def setUpClass(cls):
cls._cleanup_stack = stack = ExitStack()
cls.cols = {}
cls.dataset = {sid: df for sid, df in enumerate(earnings_cases)}
cls.finder = stack.enter_context(tmp_asset_finder(equities=cls.get_equity_info()))
cls.loader_type = EarningsCalendarLoader
def _test_id(self, df, dshape, expected, finder, add):
expr = bz.data(df, name='expr', dshape=dshape)
loader = BlazeLoader()
ds = from_blaze(
expr,
loader=loader,
no_deltas_rule=no_deltas_rules.ignore,
missing_values=self.missing_values,
)
p = Pipeline()
for a in add:
p.add(getattr(ds, a).latest, a)
dates = self.dates
with tmp_asset_finder() as finder:
result = SimplePipelineEngine(
loader,
dates,
finder,
).run_pipeline(p, dates[0], dates[-1])
assert_frame_equal(
result,
_utc_localize_index_level_0(expected),
check_dtype=False,
)
def test_novel_deltas_macro(self):
asset_info = asset_infos[0][0]
base_dates = pd.DatetimeIndex([
pd.Timestamp('2014-01-01'),
pd.Timestamp('2014-01-04')
])
baseline = pd.DataFrame({
'value': (0, 1),
'asof_date': base_dates,
'timestamp': base_dates,
})
expr = bz.data(baseline, name='expr', dshape=self.macro_dshape)
deltas = bz.data(baseline, name='deltas', dshape=self.macro_dshape)
deltas = bz.transform(
deltas,
value=deltas.value + 10,
timestamp=deltas.timestamp + timedelta(days=1),
)
nassets = len(asset_info)
expected_views = keymap(pd.Timestamp, {
'2014-01-03': repeat_last_axis(
np.array([10.0, 10.0, 10.0]),
nassets,
),
'2014-01-06': repeat_last_axis(
np.array([10.0, 10.0, 11.0]),
nassets,
),
})
cal = pd.DatetimeIndex([
pd.Timestamp('2014-01-01'),
pd.Timestamp('2014-01-02'),
pd.Timestamp('2014-01-03'),
# omitting the 4th and 5th to simulate a weekend
pd.Timestamp('2014-01-06'),
])
with tmp_asset_finder(equities=asset_info) as finder:
expected_output = pd.DataFrame(
list(concatv([10] * nassets, [11] * nassets)),
index=pd.MultiIndex.from_product((
sorted(expected_views.keys()),
finder.retrieve_all(asset_info.index),
)),
columns=('value',),
)
self._run_pipeline(
expr,
deltas,
expected_views,
expected_output,
finder,
calendar=cal,
start=cal[2],
end=cal[-1],
window_length=3,
compute_fn=op.itemgetter(-1),
)
def test_id_take_last_in_group_macro(self):
"""
output (expected):
other value
2014-01-01 Equity(65 [A]) NaN 1
Equity(66 [B]) NaN 1
Equity(67 [C]) NaN 1
2014-01-02 Equity(65 [A]) 1 2
Equity(66 [B]) 1 2
Equity(67 [C]) 1 2
2014-01-03 Equity(65 [A]) 2 2
Equity(66 [B]) 2 2
Equity(67 [C]) 2 2
"""
T = pd.Timestamp
df = pd.DataFrame(
columns=['asof_date', 'timestamp', 'other', 'value'],
data=[
[T('2014-01-01'), T('2014-01-01 00'), np.nan, 1],
[T('2014-01-01'), T('2014-01-01 01'), np.nan, np.nan],
[T('2014-01-02'), T('2014-01-02 00'), 1, np.nan],
[T('2014-01-02'), T('2014-01-02 01'), np.nan, 2],
[T('2014-01-03'), T('2014-01-03 00'), 2, np.nan],
[T('2014-01-03'), T('2014-01-03 01'), 3, 3],
],
)
fields = OrderedDict(self.macro_dshape.measure.fields)
fields['other'] = fields['value']
with tmp_asset_finder() as finder:
expected = pd.DataFrame(
columns=[
'other', 'value',
],
data=[
[np.nan, 1], # 2014-01-01 Equity(65 [A])
[np.nan, 1], # Equity(66 [B])
[np.nan, 1], # Equity(67 [C])
[1, 2], # 2014-01-02 Equity(65 [A])
[1, 2], # Equity(66 [B])
[1, 2], # Equity(67 [C])
[2, 2], # 2014-01-03 Equity(65 [A])
[2, 2], # Equity(66 [B])
[2, 2], # Equity(67 [C])
],
index=pd.MultiIndex.from_product(
(self.dates, finder.retrieve_all(self.sids)),
),
)
self._test_id(
df,
var * Record(fields),
expected,
finder,
('value', 'other'),
)
def init_class_fixtures(cls):
super(BasePipelineTestCase, cls).init_class_fixtures()
cls.__calendar = date_range("2014", "2015", freq=cls.trading_calendar.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._cleanup_stack = stack = ExitStack()
cls.finder = stack.enter_context(
tmp_asset_finder(equities=cls.get_equity_info()),
)
cls.cols = {}
cls.dataset = {sid:
frame.drop(CASH_FIELD_NAME, axis=1)
for sid, frame
in enumerate(buyback_authorizations_cases)}
cls.loader_type = ShareBuybackAuthorizationsLoader
def test_id_ffill_out_of_window_macro_dataset(self):
"""
input (df):
asof_date timestamp other value
0 2013-12-22 2013-12-22 NaN 0
1 2013-12-23 2013-12-23 1 NaN
2 2013-12-24 2013-12-24 NaN NaN
output (expected):
other value
2014-01-01 Equity(65 [A]) 1 0
Equity(66 [B]) 1 0
Equity(67 [C]) 1 0
2014-01-02 Equity(65 [A]) 1 0
Equity(66 [B]) 1 0
Equity(67 [C]) 1 0
2014-01-03 Equity(65 [A]) 1 0
Equity(66 [B]) 1 0
Equity(67 [C]) 1 0
"""
dates = self.dates - timedelta(days=10)
df = pd.DataFrame({
'value': (0, np.nan, np.nan),
'other': (np.nan, 1, np.nan),
'asof_date': dates,
'timestamp': dates,
})
fields = OrderedDict(self.macro_dshape.measure.fields)
fields['other'] = fields['value']
with tmp_asset_finder() as finder:
expected = pd.DataFrame(
np.array([[0, 1],
[0, 1],
[0, 1],
[0, 1],
[0, 1],
[0, 1],
[0, 1],
[0, 1],
[0, 1]]),
columns=['value', 'other'],
index=pd.MultiIndex.from_product(
(self.dates, finder.retrieve_all(self.sids)),
),
).sort_index(axis=1)
self._test_id(
df,
var * Record(fields),
expected,
finder,
('value', 'other'),
)
def test_id_take_last_in_group(self):
T = pd.Timestamp
df = pd.DataFrame(
columns=['asof_date', 'timestamp', 'sid', 'other', 'value'],
data=[
[T('2014-01-01'), T('2014-01-01 00'), 65, 0, 0],
[T('2014-01-01'), T('2014-01-01 01'), 65, 1, np.nan],
[T('2014-01-01'), T('2014-01-01 00'), 66, np.nan, np.nan],
[T('2014-01-01'), T('2014-01-01 01'), 66, np.nan, 1],
[T('2014-01-01'), T('2014-01-01 00'), 67, 2, np.nan],
[T('2014-01-01'), T('2014-01-01 01'), 67, np.nan, np.nan],
[T('2014-01-02'), T('2014-01-02 00'), 65, np.nan, np.nan],
[T('2014-01-02'), T('2014-01-02 01'), 65, np.nan, 1],
[T('2014-01-02'), T('2014-01-02 00'), 66, np.nan, np.nan],
[T('2014-01-02'), T('2014-01-02 01'), 66, 2, np.nan],
[T('2014-01-02'), T('2014-01-02 00'), 67, 3, 3],
[T('2014-01-02'), T('2014-01-02 01'), 67, 3, 3],
[T('2014-01-03'), T('2014-01-03 00'), 65, 2, np.nan],
[T('2014-01-03'), T('2014-01-03 01'), 65, 2, np.nan],
[T('2014-01-03'), T('2014-01-03 00'), 66, 3, 3],
[T('2014-01-03'), T('2014-01-03 01'), 66, np.nan, np.nan],
[T('2014-01-03'), T('2014-01-03 00'), 67, np.nan, np.nan],
[T('2014-01-03'), T('2014-01-03 01'), 67, np.nan, 4],
],
)
fields = OrderedDict(self.dshape.measure.fields)
fields['other'] = fields['value']
with tmp_asset_finder() as finder:
expected = pd.DataFrame(
columns=['other', 'value'],
data=[
[1, 0], # 2014-01-01 Equity(65 [A])
[np.nan, 1], # Equity(66 [B])
[2, np.nan], # Equity(67 [C])
[1, 1], # 2014-01-02 Equity(65 [A])
[2, 1], # Equity(66 [B])
[3, 3], # Equity(67 [C])
[2, 1], # 2014-01-03 Equity(65 [A])
[3, 3], # Equity(66 [B])
[3, 3], # Equity(67 [C])
],
index=pd.MultiIndex.from_product(
(self.dates, finder.retrieve_all(self.sids)),
),
)
self._test_id(
df,
var * Record(fields),
expected,
finder,
('value', 'other'),
)
def test_id_ffill_out_of_window(self):
"""
input (df):
asof_date timestamp sid other value
0 2013-12-22 2013-12-22 65 0 0
1 2013-12-22 2013-12-22 66 NaN 1
2 2013-12-22 2013-12-22 67 2 NaN
3 2013-12-23 2013-12-23 65 NaN 1
4 2013-12-23 2013-12-23 66 2 NaN
5 2013-12-23 2013-12-23 67 3 3
6 2013-12-24 2013-12-24 65 2 NaN
7 2013-12-24 2013-12-24 66 3 3
8 2013-12-24 2013-12-24 67 NaN 4
output (expected):
other value
2014-01-01 Equity(65 [A]) 2 1
Equity(66 [B]) 3 3
Equity(67 [C]) 3 4
2014-01-02 Equity(65 [A]) 2 1
Equity(66 [B]) 3 3
Equity(67 [C]) 3 4
2014-01-03 Equity(65 [A]) 2 1
Equity(66 [B]) 3 3
Equity(67 [C]) 3 4
"""
dates = self.dates.repeat(3) - timedelta(days=10)
df = pd.DataFrame({
'sid': self.sids * 3,
'value': (0, 1, np.nan, 1, np.nan, 3, np.nan, 3, 4),
'other': (0, np.nan, 2, np.nan, 2, 3, 2, 3, np.nan),
'asof_date': dates,
'timestamp': dates,
})
fields = OrderedDict(self.dshape.measure.fields)
fields['other'] = fields['value']
with tmp_asset_finder() as finder:
expected = pd.DataFrame(
np.array([[2, 1], [3, 3], [3, 4], [2, 1], [3, 3], [3, 4],
[2, 1], [3, 3], [3, 4]]),
columns=['other', 'value'],
index=pd.MultiIndex.from_product(
(self.dates, finder.retrieve_all(self.sids)), ),
)
self._test_id(
df,
var * Record(fields),
expected,
finder,
('value', 'other'),
)
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 test_deltas_only_one_delta_in_universe(self, asset_info):
expr = bz.data(self.df, name='expr', dshape=self.dshape)
deltas = pd.DataFrame({
'sid': [65, 66],
'asof_date': [self.dates[1], self.dates[0]],
'timestamp': [self.dates[2], self.dates[1]],
'value': [10, 11],
})
deltas = bz.data(deltas, name='deltas', dshape=self.dshape)
expected_views = keymap(pd.Timestamp, {
'2014-01-02': np.array([[0.0, 11.0, 2.0],
[1.0, 2.0, 3.0]]),
'2014-01-03': np.array([[10.0, 2.0, 3.0],
[2.0, 3.0, 4.0]]),
'2014-01-04': np.array([[2.0, 3.0, 4.0],
[2.0, 3.0, 4.0]]),
})
nassets = len(asset_info)
if nassets == 4:
expected_views = valmap(
lambda view: np.c_[view, [np.nan, np.nan]],
expected_views,
)
with tmp_asset_finder(equities=asset_info) as finder:
expected_output = pd.DataFrame(
columns=[
'value',
],
data=np.array([11, 10, 4]).repeat(len(asset_info.index)),
index=pd.MultiIndex.from_product((
sorted(expected_views.keys()),
finder.retrieve_all(asset_info.index),
)),
)
dates = self.dates
dates = dates.insert(len(dates), dates[-1] + timedelta(days=1))
self._run_pipeline(
expr,
deltas,
expected_views,
expected_output,
finder,
calendar=dates,
start=dates[1],
end=dates[-1],
window_length=2,
compute_fn=np.nanmax,
)
def test_read_from_asset_finder(self):
sids = list(range(8))
exchange_names = [
'NEW YORK STOCK EXCHANGE',
'NEW YORK STOCK EXCHANGE',
'NASDAQ STOCK MARKET',
'NASDAQ STOCK MARKET',
'TOKYO STOCK EXCHANGE',
'TOKYO STOCK EXCHANGE',
'OSAKA STOCK EXCHANGE',
'OSAKA STOCK EXCHANGE',
]
equities = pd.DataFrame({
'sid': sids,
'real_sid': [str(sid) for sid in sids],
'currency': ['USD'] * len(sids),
'exchange': exchange_names,
'symbol': [chr(65 + sid) for sid in sids],
})
exchange_infos = [
ExchangeInfo('NEW YORK STOCK EXCHANGE', 'NYSE', 'US'),
ExchangeInfo('NASDAQ STOCK MARKET', 'NYSE', 'US'),
ExchangeInfo('TOKYO STOCK EXCHANGE', 'JPX', 'JP'),
ExchangeInfo('OSAKA STOCK EXCHANGE', 'JPX', 'JP'),
]
exchange_info_table = pd.DataFrame(
[(info.name, info.canonical_name, info.country_code)
for info in exchange_infos],
columns=['exchange', 'canonical_name', 'country_code'],
)
expected_exchange_info_map = {
info.name: info
for info in exchange_infos
}
ctx = tmp_asset_finder(
equities=equities,
exchanges=exchange_info_table,
)
with ctx as af:
actual_exchange_info_map = af.exchange_info
assets = af.retrieve_all(sids)
assert_equal(actual_exchange_info_map, expected_exchange_info_map)
for asset in assets:
expected_exchange_info = expected_exchange_info_map[exchange_names[
asset.sid]]
assert_equal(asset.exchange_info, expected_exchange_info)
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 test_deltas_only_one_delta_in_universe(self, asset_info):
expr = bz.data(self.df, name='expr', dshape=self.dshape)
deltas = pd.DataFrame({
'sid': [65, 66],
'asof_date': [self.dates[1], self.dates[0]],
'timestamp': [self.dates[2], self.dates[1]],
'value': [10, 11],
})
deltas = bz.data(deltas, name='deltas', dshape=self.dshape)
expected_views = keymap(
pd.Timestamp, {
'2014-01-02': np.array([[0.0, 11.0, 2.0], [1.0, 2.0, 3.0]]),
'2014-01-03': np.array([[10.0, 2.0, 3.0], [2.0, 3.0, 4.0]]),
'2014-01-04': np.array([[2.0, 3.0, 4.0], [2.0, 3.0, 4.0]]),
})
nassets = len(asset_info)
if nassets == 4:
expected_views = valmap(
lambda view: np.c_[view, [np.nan, np.nan]],
expected_views,
)
with tmp_asset_finder(equities=asset_info) as finder:
expected_output = pd.DataFrame(
columns=[
'value',
],
data=np.array([11, 10, 4]).repeat(len(asset_info.index)),
index=pd.MultiIndex.from_product((
sorted(expected_views.keys()),
finder.retrieve_all(asset_info.index),
)),
)
dates = self.dates
dates = dates.insert(len(dates), dates[-1] + timedelta(days=1))
self._run_pipeline(
expr,
deltas,
expected_views,
expected_output,
finder,
calendar=dates,
start=dates[1],
end=dates[-1],
window_length=2,
compute_fn=np.nanmax,
)
def test_id_ffill_out_of_window_macro_dataset(self):
"""
input (df):
asof_date timestamp other value
0 2013-12-22 2013-12-22 NaN 0
1 2013-12-23 2013-12-23 1 NaN
2 2013-12-24 2013-12-24 NaN NaN
output (expected):
other value
2014-01-01 Equity(65 [A]) 1 0
Equity(66 [B]) 1 0
Equity(67 [C]) 1 0
2014-01-02 Equity(65 [A]) 1 0
Equity(66 [B]) 1 0
Equity(67 [C]) 1 0
2014-01-03 Equity(65 [A]) 1 0
Equity(66 [B]) 1 0
Equity(67 [C]) 1 0
"""
dates = self.dates - timedelta(days=10)
df = pd.DataFrame({
'value': (0, np.nan, np.nan),
'other': (np.nan, 1, np.nan),
'asof_date': dates,
'timestamp': dates,
})
fields = OrderedDict(self.macro_dshape.measure.fields)
fields['other'] = fields['value']
with tmp_asset_finder() as finder:
expected = pd.DataFrame(
np.array([[0, 1], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1],
[0, 1], [0, 1], [0, 1]]),
columns=['value', 'other'],
index=pd.MultiIndex.from_product(
(self.dates, finder.retrieve_all(self.sids)), ),
).sort_index(axis=1)
self._test_id(
df,
var * Record(fields),
expected,
finder,
('value', 'other'),
)
def test_id_multiple_columns(self):
"""
input (df):
asof_date sid timestamp value other
0 2014-01-01 65 2014-01-01 0 1
1 2014-01-01 66 2014-01-01 1 2
2 2014-01-01 67 2014-01-01 2 3
3 2014-01-02 65 2014-01-02 1 2
4 2014-01-02 66 2014-01-02 2 3
5 2014-01-02 67 2014-01-02 3 4
6 2014-01-03 65 2014-01-03 2 3
7 2014-01-03 66 2014-01-03 3 4
8 2014-01-03 67 2014-01-03 4 5
output (expected):
value other
2014-01-01 Equity(65 [A]) 0 1
Equity(66 [B]) 1 2
Equity(67 [C]) 2 3
2014-01-02 Equity(65 [A]) 1 2
Equity(66 [B]) 2 3
Equity(67 [C]) 3 4
2014-01-03 Equity(65 [A]) 2 3
Equity(66 [B]) 3 4
Equity(67 [C]) 4 5
"""
df = self.df.copy()
df['other'] = df.value + 1
fields = OrderedDict(self.dshape.measure.fields)
fields['other'] = fields['value']
with tmp_asset_finder() as finder:
expected = df.drop('asof_date', axis=1).set_index(
['timestamp', 'sid'],
).sort_index(axis=1)
expected.index = pd.MultiIndex.from_product((
expected.index.levels[0],
finder.retrieve_all(expected.index.levels[1]),
))
self._test_id(
df,
var * Record(fields),
expected,
finder,
('value', 'int_value', 'other'),
)
def init_class_fixtures(cls):
super(BasePipelineTestCase, cls).init_class_fixtures()
cls.__calendar = date_range('2014',
'2015',
freq=cls.trading_calendar.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 test_id_multiple_columns(self):
"""
input (df):
asof_date sid timestamp value other
0 2014-01-01 65 2014-01-01 0 1
1 2014-01-01 66 2014-01-01 1 2
2 2014-01-01 67 2014-01-01 2 3
3 2014-01-02 65 2014-01-02 1 2
4 2014-01-02 66 2014-01-02 2 3
5 2014-01-02 67 2014-01-02 3 4
6 2014-01-03 65 2014-01-03 2 3
7 2014-01-03 66 2014-01-03 3 4
8 2014-01-03 67 2014-01-03 4 5
output (expected):
value other
2014-01-01 Equity(65 [A]) 0 1
Equity(66 [B]) 1 2
Equity(67 [C]) 2 3
2014-01-02 Equity(65 [A]) 1 2
Equity(66 [B]) 2 3
Equity(67 [C]) 3 4
2014-01-03 Equity(65 [A]) 2 3
Equity(66 [B]) 3 4
Equity(67 [C]) 4 5
"""
df = self.df.copy()
df['other'] = df.value + 1
fields = OrderedDict(self.dshape.measure.fields)
fields['other'] = fields['value']
with tmp_asset_finder() as finder:
expected = df.drop('asof_date', axis=1).set_index(
['timestamp', 'sid'], ).sort_index(axis=1)
expected.index = pd.MultiIndex.from_product((
expected.index.levels[0],
finder.retrieve_all(expected.index.levels[1]),
))
self._test_id(
df,
var * Record(fields),
expected,
finder,
('value', 'int_value', 'other'),
)
def test_id_macro_dataset_multiple_columns(self):
"""
input (df):
asof_date timestamp other value
0 2014-01-01 2014-01-01 1 0
3 2014-01-02 2014-01-02 2 1
6 2014-01-03 2014-01-03 3 2
output (expected):
other value
2014-01-01 Equity(65 [A]) 1 0
Equity(66 [B]) 1 0
Equity(67 [C]) 1 0
2014-01-02 Equity(65 [A]) 2 1
Equity(66 [B]) 2 1
Equity(67 [C]) 2 1
2014-01-03 Equity(65 [A]) 3 2
Equity(66 [B]) 3 2
Equity(67 [C]) 3 2
"""
df = self.macro_df.copy()
df['other'] = df.value + 1
fields = OrderedDict(self.macro_dshape.measure.fields)
fields['other'] = fields['value']
asset_info = asset_infos[0][0]
with tmp_asset_finder(equities=asset_info) as finder:
expected = pd.DataFrame(
np.array([[0, 1],
[1, 2],
[2, 3]]).repeat(3, axis=0),
index=pd.MultiIndex.from_product((
df.timestamp,
finder.retrieve_all(asset_info.index),
)),
columns=('value', 'other'),
).sort_index(axis=1)
self._test_id(
df,
var * Record(fields),
expected,
finder,
('value', 'other'),
)
def test_deltas_macro(self):
asset_info = asset_infos[0][0]
expr = bz.data(self.macro_df, name='expr', dshape=self.macro_dshape)
deltas = bz.data(
self.macro_df.iloc[:-1],
name='deltas',
dshape=self.macro_dshape,
)
deltas = bz.transform(
deltas,
value=deltas.value + 10,
timestamp=deltas.timestamp + timedelta(days=1),
)
nassets = len(asset_info)
expected_views = keymap(
pd.Timestamp, {
'2014-01-02': repeat_last_axis(np.array([10.0, 1.0]), nassets),
'2014-01-03': repeat_last_axis(np.array([11.0, 2.0]), nassets),
})
with tmp_asset_finder(equities=asset_info) as finder:
expected_output = pd.DataFrame(
list(concatv([10] * nassets, [11] * nassets)),
index=pd.MultiIndex.from_product((
sorted(expected_views.keys()),
finder.retrieve_all(asset_info.index),
)),
columns=('value', ),
)
dates = self.dates
self._run_pipeline(
expr,
deltas,
expected_views,
expected_output,
finder,
calendar=dates,
start=dates[1],
end=dates[-1],
window_length=2,
compute_fn=np.nanmax,
)
def test_deltas_macro(self):
asset_info = asset_infos[0][0]
expr = bz.data(self.macro_df, name='expr', dshape=self.macro_dshape)
deltas = bz.data(
self.macro_df.iloc[:-1],
name='deltas',
dshape=self.macro_dshape,
)
deltas = bz.transform(
deltas,
value=deltas.value + 10,
timestamp=deltas.timestamp + timedelta(days=1),
)
nassets = len(asset_info)
expected_views = keymap(pd.Timestamp, {
'2014-01-02': repeat_last_axis(np.array([10.0, 1.0]), nassets),
'2014-01-03': repeat_last_axis(np.array([11.0, 2.0]), nassets),
})
with tmp_asset_finder(equities=asset_info) as finder:
expected_output = pd.DataFrame(
list(concatv([10] * nassets, [11] * nassets)),
index=pd.MultiIndex.from_product((
sorted(expected_views.keys()),
finder.retrieve_all(asset_info.index),
)),
columns=('value',),
)
dates = self.dates
self._run_pipeline(
expr,
deltas,
expected_views,
expected_output,
finder,
calendar=dates,
start=dates[1],
end=dates[-1],
window_length=2,
compute_fn=np.nanmax,
)
def test_id_macro_dataset_multiple_columns(self):
"""
input (df):
asof_date timestamp other value
0 2014-01-01 2014-01-01 1 0
3 2014-01-02 2014-01-02 2 1
6 2014-01-03 2014-01-03 3 2
output (expected):
other value
2014-01-01 Equity(65 [A]) 1 0
Equity(66 [B]) 1 0
Equity(67 [C]) 1 0
2014-01-02 Equity(65 [A]) 2 1
Equity(66 [B]) 2 1
Equity(67 [C]) 2 1
2014-01-03 Equity(65 [A]) 3 2
Equity(66 [B]) 3 2
Equity(67 [C]) 3 2
"""
df = self.macro_df.copy()
df['other'] = df.value + 1
fields = OrderedDict(self.macro_dshape.measure.fields)
fields['other'] = fields['value']
asset_info = asset_infos[0][0]
with tmp_asset_finder(equities=asset_info) as finder:
expected = pd.DataFrame(
np.array([[0, 1], [1, 2], [2, 3]]).repeat(3, axis=0),
index=pd.MultiIndex.from_product((
df.timestamp,
finder.retrieve_all(asset_info.index),
)),
columns=('value', 'other'),
).sort_index(axis=1)
self._test_id(
df,
var * Record(fields),
expected,
finder,
('value', 'other'),
)
def test_custom_query_time_tz(self):
df = self.df.copy()
df['timestamp'] = (
pd.DatetimeIndex(df['timestamp'], tz='EST') +
timedelta(hours=8, minutes=44)
).tz_convert('utc').tz_localize(None)
df.ix[3:5, 'timestamp'] = pd.Timestamp('2014-01-01 13:45')
expr = bz.data(df, name='expr', dshape=self.dshape)
loader = BlazeLoader(data_query_time=time(8, 45), data_query_tz='EST')
ds = from_blaze(
expr,
loader=loader,
no_deltas_rule=no_deltas_rules.ignore,
missing_values=self.missing_values,
)
p = Pipeline()
p.add(ds.value.latest, 'value')
p.add(ds.int_value.latest, 'int_value')
dates = self.dates
with tmp_asset_finder() as finder:
result = SimplePipelineEngine(
loader,
dates,
finder,
).run_pipeline(p, dates[0], dates[-1])
expected = df.drop('asof_date', axis=1)
expected['timestamp'] = expected['timestamp'].dt.normalize().astype(
'datetime64[ns]',
).dt.tz_localize('utc')
expected.ix[3:5, 'timestamp'] += timedelta(days=1)
expected.set_index(['timestamp', 'sid'], inplace=True)
expected.index = pd.MultiIndex.from_product((
expected.index.levels[0],
finder.retrieve_all(expected.index.levels[1]),
))
assert_frame_equal(result, expected, check_dtype=False)
def test_id_macro_dataset(self):
"""
input (self.macro_df)
asof_date timestamp value
0 2014-01-01 2014-01-01 0
3 2014-01-02 2014-01-02 1
6 2014-01-03 2014-01-03 2
output (expected):
value
2014-01-01 Equity(65 [A]) 0
Equity(66 [B]) 0
Equity(67 [C]) 0
2014-01-02 Equity(65 [A]) 1
Equity(66 [B]) 1
Equity(67 [C]) 1
2014-01-03 Equity(65 [A]) 2
Equity(66 [B]) 2
Equity(67 [C]) 2
"""
asset_info = asset_infos[0][0]
nassets = len(asset_info)
with tmp_asset_finder() as finder:
expected = pd.DataFrame(
list(concatv([0] * nassets, [1] * nassets, [2] * nassets)),
index=pd.MultiIndex.from_product((
self.macro_df.timestamp,
finder.retrieve_all(asset_info.index),
)),
columns=('value',),
)
self._test_id(
self.macro_df,
self.macro_dshape,
expected,
finder,
('value',),
)
def test_id_macro_dataset(self):
"""
input (self.macro_df)
asof_date timestamp value
0 2014-01-01 2014-01-01 0
3 2014-01-02 2014-01-02 1
6 2014-01-03 2014-01-03 2
output (expected):
value
2014-01-01 Equity(65 [A]) 0
Equity(66 [B]) 0
Equity(67 [C]) 0
2014-01-02 Equity(65 [A]) 1
Equity(66 [B]) 1
Equity(67 [C]) 1
2014-01-03 Equity(65 [A]) 2
Equity(66 [B]) 2
Equity(67 [C]) 2
"""
asset_info = asset_infos[0][0]
nassets = len(asset_info)
with tmp_asset_finder() as finder:
expected = pd.DataFrame(
list(concatv([0] * nassets, [1] * nassets, [2] * nassets)),
index=pd.MultiIndex.from_product((
self.macro_df.timestamp,
finder.retrieve_all(asset_info.index),
)),
columns=('value', ),
)
self._test_id(
self.macro_df,
self.macro_dshape,
expected,
finder,
('value', ),
)
def test_id(self):
"""
input (self.df):
asof_date sid timestamp value
0 2014-01-01 65 2014-01-01 0
1 2014-01-01 66 2014-01-01 1
2 2014-01-01 67 2014-01-01 2
3 2014-01-02 65 2014-01-02 1
4 2014-01-02 66 2014-01-02 2
5 2014-01-02 67 2014-01-02 3
6 2014-01-03 65 2014-01-03 2
7 2014-01-03 66 2014-01-03 3
8 2014-01-03 67 2014-01-03 4
output (expected)
value
2014-01-01 Equity(65 [A]) 0
Equity(66 [B]) 1
Equity(67 [C]) 2
2014-01-02 Equity(65 [A]) 1
Equity(66 [B]) 2
Equity(67 [C]) 3
2014-01-03 Equity(65 [A]) 2
Equity(66 [B]) 3
Equity(67 [C]) 4
"""
with tmp_asset_finder() as finder:
expected = self.df.drop('asof_date',
axis=1).set_index(['timestamp', 'sid'], )
expected.index = pd.MultiIndex.from_product((
expected.index.levels[0],
finder.retrieve_all(expected.index.levels[1]),
))
self._test_id(self.df, self.dshape, expected, finder, (
'int_value',
'value',
))
def test_id(self):
"""
input (self.df):
asof_date sid timestamp value
0 2014-01-01 65 2014-01-01 0
1 2014-01-01 66 2014-01-01 1
2 2014-01-01 67 2014-01-01 2
3 2014-01-02 65 2014-01-02 1
4 2014-01-02 66 2014-01-02 2
5 2014-01-02 67 2014-01-02 3
6 2014-01-03 65 2014-01-03 2
7 2014-01-03 66 2014-01-03 3
8 2014-01-03 67 2014-01-03 4
output (expected)
value
2014-01-01 Equity(65 [A]) 0
Equity(66 [B]) 1
Equity(67 [C]) 2
2014-01-02 Equity(65 [A]) 1
Equity(66 [B]) 2
Equity(67 [C]) 3
2014-01-03 Equity(65 [A]) 2
Equity(66 [B]) 3
Equity(67 [C]) 4
"""
with tmp_asset_finder() as finder:
expected = self.df.drop('asof_date', axis=1).set_index(
['timestamp', 'sid'],
)
expected.index = pd.MultiIndex.from_product((
expected.index.levels[0],
finder.retrieve_all(expected.index.levels[1]),
))
self._test_id(
self.df, self.dshape, expected, finder, ('int_value', 'value',)
)
def test_custom_query_time_tz(self):
df = self.df.copy()
df['timestamp'] = (
pd.DatetimeIndex(df['timestamp'], tz='EST') +
timedelta(hours=8, minutes=44)).tz_convert('utc').tz_localize(None)
df.ix[3:5, 'timestamp'] = pd.Timestamp('2014-01-01 13:45')
expr = bz.data(df, name='expr', dshape=self.dshape)
loader = BlazeLoader(data_query_time=time(8, 45), data_query_tz='EST')
ds = from_blaze(
expr,
loader=loader,
no_deltas_rule=no_deltas_rules.ignore,
missing_values=self.missing_values,
)
p = Pipeline()
p.add(ds.value.latest, 'value')
p.add(ds.int_value.latest, 'int_value')
dates = self.dates
with tmp_asset_finder() as finder:
result = SimplePipelineEngine(
loader,
dates,
finder,
).run_pipeline(p, dates[0], dates[-1])
expected = df.drop('asof_date', axis=1)
expected['timestamp'] = expected['timestamp'].dt.normalize().astype(
'datetime64[ns]', ).dt.tz_localize('utc')
expected.ix[3:5, 'timestamp'] += timedelta(days=1)
expected.set_index(['timestamp', 'sid'], inplace=True)
expected.index = pd.MultiIndex.from_product((
expected.index.levels[0],
finder.retrieve_all(expected.index.levels[1]),
))
assert_frame_equal(result, expected, check_dtype=False)
def test_novel_deltas(self, asset_info):
base_dates = pd.DatetimeIndex([
pd.Timestamp('2014-01-01'),
pd.Timestamp('2014-01-04')
])
repeated_dates = base_dates.repeat(3)
baseline = pd.DataFrame({
'sid': self.sids * 2,
'value': (0., 1., 2., 1., 2., 3.),
'int_value': (0, 1, 2, 1, 2, 3),
'asof_date': repeated_dates,
'timestamp': repeated_dates,
})
expr = bz.data(baseline, name='expr', dshape=self.dshape)
deltas = bz.data(
odo(
bz.transform(
expr,
value=expr.value + 10,
timestamp=expr.timestamp + timedelta(days=1),
),
pd.DataFrame,
),
name='delta',
dshape=self.dshape,
)
expected_views = keymap(pd.Timestamp, {
'2014-01-03': np.array([[10.0, 11.0, 12.0],
[10.0, 11.0, 12.0],
[10.0, 11.0, 12.0]]),
'2014-01-06': np.array([[10.0, 11.0, 12.0],
[10.0, 11.0, 12.0],
[11.0, 12.0, 13.0]]),
})
if len(asset_info) == 4:
expected_views = valmap(
lambda view: np.c_[view, [np.nan, np.nan, np.nan]],
expected_views,
)
expected_output_buffer = [10, 11, 12, np.nan, 11, 12, 13, np.nan]
else:
expected_output_buffer = [10, 11, 12, 11, 12, 13]
cal = pd.DatetimeIndex([
pd.Timestamp('2014-01-01'),
pd.Timestamp('2014-01-02'),
pd.Timestamp('2014-01-03'),
# omitting the 4th and 5th to simulate a weekend
pd.Timestamp('2014-01-06'),
])
with tmp_asset_finder(equities=asset_info) as finder:
expected_output = pd.DataFrame(
expected_output_buffer,
index=pd.MultiIndex.from_product((
sorted(expected_views.keys()),
finder.retrieve_all(asset_info.index),
)),
columns=('value',),
)
self._run_pipeline(
expr,
deltas,
expected_views,
expected_output,
finder,
calendar=cal,
start=cal[2],
end=cal[-1],
window_length=3,
compute_fn=op.itemgetter(-1),
)
def test_novel_deltas_macro(self):
asset_info = asset_infos[0][0]
base_dates = pd.DatetimeIndex(
[pd.Timestamp('2014-01-01'),
pd.Timestamp('2014-01-04')])
baseline = pd.DataFrame({
'value': (0, 1),
'asof_date': base_dates,
'timestamp': base_dates,
})
expr = bz.data(baseline, name='expr', dshape=self.macro_dshape)
deltas = bz.data(baseline, name='deltas', dshape=self.macro_dshape)
deltas = bz.transform(
deltas,
value=deltas.value + 10,
timestamp=deltas.timestamp + timedelta(days=1),
)
nassets = len(asset_info)
expected_views = keymap(
pd.Timestamp, {
'2014-01-03':
repeat_last_axis(
np.array([10.0, 10.0, 10.0]),
nassets,
),
'2014-01-06':
repeat_last_axis(
np.array([10.0, 10.0, 11.0]),
nassets,
),
})
cal = pd.DatetimeIndex([
pd.Timestamp('2014-01-01'),
pd.Timestamp('2014-01-02'),
pd.Timestamp('2014-01-03'),
# omitting the 4th and 5th to simulate a weekend
pd.Timestamp('2014-01-06'),
])
with tmp_asset_finder(equities=asset_info) as finder:
expected_output = pd.DataFrame(
list(concatv([10] * nassets, [11] * nassets)),
index=pd.MultiIndex.from_product((
sorted(expected_views.keys()),
finder.retrieve_all(asset_info.index),
)),
columns=('value', ),
)
self._run_pipeline(
expr,
deltas,
expected_views,
expected_output,
finder,
calendar=cal,
start=cal[2],
end=cal[-1],
window_length=3,
compute_fn=op.itemgetter(-1),
)
def test_novel_deltas(self, asset_info):
base_dates = pd.DatetimeIndex(
[pd.Timestamp('2014-01-01'),
pd.Timestamp('2014-01-04')])
repeated_dates = base_dates.repeat(3)
baseline = pd.DataFrame({
'sid': self.ASSET_FINDER_EQUITY_SIDS * 2,
'value': (0., 1., 2., 1., 2., 3.),
'int_value': (0, 1, 2, 1, 2, 3),
'asof_date': repeated_dates,
'timestamp': repeated_dates,
})
expr = bz.data(baseline, name='expr', dshape=self.dshape)
deltas = bz.data(
odo(
bz.transform(
expr,
value=expr.value + 10,
timestamp=expr.timestamp + timedelta(days=1),
),
pd.DataFrame,
),
name='delta',
dshape=self.dshape,
)
expected_views = keymap(
pd.Timestamp, {
'2014-01-03':
np.array([[10.0, 11.0, 12.0], [10.0, 11.0, 12.0],
[10.0, 11.0, 12.0]]),
'2014-01-06':
np.array([[10.0, 11.0, 12.0], [10.0, 11.0, 12.0],
[11.0, 12.0, 13.0]]),
})
if len(asset_info) == 4:
expected_views = valmap(
lambda view: np.c_[view, [np.nan, np.nan, np.nan]],
expected_views,
)
expected_output_buffer = [10, 11, 12, np.nan, 11, 12, 13, np.nan]
else:
expected_output_buffer = [10, 11, 12, 11, 12, 13]
cal = pd.DatetimeIndex([
pd.Timestamp('2014-01-01'),
pd.Timestamp('2014-01-02'),
pd.Timestamp('2014-01-03'),
# omitting the 4th and 5th to simulate a weekend
pd.Timestamp('2014-01-06'),
])
with tmp_asset_finder(equities=asset_info) as finder:
expected_output = pd.DataFrame(
expected_output_buffer,
index=pd.MultiIndex.from_product((
sorted(expected_views.keys()),
finder.retrieve_all(asset_info.index),
)),
columns=('value', ),
)
self._run_pipeline(
expr,
deltas,
expected_views,
expected_output,
finder,
calendar=cal,
start=cal[2],
end=cal[-1],
window_length=3,
compute_fn=op.itemgetter(-1),
)
def test_id_ffill_out_of_window(self):
"""
input (df):
asof_date timestamp sid other value
0 2013-12-22 2013-12-22 65 0 0
1 2013-12-22 2013-12-22 66 NaN 1
2 2013-12-22 2013-12-22 67 2 NaN
3 2013-12-23 2013-12-23 65 NaN 1
4 2013-12-23 2013-12-23 66 2 NaN
5 2013-12-23 2013-12-23 67 3 3
6 2013-12-24 2013-12-24 65 2 NaN
7 2013-12-24 2013-12-24 66 3 3
8 2013-12-24 2013-12-24 67 NaN 4
output (expected):
other value
2014-01-01 Equity(65 [A]) 2 1
Equity(66 [B]) 3 3
Equity(67 [C]) 3 4
2014-01-02 Equity(65 [A]) 2 1
Equity(66 [B]) 3 3
Equity(67 [C]) 3 4
2014-01-03 Equity(65 [A]) 2 1
Equity(66 [B]) 3 3
Equity(67 [C]) 3 4
"""
dates = self.dates.repeat(3) - timedelta(days=10)
df = pd.DataFrame({
'sid': self.sids * 3,
'value': (0, 1, np.nan, 1, np.nan, 3, np.nan, 3, 4),
'other': (0, np.nan, 2, np.nan, 2, 3, 2, 3, np.nan),
'asof_date': dates,
'timestamp': dates,
})
fields = OrderedDict(self.dshape.measure.fields)
fields['other'] = fields['value']
with tmp_asset_finder() as finder:
expected = pd.DataFrame(
np.array([[2, 1],
[3, 3],
[3, 4],
[2, 1],
[3, 3],
[3, 4],
[2, 1],
[3, 3],
[3, 4]]),
columns=['other', 'value'],
index=pd.MultiIndex.from_product(
(self.dates, finder.retrieve_all(self.sids)),
),
)
self._test_id(
df,
var * Record(fields),
expected,
finder,
('value', 'other'),
)
def test_deltas(self, asset_info, add_extra_sid):
df = self.df.copy()
if add_extra_sid:
extra_sid_df = pd.DataFrame({
'asof_date': self.dates,
'timestamp': self.dates,
'sid': (ord('E'), ) * 3,
'value': (
3.,
4.,
5.,
),
'int_value': (3, 4, 5),
})
df = df.append(extra_sid_df, ignore_index=True)
expr = bz.data(df, name='expr', dshape=self.dshape)
deltas = bz.data(df, dshape=self.dshape)
deltas = bz.data(
odo(
bz.transform(
deltas,
value=deltas.value + 10,
timestamp=deltas.timestamp + timedelta(days=1),
),
pd.DataFrame,
),
name='delta',
dshape=self.dshape,
)
expected_views = keymap(
pd.Timestamp, {
'2014-01-02': np.array([[10.0, 11.0, 12.0], [1.0, 2.0, 3.0]]),
'2014-01-03': np.array([[11.0, 12.0, 13.0], [2.0, 3.0, 4.0]]),
'2014-01-04': np.array([[12.0, 13.0, 14.0], [12.0, 13.0, 14.0]
]),
})
nassets = len(asset_info)
if nassets == 4:
expected_views = valmap(
lambda view: np.c_[view, [np.nan, np.nan]],
expected_views,
)
with tmp_asset_finder(equities=asset_info) as finder:
expected_output = pd.DataFrame(
list(concatv([12] * nassets, [13] * nassets, [14] * nassets)),
index=pd.MultiIndex.from_product((
sorted(expected_views.keys()),
finder.retrieve_all(asset_info.index),
)),
columns=('value', ),
)
dates = self.dates
dates = dates.insert(len(dates), dates[-1] + timedelta(days=1))
self._run_pipeline(
expr,
deltas,
expected_views,
expected_output,
finder,
calendar=dates,
start=dates[1],
end=dates[-1],
window_length=2,
compute_fn=np.nanmax,
)
def test_id_take_last_in_group(self):
T = pd.Timestamp
df = pd.DataFrame(
columns=['asof_date', 'timestamp', 'sid', 'other', 'value'],
data=[
[T('2014-01-01'),
T('2014-01-01 00'), 65, 0, 0],
[T('2014-01-01'),
T('2014-01-01 01'), 65, 1, np.nan],
[T('2014-01-01'),
T('2014-01-01 00'), 66, np.nan, np.nan],
[T('2014-01-01'),
T('2014-01-01 01'), 66, np.nan, 1],
[T('2014-01-01'),
T('2014-01-01 00'), 67, 2, np.nan],
[T('2014-01-01'),
T('2014-01-01 01'), 67, np.nan, np.nan],
[T('2014-01-02'),
T('2014-01-02 00'), 65, np.nan, np.nan],
[T('2014-01-02'),
T('2014-01-02 01'), 65, np.nan, 1],
[T('2014-01-02'),
T('2014-01-02 00'), 66, np.nan, np.nan],
[T('2014-01-02'),
T('2014-01-02 01'), 66, 2, np.nan],
[T('2014-01-02'),
T('2014-01-02 00'), 67, 3, 3],
[T('2014-01-02'),
T('2014-01-02 01'), 67, 3, 3],
[T('2014-01-03'),
T('2014-01-03 00'), 65, 2, np.nan],
[T('2014-01-03'),
T('2014-01-03 01'), 65, 2, np.nan],
[T('2014-01-03'),
T('2014-01-03 00'), 66, 3, 3],
[T('2014-01-03'),
T('2014-01-03 01'), 66, np.nan, np.nan],
[T('2014-01-03'),
T('2014-01-03 00'), 67, np.nan, np.nan],
[T('2014-01-03'),
T('2014-01-03 01'), 67, np.nan, 4],
],
)
fields = OrderedDict(self.dshape.measure.fields)
fields['other'] = fields['value']
with tmp_asset_finder() as finder:
expected = pd.DataFrame(
columns=['other', 'value'],
data=[
[1, 0], # 2014-01-01 Equity(65 [A])
[np.nan, 1], # Equity(66 [B])
[2, np.nan], # Equity(67 [C])
[1, 1], # 2014-01-02 Equity(65 [A])
[2, 1], # Equity(66 [B])
[3, 3], # Equity(67 [C])
[2, 1], # 2014-01-03 Equity(65 [A])
[3, 3], # Equity(66 [B])
[3, 3], # Equity(67 [C])
],
index=pd.MultiIndex.from_product(
(self.dates, finder.retrieve_all(self.sids)), ),
)
self._test_id(
df,
var * Record(fields),
expected,
finder,
('value', 'other'),
)
def test_id_take_last_in_group_macro(self):
"""
output (expected):
other value
2014-01-01 Equity(65 [A]) NaN 1
Equity(66 [B]) NaN 1
Equity(67 [C]) NaN 1
2014-01-02 Equity(65 [A]) 1 2
Equity(66 [B]) 1 2
Equity(67 [C]) 1 2
2014-01-03 Equity(65 [A]) 2 2
Equity(66 [B]) 2 2
Equity(67 [C]) 2 2
"""
T = pd.Timestamp
df = pd.DataFrame(
columns=['asof_date', 'timestamp', 'other', 'value'],
data=[
[T('2014-01-01'),
T('2014-01-01 00'), np.nan, 1],
[T('2014-01-01'),
T('2014-01-01 01'), np.nan, np.nan],
[T('2014-01-02'),
T('2014-01-02 00'), 1, np.nan],
[T('2014-01-02'),
T('2014-01-02 01'), np.nan, 2],
[T('2014-01-03'),
T('2014-01-03 00'), 2, np.nan],
[T('2014-01-03'), T('2014-01-03 01'), 3, 3],
],
)
fields = OrderedDict(self.macro_dshape.measure.fields)
fields['other'] = fields['value']
with tmp_asset_finder() as finder:
expected = pd.DataFrame(
columns=[
'other',
'value',
],
data=[
[np.nan, 1], # 2014-01-01 Equity(65 [A])
[np.nan, 1], # Equity(66 [B])
[np.nan, 1], # Equity(67 [C])
[1, 2], # 2014-01-02 Equity(65 [A])
[1, 2], # Equity(66 [B])
[1, 2], # Equity(67 [C])
[2, 2], # 2014-01-03 Equity(65 [A])
[2, 2], # Equity(66 [B])
[2, 2], # Equity(67 [C])
],
index=pd.MultiIndex.from_product(
(self.dates, finder.retrieve_all(self.sids)), ),
)
self._test_id(
df,
var * Record(fields),
expected,
finder,
('value', 'other'),
)
def test_deltas(self, asset_info, add_extra_sid):
df = self.df.copy()
if add_extra_sid:
extra_sid_df = pd.DataFrame({
'asof_date': self.dates,
'timestamp': self.dates,
'sid': (ord('E'),) * 3,
'value': (3., 4., 5.,),
'int_value': (3, 4, 5),
})
df = df.append(extra_sid_df, ignore_index=True)
expr = bz.data(df, name='expr', dshape=self.dshape)
deltas = bz.data(df, dshape=self.dshape)
deltas = bz.data(
odo(
bz.transform(
deltas,
value=deltas.value + 10,
timestamp=deltas.timestamp + timedelta(days=1),
),
pd.DataFrame,
),
name='delta',
dshape=self.dshape,
)
expected_views = keymap(pd.Timestamp, {
'2014-01-02': np.array([[10.0, 11.0, 12.0],
[1.0, 2.0, 3.0]]),
'2014-01-03': np.array([[11.0, 12.0, 13.0],
[2.0, 3.0, 4.0]]),
'2014-01-04': np.array([[12.0, 13.0, 14.0],
[12.0, 13.0, 14.0]]),
})
nassets = len(asset_info)
if nassets == 4:
expected_views = valmap(
lambda view: np.c_[view, [np.nan, np.nan]],
expected_views,
)
with tmp_asset_finder(equities=asset_info) as finder:
expected_output = pd.DataFrame(
list(concatv([12] * nassets, [13] * nassets, [14] * nassets)),
index=pd.MultiIndex.from_product((
sorted(expected_views.keys()),
finder.retrieve_all(asset_info.index),
)),
columns=('value',),
)
dates = self.dates
dates = dates.insert(len(dates), dates[-1] + timedelta(days=1))
self._run_pipeline(
expr,
deltas,
expected_views,
expected_output,
finder,
calendar=dates,
start=dates[1],
end=dates[-1],
window_length=2,
compute_fn=np.nanmax,
)
