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MAINT: Add helper for multi-exchange equity info. #2336

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MAINT: Add helper for multi-exchange equity info. #2336

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3 changes: 2 additions & 1 deletion tests/pipeline/test_engine.py
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Original file line number Diff line number Diff line change
Expand Up @@ -910,11 +910,12 @@ class SyntheticBcolzTestCase(zf.WithAdjustmentReader,
first_asset_start = Timestamp('2015-04-01', tz='UTC')
START_DATE = Timestamp('2015-01-01', tz='utc')
END_DATE = Timestamp('2015-08-01', tz='utc')
ASSET_FINDER_EQUITY_SIDS = list(range(6))

@classmethod
def make_equity_info(cls):
cls.equity_info = ret = make_rotating_equity_info(
num_assets=6,
sids=cls.ASSET_FINDER_EQUITY_SIDS,
first_start=cls.first_asset_start,
frequency=cls.trading_calendar.day,
periods_between_starts=4,
Expand Down
57 changes: 29 additions & 28 deletions tests/pipeline/test_international_markets.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,9 +3,10 @@
import numpy as np
import pandas as pd

from trading_calendars import get_calendar

from zipline.assets.synthetic import make_rotating_equity_info
from zipline.assets.synthetic import (
make_rotating_equity_info,
make_multi_exchange_equity_info,
)
from zipline.data.in_memory_daily_bars import InMemoryDailyBarReader
from zipline.pipeline.domain import (
CA_EQUITIES,
Expand All @@ -18,7 +19,10 @@
from zipline.pipeline.loaders.equity_pricing_loader import EquityPricingLoader
from zipline.pipeline.loaders.synthetic import NullAdjustmentReader
from zipline.testing.predicates import assert_equal
from zipline.testing.core import parameter_space, random_tick_prices
from zipline.testing.core import (
parameter_space,
random_tick_prices,
)

import zipline.testing.fixtures as zf

Expand Down Expand Up @@ -146,29 +150,26 @@ class InternationalEquityTestCase(WithInternationalPricingPipelineEngine,

@classmethod
def make_equity_info(cls):
out = pd.concat(
[
# 15 assets on each exchange. Each asset lives for 5 days.
# A new asset starts each day.
make_rotating_equity_info(
num_assets=20,
first_start=cls.START_DATE,
frequency=get_calendar(exchange).day,
periods_between_starts=1,
# NOTE: The asset_lifetime parameter name is a bit
# misleading. It determines the number of trading
# days between each asset's start_date and end_date,
# so assets created with this method actual "live"
# for (asset_lifetime + 1) days. But, since pipeline
# doesn't show you an asset the day it IPOs, this
# number matches the number of days that each asset
# should appear in a pipeline output.
asset_lifetime=5,
exchange=exchange,
)
for exchange in cls.EXCHANGE_INFO.exchange
],
ignore_index=True,
# - 20 assets on each exchange.
# - Each asset lives for 5 days.
# - A new asset starts each day.
out = make_multi_exchange_equity_info(
factory=make_rotating_equity_info,
exchange_sids={
'XNYS': range(20),
'XTSE': range(20, 40),
'XLON': range(40, 60),
},
first_start=cls.START_DATE,
periods_between_starts=1,
# NOTE: The asset_lifetime parameter name is a bit misleading. It
# determines the number of trading days between each asset's
# start_date and end_date, so assets created with this method
# actual "live" for (asset_lifetime + 1) days. But, since
# pipeline doesn't show you an asset the day it IPOs, this
# number matches the number of days that each asset should
# appear in a pipeline output.
asset_lifetime=5,
)
assert_equal(out.end_date.max(), cls.END_DATE)
return out
Expand Down Expand Up @@ -211,7 +212,7 @@ def test_generic_pipeline_with_explicit_domain(self, domain):
expected_dates = sessions[-17:-9]

for col in pipe.columns:
# result_date should look like this:
# result_data should look like this:
#
# E F G H I J K L M N O P # noqa
# 24.17 25.17 26.17 27.17 28.17 NaN NaN NaN NaN NaN NaN NaN # noqa
Expand Down
4 changes: 2 additions & 2 deletions tests/test_algorithm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3723,7 +3723,7 @@ def make_equity_info(cls):
# 1 2015-01-05 2015-01-09 2015-01-13
# 2 2015-01-05 2015-01-13 2015-01-15
cls.asset_info = make_jagged_equity_info(
num_assets=3,
sids=list(range(3)),
start_date=cls.test_days[0],
first_end=cls.first_asset_expiration,
frequency=cls.trading_calendar.day,
Expand Down Expand Up @@ -4071,7 +4071,7 @@ def make_equity_info(cls):
# 1 2015-01-05 2015-01-09 2015-01-13
# 2 2015-01-05 2015-01-13 2015-01-15
cls.asset_info = make_jagged_equity_info(
num_assets=3,
sids=list(range(3)),
start_date=cls.test_days[0],
first_end=cls.first_asset_expiration,
frequency=cls.trading_calendar.day,
Expand Down
11 changes: 7 additions & 4 deletions tests/test_assets.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1056,27 +1056,30 @@ def test_compute_lifetimes(self):
equities = pd.concat(
[
make_rotating_equity_info(
num_assets=assets_per_exchange,
sids=range(
i * assets_per_exchange,
(i + 1) * assets_per_exchange,
),
first_start=first_start,
frequency=trading_day,
periods_between_starts=3,
asset_lifetime=5,
exchange=exchange,
)
for exchange in (
for i, exchange in enumerate((
'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)])

# shuffle up the sids so they are not contiguous per exchange
sids = np.arange(len(equities))
sids = equities.index.values[:]
np.random.RandomState(1337).shuffle(sids)
equities.index = sids
permute_sid = dict(zip(sids, range(len(sids)))).__getitem__
Expand Down
72 changes: 66 additions & 6 deletions zipline/assets/synthetic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,11 +4,13 @@
import pandas as pd
from pandas.tseries.offsets import MonthBegin
from six import iteritems
from toolz import merge
from trading_calendars import get_calendar

from .futures import CMES_CODE_TO_MONTH


def make_rotating_equity_info(num_assets,
def make_rotating_equity_info(sids,
first_start,
frequency,
periods_between_starts,
Expand Down Expand Up @@ -38,6 +40,7 @@ def make_rotating_equity_info(num_assets,
info : pd.DataFrame
DataFrame representing newly-created assets.
"""
num_assets = len(sids)
return pd.DataFrame(
{
'symbol': [chr(ord('A') + i) for i in range(num_assets)],
Expand All @@ -55,7 +58,7 @@ def make_rotating_equity_info(num_assets,
),
'exchange': exchange,
},
index=range(num_assets),
index=sids,
)


Expand Down Expand Up @@ -117,12 +120,13 @@ def make_simple_equity_info(sids,
)


def make_jagged_equity_info(num_assets,
def make_jagged_equity_info(sids,
start_date,
first_end,
frequency,
periods_between_ends,
auto_close_delta):
auto_close_delta,
exchange='TEST'):
"""
Create a DataFrame representing assets that all begin at the same start
date, but have cascading end dates.
Expand All @@ -146,6 +150,7 @@ def make_jagged_equity_info(num_assets,
info : pd.DataFrame
DataFrame representing newly-created assets.
"""
num_assets = len(sids)
frame = pd.DataFrame(
{
'symbol': [chr(ord('A') + i) for i in range(num_assets)],
Expand All @@ -155,9 +160,9 @@ def make_jagged_equity_info(num_assets,
freq=(periods_between_ends * frequency),
periods=num_assets,
),
'exchange': 'TEST',
'exchange': exchange,
},
index=range(num_assets),
index=sids,
)

# Explicitly pass None to disable setting the auto_close_date column.
Expand All @@ -167,6 +172,61 @@ def make_jagged_equity_info(num_assets,
return frame


def make_multi_exchange_equity_info(factory,
exchange_sids,
exchange_kwargs=None,
**common_kwargs):
"""
Create an "equity_info" DataFrame for multiple exchanges by calling an
existing factory function for each exchange and concatting the results.

Parameters
----------
factory : function
Function to use to create equity info for each exchange.
exchange_sids : dict[str -> list[sids]]
Map from exchange to list of sids to be created for that exchange.
exchange_kwargs : dict[str -> dict], optional
Map from exchange to additional kwargs to be passed for that exchange.
**common_kwargs
Additional keyword-arguments are forwarded to ``factory``.

Returns
-------
info : pd.DataFrame
DataFrame representing newly-created assets.
"""
if exchange_kwargs is None:
exchange_kwargs = {e: {} for e in exchange_sids}
else:
assert exchange_kwargs.keys() == exchange_sids.keys()

# When using frequency-based factories, use each calendar's trading
# calendar for frequency by default.
provide_default_frequency = (
'frequency' not in common_kwargs
and factory in (make_rotating_equity_info, make_jagged_equity_info)
)
if provide_default_frequency:
for e, kw in iteritems(exchange_kwargs):
kw.setdefault('frequency', get_calendar(e).day)

frame_per_exchange = [
factory(
sids=sids,
exchange=e,
**merge(common_kwargs, exchange_kwargs[e])
)
for e, sids in iteritems(exchange_sids)
]

result = pd.concat(frame_per_exchange)
if not result.index.is_unique:
raise AssertionError("Duplicate sids: {}".format(result.index))

return result


def make_future_info(first_sid,
root_symbols,
years,
Expand Down