Backtest with periodic rebalancing

Now we will run the backtest using the portfolio specification created in the last exercise with quarterly rebalancing to evaluate out-of-sample performance. The other backtest parameters we need to set are the training period and rolling window. The training period sets the number of data points to use for the initial optimization. The rolling window sets the number of periods to use in the window. This problem can be solved with a quadratic programming solver so we will use "ROI" for the optimization method.

Deze oefening maakt deel uit van de cursus

Intermediate Portfolio Analysis in R

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Oefeninstructies

Run the optimization with quarterly rebalancing. Set the training period and rolling window to use 5 years of data. Assign the results to a variable named opt_rebal_base.
Print the results of the optimization.
Chart the weights.
Compute the portfolio returns using Return.portfolio. Assign the returns to a variable named returns_base.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.


# Run the optimization
opt_rebal_base <- optimize.portfolio.rebalancing(R = ___, 
                                                 portfolio = ___, 
                                                 optimize_method = "ROI", 
                                                 rebalance_on = ___, 
                                                 training_period = ___,
                                                 rolling_window = ___)

# Print the results


# Chart the weights


# Compute the portfolio returns
returns_base <- Return.portfolio(R = ___, weights = ___)
colnames(returns_base) <- "base"

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