TE V

  low_te_port = portfolio.spec(assets=as.character(Sics_Winners$identifier))
  ##Rescale weights to be a fn of 1
  wb = Sics_Winners$cap_weight

  upper_bound = pmin(20*Sics_Winners$cap_weight,0.1)
  upper_bound = ifelse(Sics_Winners$long_flag==0,0,upper_bound)

  min_mult = rep(0,nrow(Sics_Winners))
  max_mult = ifelse(as.numeric(upper_bound)==0,0,as.numeric(upper_bound/wb))

  low_te_port = add.constraint(portfolio=low_te_port,type='box',min=rep(0,nrow(Sics_Winners)),max=as.numeric(upper_bound),min_mult=0,max_mult=sum(upper_bound))
  low_te_port = add.constraint(portfolio=low_te_port,type='weight_sum',min=1,max=1)

  ##Calc beta exposures (set na to 0 )
  beta_exposures = ifelse(is.na(Sics_Winners$factor_data),0,Sics_Winners$factor_data)
  low_te_port = add.constraint(portfolio=low_te_port,type='factor_exposure',B=  beta_exposures,lower=0,upper=carbon_contrib_target)
  low_te_port = add.objective(portfolio=low_te_port,type='risk',name="Expected_TE")
  ##Calc cov ledoit wolf
#   cov_sample = cov.sample(dailyprime)
#   const_corr = cov.prior.cc(cov_sample)
#   small_delta = shrinkage.intensity(dailyprime,const_corr,cov_sample)

daily_temp_mat = dailyprime[,colnames(dailyprime)%in%Sics_Winners$identifier]
  my_cov = cov.shrink(daily_temp_mat )
  my_port = optimize.portfolio(R=daily_temp_mat,portfolio=low_te_port,
                               optimize_method="DEoptim",
                               search_size=1,wb=wb,sigma=my_cov)