问题
I have the following formula to calculate Max Sharpe Ratio for a given set of returns:
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
Modifying functions like this is way above my skill level, so I was hoping somebody here might be able to advise on the following. The given formula takes a set of returns as a base:
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
I want it to take one more argument - weight matrix of 0 and 1 where 0 means that company should be excluded from the portfolio in a given month, whereas the rest of the weights should be distributed as per msr function.
Weight matrix example is below, meaning in 1926-08 "Smoke" weight should be zero, in 1926-09 and 1926-10 "Games" weight should be zero:
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
Could somebody suggest how I can incorporate this into the above function? Thank you in advance!
来源：https://stackoverflow.com/questions/64958844/adding-a-weight-constraint-to-max-sharpe-ratio-function-in-python