我正在嘗試?yán)L制一個有效的邊界。下面是我用的?；貓髤?shù)由投資組合的 9 列回報組成。我選擇了 10,000 個投資組合，這就是我的有效邊界的樣子。這不是我們熟悉的通常的邊界形狀。有人可以向我解釋這個問題。def monteCarlo_Simulation(returns):    #returns=returns.drop("Date")    returns=returns/100    stocks=list(returns)    stocks1=list(returns)    stocks1.insert(0,"ret")    stocks1.insert(1,"stdev")    stocks1.insert(2,"sharpe")    print (stocks)    #calculate mean daily return and covariance of daily returns    mean_daily_returns = returns.mean()    #print (mean_daily_returns)    cov_matrix = returns.cov()    #set number of runs of random portfolio weights    num_portfolios = 10000    #set up array to hold results    #We have increased the size of the array to hold the weight values for each stock    results = np.zeros((4+len(stocks)-1,num_portfolios))    for i in range(num_portfolios):        #select random weights for portfolio holdings        weights = np.array(np.random.random(len(stocks)))        #rebalance weights to sum to 1        weights /= np.sum(weights)        #calculate portfolio return and volatility        portfolio_return = np.sum(mean_daily_returns * weights) * 252        portfolio_std_dev = np.sqrt(np.dot(weights.T,np.dot(cov_matrix, weights))) * np.sqrt(252)        #store results in results array        results[0,i] = portfolio_return        results[1,i] = portfolio_std_dev        #store Sharpe Ratio (return / volatility) - risk free rate element excluded for simplicity        results[2,i] = results[0,i] / results[1,i]        #iterate through the weight vector and add data to results array        for j in range(len(weights)):            results[j+3,i] = weights[j]