def test_listing_currency(self):
# Test loading on all assets.
all_assets = np.array(list(self.assets))
all_results = self.daily_bar_reader.currency_codes(all_assets)
all_expected = self.make_equity_daily_bar_currency_codes(
self.DAILY_BARS_TEST_QUERY_COUNTRY_CODE, all_assets,
).values
assert_equal(all_results, all_expected)
self.assertEqual(all_results.dtype, np.dtype(object))
for code in all_results:
self.assertIsInstance(code, str)
# Check all possible subsets of assets.
for indices in map(list, powerset(range(len(all_assets)))):
# Empty queries aren't currently supported.
if not indices:
continue
assets = all_assets[indices]
results = self.daily_bar_reader.currency_codes(assets)
expected = all_expected[indices]
assert_equal(results, expected)
def test_compute_lifetimes(self):
assets_per_exchange = 4
trading_day = self.trading_calendar.day
first_start = pd.Timestamp('2015-04-01', tz='UTC')
equities = pd.concat(
[
make_rotating_equity_info(
num_assets=assets_per_exchange,
first_start=first_start,
frequency=trading_day,
periods_between_starts=3,
asset_lifetime=5,
exchange=exchange,
) for exchange in (
'US_EXCHANGE_1',
'US_EXCHANGE_2',
'CA_EXCHANGE',
'JP_EXCHANGE',
)
],
ignore_index=True,
)
# make every symbol unique
equities['symbol'] = list(string.ascii_uppercase[:len(equities)])
equities['real_sid'] = equities['symbol']
# shuffle up the sids so they are not contiguous per exchange
sids = np.arange(len(equities))
np.random.RandomState(1337).shuffle(sids)
equities.index = sids
permute_sid = dict(zip(sids, range(len(sids)))).__getitem__
exchanges = pd.DataFrame.from_records([
{
'exchange': 'US_EXCHANGE_1',
'country_code': 'US'
},
{
'exchange': 'US_EXCHANGE_2',
'country_code': 'US'
},
{
'exchange': 'CA_EXCHANGE',
'country_code': 'CA'
},
{
'exchange': 'JP_EXCHANGE',
'country_code': 'JP'
},
])
sids_by_country = {
'US': equities.index[:2 * assets_per_exchange],
'CA':
equities.index[2 * assets_per_exchange:3 * assets_per_exchange],
'JP': equities.index[3 * assets_per_exchange:],
}
self.write_assets(equities=equities, exchanges=exchanges)
finder = self.asset_finder
all_dates = pd.date_range(
start=first_start,
end=equities.end_date.max(),
freq=trading_day,
)
for dates in all_subindices(all_dates):
expected_with_start_raw = full(
shape=(len(dates), assets_per_exchange),
fill_value=False,
dtype=bool,
)
expected_no_start_raw = full(
shape=(len(dates), assets_per_exchange),
fill_value=False,
dtype=bool,
)
for i, date in enumerate(dates):
it = equities.iloc[:4][['start_date',
'end_date']].itertuples(index=False, )
for j, (start, end) in enumerate(it):
# This way of doing the checks is redundant, but very
# clear.
if start <= date <= end:
expected_with_start_raw[i, j] = True
if start < date:
expected_no_start_raw[i, j] = True
for country_codes in powerset(exchanges.country_code.unique()):
expected_sids = pd.Int64Index(
sorted(
concat(sids_by_country[country_code]
for country_code in country_codes)))
permuted_sids = [
sid for sid in sorted(expected_sids, key=permute_sid)
]
tile_count = len(country_codes) + ('US' in country_codes)
expected_with_start = pd.DataFrame(
data=np.tile(
expected_with_start_raw,
tile_count,
),
index=dates,
columns=pd.Int64Index(permuted_sids),
)
result = finder.lifetimes(
dates,
include_start_date=True,
country_codes=country_codes,
)
assert_equal(result.columns, expected_sids)
result = result[permuted_sids]
assert_equal(result, expected_with_start)
expected_no_start = pd.DataFrame(
data=np.tile(
expected_no_start_raw,
tile_count,
),
index=dates,
columns=pd.Int64Index(permuted_sids),
)
result = finder.lifetimes(
dates,
include_start_date=False,
country_codes=country_codes,
)
assert_equal(result.columns, expected_sids)
result = result[permuted_sids]
assert_equal(result, expected_no_start)
