plotPerformanceVsParams {portfolioBacktest}

Plot performance of portfolio function vs choice of parameters

Description

Portfolio functions usually contain some parameters that can be tuned. After generating multiple versions of a portfolio function with randomly chosen parameters with the function genRandomFuns and doing the backtesting, this function can be used to plot the performance vs choice of parameters.

Usage

plotPerformanceVsParams(
  bt_all_portfolios,
  params_subset = NULL,
  name_performance = "Sharpe ratio",
  summary_fun = median
)

Arguments

bt_all_portfolios

Backtest results as produced by the function portfolioBacktest.

params_subset

List of named parameters with a subset of the values to be considered. By default all the possible values will be considered.

name_performance

String with the name of the performance measure to be used.

summary_fun

Summary function to be employed (e.g., median or mean). Defult is median.

Author(s)

Daniel P. Palomar and Rui Zhou

See Also

genRandomFuns

Examples

library(portfolioBacktest)

# define GMVP with parameters "delay", "lookback", and "regularize"
GMVP_portfolio_fun <- function(dataset, ...) {
  prices <- tail(lag(dataset$adjusted, delay), lookback)
  X <- diff(log(prices))[-1]
  Sigma <- cov(X)
  if (regularize)
    Sigma <- Sigma + 0.01*diag(ncol(Sigma))
  # design GMVP
  w <- solve(Sigma, rep(1, ncol(Sigma)))
  return(w/sum(w))
}

# generate the functions with random parameters
portfolio_list <- genRandomFuns(portfolio_fun = GMVP_portfolio_fun, 
                                params_grid = list(lookback = c(100, 120, 140, 160),
                                                   delay = c(0, 5, 10, 15, 20),
                                                   regularize = c(FALSE, TRUE)),
                                name = "GMVP", 
                                N_funs = 40)
                                
# backtest portfolios
bt <- portfolioBacktest(portfolio_list, dataset10)

# plot
plotPerformanceVsParams(bt)
plotPerformanceVsParams(bt, params_subset = list(regularize = TRUE))
plotPerformanceVsParams(bt, params_subset = list(delay = 5))
plotPerformanceVsParams(bt, params_subset = list(delay = 5, regularize = TRUE))