def test_compute_lifetimes(self, env=None):
num_assets = 4
trading_day = env.trading_day
first_start = pd.Timestamp('2015-04-01', tz='UTC')
frame = make_rotating_asset_info(
num_assets=num_assets,
first_start=first_start,
frequency=env.trading_day,
periods_between_starts=3,
asset_lifetime=5
)
finder = AssetFinder(frame)
all_dates = pd.date_range(
start=first_start,
end=frame.end_date.max(),
freq=trading_day,
)
for dates in all_subindices(all_dates):
expected_mask = full(
shape=(len(dates), num_assets),
fill_value=False,
dtype=bool,
)
for i, date in enumerate(dates):
it = frame[['start_date', 'end_date']].itertuples()
for j, start, end in it:
if start <= date <= end:
expected_mask[i, j] = True
# Filter out columns with all-empty columns.
expected_result = pd.DataFrame(
data=expected_mask,
index=dates,
columns=frame.sid.values,
)
actual_result = finder.lifetimes(dates)
assert_frame_equal(actual_result, expected_result)
def test_compute_lifetimes(self, env=None):
num_assets = 4
trading_day = env.trading_day
first_start = pd.Timestamp('2015-04-01', tz='UTC')
frame = make_rotating_asset_info(num_assets=num_assets,
first_start=first_start,
frequency=env.trading_day,
periods_between_starts=3,
asset_lifetime=5)
finder = AssetFinder(frame)
all_dates = pd.date_range(
start=first_start,
end=frame.end_date.max(),
freq=trading_day,
)
for dates in all_subindices(all_dates):
expected_mask = full(
shape=(len(dates), num_assets),
fill_value=False,
dtype=bool,
)
for i, date in enumerate(dates):
it = frame[['start_date', 'end_date']].itertuples()
for j, start, end in it:
if start <= date <= end:
expected_mask[i, j] = True
# Filter out columns with all-empty columns.
expected_result = pd.DataFrame(
data=expected_mask,
index=dates,
columns=frame.sid.values,
)
actual_result = finder.lifetimes(dates)
assert_frame_equal(actual_result, expected_result)
class BaseFFCTestCase(TestCase):
def setUp(self):
self.__calendar = date_range('2014', '2015', freq=trading_day)
self.__assets = assets = Int64Index(arange(1, 20))
self.__finder = AssetFinder(
make_simple_asset_info(
assets,
self.__calendar[0],
self.__calendar[-1],
),
db_path=':memory:',
create_table=True,
)
self.__mask = self.__finder.lifetimes(self.__calendar[-10:])
@property
def default_shape(self):
"""Default shape for methods that build test data."""
return self.__mask.shape
def run_terms(self, terms, initial_workspace, mask=None):
"""
Compute the given terms, seeding the workspace of our FFCEngine with
`initial_workspace`.
Parameters
----------
terms : dict
Mapping from termname -> term object.
Returns
-------
results : dict
Mapping from termname -> computed result.
"""
engine = SimpleFFCEngine(
ExplodingObject(),
self.__calendar,
self.__finder,
)
mask = mask if mask is not None else self.__mask
return engine.compute_chunk(TermGraph(terms), mask, initial_workspace)
def build_mask(self, array):
ndates, nassets = array.shape
return DataFrame(
array,
# Use the **last** N dates rather than the first N so that we have
# space for lookbacks.
index=self.__calendar[-ndates:],
columns=self.__assets[:nassets],
dtype=bool,
)
@with_default_shape
def arange_data(self, shape, dtype=float):
"""
Build a block of testing data from numpy.arange.
"""
return arange(prod(shape), dtype=dtype).reshape(shape)
@with_default_shape
def randn_data(self, seed, shape):
"""
Build a block of testing data from numpy.random.randn.
"""
random_seed(seed)
return randn(*shape)
class BaseFFCTestCase(TestCase):
def setUp(self):
self.__calendar = date_range('2014', '2015', freq=trading_day)
self.__assets = assets = Int64Index(arange(1, 20))
self.__finder = AssetFinder(
make_simple_asset_info(
assets,
self.__calendar[0],
self.__calendar[-1],
),
db_path=':memory:',
create_table=True,
)
self.__mask = self.__finder.lifetimes(self.__calendar[-10:])
@property
def default_shape(self):
"""Default shape for methods that build test data."""
return self.__mask.shape
def run_terms(self, terms, initial_workspace, mask=None):
"""
Compute the given terms, seeding the workspace of our FFCEngine with
`initial_workspace`.
Parameters
----------
terms : dict
Mapping from termname -> term object.
Returns
-------
results : dict
Mapping from termname -> computed result.
"""
engine = SimpleFFCEngine(
ExplodingObject(),
self.__calendar,
self.__finder,
)
mask = mask if mask is not None else self.__mask
return engine.compute_chunk(TermGraph(terms), mask, initial_workspace)
def build_mask(self, array):
ndates, nassets = array.shape
return DataFrame(
array,
# Use the **last** N dates rather than the first N so that we have
# space for lookbacks.
index=self.__calendar[-ndates:],
columns=self.__assets[:nassets],
dtype=bool,
)
@with_default_shape
def arange_data(self, shape, dtype=float):
"""
Build a block of testing data from numpy.arange.
"""
return arange(prod(shape), dtype=dtype).reshape(shape)
@with_default_shape
def randn_data(self, seed, shape):
"""
Build a block of testing data from numpy.random.randn.
"""
random_seed(seed)
return randn(*shape)
