def test_sharpe_ratio(self):
array = np.array([1, 1.5, 3, 4, 4.3])
array_2 = np.array([5, 4.3, 3, 3.5, 1])
matrix = np.array([array, array_2]).T
series = pd.Series(array)
time_series = pd.Series(array_2)
df = pd.DataFrame(matrix, columns=['c1', 'c2'])
# Input is np.array of 1 dimmension => float
ans = Calculator.sharpe_ratio(array)
self.assertFloat(ans)
self.assertAlmostEquals(ans, 2.38842, 5)
# Input is np.array of 2 dimmensions => array
ans = Calculator.sharpe_ratio(matrix)
self.assertArray(ans)
sol = np.array([2.38842482, -1.4708528])
self.assertEqual(ans, sol, 5)
# Input is pandas.Series => float
ans = Calculator.sharpe_ratio(series)
self.assertFloat(ans)
self.assertAlmostEquals(ans, 2.38842, 5)
# Input is pandas.TimeSeries => float
ans = Calculator.sharpe_ratio(time_series)
self.assertFloat(ans)
self.assertAlmostEquals(ans, -1.4708528, 5)
# Input is pandas.DataFrame with col parameter => float
ans = Calculator.sharpe_ratio(df, col='c1')
self.assertFloat(ans)
self.assertAlmostEquals(ans, 2.38842, 5)
# --
ans = Calculator.sharpe_ratio(df, col='c2')
self.assertFloat(ans)
self.assertAlmostEqual(ans, -1.4708528, 5)
# Input is pandas.DataFrame without col parameter => pd.Series
ans = Calculator.sharpe_ratio(df)
self.assertSeries(ans)
sol = pd.Series([2.38842482, -1.4708528],
index=['c1', 'c2'],
name='Sharpe Ratios')
self.assertEqual(ans, sol)
def test_sharpe_ratio(self):
array = np.array([1,1.5,3,4,4.3])
array_2 = np.array([5,4.3,3,3.5,1])
matrix = np.array([array, array_2]).T
series = pd.Series(array)
time_series = pd.Series(array_2)
df = pd.DataFrame(matrix, columns=['c1', 'c2'])
# Input is np.array of 1 dimmension => float
ans = Calculator.sharpe_ratio(array)
self.assertFloat(ans)
self.assertAlmostEquals(ans, 2.38842, 5)
# Input is np.array of 2 dimmensions => array
ans = Calculator.sharpe_ratio(matrix)
self.assertArray(ans)
sol = np.array([2.38842482, -1.4708528])
self.assertEqual(ans, sol, 5)
# Input is pandas.Series => float
ans = Calculator.sharpe_ratio(series)
self.assertFloat(ans)
self.assertAlmostEquals(ans, 2.38842, 5)
# Input is pandas.TimeSeries => float
ans = Calculator.sharpe_ratio(time_series)
self.assertFloat(ans)
self.assertAlmostEquals(ans, -1.4708528, 5)
# Input is pandas.DataFrame with col parameter => float
ans = Calculator.sharpe_ratio(df, col='c1')
self.assertFloat(ans)
self.assertAlmostEquals(ans, 2.38842, 5)
# --
ans = Calculator.sharpe_ratio(df, col='c2')
self.assertFloat(ans)
self.assertAlmostEqual(ans, -1.4708528, 5)
# Input is pandas.DataFrame without col parameter => pd.Series
ans = Calculator.sharpe_ratio(df)
self.assertSeries(ans)
sol = pd.Series([2.38842482, -1.4708528], index=['c1', 'c2'], name='Sharpe Ratios')
self.assertEqual(ans, sol)
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()
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.tail())
print('Total Return:', Calculator.ret(sim.portfolio))
print('Sharpe Ratio:', Calculator.sharpe_ratio(sim.portfolio))
sim.portfolio.plot()
plt.grid(True)
plt.show()
