Portfolio optimisation in R with Long Panel data

Question

In regards to this question Portfolio Optimisation under weight constraints,

I have a long unbalanced panel data of company returns as well as sector dummy variables taking values of 1 if that company belongs to this sector and 0 otherwise. The data looks like this:

CompanyID Date PR Sector1 Sector2
1 00003 2000-01-31 0.00947 0 1

2 00003 2000-07-31 0.0473 0 1

3 00003 2000-09-30 0.0399 0 1

I want to perform portfolio diversification with the aim to diversify my portfolio across the sectors and achieve maximum Sharpe ratio or achieve maximum diversification. My weights should sum to 1 and be non negative. I want to see the output as several combination between the assets and the sectors and to see the return, risk and Sharpe

Is there a way that I can do so?

I had the following code on R but It is not working:

assets=unique(return_dat$CompanyID)
asset_min=5 # Minimum amount of Assets to go in the portfolio
asset_max=205 #max  amount of Assets to go in the portfolio
asset_step=5
asset_range=seq(from=asset_min,to=asset_max,by=asset_step)

sectors=unique(return_dat$Sector)
sector_min=2 #Minimum amount of Sectors to go in the portfolio
sector_max=10 #max Minimum amount of Sectors to go in the portfolio
sector_step=2
sector_range=seq(from=sector_min,to=sector_max,by=sector_step)

# Initialize an empty data frame to store results
Results <- data.frame()

# Loop through the range of sectors and assets
for (k in sector_range) {
  for (i in asset_range) {


    # Select random sectors
    sector_sample <- sample(sectors, k, replace = TRUE)
    asset_dat <- return_data %>% filter(Sector %in% sector_sample)
    
    # Select random assets within those sectors
    asset_sample <- sample(asset_dat$CompanyID, i, replace = TRUE)
    asset_dat <- asset_dat %>% filter(CompanyID %in% asset_sample)
    
    # Prepare asset returns data
    asset_returns <- asset_dat %>% 
      select(CompanyID, Date, PR) %>% 
      pivot_wider(names_from = CompanyID, values_from = PR)
    asset_returns <- xts(asset_returns[,-1], order.by = as.Date(asset_returns$Date))
    
    
    # Calculate expected returns and covariance matrix
    exp_returns <- colMeans(asset_returns, na.rm = TRUE) * 12 # annualized returns
    cov_matrix <- cov(asset_returns, use = "complete.obs") * 12 # annualized covariance
    
    # Set up the optimization problem
    Dmat <- 2 * cov_matrix
    dvec <- rep(0, ncol(asset_returns))
    Amat <- cbind(1, diag(ncol(asset_returns)))
    bvec <- c(1, rep(0, ncol(asset_returns)))
    meq <- 1
    
    # Solve the quadratic programming problem
    solution <- solve.QP(Dmat, dvec, t(Amat), bvec, meq)
    weights <- solution$solution
    
    # Calculate portfolio metrics
    portfolio_return <- sum(exp_returns * weights)
    portfolio_risk <- sqrt(t(weights) %*% cov_matrix %*% weights)
    sharpe_ratio <- (portfolio_return - risk_free_rate) / portfolio_risk
    
    # Append the results
    Results <- rbind(Results, c(k, i, portfolio_return, portfolio_risk, sharpe_ratio))
  }
}


colnames(Results) <- c("num_sectors", "num_assets", "return", "risk", "sharpe_ratio")

print(Results)

I get the following error:

Error in solve.QP(Dmat, dvec, t(Amat), bvec, meq) : Amat and dvec are incompatible!