def setUp(self):
self.sigma = pd.read_csv(DIR + 'sigmas.csv',
index_col=0, parse_dates=[0])
self.returns = pd.read_csv(DIR + 'returns.csv',
index_col=0, parse_dates=[0])
self.volume = pd.read_csv(DIR + 'volumes.csv',
index_col=0, parse_dates=[0])
self.a, self.b, self.s = 0.0005, 1., 0.
self.portfolio = pd.Series(index=self.returns.columns, data=1E6)
self.tcost_term = TcostModel(self.a, self.b, self.sigma, self.volume)
self.hcost_term = HcostModel(self.s)
self.Simulator = MarketSimulator(self.returns,
costs=[self.tcost_term,
self.hcost_term],
market_volumes=self.volume)
class TestSimulator(BaseTest):
def setUp(self):
self.sigma = pd.read_csv(DIR + 'sigmas.csv',
index_col=0, parse_dates=[0])
self.returns = pd.read_csv(DIR + 'returns.csv',
index_col=0, parse_dates=[0])
self.volume = pd.read_csv(DIR + 'volumes.csv',
index_col=0, parse_dates=[0])
self.a, self.b, self.s = 0.0005, 1., 0.
self.portfolio = pd.Series(index=self.returns.columns, data=1E6)
self.tcost_term = TcostModel(self.a, self.b, self.sigma, self.volume)
self.hcost_term = HcostModel(self.s)
self.Simulator = MarketSimulator(self.returns,
costs=[self.tcost_term,
self.hcost_term],
market_volumes=self.volume)
def test_propag(self):
"""Test propagation of portfolio."""
t = self.returns.index[1]
h = copy.copy(self.portfolio)
results = SimulationResult(initial_portfolio=h, policy=None,
cash_key='cash', simulator=self.Simulator)
u = pd.Series(index=self.portfolio.index, data=1E4)
h_next, u = self.Simulator.propagate(h, u=u, t=t)
results.log_simulation(t=t, u=u, h_next=h_next,
risk_free_return=0., exec_time=0)
self.assertAlmostEquals(
results.simulator_TcostModel.sum().sum(), 157.604, 3)
self.assertAlmostEquals(
results.simulator_HcostModel.sum().sum(), 0., 3)
self.assertAlmostEqual(sum(h_next), 28906767.251, 3)
def test_propag_list(self):
"""Test propagation of portfolio, list of trades."""
t = self.returns.index[1]
h = copy.copy(self.portfolio)
results = SimulationResult(initial_portfolio=h, policy=None,
cash_key='cash', simulator=self.Simulator)
u = pd.Series(index=self.portfolio.index, data=[1E4] * 29)
h_next, u = self.Simulator.propagate(h, u, t=t)
results.log_simulation(t=t, u=u, h_next=h_next,
risk_free_return=0., exec_time=0)
self.assertAlmostEquals(
results.simulator_TcostModel.sum().sum(), 157.604, 3)
self.assertAlmostEquals(
results.simulator_HcostModel.sum().sum(), 0., 3)
self.assertAlmostEqual(sum(h_next), 28906767.251, 3)
def test_propag_neg(self):
"""Test propagation of portfolio, negative trades."""
t = self.returns.index[1]
h = copy.copy(self.portfolio)
results = SimulationResult(initial_portfolio=h, policy=None,
cash_key='cash', simulator=self.Simulator)
u = pd.Series(index=self.portfolio.index, data=[-1E4] * 29)
h_next, u = self.Simulator.propagate(h, u, t=t)
results.log_simulation(t=t, u=u, h_next=h_next,
risk_free_return=0., exec_time=0)
self.assertAlmostEquals(
results.simulator_TcostModel.sum().sum(), 157.604, 3)
self.assertAlmostEquals(
results.simulator_HcostModel.sum().sum(), 0., 3)
self.assertAlmostEqual(sum(h_next), 28908611.931, 3)
def test_hcost_pos(self):
"""Test hcost function, positive positions."""
self.hcost_term.borrow_costs += 0
t = self.returns.index[1]
h = copy.copy(self.portfolio)
results = SimulationResult(initial_portfolio=h, policy=None,
cash_key='cash', simulator=self.Simulator)
u = pd.Series(index=self.portfolio.index, data=1E4)
h_next, u = self.Simulator.propagate(h, u, t=t)
results.log_simulation(t=t, u=u, h_next=h_next,
risk_free_return=0., exec_time=0)
self.assertAlmostEquals(results.simulator_HcostModel.sum().sum(), 0.)
def test_hcost_neg(self):
"""Test hcost function, negative positions."""
self.hcost_term.borrow_costs += .0001
t = self.returns.index[1]
h = copy.copy(self.portfolio)
results = SimulationResult(initial_portfolio=h, policy=None,
cash_key='cash', simulator=self.Simulator)
u = pd.Series(index=self.portfolio.index, data=-2E6)
h_next, u = self.Simulator.propagate(h, u, t=t)
results.log_simulation(t=t, u=u, h_next=h_next,
risk_free_return=0.,
exec_time=0)
self.assertAlmostEquals(
results.simulator_HcostModel.sum().sum(), 2800.0)