portfolio_optimization.py

import numpy as np
from scipy.optimize import minimize

def portfolio_annualized_performance(weights, mean_returns, cov_matrix):
    returns = np.sum(mean_returns * weights) * 252
    std = np.sqrt(np.dot(weights.T, np.dot(cov_matrix, weights))) * np.sqrt(252)
    return std, returns

def minimize_volatility(weights, mean_returns, cov_matrix):
    return portfolio_annualized_performance(weights, mean_returns, cov_matrix)[0]

def optimize_portfolio(mean_returns, cov_matrix):
    num_assets = len(mean_returns)
    args = (mean_returns, cov_matrix)
    constraints = ({'type': 'eq', 'fun': lambda x: np.sum(x) - 1})
    bounds = tuple((0, 1) for asset in range(num_assets))
    result = minimize(minimize_volatility, num_assets*[1./num_assets,], args=args, 
                      method='SLSQP', bounds=bounds, constraints=constraints)
    return result