def test_calculate_impact_without_history(self):
model = VolatilityVolumeShare(volume_limit=1)
late_start_asset = self.asset_finder.retrieve_asset(1000)
early_start_asset = self.asset_finder.retrieve_asset(1001)
cases = [
# History will look for data before the start date.
(pd.Timestamp('2006-01-05 11:35AM', tz='UTC'), early_start_asset),
# Start day of the futures contract; no history yet.
(pd.Timestamp('2006-02-10 11:35AM', tz='UTC'), late_start_asset),
# Only a week's worth of history data.
(pd.Timestamp('2006-02-17 11:35AM', tz='UTC'), late_start_asset),
]
for minute, asset in cases:
data = self.create_bardata(simulation_dt_func=lambda: minute)
order = Order(dt=data.current_dt, asset=asset, amount=10)
price, amount = model.process_order(data, order)
avg_price = (
data.current(asset, 'high') + data.current(asset, 'low')
) / 2
expected_price = \
avg_price * (1 + model.NO_DATA_VOLATILITY_SLIPPAGE_IMPACT)
self.assertAlmostEqual(price, expected_price, delta=0.001)
self.assertEqual(amount, 10)
def test_calculate_impact_without_history(self):
model = VolatilityVolumeShare(volume_limit=1)
late_start_asset = self.asset_finder.retrieve_asset(1000)
early_start_asset = self.asset_finder.retrieve_asset(1001)
cases = [
# History will look for data before the start date.
(pd.Timestamp('2006-01-05 11:35AM', tz='UTC'), early_start_asset),
# Start day of the futures contract; no history yet.
(pd.Timestamp('2006-02-10 11:35AM', tz='UTC'), late_start_asset),
# Only a week's worth of history data.
(pd.Timestamp('2006-02-17 11:35AM', tz='UTC'), late_start_asset),
]
for minute, asset in cases:
data = self.create_bardata(simulation_dt_func=lambda: minute)
order = Order(dt=data.current_dt, asset=asset, amount=10)
price, amount = model.process_order(data, order)
avg_price = (data.current(asset, 'high') +
data.current(asset, 'low')) / 2
expected_price = \
avg_price * (1 + model.NO_DATA_VOLATILITY_SLIPPAGE_IMPACT)
self.assertAlmostEqual(price, expected_price, delta=0.001)
self.assertEqual(amount, 10)
def test_low_transaction_volume(self):
# With a volume limit of 0.001, and a bar volume of 100, we should
# compute a transaction volume of 100 * 0.001 = 0.1, which gets rounded
# down to zero. In this case we expect no amount to be transacted.
model = VolatilityVolumeShare(volume_limit=0.001)
minute = pd.Timestamp('2006-03-01 11:35AM', tz='UTC')
data = self.create_bardata(simulation_dt_func=lambda: minute)
order = Order(dt=data.current_dt, asset=self.ASSET, amount=10)
price, amount = model.process_order(data, order)
self.assertIsNone(price)
self.assertIsNone(amount)
def test_low_transaction_volume(self):
# With a volume limit of 0.001, and a bar volume of 100, we should
# compute a transaction volume of 100 * 0.001 = 0.1, which gets rounded
# down to zero. In this case we expect no amount to be transacted.
model = VolatilityVolumeShare(volume_limit=0.001)
minute = pd.Timestamp('2006-03-01 11:35AM', tz='UTC')
data = self.create_bardata(simulation_dt_func=lambda: minute)
order = Order(dt=data.current_dt, asset=self.ASSET, amount=10)
price, amount = model.process_order(data, order)
self.assertIsNone(price)
self.assertIsNone(amount)
def test_impacted_price_worse_than_limit(self):
model = VolatilityVolumeShare(volume_limit=0.05)
# Use all the same numbers from the 'calculate_impact' tests. Since the
# impacted price is 59805.5, which is worse than the limit price of
# 59800, the model should return None.
minute = pd.Timestamp('2006-03-01 11:35AM', tz='UTC')
data = self.create_bardata(simulation_dt_func=lambda: minute)
order = Order(
dt=data.current_dt, asset=self.ASSET, amount=10, limit=59800,
)
price, amount = model.process_order(data, order)
self.assertIsNone(price)
self.assertIsNone(amount)
def test_impacted_price_worse_than_limit(self):
model = VolatilityVolumeShare(volume_limit=0.05)
# Use all the same numbers from the 'calculate_impact' tests. Since the
# impacted price is 59805.5, which is worse than the limit price of
# 59800, the model should return None.
minute = pd.Timestamp('2006-03-01 11:35AM', tz='UTC')
data = self.create_bardata(simulation_dt_func=lambda: minute)
order = Order(
dt=data.current_dt,
asset=self.ASSET,
amount=10,
limit=59800,
)
price, amount = model.process_order(data, order)
self.assertIsNone(price)
self.assertIsNone(amount)
def _calculate_impact(self, test_order, answer_key):
model = VolatilityVolumeShare(volume_limit=0.05)
first_minute = pd.Timestamp('2006-03-31 11:35AM', tz='UTC')
next_3_minutes = self.trading_calendar.minutes_window(first_minute, 3)
remaining_shares = test_order.open_amount
for i, minute in enumerate(next_3_minutes):
data = self.create_bardata(simulation_dt_func=lambda: minute)
new_order = Order(
dt=data.current_dt, asset=self.ASSET, amount=remaining_shares,
)
price, amount = model.process_order(data, new_order)
self.assertEqual(price, answer_key[i][0])
self.assertEqual(amount, answer_key[i][1])
amount = amount or 0
if remaining_shares < 0:
remaining_shares = min(0, remaining_shares - amount)
else:
remaining_shares = max(0, remaining_shares - amount)
def _calculate_impact(self, test_order, answer_key):
model = VolatilityVolumeShare(volume_limit=0.05)
first_minute = pd.Timestamp('2006-03-31 11:35AM', tz='UTC')
next_3_minutes = self.trading_calendar.minutes_window(first_minute, 3)
remaining_shares = test_order.open_amount
for i, minute in enumerate(next_3_minutes):
data = self.create_bardata(simulation_dt_func=lambda: minute)
new_order = Order(
dt=data.current_dt,
asset=self.ASSET,
amount=remaining_shares,
)
price, amount = model.process_order(data, new_order)
self.assertEqual(price, answer_key[i][0])
self.assertEqual(amount, answer_key[i][1])
amount = amount or 0
if remaining_shares < 0:
remaining_shares = min(0, remaining_shares - amount)
else:
remaining_shares = max(0, remaining_shares - amount)