I have written a code to get the sharp ratio. now i need to constrain the weight based on
The minimum - maximum permissible weight in any single asset class
ii. The minimum - maximum weight to an asset type/ product within the asset class. How can i do it. Also is there a way to get sharp ratio using cvxopt. I got lot of examples on effective frontier but how to get the same result as my code is a problem.
my full code is here
https://github.com/SouravRoy1989/portfolio-analysis/blob/master/Final%20Optimization.ipynb
def get_ret_vol_sr(weights):
"""
Takes in weights, returns array or return,volatility, sharpe ratio
"""
weights = np.array(weights)
ret = np.sum(stocks.mean() * weights)
vol = np.sqrt(np.dot(weights.T, np.dot(stocks.cov(), weights)))
sr = ret/vol
return np.array([ret,vol,sr])
from scipy.optimize import minimize
def neg_sharpe(weights):
return get_ret_vol_sr(weights)[2] * -1
def check_sum(weights):
'''
Returns 0 if sum of weights is 1.0
'''
return np.sum(weights) - 1
cons = ({'type':'eq','fun': check_sum})
initial=np.random.rand(n)
initial = initial / np.sum(initial)
opt_results = minimize(neg_sharpe,initial,method='SLSQP',bounds=bounds,constraints=cons)
print ('The Optimum weights',opt_results.x)
#opt_results.x.sum() # Checking sum is 1 or not
get_ret_vol_sr(opt_results.x)
get_ret_vol_sr(opt_results.x)[0].max()
