code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
|---|---|---|---|---|---|---|---|
def test_end_on_asset_start(self):
"""
Test loading with queries that end on the first day of each asset's
lifetime.
"""
columns = ['close', 'volume']
for asset in self.assets:
self._check_read_results(
columns,
self.assets,
... |
Test loading with queries that end on the first day of each asset's
lifetime.
| test_end_on_asset_start | python | quantopian/zipline | tests/data/test_daily_bars.py | https://github.com/quantopian/zipline/blob/master/tests/data/test_daily_bars.py | Apache-2.0 |
def test_end_on_asset_end(self):
"""
Test loading with queries that end on the last day of each asset's
lifetime.
"""
columns = [CLOSE, VOLUME]
for asset in self.assets:
self._check_read_results(
columns,
self.assets,
... |
Test loading with queries that end on the last day of each asset's
lifetime.
| test_end_on_asset_end | python | quantopian/zipline | tests/data/test_daily_bars.py | https://github.com/quantopian/zipline/blob/master/tests/data/test_daily_bars.py | Apache-2.0 |
def test_read_known_and_unknown_sids(self):
"""
Test a query with some known sids mixed in with unknown sids.
"""
# Construct a list of alternating valid and invalid query sids,
# bookended by invalid sids.
#
# E.g.
# INVALID VALID INVALID VALID ... VAL... |
Test a query with some known sids mixed in with unknown sids.
| test_read_known_and_unknown_sids | python | quantopian/zipline | tests/data/test_daily_bars.py | https://github.com/quantopian/zipline/blob/master/tests/data/test_daily_bars.py | Apache-2.0 |
def test_unadjusted_get_value(self):
"""Test get_value() on both a price field (CLOSE) and VOLUME."""
reader = self.daily_bar_reader
def make_failure_msg(asset, date, field):
return "Unexpected value for sid={}; date={}; field={}.".format(
asset,
date... | Test get_value() on both a price field (CLOSE) and VOLUME. | test_unadjusted_get_value | python | quantopian/zipline | tests/data/test_daily_bars.py | https://github.com/quantopian/zipline/blob/master/tests/data/test_daily_bars.py | Apache-2.0 |
def test_unadjusted_get_value_no_data(self):
"""Test behavior of get_value() around missing data."""
reader = self.daily_bar_reader
for asset in self.assets:
before_start = self.trading_calendar.previous_session_label(
self.asset_start(asset)
)
... | Test behavior of get_value() around missing data. | test_unadjusted_get_value_no_data | python | quantopian/zipline | tests/data/test_daily_bars.py | https://github.com/quantopian/zipline/blob/master/tests/data/test_daily_bars.py | Apache-2.0 |
def test_write_empty_country(self):
"""
Test that we can write an empty country to an HDF5 daily bar writer.
This is useful functionality for some tests, but it requires a bunch of
special cased logic in the writer.
"""
path = self.tmpdir.getpath('empty.h5')
writ... |
Test that we can write an empty country to an HDF5 daily bar writer.
This is useful functionality for some tests, but it requires a bunch of
special cased logic in the writer.
| test_write_empty_country | python | quantopian/zipline | tests/data/test_hdf5_daily_bars.py | https://github.com/quantopian/zipline/blob/master/tests/data/test_hdf5_daily_bars.py | Apache-2.0 |
def test_precision_after_scaling(self):
'''For numbers that don't have an exact float representation,
assert that scaling the value does not cause a loss in precision.
'''
minute = self.market_opens[self.test_calendar_start]
sid = 1
data = DataFrame(
data={
... | For numbers that don't have an exact float representation,
assert that scaling the value does not cause a loss in precision.
| test_precision_after_scaling | python | quantopian/zipline | tests/data/test_minute_bars.py | https://github.com/quantopian/zipline/blob/master/tests/data/test_minute_bars.py | Apache-2.0 |
def test_append_to_same_day(self):
"""
Test writing data with the same date as existing data in our file.
"""
sid = 1
first_minute = self.market_opens[TEST_CALENDAR_START]
data = DataFrame(
data={
'open': [10.0],
'high': [20.0]... |
Test writing data with the same date as existing data in our file.
| test_append_to_same_day | python | quantopian/zipline | tests/data/test_minute_bars.py | https://github.com/quantopian/zipline/blob/master/tests/data/test_minute_bars.py | Apache-2.0 |
def test_write_multiple_sids(self):
"""
Test writing multiple sids.
Tests both that the data is written to the correct sid, as well as
ensuring that the logic for creating the subdirectory path to each sid
does not cause issues from attempts to recreate existing paths.
(... |
Test writing multiple sids.
Tests both that the data is written to the correct sid, as well as
ensuring that the logic for creating the subdirectory path to each sid
does not cause issues from attempts to recreate existing paths.
(Calling out this coverage, because an assertion... | test_write_multiple_sids | python | quantopian/zipline | tests/data/test_minute_bars.py | https://github.com/quantopian/zipline/blob/master/tests/data/test_minute_bars.py | Apache-2.0 |
def test_differing_nans(self):
"""
Also test nans of differing values/construction.
"""
sid = 1
last_date = self.writer.last_date_in_output_for_sid(sid)
self.assertIs(last_date, NaT)
self.writer.pad(sid, TEST_CALENDAR_START)
last_date = self.writer.last_... |
Also test nans of differing values/construction.
| test_differing_nans | python | quantopian/zipline | tests/data/test_minute_bars.py | https://github.com/quantopian/zipline/blob/master/tests/data/test_minute_bars.py | Apache-2.0 |
def test_unadjusted_minutes_early_close(self):
"""
Test unadjusted minute window, ensuring that early closes are filtered
out.
"""
day_before_thanksgiving = Timestamp('2015-11-25', tz='UTC')
xmas_eve = Timestamp('2015-12-24', tz='UTC')
market_day_after_xmas = Time... |
Test unadjusted minute window, ensuring that early closes are filtered
out.
| test_unadjusted_minutes_early_close | python | quantopian/zipline | tests/data/test_minute_bars.py | https://github.com/quantopian/zipline/blob/master/tests/data/test_minute_bars.py | Apache-2.0 |
def test_set_sid_attrs(self):
"""Confirm that we can set the attributes of a sid's file correctly.
"""
sid = 1
start_day = Timestamp('2015-11-27', tz='UTC')
end_day = Timestamp('2015-06-02', tz='UTC')
attrs = {
'start_day': start_day.value / int(1e9),
... | Confirm that we can set the attributes of a sid's file correctly.
| test_set_sid_attrs | python | quantopian/zipline | tests/data/test_minute_bars.py | https://github.com/quantopian/zipline/blob/master/tests/data/test_minute_bars.py | Apache-2.0 |
def _empty_ingest(self, _wrote_to=[]):
"""Run the nth empty ingest.
Returns
-------
wrote_to : str
The timestr of the bundle written.
"""
if not self.bundles:
@self.register('bundle',
calendar_name='NYSE',
... | Run the nth empty ingest.
Returns
-------
wrote_to : str
The timestr of the bundle written.
| _empty_ingest | python | quantopian/zipline | tests/data/bundles/test_core.py | https://github.com/quantopian/zipline/blob/master/tests/data/bundles/test_core.py | Apache-2.0 |
def minutes_for_days(cal, ordered_days=False):
"""
500 randomly selected days.
This is used to make sure our test coverage is unbiased towards any rules.
We use a random sample because testing on all the trading days took
around 180 seconds on my laptop, which is far too much for normal unit
tes... |
500 randomly selected days.
This is used to make sure our test coverage is unbiased towards any rules.
We use a random sample because testing on all the trading days took
around 180 seconds on my laptop, which is far too much for normal unit
testing.
We manually set the seed so that this will ... | minutes_for_days | python | quantopian/zipline | tests/events/test_events.py | https://github.com/quantopian/zipline/blob/master/tests/events/test_events.py | Apache-2.0 |
def test_completeness(self):
"""
Tests that all rules are being tested.
"""
if not self.class_:
return # This is the base class testing, it is always complete.
classes_to_ignore = [TradingDayOfWeekRule, TradingDayOfMonthRule]
dem = {
k for k, v ... |
Tests that all rules are being tested.
| test_completeness | python | quantopian/zipline | tests/events/test_events.py | https://github.com/quantopian/zipline/blob/master/tests/events/test_events.py | Apache-2.0 |
def test_NthTradingDayOfWeek_day_zero(self):
"""
Test that we don't blow up when trying to call week_start's
should_trigger on the first day of a trading environment.
"""
rule = NthTradingDayOfWeek(0)
rule.cal = self.cal
first_open = self.cal.open_and_close_for_se... |
Test that we don't blow up when trying to call week_start's
should_trigger on the first day of a trading environment.
| test_NthTradingDayOfWeek_day_zero | python | quantopian/zipline | tests/events/test_events.py | https://github.com/quantopian/zipline/blob/master/tests/events/test_events.py | Apache-2.0 |
def test_edge_cases_for_TradingDayOfWeek(self):
"""
Test that we account for midweek holidays. Monday 01/20 is a holiday.
Ensure that the trigger date for that week is adjusted
appropriately, or thrown out if not enough trading days. Also, test
that if we start the simulation on ... |
Test that we account for midweek holidays. Monday 01/20 is a holiday.
Ensure that the trigger date for that week is adjusted
appropriately, or thrown out if not enough trading days. Also, test
that if we start the simulation on a day where we miss the trigger
for that week, that... | test_edge_cases_for_TradingDayOfWeek | python | quantopian/zipline | tests/events/test_events_nyse.py | https://github.com/quantopian/zipline/blob/master/tests/events/test_events_nyse.py | Apache-2.0 |
def check_metrics(self, metrics, total_months, start_date):
"""
confirm that the right number of riskmetrics were calculated for each
window length.
"""
for period, length in zip(PERIODS, [1, 3, 6, 12]):
self.assert_range_length(
metrics[period],
... |
confirm that the right number of riskmetrics were calculated for each
window length.
| check_metrics | python | quantopian/zipline | tests/finance/test_risk.py | https://github.com/quantopian/zipline/blob/master/tests/finance/test_risk.py | Apache-2.0 |
def generate_minute_test_data(first_day,
last_day,
starting_open,
starting_volume,
multipliers_list,
path):
"""
Utility method to generate fake minute-level CSV d... |
Utility method to generate fake minute-level CSV data.
:param first_day: first trading day
:param last_day: last trading day
:param starting_open: first open value, raw value.
:param starting_volume: first volume value, raw value.
:param multipliers_list: ordered list of pd.Timestamp -> float, ... | generate_minute_test_data | python | quantopian/zipline | tests/history/generate_csvs.py | https://github.com/quantopian/zipline/blob/master/tests/history/generate_csvs.py | Apache-2.0 |
def portfolio_snapshot(p):
"""Extract all of the fields from the portfolio as a new dictionary.
"""
fields = (
'cash_flow',
'starting_cash',
'portfolio_value',
'pnl',
'returns',
'cash',
'positions',
'positions_value',
'positions_exposur... | Extract all of the fields from the portfolio as a new dictionary.
| portfolio_snapshot | python | quantopian/zipline | tests/metrics/test_metrics.py | https://github.com/quantopian/zipline/blob/master/tests/metrics/test_metrics.py | Apache-2.0 |
def with_defaults(**default_funcs):
"""
Decorator for providing dynamic default values for a method.
Usages:
@with_defaults(foo=lambda self: self.x + self.y)
def func(self, foo):
...
If a value is passed for `foo`, it will be used. Otherwise the function
supplied to `with_defaults... |
Decorator for providing dynamic default values for a method.
Usages:
@with_defaults(foo=lambda self: self.x + self.y)
def func(self, foo):
...
If a value is passed for `foo`, it will be used. Otherwise the function
supplied to `with_defaults` will be called with `self` as an argument... | with_defaults | python | quantopian/zipline | tests/pipeline/base.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/base.py | Apache-2.0 |
def run_graph(self, graph, initial_workspace, mask=None):
"""
Compute the given TermGraph, seeding the workspace of our engine with
`initial_workspace`.
Parameters
----------
graph : zipline.pipeline.graph.ExecutionPlan
Graph to run.
initial_workspace... |
Compute the given TermGraph, seeding the workspace of our engine with
`initial_workspace`.
Parameters
----------
graph : zipline.pipeline.graph.ExecutionPlan
Graph to run.
initial_workspace : dict
Initial workspace to forward to SimplePipelineEng... | run_graph | python | quantopian/zipline | tests/pipeline/base.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/base.py | Apache-2.0 |
def check_terms(self,
terms,
expected,
initial_workspace,
mask,
check=check_arrays):
"""
Compile the given terms into a TermGraph, compute it with
initial_workspace, and compare the results with `... |
Compile the given terms into a TermGraph, compute it with
initial_workspace, and compare the results with ``expected``.
| check_terms | python | quantopian/zipline | tests/pipeline/base.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/base.py | Apache-2.0 |
def build_mask(self, array):
"""
Helper for constructing an AssetExists mask from a boolean-coercible
array.
"""
ndates, nassets = array.shape
return DataFrame(
array,
# Use the **last** N dates rather than the first N so that we have
#... |
Helper for constructing an AssetExists mask from a boolean-coercible
array.
| build_mask | python | quantopian/zipline | tests/pipeline/base.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/base.py | Apache-2.0 |
def rand_ints(self, seed, shape, low=0, high=10):
"""
Build a block of random numerical data.
"""
rand = np.random.RandomState(seed)
return rand.randint(low, high, shape, dtype='i8') |
Build a block of random numerical data.
| rand_ints | python | quantopian/zipline | tests/pipeline/base.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/base.py | Apache-2.0 |
def rand_categoricals(self, categories, seed, shape, missing_value=None):
"""Build a block of random categorical data.
Categories should not include ``missing_value``.
"""
categories = list(categories) + [missing_value]
data = np.random.RandomState(seed).choice(categories, shape... | Build a block of random categorical data.
Categories should not include ``missing_value``.
| rand_categoricals | python | quantopian/zipline | tests/pipeline/base.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/base.py | Apache-2.0 |
def moving_window(array, nrows):
"""
Simple moving window generator over a 2D numpy array.
"""
count = num_windows_of_length_M_on_buffers_of_length_N(nrows, len(array))
for i in range(count):
yield array[i:i + nrows] |
Simple moving window generator over a 2D numpy array.
| moving_window | python | quantopian/zipline | tests/pipeline/test_adjusted_array.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_adjusted_array.py | Apache-2.0 |
def as_labelarray(initial_dtype, missing_value, array):
"""
Curried wrapper around LabelArray, that round-trips the input data through
`initial_dtype` first.
"""
return LabelArray(
array.astype(initial_dtype),
missing_value=initial_dtype.type(missing_value),
) |
Curried wrapper around LabelArray, that round-trips the input data through
`initial_dtype` first.
| as_labelarray | python | quantopian/zipline | tests/pipeline/test_adjusted_array.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_adjusted_array.py | Apache-2.0 |
def _gen_multiplicative_adjustment_cases(dtype):
"""
Generate expected moving windows on a buffer with adjustments.
We proceed by constructing, at each row, the view of the array we expect in
in all windows anchored on that row.
In general, if we have an adjustment to be applied once we process th... |
Generate expected moving windows on a buffer with adjustments.
We proceed by constructing, at each row, the view of the array we expect in
in all windows anchored on that row.
In general, if we have an adjustment to be applied once we process the row
at index N, should see that adjustment applied... | _gen_multiplicative_adjustment_cases | python | quantopian/zipline | tests/pipeline/test_adjusted_array.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_adjusted_array.py | Apache-2.0 |
def _gen_overwrite_adjustment_cases(dtype):
"""
Generate test cases for overwrite adjustments.
The algorithm used here is the same as the one used above for
multiplicative adjustments. The only difference is the semantics of how
the adjustments are expected to modify the arrays.
This is param... |
Generate test cases for overwrite adjustments.
The algorithm used here is the same as the one used above for
multiplicative adjustments. The only difference is the semantics of how
the adjustments are expected to modify the arrays.
This is parameterized on `make_input` and `make_expected_output`... | _gen_overwrite_adjustment_cases | python | quantopian/zipline | tests/pipeline/test_adjusted_array.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_adjusted_array.py | Apache-2.0 |
def _gen_overwrite_1d_array_adjustment_case(dtype):
"""
Generate test cases for overwrite adjustments.
The algorithm used here is the same as the one used above for
multiplicative adjustments. The only difference is the semantics of how
the adjustments are expected to modify the arrays.
This ... |
Generate test cases for overwrite adjustments.
The algorithm used here is the same as the one used above for
multiplicative adjustments. The only difference is the semantics of how
the adjustments are expected to modify the arrays.
This is parameterized on `make_input` and `make_expected_output`... | _gen_overwrite_1d_array_adjustment_case | python | quantopian/zipline | tests/pipeline/test_adjusted_array.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_adjusted_array.py | Apache-2.0 |
def test_cols_with_all_missing_vals(self):
"""
Tests that when there is no known data, we get output where the
columns have the right dtypes and the right missing values filled in.
input (self.df):
Empty DataFrame
Columns: [sid, float_value, str_value, int_value, bool_va... |
Tests that when there is no known data, we get output where the
columns have the right dtypes and the right missing values filled in.
input (self.df):
Empty DataFrame
Columns: [sid, float_value, str_value, int_value, bool_value, dt_value,
asof_date, timestamp]
... | test_cols_with_all_missing_vals | python | quantopian/zipline | tests/pipeline/test_blaze.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_blaze.py | Apache-2.0 |
def test_cols_with_some_missing_vals(self):
"""
Tests the following:
1) Forward filling replaces missing values correctly for the data
types supported in pipeline.
2) We don't forward fill when the missing value is the actual value
we got for a date in th... |
Tests the following:
1) Forward filling replaces missing values correctly for the data
types supported in pipeline.
2) We don't forward fill when the missing value is the actual value
we got for a date in the case of int/bool columns.
3) We get the c... | test_cols_with_some_missing_vals | python | quantopian/zipline | tests/pipeline/test_blaze.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_blaze.py | Apache-2.0 |
def test_custom_query_time_tz(self):
"""
input (df):
asof_date int_value sid timestamp value
0 2013-12-31 0 65 2014-01-01 13:44:00 0.0
1 2013-12-31 1 66 2014-01-01 13:44:00 1.0
2 2013-12-31 2 67 2014-01-01 13:44:00 ... |
input (df):
asof_date int_value sid timestamp value
0 2013-12-31 0 65 2014-01-01 13:44:00 0.0
1 2013-12-31 1 66 2014-01-01 13:44:00 1.0
2 2013-12-31 2 67 2014-01-01 13:44:00 2.0
3 2013-12-31 1 65 2014-0... | test_custom_query_time_tz | python | quantopian/zipline | tests/pipeline/test_blaze.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_blaze.py | Apache-2.0 |
def test_id(self):
"""
input (self.df):
asof_date sid timestamp int_value value
0 2014-01-01 65 2014-01-01 0 0
1 2014-01-01 66 2014-01-01 1 1
2 2014-01-01 67 2014-01-01 2 2
3 2014-01-02 65 2014-01-02 1 1... |
input (self.df):
asof_date sid timestamp int_value value
0 2014-01-01 65 2014-01-01 0 0
1 2014-01-01 66 2014-01-01 1 1
2 2014-01-01 67 2014-01-01 2 2
3 2014-01-02 65 2014-01-02 1 1
4 2014-01-02 66 201... | test_id | python | quantopian/zipline | tests/pipeline/test_blaze.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_blaze.py | Apache-2.0 |
def test_id_with_asof_date(self):
"""
input (self.df):
asof_date sid timestamp int_value value
0 2014-01-01 65 2014-01-01 0 0
1 2014-01-01 66 2014-01-01 1 1
2 2014-01-01 67 2014-01-01 2 2
3 2014-01-02 65 2014-01-02 ... |
input (self.df):
asof_date sid timestamp int_value value
0 2014-01-01 65 2014-01-01 0 0
1 2014-01-01 66 2014-01-01 1 1
2 2014-01-01 67 2014-01-01 2 2
3 2014-01-02 65 2014-01-02 1 1
4 2014-01-02 66 201... | test_id_with_asof_date | python | quantopian/zipline | tests/pipeline/test_blaze.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_blaze.py | Apache-2.0 |
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 ... |
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... | test_id_ffill_out_of_window | python | quantopian/zipline | tests/pipeline/test_blaze.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_blaze.py | Apache-2.0 |
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 ... |
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 ... | test_id_multiple_columns | python | quantopian/zipline | tests/pipeline/test_blaze.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_blaze.py | Apache-2.0 |
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 0
2... |
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 0
2014-01-02 1
2014-01-03 2
... | test_id_macro_dataset | python | quantopian/zipline | tests/pipeline/test_blaze.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_blaze.py | Apache-2.0 |
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):
oth... |
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 1 0
2014-01-02 ... | test_id_ffill_out_of_window_macro_dataset | python | quantopian/zipline | tests/pipeline/test_blaze.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_blaze.py | Apache-2.0 |
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 ... |
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 1 0
2014-01-02 ... | test_id_macro_dataset_multiple_columns | python | quantopian/zipline | tests/pipeline/test_blaze.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_blaze.py | Apache-2.0 |
def _test_checkpoints_macro(self, checkpoints, ffilled_value=-1.0):
"""Simple checkpoints test that accepts a checkpoints dataframe and
the expected value for 2014-01-03 for macro datasets.
The underlying data has value -1.0 on 2014-01-01 and 1.0 on 2014-01-04.
Parameters
-----... | Simple checkpoints test that accepts a checkpoints dataframe and
the expected value for 2014-01-03 for macro datasets.
The underlying data has value -1.0 on 2014-01-01 and 1.0 on 2014-01-04.
Parameters
----------
checkpoints : pd.DataFrame
The checkpoints data.
... | _test_checkpoints_macro | python | quantopian/zipline | tests/pipeline/test_blaze.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_blaze.py | Apache-2.0 |
def _test_checkpoints(self, checkpoints, ffilled_values=None):
"""Simple checkpoints test that accepts a checkpoints dataframe and
the expected value for 2014-01-03.
The underlying data has value -(sid + 1) on 2014-01-01 and sid + 1 on
2014-01-04.
Parameters
----------
... | Simple checkpoints test that accepts a checkpoints dataframe and
the expected value for 2014-01-03.
The underlying data has value -(sid + 1) on 2014-01-01 and sid + 1 on
2014-01-04.
Parameters
----------
checkpoints : pd.DataFrame
The checkpoints data.
... | _test_checkpoints | python | quantopian/zipline | tests/pipeline/test_blaze.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_blaze.py | Apache-2.0 |
def test_id_take_last_in_group_sorted(self):
"""
input
asof_date timestamp other value
2014-01-03 2014-01-04 00 3 3
2014-01-02 2014-01-04 00 2 2
output (expected):
other value
2014-01-02 NaN NaN
... |
input
asof_date timestamp other value
2014-01-03 2014-01-04 00 3 3
2014-01-02 2014-01-04 00 2 2
output (expected):
other value
2014-01-02 NaN NaN
2014-01-03 NaN NaN
2014-01-06 3 ... | test_id_take_last_in_group_sorted | python | quantopian/zipline | tests/pipeline/test_blaze.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_blaze.py | Apache-2.0 |
def test_element_of_integral(self):
"""
Element of is well-defined for integral classifiers.
"""
class C(Classifier):
dtype = int64_dtype
missing_value = -1
inputs = ()
window_length = 0
c = C()
# There's no significance t... |
Element of is well-defined for integral classifiers.
| test_element_of_integral | python | quantopian/zipline | tests/pipeline/test_classifier.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_classifier.py | Apache-2.0 |
def test_element_of_rejects_missing_value(self):
"""
Test that element_of raises a useful error if we attempt to pass it an
array of choices that include the classifier's missing_value.
"""
missing = "not in the array"
class C(Classifier):
dtype = categorical... |
Test that element_of raises a useful error if we attempt to pass it an
array of choices that include the classifier's missing_value.
| test_element_of_rejects_missing_value | python | quantopian/zipline | tests/pipeline/test_classifier.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_classifier.py | Apache-2.0 |
def test_mixed_generics(self):
"""
Test that we can run pipelines with mixed generic/non-generic terms.
This test is a regression test for failures encountered during
development where having a mix of generic and non-generic columns in
the term graph caused bugs in our extra row... |
Test that we can run pipelines with mixed generic/non-generic terms.
This test is a regression test for failures encountered during
development where having a mix of generic and non-generic columns in
the term graph caused bugs in our extra row accounting.
| test_mixed_generics | python | quantopian/zipline | tests/pipeline/test_domain.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_domain.py | Apache-2.0 |
def check_extra_row_calculations(self,
terms,
all_sessions,
start_session,
end_session,
min_extra_rows,
... |
Check that each term in ``terms`` computes an expected number of extra
rows for the given parameters.
| check_extra_row_calculations | python | quantopian/zipline | tests/pipeline/test_downsampling.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_downsampling.py | Apache-2.0 |
def assert_multi_index_is_product(testcase, index, *levels):
"""Assert that a MultiIndex contains the product of `*levels`."""
testcase.assertIsInstance(
index, MultiIndex, "%s is not a MultiIndex" % index
)
testcase.assertEqual(set(index), set(product(*levels))) | Assert that a MultiIndex contains the product of `*levels`. | assert_multi_index_is_product | python | quantopian/zipline | tests/pipeline/test_engine.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_engine.py | Apache-2.0 |
def test_masked_factor(self):
"""
Test that a Custom Factor computes the correct values when passed a
mask. The mask/filter should be applied prior to computing any values,
as opposed to computing the factor across the entire universe of
assets. Any assets that are filtered out s... |
Test that a Custom Factor computes the correct values when passed a
mask. The mask/filter should be applied prior to computing any values,
as opposed to computing the factor across the entire universe of
assets. Any assets that are filtered out should be filled with missing
valu... | test_masked_factor | python | quantopian/zipline | tests/pipeline/test_engine.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_engine.py | Apache-2.0 |
def test_factor_with_single_output(self):
"""
Test passing an `outputs` parameter of length 1 to a CustomFactor.
"""
dates = self.dates[5:10]
assets = self.assets
num_dates = len(dates)
open = EquityPricing.open
open_values = [self.constants[open]] * num_d... |
Test passing an `outputs` parameter of length 1 to a CustomFactor.
| test_factor_with_single_output | python | quantopian/zipline | tests/pipeline/test_engine.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_engine.py | Apache-2.0 |
def test_instance_of_factor_with_multiple_outputs(self):
"""
Test adding a CustomFactor instance, which has multiple outputs, as a
pipeline column directly. Its computed values should be tuples
containing the computed values of each of its outputs.
"""
dates = self.dates[... |
Test adding a CustomFactor instance, which has multiple outputs, as a
pipeline column directly. Its computed values should be tuples
containing the computed values of each of its outputs.
| test_instance_of_factor_with_multiple_outputs | python | quantopian/zipline | tests/pipeline/test_engine.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_engine.py | Apache-2.0 |
def write_nans(self, df):
"""
Write nans to the locations in data corresponding to the (date, asset)
pairs for which we wouldn't have data for `asset` on `date` in a
backtest.
Parameters
----------
df : pd.DataFrame
A DataFrame with a DatetimeIndex as... |
Write nans to the locations in data corresponding to the (date, asset)
pairs for which we wouldn't have data for `asset` on `date` in a
backtest.
Parameters
----------
df : pd.DataFrame
A DataFrame with a DatetimeIndex as index and an object index of
... | write_nans | python | quantopian/zipline | tests/pipeline/test_engine.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_engine.py | Apache-2.0 |
def test_string_classifiers_produce_categoricals(self):
"""
Test that string-based classifiers produce pandas categoricals as their
outputs.
"""
col = TestingDataSet.categorical_col
pipe = Pipeline(columns={'c': col.latest})
run_dates = self.trading_days[-10:]
... |
Test that string-based classifiers produce pandas categoricals as their
outputs.
| test_string_classifiers_produce_categoricals | python | quantopian/zipline | tests/pipeline/test_engine.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_engine.py | Apache-2.0 |
def test_run_chunked_pipeline(self):
"""
Test that running a pipeline in chunks produces the same result as if
it were run all at once
"""
pipe = Pipeline(
columns={
'float': TestingDataSet.float_col.latest,
'custom_factor': SimpleMovi... |
Test that running a pipeline in chunks produces the same result as if
it were run all at once
| test_run_chunked_pipeline | python | quantopian/zipline | tests/pipeline/test_engine.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_engine.py | Apache-2.0 |
def make_null_event_date_events(all_sids, timestamp):
"""
Make an event with a null event_date for all sids.
Used to test that EventsLoaders filter out null events.
"""
return pd.DataFrame({
'sid': all_sids,
'timestamp': timestamp,
'event_date': pd.Timestamp('NaT'),
... |
Make an event with a null event_date for all sids.
Used to test that EventsLoaders filter out null events.
| make_null_event_date_events | python | quantopian/zipline | tests/pipeline/test_events.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_events.py | Apache-2.0 |
def make_events(add_nulls):
"""
Every event has at least three pieces of data associated with it:
1. sid : The ID of the asset associated with the event.
2. event_date : The date on which an event occurred.
3. timestamp : The date on which we learned about the event.
This can be ... |
Every event has at least three pieces of data associated with it:
1. sid : The ID of the asset associated with the event.
2. event_date : The date on which an event occurred.
3. timestamp : The date on which we learned about the event.
This can be before the occurence_date in the ca... | make_events | python | quantopian/zipline | tests/pipeline/test_events.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_events.py | Apache-2.0 |
def test_load_empty(self):
"""
For the case where raw data is empty, make sure we have a result for
all sids, that the dimensions are correct, and that we have the
correct missing value.
"""
raw_events = pd.DataFrame(
columns=["sid",
"time... |
For the case where raw data is empty, make sure we have a result for
all sids, that the dimensions are correct, and that we have the
correct missing value.
| test_load_empty | python | quantopian/zipline | tests/pipeline/test_events.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_events.py | Apache-2.0 |
def check_previous_value_results(self, column, results, dates):
"""
Check previous value results for a single column.
"""
# Verify that we got a result for every sid.
self.assert_result_contains_all_sids(results)
events = self.raw_events_no_nulls
# Remove timezon... |
Check previous value results for a single column.
| check_previous_value_results | python | quantopian/zipline | tests/pipeline/test_events.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_events.py | Apache-2.0 |
def scipy_winsorize_with_nan_handling(array, limits):
"""
Wrapper around scipy.stats.mstats.winsorize that handles NaNs correctly.
scipy's winsorize sorts NaNs to the end of the array when calculating
percentiles.
"""
# The basic idea of this function is to do the following:
# 1. Sort the i... |
Wrapper around scipy.stats.mstats.winsorize that handles NaNs correctly.
scipy's winsorize sorts NaNs to the end of the array when calculating
percentiles.
| scipy_winsorize_with_nan_handling | python | quantopian/zipline | tests/pipeline/test_factor.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_factor.py | Apache-2.0 |
def test_normalizations_hand_computed(self):
"""
Test the hand-computed example in factor.demean.
"""
f = self.f
m = Mask()
c = C()
str_c = C(dtype=categorical_dtype, missing_value=None)
factor_data = array(
[[1.0, 2.0, 3.0, 4.0],
... |
Test the hand-computed example in factor.demean.
| test_normalizations_hand_computed | python | quantopian/zipline | tests/pipeline/test_factor.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_factor.py | Apache-2.0 |
def test_winsorize_hand_computed(self):
"""
Test the hand-computed example in factor.winsorize.
"""
f = self.f
m = Mask()
c = C()
str_c = C(dtype=categorical_dtype, missing_value=None)
factor_data = array([
[1., 2., 3., 4., 5., 6., ... |
Test the hand-computed example in factor.winsorize.
| test_winsorize_hand_computed | python | quantopian/zipline | tests/pipeline/test_factor.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_factor.py | Apache-2.0 |
def test_winsorize_bad_bounds(self):
"""
Test out of bounds input for factor.winsorize.
"""
f = self.f
bad_percentiles = [
(-.1, 1),
(0, 95),
(5, 95),
(5, 5),
(.6, .4)
]
for min_, max_ in bad_percentiles... |
Test out of bounds input for factor.winsorize.
| test_winsorize_bad_bounds | python | quantopian/zipline | tests/pipeline/test_factor.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_factor.py | Apache-2.0 |
def test_summary_methods(self, seed, mask):
"""Test that summary funcs work the same as numpy NaN-aware funcs.
"""
rand = np.random.RandomState(seed)
shape = (10, 5)
data = rand.randn(*shape)
data[~mask] = np.nan
workspace = {F(): data}
terms = {
... | Test that summary funcs work the same as numpy NaN-aware funcs.
| test_summary_methods | python | quantopian/zipline | tests/pipeline/test_factor.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_factor.py | Apache-2.0 |
def test_built_in_vs_summary(self, seed, mask):
"""Test that summary funcs match normalization functions.
"""
rand = np.random.RandomState(seed)
shape = (10, 5)
data = rand.randn(*shape)
data[~mask] = np.nan
workspace = {F(): data}
terms = {
'... | Test that summary funcs match normalization functions.
| test_built_in_vs_summary | python | quantopian/zipline | tests/pipeline/test_factor.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_factor.py | Apache-2.0 |
def rowwise_rank(array, mask=None):
"""
Take a 2D array and return the 0-indexed sorted position of each element in
the array for each row.
Examples
--------
In [5]: data
Out[5]:
array([[-0.141, -1.103, -1.0171, 0.7812, 0.07 ],
[ 0.926, 0.235, -0.7698, 1.4552, 0.2061],
... |
Take a 2D array and return the 0-indexed sorted position of each element in
the array for each row.
Examples
--------
In [5]: data
Out[5]:
array([[-0.141, -1.103, -1.0171, 0.7812, 0.07 ],
[ 0.926, 0.235, -0.7698, 1.4552, 0.2061],
[ 1.579, 0.929, -0.557 , 0.789... | rowwise_rank | python | quantopian/zipline | tests/pipeline/test_filter.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_filter.py | Apache-2.0 |
def test_all_present_float_factor_input(self):
"""Test float factor input to `AllPresent`
"""
class SomeWindowSafeFactor(Factor):
dtype = float64_dtype
inputs = ()
window_length = 0
window_safe = True
input_factor = SomeWindowSafeFactor()
... | Test float factor input to `AllPresent`
| test_all_present_float_factor_input | python | quantopian/zipline | tests/pipeline/test_filter.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_filter.py | Apache-2.0 |
def test_all_present_int_factor_input(self):
"""Test int factor input to `AllPresent`
"""
class SomeWindowSafeIntFactor(Factor):
dtype = int64_dtype
inputs = ()
window_length = 0
window_safe = True
missing_value = 0
input_facto... | Test int factor input to `AllPresent`
| test_all_present_int_factor_input | python | quantopian/zipline | tests/pipeline/test_filter.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_filter.py | Apache-2.0 |
def test_all_present_classifier_input(self):
"""Test classifier factor input to `AllPresent`
"""
class SomeWindowSafeStringClassifier(Classifier):
dtype = object_dtype
inputs = ()
window_length = 0
missing_value = ''
window_safe = True
... | Test classifier factor input to `AllPresent`
| test_all_present_classifier_input | python | quantopian/zipline | tests/pipeline/test_filter.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_filter.py | Apache-2.0 |
def test_all_present_filter_input(self):
"""Test error is raised when filter factor is input to `AllPresent`
"""
with self.assertRaises(TypeError) as err:
AllPresent([Mask()], window_length=4)
self.assertEqual(
"Input to filter `AllPresent` cannot be a Filter.",
... | Test error is raised when filter factor is input to `AllPresent`
| test_all_present_filter_input | python | quantopian/zipline | tests/pipeline/test_filter.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_filter.py | Apache-2.0 |
def split_by_chunk(self, trace):
"""
Split a trace of a chunked pipeline execution into a list of traces for
each chunk.
"""
def is_end_of_chunk(call):
return (
call.method_name == 'computing_chunk' and call.state == 'exit'
)
to_yi... |
Split a trace of a chunked pipeline execution into a list of traces for
each chunk.
| split_by_chunk | python | quantopian/zipline | tests/pipeline/test_hooks.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_hooks.py | Apache-2.0 |
def expected_chunk_progress(self, pipeline_start, pipeline_end, chunk_end):
"""Get expected progress after finishing a chunk ending at ``chunk_end``.
"""
# +1 to be inclusive of end dates
total_days = (pipeline_end - pipeline_start).days + 1
days_complete = (chunk_end - pipeline_... | Get expected progress after finishing a chunk ending at ``chunk_end``.
| expected_chunk_progress | python | quantopian/zipline | tests/pipeline/test_hooks.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_hooks.py | Apache-2.0 |
def check_expected_latest_value(self, calendar, col, date, asset, value):
"""Check the expected result of column.latest from a pipeline.
"""
if np.isnan(value):
# If we got a NaN, we should be outside the asset's
# lifetime.
self.assertTrue(date <= asset.start... | Check the expected result of column.latest from a pipeline.
| check_expected_latest_value | python | quantopian/zipline | tests/pipeline/test_international_markets.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_international_markets.py | Apache-2.0 |
def assert_identical_results(self, left, right, start_date, end_date):
"""Assert that two pipelines produce the same results.
"""
left_result = self.run_pipeline(left, start_date, end_date)
right_result = self.run_pipeline(right, start_date, end_date)
assert_equal(left_result, ri... | Assert that two pipelines produce the same results.
| assert_identical_results | python | quantopian/zipline | tests/pipeline/test_international_markets.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_international_markets.py | Apache-2.0 |
def alive_in_range(asset, start, end, include_asset_start_date=False):
"""
Check if an asset was alive in the range from start to end.
Parameters
----------
asset : Asset
The asset to check
start : pd.Timestamp
Start of the interval.
end : pd.Timestamp
End of the int... |
Check if an asset was alive in the range from start to end.
Parameters
----------
asset : Asset
The asset to check
start : pd.Timestamp
Start of the interval.
end : pd.Timestamp
End of the interval.
include_asset_start_date : bool
Whether to include the star... | alive_in_range | python | quantopian/zipline | tests/pipeline/test_international_markets.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_international_markets.py | Apache-2.0 |
def intervals_overlap(a, b):
"""
Check whether a pair of datetime intervals overlap.
Parameters
----------
a : (pd.Timestamp, pd.Timestamp)
b : (pd.Timestamp, pd.Timestamp)
Returns
-------
have_overlap : bool
Bool indicating whether there there is a non-empty intersection b... |
Check whether a pair of datetime intervals overlap.
Parameters
----------
a : (pd.Timestamp, pd.Timestamp)
b : (pd.Timestamp, pd.Timestamp)
Returns
-------
have_overlap : bool
Bool indicating whether there there is a non-empty intersection between
the intervals.
| intervals_overlap | python | quantopian/zipline | tests/pipeline/test_international_markets.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_international_markets.py | Apache-2.0 |
def gen_boolops(x, y, z):
"""
Generate all possible interleavings of & and | between all possible
orderings of x, y, and z.
"""
for a, b, c in permutations([x, y, z]):
yield (a & b) & c
yield (a & b) | c
yield (a... |
Generate all possible interleavings of & and | between all possible
orderings of x, y, and z.
| gen_boolops | python | quantopian/zipline | tests/pipeline/test_numerical_expression.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_numerical_expression.py | Apache-2.0 |
def rolling_vwap(df, length):
"Simple rolling vwap implementation for testing"
closes = df['close'].values
volumes = df['volume'].values
product = closes * volumes
out = full_like(closes, nan)
for upper_bound in range(length, len(closes) + 1):
bounds = slice(upper_bound - length, upper_b... | Simple rolling vwap implementation for testing | rolling_vwap | python | quantopian/zipline | tests/pipeline/test_pipeline_algo.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_pipeline_algo.py | Apache-2.0 |
def test_attach_pipeline_after_initialize(self):
"""
Assert that calling attach_pipeline after initialize raises correctly.
"""
def initialize(context):
pass
def late_attach(context, data):
attach_pipeline(Pipeline(), 'test')
raise AssertionEr... |
Assert that calling attach_pipeline after initialize raises correctly.
| test_attach_pipeline_after_initialize | python | quantopian/zipline | tests/pipeline/test_pipeline_algo.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_pipeline_algo.py | Apache-2.0 |
def test_pipeline_output_after_initialize(self):
"""
Assert that calling pipeline_output after initialize raises correctly.
"""
def initialize(context):
attach_pipeline(Pipeline(), 'test')
pipeline_output('test')
raise AssertionError("Shouldn't make it... |
Assert that calling pipeline_output after initialize raises correctly.
| test_pipeline_output_after_initialize | python | quantopian/zipline | tests/pipeline/test_pipeline_algo.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_pipeline_algo.py | Apache-2.0 |
def test_get_output_nonexistent_pipeline(self):
"""
Assert that calling add_pipeline after initialize raises appropriately.
"""
def initialize(context):
attach_pipeline(Pipeline(), 'test')
def handle_data(context, data):
raise AssertionError("Shouldn't ma... |
Assert that calling add_pipeline after initialize raises appropriately.
| test_get_output_nonexistent_pipeline | python | quantopian/zipline | tests/pipeline/test_pipeline_algo.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_pipeline_algo.py | Apache-2.0 |
def test_assets_appear_on_correct_days(self, test_name, chunks):
"""
Assert that assets appear at correct times during a backtest, with
correctly-adjusted close price values.
"""
if chunks == 'all_but_one_day':
chunks = (
self.dates.get_loc(self.last_... |
Assert that assets appear at correct times during a backtest, with
correctly-adjusted close price values.
| test_assets_appear_on_correct_days | python | quantopian/zipline | tests/pipeline/test_pipeline_algo.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_pipeline_algo.py | Apache-2.0 |
def test_multiple_pipelines(self):
"""
Test that we can attach multiple pipelines and access the correct
output based on the pipeline name.
"""
def initialize(context):
pipeline_close = attach_pipeline(Pipeline(), 'test_close')
pipeline_volume = attach_pip... |
Test that we can attach multiple pipelines and access the correct
output based on the pipeline name.
| test_multiple_pipelines | python | quantopian/zipline | tests/pipeline/test_pipeline_algo.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_pipeline_algo.py | Apache-2.0 |
def test_duplicate_pipeline_names(self):
"""
Test that we raise an error when we try to attach a pipeline with a
name that already exists for another attached pipeline.
"""
def initialize(context):
attach_pipeline(Pipeline(), 'test')
attach_pipeline(Pipeli... |
Test that we raise an error when we try to attach a pipeline with a
name that already exists for another attached pipeline.
| test_duplicate_pipeline_names | python | quantopian/zipline | tests/pipeline/test_pipeline_algo.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_pipeline_algo.py | Apache-2.0 |
def test_pipeline_beyond_daily_bars(self):
"""
Ensure that we can run an algo with pipeline beyond the max date
of the daily bars.
"""
# For ensuring we call before_trading_start.
count = [0]
current_day = self.trading_calendar.next_session_label(
se... |
Ensure that we can run an algo with pipeline beyond the max date
of the daily bars.
| test_pipeline_beyond_daily_bars | python | quantopian/zipline | tests/pipeline/test_pipeline_algo.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_pipeline_algo.py | Apache-2.0 |
def create_expected_df_for_factor_compute(start_date,
sids,
tuples,
end_date):
"""
Given a list of tuples of new data we get for each sid on each critical
date (when information chan... |
Given a list of tuples of new data we get for each sid on each critical
date (when information changes), create a DataFrame that fills that
data through a date range ending at `end_date`.
| create_expected_df_for_factor_compute | python | quantopian/zipline | tests/pipeline/test_quarters_estimates.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_quarters_estimates.py | Apache-2.0 |
def make_events(cls):
"""
In order to determine which estimate we care about for a particular
sid, we need to look at all estimates that we have for that sid and
their associated event dates.
We define q1 < q2, and thus event1 < event2 since event1 occurs
during q1 and e... |
In order to determine which estimate we care about for a particular
sid, we need to look at all estimates that we have for that sid and
their associated event dates.
We define q1 < q2, and thus event1 < event2 since event1 occurs
during q1 and event2 occurs during q2 and we ass... | make_events | python | quantopian/zipline | tests/pipeline/test_quarters_estimates.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_quarters_estimates.py | Apache-2.0 |
def test_slice(self, my_asset_column, window_length_):
"""
Test that slices can be created by indexing into a term, and that they
have the correct shape when used as inputs.
"""
sids = self.sids
my_asset = self.asset_finder.retrieve_asset(self.sids[my_asset_column])
... |
Test that slices can be created by indexing into a term, and that they
have the correct shape when used as inputs.
| test_slice | python | quantopian/zipline | tests/pipeline/test_slice.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_slice.py | Apache-2.0 |
def test_slice_with_masking(self, unmasked_column, slice_column):
"""
Test that masking a factor that uses slices as inputs does not mask the
slice data.
"""
sids = self.sids
asset_finder = self.asset_finder
start_date = self.pipeline_start_date
end_date =... |
Test that masking a factor that uses slices as inputs does not mask the
slice data.
| test_slice_with_masking | python | quantopian/zipline | tests/pipeline/test_slice.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_slice.py | Apache-2.0 |
def test_adding_slice_column(self):
"""
Test that slices cannot be added as a pipeline column.
"""
my_asset = self.asset_finder.retrieve_asset(self.sids[0])
open_slice = OpenPrice()[my_asset]
with self.assertRaises(UnsupportedPipelineOutput):
Pipeline(columns... |
Test that slices cannot be added as a pipeline column.
| test_adding_slice_column | python | quantopian/zipline | tests/pipeline/test_slice.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_slice.py | Apache-2.0 |
def test_loadable_term_slices(self):
"""
Test that slicing loadable terms raises the proper error.
"""
my_asset = self.asset_finder.retrieve_asset(self.sids[0])
with self.assertRaises(NonSliceableTerm):
USEquityPricing.close[my_asset] |
Test that slicing loadable terms raises the proper error.
| test_loadable_term_slices | python | quantopian/zipline | tests/pipeline/test_slice.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_slice.py | Apache-2.0 |
def test_non_existent_asset(self):
"""
Test that indexing into a term with a non-existent asset raises the
proper exception.
"""
my_asset = Asset(
0,
exchange_info=ExchangeInfo('TEST FULL', 'TEST', 'US'),
)
returns = Returns(window_length=2... |
Test that indexing into a term with a non-existent asset raises the
proper exception.
| test_non_existent_asset | python | quantopian/zipline | tests/pipeline/test_slice.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_slice.py | Apache-2.0 |
def test_window_safety_of_slices(self):
"""
Test that slices correctly inherit the `window_safe` property of the
term from which they are derived.
"""
col = self.col
my_asset = self.asset_finder.retrieve_asset(self.sids[0])
# SimpleMovingAverage is not window saf... |
Test that slices correctly inherit the `window_safe` property of the
term from which they are derived.
| test_window_safety_of_slices | python | quantopian/zipline | tests/pipeline/test_slice.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_slice.py | Apache-2.0 |
def test_single_column_output(self):
"""
Tests for custom factors that compute a 1D out.
"""
start_date = self.pipeline_start_date
end_date = self.pipeline_end_date
alternating_mask = (AssetIDPlusDay() % 2).eq(0)
cascading_mask = AssetIDPlusDay() < (self.sids[-1]... |
Tests for custom factors that compute a 1D out.
| test_single_column_output | python | quantopian/zipline | tests/pipeline/test_slice.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_slice.py | Apache-2.0 |
def test_masked_single_column_output(self):
"""
Tests for masking custom factors that compute a 1D out.
"""
start_date = self.pipeline_start_date
end_date = self.pipeline_end_date
alternating_mask = (AssetIDPlusDay() % 2).eq(0)
cascading_mask = AssetIDPlusDay() <... |
Tests for masking custom factors that compute a 1D out.
| test_masked_single_column_output | python | quantopian/zipline | tests/pipeline/test_slice.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_slice.py | Apache-2.0 |
def test_factor_correlation_methods(self,
returns_length,
correlation_length):
"""
Ensure that `Factor.pearsonr` and `Factor.spearmanr` are consistent
with the built-in factors `RollingPearsonOfReturns` and
`... |
Ensure that `Factor.pearsonr` and `Factor.spearmanr` are consistent
with the built-in factors `RollingPearsonOfReturns` and
`RollingSpearmanOfReturns`.
| test_factor_correlation_methods | python | quantopian/zipline | tests/pipeline/test_slice.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_slice.py | Apache-2.0 |
def test_factor_regression_method(self, returns_length, regression_length):
"""
Ensure that `Factor.linear_regression` is consistent with the built-in
factor `RollingLinearRegressionOfReturns`.
"""
my_asset = self.asset_finder.retrieve_asset(self.sids[0])
returns = Retur... |
Ensure that `Factor.linear_regression` is consistent with the built-in
factor `RollingLinearRegressionOfReturns`.
| test_factor_regression_method | python | quantopian/zipline | tests/pipeline/test_slice.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_slice.py | Apache-2.0 |
def test_correlation_factors(self, returns_length, correlation_length):
"""
Tests for the built-in factors `RollingPearsonOfReturns` and
`RollingSpearmanOfReturns`.
"""
assets = self.assets
my_asset = self.my_asset
my_asset_column = self.my_asset_column
da... |
Tests for the built-in factors `RollingPearsonOfReturns` and
`RollingSpearmanOfReturns`.
| test_correlation_factors | python | quantopian/zipline | tests/pipeline/test_statistical.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_statistical.py | Apache-2.0 |
def test_regression_of_returns_factor(self,
returns_length,
regression_length):
"""
Tests for the built-in factor `RollingLinearRegressionOfReturns`.
"""
assets = self.assets
my_asset = self.my_as... |
Tests for the built-in factor `RollingLinearRegressionOfReturns`.
| test_regression_of_returns_factor | python | quantopian/zipline | tests/pipeline/test_statistical.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_statistical.py | Apache-2.0 |
def test_correlation_and_regression_with_bad_asset(self):
"""
Test that `RollingPearsonOfReturns`, `RollingSpearmanOfReturns` and
`RollingLinearRegressionOfReturns` raise the proper exception when
given a nonexistent target asset.
"""
my_asset = Equity(
0,
... |
Test that `RollingPearsonOfReturns`, `RollingSpearmanOfReturns` and
`RollingLinearRegressionOfReturns` raise the proper exception when
given a nonexistent target asset.
| test_correlation_and_regression_with_bad_asset | python | quantopian/zipline | tests/pipeline/test_statistical.py | https://github.com/quantopian/zipline/blob/master/tests/pipeline/test_statistical.py | Apache-2.0 |
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