MAINT: Add helper for multi-exchange equity info.

tests/pipeline/test_engine.py

@@ -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,

tests/pipeline/test_international_markets.py

@@ -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,
@@ -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
 
@@ -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
@@ -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

tests/test_assets.py

@@ -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__

zipline/assets/synthetic.py

@@ -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,
@@ -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)],
@@ -55,7 +58,7 @@ def make_rotating_equity_info(num_assets,
             ),
             'exchange': exchange,
         },
-        index=range(num_assets),
+        index=sids,
     )
 
 
@@ -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.
@@ -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)],
@@ -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.
@@ -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,