PyDataBlog

#  variance and standard deviation of each investment
var1 = cov_matrix[0,0]  # variance of any asset is the covariance of its returns WITH its returns
var2 = cov_matrix[1,1]  # variance of any asset is the covariance of its returns WITH its returns
std1 = np.sqrt(var1)  # std deviation is simply the square root of the variance
std2 = np.sqrt(var2)  # std deviation is simply the square root of the variance

#  correlation between Asset 1 & 2's returns
cov = cov_matrix[0,1]
corr = cov / (std1 * std2)  # correlation of returns between 2 assets = covariance of their returns / (their std multiplied)

PyDataBlog

#  just a function that returns a percentile for a given float
def percentage (number):
    return str(round(number, 4) * 100) + '%'

#  print the various variables for intepretation 
print('Expected Return of Investment 1 = {}'.format(percentage(expected_return1)))
print('Expected Return of Investment 2 = {}'.format(percentage(expected_return2)))
print('Expected Return of Portfolio = {}'.format(percentage(portfolio_returns)))
print('Standard Deviation of Investment 1 = {}'.format(percentage(std1)))
print('Standard Deviation of Investment 1 = {}'.format(percentage(std2)))

PyDataBlog

#  Import numpy
import numpy as np

#  store the variables in arrays
prob = np.array([0.25, 0.5, 0.25])
rate_1 = np.array([0.05, 0.075, 0.10])
rate_2 = np.array([0.2, 0.15, 0.1])

#  expected return of each investment
expected_return1 = np.sum(prob * rate_1)

PyDataBlog

import quandl
quandl.ApiConfig.api_key = 'INSERT YOU API KEY HERE'

# get the table for daily stock prices and,
# filter the table for selected tickers, columns within a time range
# set paginate to True because Quandl limits tables API to 10,000 rows per call

data = quandl.get_table('WIKI/PRICES', ticker = ['AAPL', 'MSFT', 'WMT'], 
                        qopts = { 'columns': ['ticker', 'date', 'adj_close'] }, 
                        date = { 'gte': '2015-12-31', 'lte': '2016-12-31' }, 

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       ticker	date	        adj_close
None			
  0	AAPL	2015-12-31	101.696810
  1	AAPL	2016-01-04	101.783763
  2	AAPL	2016-01-05	99.233131
  3	AAPL	2016-01-06	97.291172
  4	AAPL	2016-01-07	93.185040

PyDataBlog

# create a new dataframe with 'date' column as index
new = data.set_index('date')

# use pandas pivot function to sort adj_close by tickers
clean_data = new.pivot(columns='ticker')

# check the head of the output
clean_data.head()

PyDataBlog

			adj_close
ticker		AAPL		MSFT		WMT
date			
2015-12-31	101.696810	53.096499	58.379766
2016-01-04	101.783763	52.445713	58.532144
2016-01-05	99.233131	52.684973	59.922592
2016-01-06	97.291172	51.727934	60.522580
2016-01-07	93.185040	49.928701	61.932075

PyDataBlog

# import needed libraries
import quandl
import pandas as pd

# add quandl API key for unrestricted
quandl.ApiConfig.api_key = 'INSERT YOU API KEY HERE'

# get the table for daily stock prices and,
# filter the table for selected tickers, columns within a time range
# set paginate to True because Quandl limits tables API to 10,000 rows per call

PyDataBlog

# import needed modules
import quandl
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# get adjusted closing prices of 5 selected companies with Quandl
quandl.ApiConfig.api_key = 'PASTE YOUR API KEY HERE'
selected = ['CNP', 'F', 'WMT', 'GE', 'TSLA']
data = quandl.get_table('WIKI/PRICES', ticker = selected,

PyDataBlog

# reorganise data pulled by setting date as index with
# columns of tickers and their corresponding adjusted prices
clean = data.set_index('date')
table = clean.pivot(columns='ticker')
table.head()