I'm writing an R shiny application. I'm facing much trouble, particularly the checkboxGroupInput function. I'm hoping that I will be able to create a dynamic list that will automatically list down all columns except the first column, source_file$Date of a dataset named source_file, and I'm not entirely sure on it. Would greatly appreciate any help you can provide!
Sample dataset of source_file would look something like this:
| Date | Index 1 | Index 2 | Index 3 | Index 4 | Index 5 |
|---|---|---|---|---|---|
| 2016-01-01 | +5% | -2% | +5% | +10% | +12% |
| 2016-01-08 | +3% | +13% | -8% | -3% | +10% |
| 2016-01-15 | +2% | +11% | -3% | +4% | -15% |
The end goal is that I hope the checkboxGroupInput function will be able to automatically read all columns starting from the second column (ignore Date). In this case, the check box would load up 5 options, Index 1 to Index 5. It should be replicable such that it can load any number of indexes depending on the data specified. I tried hard-coding each individual index in but it's definitely counter-intuitive and so frustrating to do.
tabPanel("Target Volatility Portfolio",
sidebarPanel(
tags$h3("Find an optimised portfolio to achieve maximum return for a given level of risk/volatility"),
tags$h4("Input:"),
checkboxGroupInput("portfolio_selection",
"Select Number of Indexes for Portfolio",
choices = list(#####please send help here#####)
Edits: Would appreciate if you could help me fix this.
I want to reference the output that comes from the checkbox into my global.R in this format. Basically, I want to use the selected variables to plot a graph. A selection of 2 variables will result in a graph plotting a graph related to the 2 variables, whereas a selection of 10 variables will create a plot involving all 10 variables. (I'm basically plotting the efficient market frontier of x number of stocks where x is the number of variables selected. Its a little hard to explain but I hope attaching the code can provide you some insight) The hashed line is what I need help fixing. Thank you!
plot_emf = function(n_points, target_vol, portfolio_selection)
{
first <- portfolio_selection[1]
last <- portfolio_selection[length(portfolio_selection)]
#######asset_returns = source_file[first:last]########
# Extract necessary parameters
n_assets = ncol(asset_returns)
n_obs = nrow(asset_returns)
n_years = n_obs / 52
# Initialize containers for holding return and vol simulations
return_vector = c()
vol_vector = c()
sharpe_vector = c()
for (i in 1:n_points)
{
# Generate random weights for n assets from uniform(0,1)
asset_weights = runif(n_assets, min = 0, max = 1)
normalization_ratio = sum(asset_weights)
# Asset weights need to add up to 100%
asset_weights = asset_weights / normalization_ratio
# print(asset_weights)
# print(asset_returns)
# Generate the portfolio return vector using these weights
random_portfolio_returns = emf_portfolio_returns(
asset_weights,
asset_returns)
# print(random_portfolio_returns)
# plot_returns_histogram(random_portfolio_returns$portfolio_returns)
cumulative_return = calculate_cumulative_return(random_portfolio_returns$portfolio_returns)
annualized_return = 100*((1 + cumulative_return/100)^(1/n_years) - 1)
annualized_vol = sd(random_portfolio_returns$portfolio_returns)*(52^0.5)
sharpe = annualized_return / annualized_vol
return_vector = append(return_vector, annualized_return)
vol_vector = append(vol_vector, annualized_vol)
sharpe_vector = append(sharpe_vector, sharpe)
#print(paste("Asset weights:",asset_weights))
#print(paste("Anualized return:",annualized_return))
#print(paste("Annualized vol:",annualized_vol))
}
g = ggplot(data = data.frame(vol_vector, return_vector, sharpe_vector),
aes(x = vol_vector, y = return_vector, color = sharpe_vector)) +
scale_color_gradient(low = "red", high = "blue", name = "Sharpe Ratio\n(Return/Risk)") +
ggtitle("Efficient Market Frontier") +
xlab("Annualized Vol (%)") +
ylab("Annualized Return (%)") +
theme(plot.title = element_text(hjust=0.5)) + geom_vline(xintercept=target_vol) +
geom_point()
print(g)
}