inflation.returns[j, ]) - monthly.withdrawals } # once nav is below 0 => run out of money nav[ nav < 0 ] = NA # convert to millions nav = nav / 1000000 #------------------------------------- # Plots #------------------------------------- layout(matrix(c(1,2,1,3),2,2)) # plot all scenarios matplot(nav, type = 'l', las = 1, xlab = 'Months', ylab = 'Millions', main = 'Projected Value of initial capital') # plot % of scenarios that are still paying p.alive = 1 - rowSums(is.na(nav)) / n.sim plot(100 * p.alive, las = 1, xlab = 'Months', ylab = 'Percentage Paying', main = 'Percentage of Paying Scenarios', ylim=c(0,100)) grid() # plot distribution of final wealth final.nav = nav[n.obs + 1, ] final.nav = final.nav[!is.na(final.nav)] plot(density(final.nav, from=0, to=max(final.nav)), las = 1, xlab = 'Final Capital', main = paste('Distribution of Final Capital,', 100 * p.alive[n.obs + 1], '% are still paying')) grid()

The corresponding Shiny web application consists of two files:

- ui.r – User Interface
- server.r – R simulations and calculations

Following is the user interface (ui.r) that maps and describes required inputs for the retirement simulation:

# Define UI for application that plots random distributions shinyUI(pageWithSidebar( # Application title headerPanel("Retirement : simulating wealth with random returns, inflation and withdrawals"), # Sidebar with a slider input for number of observations sidebarPanel( sliderInput("n.obs", "Number of observations (in Years):", min = 0, max = 40, value = 20), sliderInput("start.capital", "Initial capital invested :", min = 100000, max = 10000000, value = 2000000, step = 100000, format="$#,##0", locale="us"), sliderInput("annual.mean.return", "Annual return from investments (in %):", min = 0.0, max = 30.0, value = 5.0, step = 0.5), sliderInput("annual.ret.std.dev", "Annual volatility from investments (in %):", min = 0.0, max = 25.0, value = 7.0, step = 0.1), sliderInput("annual.inflation", "Annual inflation (in %):", min = 0, max = 20, value = 2.5, step = 0.1), sliderInput("annual.inf.std.dev", "Annual inflation volatility. (in %):", min = 0.0, max = 5.0, value = 1.5, step = 0.05), sliderInput("monthly.withdrawals", "Monthly capital withdrawals:", min = 1000, max = 100000, value = 10000, step = 1000, format="$#,##0", locale="us",), sliderInput("n.sim", "Number of simulations:", min = 0, max = 2000, value = 200) ), # Show a plot of the generated distribution mainPanel( plotOutput("distPlot", height = "600px") ) ))

The last step is modify the retirement simulation logic to use user inputs:

library(shiny) # Define server logic required to generate and plot a random distribution # # Idea and original code by Pierre Chretien # Small updates by Michael Kapler # shinyServer(function(input, output) { # Function that generates scenarios and computes NAV. getNav <- reactive({ #------------------------------------- # Inputs #------------------------------------- # Initial capital start.capital = input$start.capital # Investment annual.mean.return = input$annual.mean.return / 100 annual.ret.std.dev = input$annual.ret.std.dev / 100 # Inflation annual.inflation = input$annual.inflation / 100 annual.inf.std.dev = input$annual.inf.std.dev / 100 # Withdrawals monthly.withdrawals = input$monthly.withdrawals # Number of observations (in Years) n.obs = input$n.obs # Number of simulations n.sim = input$n.sim #------------------------------------- # Simulation #------------------------------------- # number of months to simulate n.obs = 12 * n.obs # monthly Investment and Inflation assumptions monthly.mean.return = annual.mean.return / 12 monthly.ret.std.dev = annual.ret.std.dev / sqrt(12) monthly.inflation = annual.inflation / 12 monthly.inf.std.dev = annual.inf.std.dev / sqrt(12) # simulate Returns monthly.invest.returns = matrix(0, n.obs, n.sim) monthly.inflation.returns = matrix(0, n.obs, n.sim) monthly.invest.returns[] = rnorm(n.obs * n.sim, mean = monthly.mean.return, sd = monthly.ret.std.dev) monthly.inflation.returns[] = rnorm(n.obs * n.sim, mean = monthly.inflation, sd = monthly.inf.std.dev) # simulate Withdrawals nav = matrix(start.capital, n.obs + 1, n.sim) for (j in 1:n.obs) { nav[j + 1, ] = nav[j, ] * (1 + monthly.invest.returns[j, ] - monthly.inflation.returns[j, ]) - monthly.withdrawals } # once nav is below 0 => run out of money nav[ nav < 0 ] = NA # convert to millions nav = nav / 1000000 return(nav) }) # Expression that plot NAV paths. output$distPlot <- renderPlot({ nav = getNav() layout(matrix(c(1,2,1,3),2,2)) # plot all scenarios matplot(nav, type = 'l', las = 1, xlab = 'Months', ylab = 'Millions', main = 'Projected Value of initial capital') # plot % of scenarios that are still paying p.alive = 1 - rowSums(is.na(nav)) / ncol(nav) plot(100 * p.alive, las = 1, xlab = 'Months', ylab = 'Percentage Paying', main = 'Percentage of Paying Scenarios', ylim=c(0,100)) grid() last.period = nrow(nav) # plot distribution of final wealth final.nav = nav[last.period, ] final.nav = final.nav[!is.na(final.nav)] if(length(final.nav) == 0) return() plot(density(final.nav, from=0, to=max(final.nav)), las = 1, xlab = 'Final Capital', main = paste('Distribution of Final Capital,', 100 * p.alive[last.period], '% are still paying')) grid() }) })

We all done now!!! Shiny is amazing in the way it allows you to convert your script into interactive web application with just two simple steps.

Please play around with the Retirement : simulating wealth with random returns, inflation and withdrawals – Shiny web application (code at GitHub).

Have a good weekend

## Sector Rotation Back Test Shiny web application

Today, I want to share the Sector Rotation Back Test application (code at GitHub).

This is the last application in the series of examples (I have shared 5 examples) that will demonstrate the amazing Shiny framework and Systematic Investor Toolbox to analyze stocks, make back-tests, and create summary reports.

The motivation for this series of posts is to show how to translate, with minimal effort, your back-test scripts into live web applications that can either be run from the Shiny server or your personal computer.

Please note that Back Test applications take longer time to update the plots/tables and hence maybe more appropriate to run from your local computer.

For example to run Sector Rotation application from your computer, you first need to install Shiny. And next execute following commands:

library(shiny) runGitHub('SIT','systematicinvestor', subdir = 'Shiny/sector.rotation')

## Market Filter Back Test Shiny web application

Today, I want to share the Market Filter Back Test application (code at GitHub).

This is the forth application in the series of examples (I plan to share 5 examples) that will demonstrate the amazing Shiny framework and Systematic Investor Toolbox to analyze stocks, make back-tests, and create summary reports.

The motivation for this series of posts is to show how to translate, with minimal effort, your back-test scripts into live web applications that can either be run from the Shiny server or your personal computer.

Please note that Back Test applications take longer time to update the plots/tables and hence maybe more appropriate to run from your local computer.

I will post the next example application on Monday.

Have a good long weekend

## January Seasonality Shiny web application

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This example is based on the An Example of Seasonality Analysis post.

This is the third application in the series of examples (I plan to share 5 examples) that will demonstrate the amazing Shiny framework and Systematic Investor Toolbox to analyze stocks, make back-tests, and create summary reports.

The motivation for this series of posts is to show how to translate, with minimal effort, your back-test scripts into live web applications that can either be run from the Shiny server or your personal computer.

I will post the fourth example application tomorrow.

## Multiple Stocks Plot Shiny web application

Today, I want to share the Multiple Stocks Plot application (code at GitHub).

This is the second application in the series of examples (I plan to share 5 examples) that will demonstrate the amazing Shiny framework and Systematic Investor Toolbox to analyze stocks, make back-tests, and create summary reports.

I will post the third example application tomorrow.

## Single Stock Plot Shiny web application

Today, I want to share the Single Stock Plot application (code at GitHub).

This is the first application in the series of examples (I plan to share 5 examples) that will demonstrate the amazing Shiny framework and Systematic Investor Toolbox to analyze stocks, make back-tests, and create summary reports.

I will post the second example application tomorrow.