Hi, we are Onyx Alpha.

import numpy as np
from scipy.optimize import minimize
 
# Define inputs
N = 5
# Number of assets
R_i = np.array([0.1, 0.2, 0.15, 0.12, 0.25]) # Expected returns of each asset
sigma_i = np.array([0.1, 0.15, 0.2, 0.08, 0.3]) # Standard deviations of each asset
rho_ij = np.array([[1.0, 0.3, -0.2, 0.1, 0.4],
[0.3, 1.0, 0.1, -0.3, 0.2],
[-0.2, 0.1, 1.0, 0.4, -0.1],
[0.1, -0.3, 0.4, 1.0, -0.2],
[0.4, 0.2, -0.1, -0.2, 1.0]]) # Correlation matrix
r_f = 0.03 # Risk-free rate
 
# Define objective function to maximize Sharpe ratiodef objective(w):
R_p = np.sum(w * R_i)
sigma_p = np.sqrt(np.dot(w, np.dot(rho_ij, w)) * np.dot(sigma_i, sigma_i))
return -(R_p - r_f) / sigma_p
 
# Define constraint function for weights to sum to 1
def constraint(w):
return np.sum(w) - 1
 
# Define bounds for weights
bounds = [(0, 1)] * N
 
# Solve optimization problem
w0 = np.ones(N) / N # Starting point
result = minimize(objective, w0, method=‘SLSQP’, bounds=bounds, constraints={‘type’: ‘eq’, ‘fun’: constraint})
 
# Print optimal weights and maximum Sharpe ratio
print(‘Optimal weights:’, result.x)
print(‘Maximum Sharpe ratio:’, -result.fun)