Results & Discussion

This project involves exploring the U.S. National Oceanic and Atmospheric Administration's (NOAA) storm database.

port_2_max_ret <- portfolio.spec(assets = colnames(returns_date_droped_dividened2)) # Add objectives - here we minimize risk (VAR) port_2_max_ret <- add.objective(portfolio = port_2_max_ret, type = "risk", name = "CVaR")#added #port_2_max_ret <- add.objective(portfolio=port_2_max_ret, type = "risk_adjusted_return", name = "SharpeRatio", risk_free_rate = risk_free_rate) # Add constraints - fully invested portfolio with no short sales port_2_max_ret <- add.constraint(portfolio = port_2_max_ret, type = "long_only") port_2_max_ret <- add.constraint(portfolio = port_2_max_ret, type = "full_investment") # Optimize the portfolio to minimize risk optimal_portfolio_2 <- optimize.portfolio(R = returns_date_droped_dividened2, portfolio = port_2_max_ret, optimize_method = "ROI") optimal_weights <- extractWeights(optimal_portfolio_2) asset_names <- names(optimal_weights) weights_df <- data.frame(Asset = asset_names, Weight = optimal_weights) plot_ly(data = weights_df, x = ~Asset, y = ~Weight, type = 'bar') %>% layout(title = "Interactive Portfolio Weights", xaxis = list(title = "Asset"), yaxis = list(title = "Weight"))

initial_portfolio <- portfolio.spec(assets = colnames(returns_date_droped_dividened2)) # Add objectives - here we minimize risk (VAR) initial_portfolio <- add.objective(portfolio = initial_portfolio, type = "risk", name = "VaR") initial_portfolio <- add.objective(portfolio = initial_portfolio, type = "return", name = "mean") initial_portfolio <- add.objective(portfolio = initial_portfolio, type = "risk-adjusted", name = "SortinoRatio") # Add constraints - fully invested portfolio with no short sales initial_portfolio <- add.constraint(portfolio = initial_portfolio, type = "long_only") initial_portfolio <- add.constraint(portfolio = initial_portfolio, type = "full_investment") initial_portfolio <- add.constraint(portfolio = initial_portfolio, type = "box", min = 0, max = 1) # Optimize the portfolio to minimize risk optimal_portfolio <- optimize.portfolio(R = returns_date_droped_dividened2, portfolio = initial_portfolio, optimize_method = "DEoptim") # View the optimal weights optimal_weights <- extractWeights(optimal_portfolio) asset_names <- names(optimal_weights) weights_df <- data.frame(Asset = asset_names, Weight = optimal_weights) plot_ly(data = weights_df, x = ~Asset, y = ~Weight, type = 'bar') %>% layout(title = "Interactive Portfolio Weights", xaxis = list(title = "Asset"), yaxis = list(title = "Weight"))

mtcars - Project