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"))

My Initial Publication

The primary objective of this analysis is to identify key factors influencing loan defaults and provide actionable insights to enhance risk management and lending strategies.

Example of caching for ConRR module