def test_ret(self):
# Variables
array = np.array([1, 2, 3, 4, 5])
array_2 = np.array([5, 4, 3, 2, 1])
matrix = np.array([array, array_2]).T
series = pd.Series(array, index=[5, 7, 8, 10, 11])
time_series = pd.Series(array)
df = pd.DataFrame(matrix,
columns=['c1', 'c2'],
index=[5, 7, 8, 10, 11])
# Input is numpy.ndarray of 1 dimmension => float
ans = Calculator.ret(array)
self.assertFloat(ans)
self.assertEqual(ans, 4)
# Input is numpy.ndarray of 2 dimmensions => np.ndarray
ans = Calculator.ret(matrix)
self.assertArray(ans)
self.assertEqual(ans, np.array([4, -0.8]))
# Input is pandas.Series => float
ans = Calculator.ret(series)
self.assertFloat(ans)
self.assertEqual(ans, 4)
# Input is pandas.TimeSeries => float
ans = Calculator.ret(time_series)
self.assertFloat(ans)
self.assertEqual(ans, 4)
# Input is pandas.DataFrame with col parameter => float
ans = Calculator.ret(df, col='c1')
self.assertFloat(ans)
self.assertEqual(ans, 4)
# --
ans = Calculator.ret(df, col='c2')
self.assertFloat(ans)
self.assertEqual(ans, -0.8)
# Input is pandas.DataFrame without col parameter => Return pd.Series
ans = Calculator.ret(df)
self.assertSeries(ans)
sol = pd.Series([4, -0.8], index=['c1', 'c2'], name='Total Returns')
self.assertEqual(ans, sol)
def test_ret(self):
# Variables
array = np.array([1,2,3,4,5])
array_2 = np.array([5,4,3,2,1])
matrix = np.array([array, array_2]).T
series = pd.Series(array, index=[5,7,8,10,11])
time_series = pd.Series(array)
df = pd.DataFrame(matrix, columns=['c1', 'c2'], index=[5,7,8,10,11])
# Input is numpy.ndarray of 1 dimmension => float
ans = Calculator.ret(array)
self.assertFloat(ans)
self.assertEqual(ans, 4)
# Input is numpy.ndarray of 2 dimmensions => np.ndarray
ans = Calculator.ret(matrix)
self.assertArray(ans)
self.assertEqual(ans, np.array([4, -0.8]))
# Input is pandas.Series => float
ans = Calculator.ret(series)
self.assertFloat(ans)
self.assertEqual(ans, 4)
# Input is pandas.TimeSeries => float
ans = Calculator.ret(time_series)
self.assertFloat(ans)
self.assertEqual(ans, 4)
# Input is pandas.DataFrame with col parameter => float
ans = Calculator.ret(df, col='c1')
self.assertFloat(ans)
self.assertEqual(ans, 4)
# --
ans = Calculator.ret(df, col='c2')
self.assertFloat(ans)
self.assertEqual(ans, -0.8)
# Input is pandas.DataFrame without col parameter => Return pd.Series
ans = Calculator.ret(df)
self.assertSeries(ans)
sol = pd.Series([4, -0.8], index=['c1', 'c2'], name='Total Returns')
self.assertEqual(ans, sol)
# Create the data
data = [['December, 2004', 31.18], ['January, 2005', 27.00],['February, 2005', 25.91],
['March, 2005', 25.83],['April, 2005', 24.76],['May, 2005', 27.40],
['June, 2005', 25.83],['July, 2005', 26.27],['August, 2005', 24.51],
['September, 2005', 25.05],['October, 2005', 28.28],['November, 2005', 30.45],
['December, 2005', 30.51]]
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%
'''
from datetime import datetime
import matplotlib.pyplot as plt
from finance.utils import Calculator
from finance.sim import MarketSimulator
# from finance.utils import DataAccess
# DataAccess.path = 'data'
sim = MarketSimulator()
sim.initial_cash = 1000000
sim.load_trades("MarketSimulator_orders.csv")
sim.simulate()
print(sim.portfolio[0:10])
print('Total Return:', Calculator.ret(sim.portfolio))
print(Calculator.sharpe_ratio(sim.portfolio))
sim.portfolio.plot()
# plt.grid(True)
plt.show()
plt.show() # Question 9 W0 = 100000 R = stats.norm(loc=0.04, scale=0.09) print(9, W0 * R.ppf(0.01), W0 * R.ppf(0.05)) # Question 10 W0 = 100000 r = stats.norm(loc=0.04, scale=0.09) r_1, r_5 = r.ppf(0.01), r.ppf(0.05) R_1, R_5 = math.exp(r_1) - 1, math.exp(r_5) - 1 print(10, W0 * R_1, W0 * R_5) # Question 11 q11_amzn = Calculator.ret([38.23, 41.29]) # q11_amzn = Calculator.R(PV=38.23, FV=41.29) # Other option q11_cost = Calculator.ret([41.11, 41.74]) print(11, q11_amzn, q11_cost) # Question 12 q12_amzn = Calculator.ret([38.23, 41.29], cc=True) q12_cost = Calculator.ret([41.11, 41.74], cc=True) print(12, q12_amzn, q12_cost) # Question 13 q13_amzn = Calculator.ret([38.23, 41.29], dividends=[0, 0.1]) print(13, q13_amzn, 0.1 / 41.29) print(13, (41.29 + 0.1) / 38.23 - 1, 0.1 / 41.29) # Question 14
plt.show() # Question 9 W0 = 100000 R = stats.norm(loc=0.04, scale=0.09) print(9, W0 * R.ppf(0.01), W0 * R.ppf(0.05)) # Question 10 W0 = 100000 r = stats.norm(loc=0.04, scale=0.09) r_1, r_5 = r.ppf(0.01), r.ppf(0.05) R_1, R_5 = math.exp(r_1) - 1, math.exp(r_5) - 1 print(10, W0 * R_1, W0 * R_5) # Question 11 q11_amzn = Calculator.ret([38.23, 41.29]) # q11_amzn = Calculator.R(PV=38.23, FV=41.29) # Other option q11_cost = Calculator.ret([41.11, 41.74]) print(11, q11_amzn, q11_cost) # Question 12 q12_amzn = Calculator.ret([38.23, 41.29], cc=True) q12_cost = Calculator.ret([41.11, 41.74], cc=True) print(12, q12_amzn, q12_cost) # Question 13 q13_amzn = Calculator.ret([38.23, 41.29], dividends=[0, 0.1]) print(13, q13_amzn, 0.1/41.29) print(13, (41.29 + 0.1)/38.23 - 1, 0.1/41.29) # Question 14
da = DataAccess()
# Question 1
symbols = ['SBUX']
start_date = datetime(1993, 3, 31)
end_date = datetime(2008, 3, 31)
fields = 'adjusted_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)
da = DataAccess()
# 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)
data = [['December, 2004', 31.18], ['January, 2005', 27.00],
['February, 2005', 25.91], ['March, 2005', 25.83],
['April, 2005', 24.76], ['May, 2005', 27.40], ['June, 2005', 25.83],
['July, 2005', 26.27], ['August, 2005', 24.51],
['September, 2005', 25.05], ['October, 2005', 28.28],
['November, 2005', 30.45], ['December, 2005', 30.51]]
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)
