def market_return(self):
# 1. Regression: On the period before the window
dr_data = bu.daily_returns(self.data)
dr_market = bu.daily_returns(self.market)
dr_market['intercept'] = 1
reg_results = sm.OLS(dr_data[self.start_period:self.end_period],
dr_market[self.start_period:self.end_period]).fit()
del dr_market['intercept']
# Solution to the regresion
slope = reg_results.params[0]
intercept = reg_results.params[1]
std_error = dr_market[self.start_period:self.end_period].std()['Daily Return']
expected_return = lambda x: x * slope + intercept
# 2. Analysis on the event window
# Expexted Return:
self.er = dr_market['Daily Return'][self.start_window:self.end_window].apply(expected_return)
self.er.name = 'Expected Return'
# Abnormal return: Return of the data - expected return
self.ar = dr_data['Daily Return'][self.start_window:self.end_window] - self.er
self.ar.name = 'Abnormal Return'
# Cumulative abnormal return
self.car = self.ar.apply(np.cumsum)
self.car.name = 'Cumulative Abnormal Return'
# t-test
t_test_calc = lambda x: x / std_error
self.t_test = self.car.apply(t_test_calc)
self.t_test.name = 't test'
self.prob = self.t_test.apply(stats.norm.cdf)
self.prob.name = 'Probability'
def test_total_return(self):
sim = MarketSimulator('./data')
sim.initial_cash = 1000000
sim.simulate("../../sim/test/orders.csv")
# Test without giving the column
tr1 = BasicUtils.total_return(sim.portfolio)
self.assertEquals(tr1, 0.1332629999999999)
# Test giving the column we want to calculate
tr2 = BasicUtils.total_return(sim.portfolio, 'Portfolio')
self.assertEquals(tr2, 0.1332629999999999)
def test_sharpe_ratio(self):
sim = MarketSimulator('./data')
sim.initial_cash = 1000000
sim.simulate("../../sim/test/orders.csv")
# Test without giving the column
sr1 = BasicUtils.sharpe_ratio(sim.portfolio, extraAnswers=True)
self.assertEquals(sr1['sharpe_ratio'], 1.1825359272456812)
self.assertEquals(sr1['std'], 0.0071658104790118396)
self.assertEquals(sr1['mean'], 0.00054698326727656884)
# Test giving the column we want to calculate
sr2 = BasicUtils.sharpe_ratio(sim.portfolio, 'Portfolio', extraAnswers=True)
self.assertEquals(sr2['sharpe_ratio'], 1.1825359272456812)
self.assertEquals(sr2['std'], 0.0071658104790118396)
self.assertEquals(sr2['mean'], 0.00054698326727656884)