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import numpy as np
from scipy.optimize import minimize
w0 = 0
w1 = 0
w2 = 0
w3 = 0
ws = np.array([w0, w1, w2, w3])
## Constraints ################################################################
# sum of the weight should be 1
cons = [
{'type': 'eq', 'fun': lambda x: np.array([1 - np.sum(x)])}
]
# # All weights should be >= 0 and <= 1
# for i, w in enumerate(ws):
# cons.extend([
# {'type': 'ineq', 'fun': lambda x: np.array([ws[i]])},
# {'type': 'ineq', 'fun': lambda x: np.array([1 - ws[i]])}
# ])
## function to minimize #######################################################
tir = np.array([0.04, 0.045, 0.05, 0.0375])
initial_guess = np.array([0.1, 0.2, 0.5, 0.2])
bounds = [(0, 1), ] * len(ws)
def tir_portfolio(weights):
return (-1) * np.sum(weights * tir)
## Let's minimize!! ##########################################################
res = minimize(
fun=tir_portfolio,
x0=initial_guess,
bounds=bounds,
method='SLSQP',
constraints=cons,
options={'disp': True}
)
print(res)
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