# Question 1
symbols = ['SBUX']
start_date = datetime(1993, 3, 31)
end_date = datetime(2008, 3, 31)
fields = "Adj Close"
data = da.get_data(symbols, start_date, end_date, fields)
monthly = data.asfreq('M', method='ffill')
monthly.plot()
plt.title('Montly Data')
plt.draw()
# Question 2 and 3
total_return = Calculator.ret(data)
q2 = Calculator.FV(PV=10000, R=total_return)
print(2, q2)
# Question 3
q3 = Calculator.ann_ret(R=total_return, m=1 / 15)
print(3, q3)
# Question 4
monthly_ln = monthly.apply(np.log)
monthly_ln.plot()
plt.title('Montly Natural Logarithm')
plt.draw()
# Question 5
monthly_returns = Calculator.returns(monthly)
monthly_returns.plot()
starbucks = pd.DataFrame(data, columns=['Date',
'Value']).set_index('Date')['Value']
'''
Question 1: Using the data in the table, what is the simple monthly return between the
end of December 2004 and the end of January 2005?
Ans: -13.40%
'''
q1 = Calculator.ret(starbucks, pos=1)
# q1 = Calculator.R(PV=data[0][1], FV=data[1][1]) # Other option
print(1, q1)
'''
Question 2: If you invested $10,000 in Starbucks at the end of December 2004, how much
would the investment be worth at the end of January 2005?
Ans: $8659.39
'''
q2 = Calculator.FV(PV=10000, R=q1)
print(2, q2)
'''
Question 3: Using the data in the table, what is the continuously compounded monthly
return between December 2004 and January 2005?
Ans: -14.39%
'''
q3 = Calculator.ret(starbucks, pos=1, cc=True)
print(3, q3)
'''
Question 4: Assuming that the simple monthly return you computed in Question 1 is
the same for 12 months, what is the annual return with monthly compounding?
Ans: -82.22%
'''
q4 = Calculator.ann_ret(R=q1, m=12)
print(4, q4)
