# AI For Trading:Portfolio Risk and Return (42)

## Portfolio Variance

### Derivation of Portfolio Variance Mmmmk. So now, we do the same thing we did in the derivation of the portfolio mean. Instead of putting everything into one big sum, we break the big sum up into sub-sums, and pull out the weights, which aren't indexed by i.  ## Reducing Risk

### Reducing Risk with Imperfectly Correlated Stocks(通过不完全相关的股票降低风险)

We just noted that when the correlation is less than one (ρ\<1) , the portfolio standard deviation is less than the weighted average of the individual standard deviations: Let’s walk through this together to see how this helps us as investors.  The nice benefit of putting two stocks into a portfolio is that, as long as they’re not perfectly correlated, we’ll end up with a portfolio whose risk is less than the the weighted sum of the individual risks. A key benefit of portfolio diversification is that it helps us to reduce risk!

## Varince of a 3-Asset Portfolio

What is the variance of a portfolio with 3 assets, A, B and C, with weights $x_A , x_B , and x_C$ ?  ## The Covariance Matrix and Quadratic Forms

### The Covariance Matrix(协方差矩阵)

Let's take a moment to learn a compact way to represent the portfolio variance using matrices and vectors.

Remember that the portfolio variance we calculated for our two-stock portfolio was:  A polynomial where the sums of the exponents of the variables in each term equals 2 is called a quadratic form. An example: ## Calculate a Covariance Matrix    ## Quize:np.cov

Covariance Matrix

## Install libraries

import sys
!{sys.executable} -m pip install -r requirements.txt
Collecting numpy==1.14.5 (from -r requirements.txt (line 1))
[K    100% |████████████████████████████████| 12.2MB 38kB/s  eta 0:00:01   19% |██████▎                         | 2.4MB 41.2MB/s eta 0:00:01
[?25hInstalling collected packages: numpy
Found existing installation: numpy 1.12.1
Uninstalling numpy-1.12.1:
Successfully uninstalled numpy-1.12.1
Successfully installed numpy-1.14.5
[33mYou are using pip version 9.0.1, however version 19.0.3 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.[0m

## Imports

import numpy as np
import quiz_tests

## Hints

### covariance matrix

If we have $m$ stock series, the covariance matrix is an $m \times m$ matrix containing the covariance between each pair of stocks. We can use numpy.cov to get the covariance. We give it a 2D array in which each row is a stock series, and each column is an observation at the same period of time. ## Quiz

import numpy as np

def covariance_matrix(returns):
"""
Create a function that takes the return series of a set of stocks
and calculates the covariance matrix.

Parameters
----------
returns : numpy.ndarray
2D array containing stock return series in each row.

Returns
-------
x : np.ndarray
A numpy ndarray containing the covariance matrix
"""

#covariance matrix of returns
cov = np.cov(returns)

return cov

quiz_tests.test_covariance_matrix(covariance_matrix)
Tests Passed
"""Test with a 3 simulated stock return series"""
days_per_year = 252
years = 3
total_days = days_per_year * years

return_market = np.random.normal(loc=0.05, scale=0.3, size=days_per_year)
return_1 = np.random.uniform(low=-0.000001, high=.000001, size=days_per_year) + return_market
return_2 = np.random.uniform(low=-0.000001, high=.000001, size=days_per_year) + return_market
return_3 = np.random.uniform(low=-0.000001, high=.000001, size=days_per_year) + return_market
returns = np.array([return_1, return_2, return_3])

cov = covariance_matrix(returns)

print(f"The covariance matrix is \n{cov}")
The covariance matrix is
[[0.07918553 0.07918553 0.07918554]
[0.07918553 0.07918554 0.07918555]
[0.07918554 0.07918555 0.07918555]]

## Efficient Frontier(高效的前沿)

### Efficient Frontier

The efficient frontier for a given set of stocks gives us the set of portfolios that achieve the highest return for each level of risk. The efficient frontier also contains the set of portfolios that achieve the lowest level of risk for each level of return. We refer to these portfolios along the efficient frontier as market portfolios. As portfolio managers, we would be interested in finding the weights for each stock that create these market portfolios on the efficient frontier.

### Minimum Variance Portfolio(最小方差投资组合)

The minimum variance portfolio is a single portfolio in the efficient frontier that has the lowest risk. ## Capital Market Line ### Treasury Securities(国库券)

Treasury securities are IOUs from a government. They are government debt instruments used to finance government spending as an alternative to taxation. In the case of stable governments, they are considered relatively risk-free. In the U.S., Treasury bills mature in 1 year or less, Treasury notes mature in 2 to 10 years, and Treasury bonds mature in 20 to 30 years.