cryptocurrencyportfolio_8

array_returns = np.asarray(return_data.dropna())
array_cov = np.asarray(var_matrix)

mean_returns = np.mean(array_returns, axis = 0)

portfolios_V1_div = portfolios_V1.copy()

div_ratio = []

for i in range(portfolios_V1.shape[0]):
  weight_vector = list(portfolios_V1.iloc[i])[2:]
  portfolio_risk = np.sqrt(np.matmul((np.matmul(weight_vector,array_cov)), np.transpose(weight_vector)))
  ann_portfolio_risk = portfolio_risk*np.sqrt(252)*100
  portfolio_return = np.matmul(weight_vector, np.transpose(mean_returns))
  ann_portfolio_return = 252*portfolio_return * 100

  portfolio_asset_sdv = np.sqrt(np.diagonal(array_cov))
  portfolio_div_ratio = np.sum(np.multiply(portfolio_asset_sdv, weight_vector)) \
                         / portfolio_risk
  div_ratio.append(portfolio_div_ratio)
  
portfolios_V1_div['Diversification Ratio'] = div_ratio

div_port = portfolios_V1_div.iloc[portfolios_V1_div['Diversification Ratio'].idxmax()]
df_weights = df_weights.append(div_port[:-1].rename("Maximum Diversification").to_frame().T)

div_port[-1]

1.327768493018246

weights_div = np.array(portfolios_V1_div.iloc[portfolios_V1_div['Diversification Ratio'].idxmax()][2:-1])