def gen_calendars(start, stop, critical_dates):
"""
Generate calendars to use as inputs.
"""
all_dates = pd.date_range(start, stop, tz='utc')
for to_drop in map(list, powerset(critical_dates)):
# Have to yield tuples.
yield (all_dates.drop(to_drop),)
# Also test with the trading calendar.
yield (trading_days[trading_days.slice_indexer(start, stop)],)
def gen_calendars():
"""
Generate calendars to use as inputs to test_compute_latest.
"""
start, stop = '2014-01-01', '2014-01-31'
all_dates = pd.date_range(start, stop, tz='utc')
# These dates are the points where announcements or knowledge dates happen.
# Test every combination of them being absent.
critical_dates = pd.to_datetime([
'2014-01-05',
'2014-01-10',
'2014-01-15',
'2014-01-20',
])
for to_drop in map(list, powerset(critical_dates)):
# Have to yield tuples.
yield (all_dates.drop(to_drop), )
# Also test with the trading calendar.
yield (trading_days[trading_days.slice_indexer(start, stop)], )
def gen_calendars():
"""
Generate calendars to use as inputs to test_compute_latest.
"""
start, stop = '2014-01-01', '2014-01-31'
all_dates = pd.date_range(start, stop, tz='utc')
# These dates are the points where announcements or knowledge dates happen.
# Test every combination of them being absent.
critical_dates = pd.to_datetime([
'2014-01-05',
'2014-01-10',
'2014-01-15',
'2014-01-20',
])
for to_drop in map(list, powerset(critical_dates)):
# Have to yield tuples.
yield (all_dates.drop(to_drop),)
# Also test with the trading calendar.
yield (trading_days[trading_days.slice_indexer(start, stop)],)
def test_ingest(self):
zipline_root = self.enter_instance_context(tmp_dir()).path
env = self.enter_instance_context(tmp_trading_env())
start = pd.Timestamp('2014-01-06', tz='utc')
end = pd.Timestamp('2014-01-10', tz='utc')
calendar = trading_days[trading_days.slice_indexer(start, end)]
minutes = env.minutes_for_days_in_range(calendar[0], calendar[-1])
outer_environ = {
'ZIPLINE_ROOT': zipline_root,
}
sids = tuple(range(3))
equities = make_simple_equity_info(
sids,
calendar[0],
calendar[-1],
)
daily_bar_data = make_bar_data(equities, calendar)
minute_bar_data = make_bar_data(equities, minutes)
first_split_ratio = 0.5
second_split_ratio = 0.1
splits = pd.DataFrame.from_records([
{
'effective_date': str_to_seconds('2014-01-08'),
'ratio': first_split_ratio,
'sid': 0,
},
{
'effective_date': str_to_seconds('2014-01-09'),
'ratio': second_split_ratio,
'sid': 1,
},
])
@self.register('bundle',
calendar=calendar,
opens=env.opens_in_range(calendar[0], calendar[-1]),
closes=env.closes_in_range(calendar[0], calendar[-1]))
def bundle_ingest(environ,
asset_db_writer,
minute_bar_writer,
daily_bar_writer,
adjustment_writer,
calendar,
cache,
show_progress):
assert_is(environ, outer_environ)
asset_db_writer.write(equities=equities)
minute_bar_writer.write(minute_bar_data)
daily_bar_writer.write(daily_bar_data)
adjustment_writer.write(splits=splits)
assert_is_instance(calendar, pd.DatetimeIndex)
assert_is_instance(cache, dataframe_cache)
assert_is_instance(show_progress, bool)
self.ingest('bundle', environ=outer_environ)
bundle = load('bundle', environ=outer_environ)
assert_equal(set(bundle.asset_finder.sids), set(sids))
columns = 'open', 'high', 'low', 'close', 'volume'
actual = bundle.minute_bar_reader.load_raw_arrays(
columns,
minutes[0],
minutes[-1],
sids,
)
for actual_column, colname in zip(actual, columns):
assert_equal(
actual_column,
expected_bar_values_2d(minutes, equities, colname),
msg=colname,
)
actual = bundle.daily_bar_reader.load_raw_arrays(
columns,
calendar[0],
calendar[-1],
sids,
)
for actual_column, colname in zip(actual, columns):
assert_equal(
actual_column,
expected_bar_values_2d(calendar, equities, colname),
msg=colname,
)
adjustments_for_cols = bundle.adjustment_reader.load_adjustments(
columns,
calendar,
pd.Index(sids),
)
for column, adjustments in zip(columns, adjustments_for_cols[:-1]):
# iterate over all the adjustments but `volume`
assert_equal(
adjustments,
{
2: [Float64Multiply(
first_row=0,
last_row=2,
first_col=0,
last_col=0,
value=first_split_ratio,
)],
3: [Float64Multiply(
first_row=0,
last_row=3,
first_col=1,
last_col=1,
value=second_split_ratio,
)],
},
msg=column,
)
# check the volume, the value should be 1/ratio
assert_equal(
adjustments_for_cols[-1],
{
2: [Float64Multiply(
first_row=0,
last_row=2,
first_col=0,
last_col=0,
value=1 / first_split_ratio,
)],
3: [Float64Multiply(
first_row=0,
last_row=3,
first_col=1,
last_col=1,
value=1 / second_split_ratio,
)],
},
msg='volume',
)
def test_ingest(self):
env = self.enter_instance_context(tmp_trading_env())
start = pd.Timestamp('2014-01-06', tz='utc')
end = pd.Timestamp('2014-01-10', tz='utc')
calendar = trading_days[trading_days.slice_indexer(start, end)]
minutes = env.minutes_for_days_in_range(calendar[0], calendar[-1])
sids = tuple(range(3))
equities = make_simple_equity_info(
sids,
calendar[0],
calendar[-1],
)
daily_bar_data = make_bar_data(equities, calendar)
minute_bar_data = make_bar_data(equities, minutes)
first_split_ratio = 0.5
second_split_ratio = 0.1
splits = pd.DataFrame.from_records([
{
'effective_date': str_to_seconds('2014-01-08'),
'ratio': first_split_ratio,
'sid': 0,
},
{
'effective_date': str_to_seconds('2014-01-09'),
'ratio': second_split_ratio,
'sid': 1,
},
])
@self.register('bundle',
calendar=calendar,
opens=env.opens_in_range(calendar[0], calendar[-1]),
closes=env.closes_in_range(calendar[0], calendar[-1]))
def bundle_ingest(environ,
asset_db_writer,
minute_bar_writer,
daily_bar_writer,
adjustment_writer,
calendar,
cache,
show_progress,
output_dir):
assert_is(environ, self.environ)
asset_db_writer.write(equities=equities)
minute_bar_writer.write(minute_bar_data)
daily_bar_writer.write(daily_bar_data)
adjustment_writer.write(splits=splits)
assert_is_instance(calendar, pd.DatetimeIndex)
assert_is_instance(cache, dataframe_cache)
assert_is_instance(show_progress, bool)
self.ingest('bundle', environ=self.environ)
bundle = self.load('bundle', environ=self.environ)
assert_equal(set(bundle.asset_finder.sids), set(sids))
columns = 'open', 'high', 'low', 'close', 'volume'
actual = bundle.minute_bar_reader.load_raw_arrays(
columns,
minutes[0],
minutes[-1],
sids,
)
for actual_column, colname in zip(actual, columns):
assert_equal(
actual_column,
expected_bar_values_2d(minutes, equities, colname),
msg=colname,
)
actual = bundle.daily_bar_reader.load_raw_arrays(
columns,
calendar[0],
calendar[-1],
sids,
)
for actual_column, colname in zip(actual, columns):
assert_equal(
actual_column,
expected_bar_values_2d(calendar, equities, colname),
msg=colname,
)
adjustments_for_cols = bundle.adjustment_reader.load_adjustments(
columns,
calendar,
pd.Index(sids),
)
for column, adjustments in zip(columns, adjustments_for_cols[:-1]):
# iterate over all the adjustments but `volume`
assert_equal(
adjustments,
{
2: [Float64Multiply(
first_row=0,
last_row=2,
first_col=0,
last_col=0,
value=first_split_ratio,
)],
3: [Float64Multiply(
first_row=0,
last_row=3,
first_col=1,
last_col=1,
value=second_split_ratio,
)],
},
msg=column,
)
# check the volume, the value should be 1/ratio
assert_equal(
adjustments_for_cols[-1],
{
2: [Float64Multiply(
first_row=0,
last_row=2,
first_col=0,
last_col=0,
value=1 / first_split_ratio,
)],
3: [Float64Multiply(
first_row=0,
last_row=3,
first_col=1,
last_col=1,
value=1 / second_split_ratio,
)],
},
msg='volume',
)
