def setUp(self):
self.constants = {
# Every day, assume every stock starts at 2, goes down to 1,
# goes up to 4, and finishes at 3.
USEquityPricing.low: 1,
USEquityPricing.open: 2,
USEquityPricing.close: 3,
USEquityPricing.high: 4,
}
self.asset_ids = [1, 2, 3, 4]
self.dates = date_range('2014-01', '2014-03', freq='D', tz='UTC')
self.loader = PrecomputedLoader(
constants=self.constants,
dates=self.dates,
sids=self.asset_ids,
)
self.asset_info = make_simple_equity_info(
self.asset_ids,
start_date=self.dates[0],
end_date=self.dates[-1],
)
environment = TradingEnvironment()
environment.write_data(equities_df=self.asset_info)
self.asset_finder = environment.asset_finder
self.assets = self.asset_finder.retrieve_all(self.asset_ids)
def setUpClass(cls):
cls.AAPL = 1
cls.MSFT = 2
cls.BRK_A = 3
cls.assets = [cls.AAPL, cls.MSFT, cls.BRK_A]
asset_info = make_simple_equity_info(
cls.assets,
Timestamp('2014'),
Timestamp('2015'),
['AAPL', 'MSFT', 'BRK_A'],
)
cls.env = trading.TradingEnvironment()
cls.env.write_data(equities_df=asset_info)
cls.tempdir = tempdir = TempDirectory()
tempdir.create()
try:
cls.raw_data, bar_reader = cls.create_bar_reader(tempdir)
adj_reader = cls.create_adjustment_reader(tempdir)
cls.pipeline_loader = USEquityPricingLoader(
bar_reader, adj_reader
)
except:
cls.tempdir.cleanup()
raise
cls.dates = cls.raw_data[cls.AAPL].index.tz_localize('UTC')
cls.AAPL_split_date = Timestamp("2014-06-09", tz='UTC')
def setUpClass(cls):
cls.AAPL = 1
cls.MSFT = 2
cls.BRK_A = 3
cls.assets = [cls.AAPL, cls.MSFT, cls.BRK_A]
asset_info = make_simple_equity_info(
cls.assets,
Timestamp('2014'),
Timestamp('2015'),
['AAPL', 'MSFT', 'BRK_A'],
)
cls.env = trading.TradingEnvironment()
cls.env.write_data(equities_df=asset_info)
cls.tempdir = tempdir = TempDirectory()
tempdir.create()
try:
cls.raw_data, bar_reader = cls.create_bar_reader(tempdir)
adj_reader = cls.create_adjustment_reader(tempdir)
cls.pipeline_loader = USEquityPricingLoader(bar_reader, adj_reader)
except:
cls.tempdir.cleanup()
raise
cls.dates = cls.raw_data[cls.AAPL].index.tz_localize('UTC')
cls.AAPL_split_date = Timestamp("2014-06-09", tz='UTC')
def setUpClass(cls):
cls.__calendar = date_range('2014', '2015', freq=trading_day)
cls.__assets = assets = Int64Index(arange(1, 20))
cls.__tmp_finder_ctx = tmp_asset_finder(
equities=make_simple_equity_info(
assets,
cls.__calendar[0],
cls.__calendar[-1],
))
cls.__finder = cls.__tmp_finder_ctx.__enter__()
cls.__mask = cls.__finder.lifetimes(
cls.__calendar[-30:],
include_start_date=False,
)
def setUpClass(cls):
cls.__calendar = date_range('2014', '2015', freq=trading_day)
cls.__assets = assets = Int64Index(arange(1, 20))
cls.__tmp_finder_ctx = tmp_asset_finder(
equities=make_simple_equity_info(
assets,
cls.__calendar[0],
cls.__calendar[-1],
)
)
cls.__finder = cls.__tmp_finder_ctx.__enter__()
cls.__mask = cls.__finder.lifetimes(
cls.__calendar[-30:],
include_start_date=False,
)
def setUpClass(cls):
cls.env = TradingEnvironment()
day = cls.env.trading_day
cls.sids = sids = Int64Index([1, 2, 3])
cls.dates = dates = date_range(
'2015-02-01',
'2015-02-28',
freq=day,
tz='UTC',
)
asset_info = make_simple_equity_info(
cls.sids,
start_date=Timestamp('2015-01-31', tz='UTC'),
end_date=Timestamp('2015-03-01', tz='UTC'),
)
cls.env.write_data(equities_df=asset_info)
cls.asset_finder = cls.env.asset_finder
cls.raw_data = DataFrame(
data=arange(len(dates) * len(sids), dtype=float).reshape(
len(dates),
len(sids),
),
index=dates,
columns=cls.asset_finder.retrieve_all(sids),
)
close_loader = DataFrameLoader(USEquityPricing.close, cls.raw_data)
volume_loader = DataFrameLoader(
USEquityPricing.volume,
cls.raw_data * 2,
)
cls.engine = SimplePipelineEngine(
{
USEquityPricing.close: close_loader,
USEquityPricing.volume: volume_loader,
}.__getitem__,
cls.dates,
cls.asset_finder,
)
def setUpClass(cls):
cls.env = TradingEnvironment()
day = cls.env.trading_day
cls.sids = sids = Int64Index([1, 2, 3])
cls.dates = dates = date_range(
'2015-02-01',
'2015-02-28',
freq=day,
tz='UTC',
)
asset_info = make_simple_equity_info(
cls.sids,
start_date=Timestamp('2015-01-31', tz='UTC'),
end_date=Timestamp('2015-03-01', tz='UTC'),
)
cls.env.write_data(equities_df=asset_info)
cls.asset_finder = cls.env.asset_finder
cls.raw_data = DataFrame(
data=arange(len(dates) * len(sids), dtype=float).reshape(
len(dates), len(sids),
),
index=dates,
columns=cls.asset_finder.retrieve_all(sids),
)
close_loader = DataFrameLoader(USEquityPricing.close, cls.raw_data)
volume_loader = DataFrameLoader(
USEquityPricing.volume,
cls.raw_data * 2,
)
cls.engine = SimplePipelineEngine(
{
USEquityPricing.close: close_loader,
USEquityPricing.volume: volume_loader,
}.__getitem__,
cls.dates,
cls.asset_finder,
)
def setUpClass(cls):
cls.env = TradingEnvironment()
day = cls.env.trading_day
cls.asset_ids = [1, 2, 3]
cls.dates = date_range(
'2015-01-01',
'2015-01-31',
freq=day,
tz='UTC',
)
asset_info = make_simple_equity_info(
cls.asset_ids,
start_date=cls.dates[0],
end_date=cls.dates[-1],
)
cls.env.write_data(equities_df=asset_info)
cls.asset_finder = cls.env.asset_finder
cls.assets = cls.asset_finder.retrieve_all(cls.asset_ids)
def get_equity_info(cls):
return make_simple_equity_info(
cls.get_sids(),
start_date=pd.Timestamp('2013-01-01', tz='UTC'),
end_date=pd.Timestamp('2015-01-01', tz='UTC'),
)
def get_equity_info(cls):
return make_simple_equity_info(
cls.get_sids(),
start_date=pd.Timestamp('2013-01-01', tz='UTC'),
end_date=pd.Timestamp('2015-01-01', tz='UTC'),
)
from zipline.utils.numpy_utils import (
float64_dtype,
int64_dtype,
repeat_last_axis,
)
from zipline.testing import (
tmp_asset_finder,
make_simple_equity_info,
)
nameof = op.attrgetter('name')
dtypeof = op.attrgetter('dtype')
asset_infos = (
(make_simple_equity_info(
tuple(map(ord, 'ABC')),
pd.Timestamp(0),
pd.Timestamp('2015'),
), ),
(make_simple_equity_info(
tuple(map(ord, 'ABCD')),
pd.Timestamp(0),
pd.Timestamp('2015'),
), ),
)
with_extra_sid = parameterized.expand(asset_infos)
with_ignore_sid = parameterized.expand(
product(chain.from_iterable(asset_infos), [True, False]))
def _utc_localize_index_level_0(df):
"""``tz_localize`` the first level of a multiindexed dataframe to utc.
from zipline.utils.numpy_utils import (
float64_dtype,
int64_dtype,
repeat_last_axis,
)
from zipline.testing import (
tmp_asset_finder,
make_simple_equity_info,
)
nameof = op.attrgetter('name')
dtypeof = op.attrgetter('dtype')
asset_infos = (
(make_simple_equity_info(
tuple(map(ord, 'ABC')),
pd.Timestamp(0),
pd.Timestamp('2015'),
),),
(make_simple_equity_info(
tuple(map(ord, 'ABCD')),
pd.Timestamp(0),
pd.Timestamp('2015'),
),),
)
with_extra_sid = parameterized.expand(asset_infos)
with_ignore_sid = parameterized.expand(
product(chain.from_iterable(asset_infos), [True, False])
)
def _utc_localize_index_level_0(df):
