# Tag Info

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For #1 and #2 I really enjoyed this Edx course from UC Berkeley: Quantum Mechanics and Quantum Computation And for #3 you seem to have the sources already :)

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What you learn in school are models, meant to illustrate the concepts and methods of the field. Later you will learn about other forms of utility functions (power utility most prominently). With such families of utility functions the computations aren't as clean as with quadratic utility, but by then you will have understood the concepts and methods, and you ...

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I used an example from the paper: An Introduction to Shrinkage Estimation of the Covariance Matrix: A Pedagogic Illustration I was able to get the same Shrinkage matrix. I have provided the same matrix they use in their paper. Hope this helps import numpy as np import pandas from math import pow def get_shrunk_covariance_matrix(obs, c, zeros): ...

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Convex Optimisation - CVXOpt and CVXPy. Textbook by Boyd & Vandenberghe Aside from CVXOPT (known for its cone programming, see http://cvxopt.org/) with extensive documentation by the authors, Boyd and Vandenberghe http://stanford.edu/~boyd/cvxbook/, there is CVXPY which provides an easier front end. CVXPY was designed and implemented by Steven ...

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I would suggest the qq-pat library (https://github.com/QuriQuant/qq-pat) with this library you can presently do minimum variance portfolio optimization using some simple code. This is a simple example with three assets: import pandas as pd from pandas_datareader import data import datetime import qqpat aapl = data.get_data_yahoo('AAPL', ...

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There is no solution. If $w$ is a solution to the original problem, then consider $aw$ with $a>1$ $$\beta_i(aw) = a(\beta_i w) = 0$$ and $$(aw)^T\Sigma(aw) = a^2 (w^T\Sigma w) > w^T\Sigma w$$ so the original solution $w$ was not a maximum.

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Say you want to optimize for max sharpe ratio, you could do something like this with scipy: import scipy.optimize as spopt allocations = [] #allocations def Sharpe(): #An function to compute Sharpe ratio, return negative SR compute Sharpe_Ratio return -1*Sharpe_Ratio bnd = [] #bounds cns [] #constraints result = spopt.minimize(Sharpe, ...

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