在python中为Max Sharpe Ratio函数添加权重约束

我有以下公式来计算给定收益集的最大夏普比率：

def msr(riskfree_rate, er, cov):
"""
Returns the weights of the portfolio that gives you the maximum sharpe ratio
given the riskfree rate and expected returns and a covariance matrix
"""
n = er.shape[0]
init_guess = np.repeat(1/n, n)
bounds = ((0.0, 1.0),) * n # an N-tuple of 2-tuples!
# construct the constraints
weights_sum_to_1 = {'type': 'eq',
                    'fun': lambda weights: np.sum(weights) - 1
}
def neg_sharpe(weights, riskfree_rate, er, cov):
    """
    Returns the negative of the sharpe ratio
    of the given portfolio
    """
    r = portfolio_return(weights, er)
    vol = portfolio_vol(weights, cov)
    return -(r - riskfree_rate)/vol

weights = minimize(neg_sharpe, init_guess,
                   args=(riskfree_rate, er, cov), method='SLSQP',
                   options={'disp': False},
                   constraints=(weights_sum_to_1,),
                   bounds=bounds)
return weights.x

像这样修改函数远远超出了我的技能水平，所以我希望这里的人能够就以下方面提供建议。 给定的公式以一组回报为基础：

        Food    Beer    Smoke   Games
1926-07 0.86    -5.36   1.67    2.31
1926-08 3.7     19.25   5.01    2.53
1926-09 1.38    5.63    3.1     4.13
1926-10 -4.7    -6.08   -1.63   -5.76

我想再拿一个参数——权重矩阵0和1，其中0意味着公司应该在给定的月份被排除在投资组合之外，而其余的权重应该按照msr函数进行分配

权重矩阵示例如下，即1926-08年“烟雾”权重应为零，1926-09年和1926-10年“游戏”权重应为零：

        Food    Beer    Smoke   Games
1926-07 1       1       1       1
1926-08 1       1       0       1
1926-09 1       1       1       0
1926-10 1       1       1       0

有人能建议我如何将其合并到上述函数中吗？ 提前谢谢你