Bernard Brenyah PyDataBlog

## S&P 500.ipynb

      
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                PyDataBlog
                / S&P 500.ipynb
            
            
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              September 13, 2017 08:14
            
              
                A tutorial for scraping information of all the S&P 500 listed companies
              
          
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## Returns on Single Assets.ipynb

      
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                PyDataBlog
                / Returns on Single Assets.ipynb
            
            
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              September 17, 2017 17:46
            
              
                A tutorial for calculating the rates of returns on single assets
              
          
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## Expected Returns of a Portfolio.ipynb

      
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                / Expected Returns of a Portfolio.ipynb
            
            
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              September 17, 2017 19:40
            
              
                Tutorial for calculating the expected returns of a portfolio 
              
          
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## Riskiness of Single Assets.ipynb

      
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                / Riskiness of Single Assets.ipynb
            
            
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              September 24, 2017 14:53
            
              
                A tutorial for assessing the volatility of returns of single assets with Python
              
          
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## Visualising Volatility of Single Assets.py
import pandas as pd
from pandas_datareader import data as web
import matplotlib.pyplot as plt
from datetime import datetime

start = datetime(2016,1,1)
end = datetime(2017,1,1)

assets = ['AAPL', 'FB', 'TSLA']

## Riskiness of a Portfolio.py
# import python's number crunchers
from pandas_datareader import data as web
import pandas as pd
import numpy as np

assets =  ['AAPL', 'GM', 'GE', 'FB', 'WMT']

df = pd.DataFrame()

for stock in assets:

## Assessing Riskiness of a Portfolio.ipynb

      
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                / Assessing Riskiness of a Portfolio.ipynb
            
            
              Created
              September 26, 2017 18:44
            
              
                A tutorial for assessing the risk of a portfolio made up of five stocks
              
          
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## Expected Return of Each Asset.py
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)
expected_return2 = np.sum(prob * rate_2)

## Expected Return of Portfolio.py
# expected return of the equally weighted portfolio
weights = np.array([0.5, 0.5])
individual_returns = np.array([rate_1, rate_2])
portfolio_returns = np.dot(weights, individual_returns)

## Covariance Matrix.py
# covariance matrix given probabilities
cov_matrix = np.cov(rate_1, rate_2, ddof=0, aweights=prob)
	import pandas as pd
	from pandas_datareader import data as web
	import matplotlib.pyplot as plt
	from datetime import datetime

	start = datetime(2016,1,1)
	end = datetime(2017,1,1)

	assets = ['AAPL', 'FB', 'TSLA']
	# import python's number crunchers
	from pandas_datareader import data as web
	import pandas as pd
	import numpy as np

	assets = ['AAPL', 'GM', 'GE', 'FB', 'WMT']

	df = pd.DataFrame()

	for stock in assets:
	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)
	expected_return2 = np.sum(prob * rate_2)
	# expected return of the equally weighted portfolio
	weights = np.array([0.5, 0.5])
	individual_returns = np.array([rate_1, rate_2])
	portfolio_returns = np.dot(weights, individual_returns)
	# covariance matrix given probabilities
	cov_matrix = np.cov(rate_1, rate_2, ddof=0, aweights=prob)