def test_get_datetime(self, name, tz):
algo = dedent("""
import pandas as pd
from zipline.api import get_datetime
def initialize(context):
context.tz = {tz} or 'UTC'
context.first_bar = True
def handle_data(context, data):
if context.first_bar:
dt = get_datetime({tz})
if dt.tz.zone != context.tz:
raise ValueError("Mismatched Zone")
elif dt.tz_convert("US/Eastern").hour != 9:
raise ValueError("Mismatched Hour")
elif dt.tz_convert("US/Eastern").minute != 31:
raise ValueError("Mismatched Minute")
context.first_bar = False
""".format(tz=repr(tz)))
start = to_utc('2014-01-02 9:31')
end = to_utc('2014-01-03 9:31')
source = RandomWalkSource(
start=start,
end=end,
)
sim_params = factory.create_simulation_parameters(
data_frequency='minute')
algo = TradingAlgorithm(script=algo,
sim_params=sim_params,
identifiers=[1])
algo.run(source)
self.assertFalse(algo.first_bar)
def test_get_datetime(self, name, tz):
algo = dedent(
"""
import pandas as pd
from zipline.api import get_datetime
def initialize(context):
context.tz = {tz} or 'UTC'
context.first_bar = True
def handle_data(context, data):
if context.first_bar:
dt = get_datetime({tz})
if dt.tz.zone != context.tz:
raise ValueError("Mismatched Zone")
elif dt.tz_convert("US/Eastern").hour != 9:
raise ValueError("Mismatched Hour")
elif dt.tz_convert("US/Eastern").minute != 31:
raise ValueError("Mismatched Minute")
context.first_bar = False
""".format(tz=repr(tz))
)
start = to_utc('2014-01-02 9:31')
end = to_utc('2014-01-03 9:31')
source = RandomWalkSource(
start=start,
end=end,
)
sim_params = factory.create_simulation_parameters(
data_frequency='minute'
)
algo = TradingAlgorithm(
script=algo,
sim_params=sim_params,
identifiers=[1]
)
algo.run(source)
self.assertFalse(algo.first_bar)
"""
if frequency == '1d':
# First day of this test is July 3rd, which is a half day.
if count < 210:
return [np.nan, count]
else:
return [209, count]
elif frequency == '1m':
if count == 0:
return [np.nan, count]
else:
return [count - 1, count]
MIXED_FREQUENCY_MINUTES = _cases_env.market_minute_window(
to_utc('2013-07-03 9:31AM'), 600,
)
ONE_MINUTE_PRICE_ONLY_SPECS = [
HistorySpec(1, '1m', 'price', True, _cases_env, data_frequency='minute'),
]
DAILY_OPEN_CLOSE_SPECS = [
HistorySpec(3, '1d', 'open_price', False, _cases_env,
data_frequency='minute'),
HistorySpec(3, '1d', 'close_price', False, _cases_env,
data_frequency='minute'),
]
ILLIQUID_PRICES_SPECS = [
HistorySpec(3, '1m', 'price', False, _cases_env, data_frequency='minute'),
HistorySpec(5, '1m', 'price', True, _cases_env, data_frequency='minute'),
]
MIXED_FREQUENCY_SPECS = [
"""
if frequency == '1d':
# First day of this test is July 3rd, which is a half day.
if count < 210:
return [np.nan, count]
else:
return [209, count]
elif frequency == '1m':
if count == 0:
return [np.nan, count]
else:
return [count - 1, count]
MIXED_FREQUENCY_MINUTES = _cases_env.market_minute_window(
to_utc('2013-07-03 9:31AM'),
600,
)
ONE_MINUTE_PRICE_ONLY_SPECS = [
HistorySpec(1, '1m', 'price', True, _cases_env, data_frequency='minute'),
]
DAILY_OPEN_CLOSE_SPECS = [
HistorySpec(3,
'1d',
'open_price',
False,
_cases_env,
data_frequency='minute'),
HistorySpec(3,
'1d',
'close_price',