Long Branch

Contoh pengolahan data Sakernas Agustus 2023

Leaflet map with the current NGOs in the city of Araruama-Brazil

es weno

test dummy

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 = "StdDev")#added port_2_max_ret <- add.objective(portfolio = port_2_max_ret, type = "return", name = "mean") # 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") port_2_max_ret <- add.constraint(portfolio = port_2_max_ret, type = "box", min = 0, max = 1) # Optimize the portfolio to minimize risk optimal_portfolio_2 <- optimize.portfolio(R = returns_date_droped_dividened2, portfolio = port_2_max_ret, optimize_method = "DEoptim") # expected_return_optimal_portfolio_2 <- sum(optimal_portfolio_2$weights * colMeans(returns_date_droped_dividened2)) # expected_risk_optimal_portfolio_2 <- sqrt(t(weights) %*% cov(returns_df) %*% weights) # # annualized_return_optimal_portfolio_2 <- Return.annualized(expected_return_optimal_portfolio_2, scale = 252) # # # Step 2: Annualize the risk # trading_days <- 252 # Number of trading days in a year # annualized_risk_tan <- tanRisk * sqrt(trading_days) 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"))