MPT_Medium_1

generate_portfolios.py

#- How many assests to include in each portfolio
n_assets = 5
#-- How many portfolios to generate
n_portfolios = 1000

#-- Initialize empty list to store mean-variance pairs for plotting
mean_variance_pairs = []

np.random.seed(75)
#-- Loop through and generate lots of random portfolios
for i in range(n_portfolios):
    #- Choose assets randomly without replacement
    assets = np.random.choice(list(daily_returns.columns), n_assets, replace=False)
    #- Choose weights randomly
    weights = np.random.rand(n_assets)
    #- Ensure weights sum to 1
    weights = weights/sum(weights)

    #-- Loop over asset pairs and compute portfolio return and variance
    #- https://quant.stackexchange.com/questions/43442/portfolio-variance-explanation-for-equation-investments-by-zvi-bodie
    portfolio_E_Variance = 0
    portfolio_E_Return = 0
    for i in range(len(assets)):
        portfolio_E_Return += weights[i] * mus.loc[assets[i]]
        for j in range(len(assets)):
            #-- Add variance/covariance for each asset pair
            #- Note that when i==j this adds the variance
            portfolio_E_Variance += weights[i] * weights[j] * cov.loc[assets[i], assets[j]]
            
    #-- Add the mean/variance pairs to a list for plotting
    mean_variance_pairs.append([portfolio_E_Return, portfolio_E_Variance])