TST: Use testing market data with run_algorithm

so env doesn't need to download it

tests/test_algorithm.py

@@ -88,6 +88,7 @@
 )
 from zipline.testing import (
     FakeDataPortal,
+    copy_market_data,
     create_daily_df_for_asset,
     create_data_portal,
     create_data_portal_from_trade_history,
@@ -99,6 +100,7 @@
     tmp_trading_env,
     to_utc,
     trades_by_sid_to_dfs,
+    tmp_dir,
 )
 from zipline.testing import RecordBatchBlotter
 from zipline.testing.fixtures import (
@@ -4760,13 +4762,18 @@ def check_panels():
             check_panels()
             price_record.loc[:] = np.nan
 
-        run_algorithm(
-            start=start_dt,
-            end=end_dt,
-            capital_base=1,
-            initialize=initialize,
-            handle_data=handle_data,
-            data_frequency=data_frequency,
-            data=panel
-        )
-        check_panels()
+        with tmp_dir() as tmpdir:
+            root = tmpdir.getpath('example_data/root')
+            copy_market_data(self.MARKET_DATA_DIR, root)
+
+            run_algorithm(
+                start=start_dt,
+                end=end_dt,
+                capital_base=1,
+                initialize=initialize,
+                handle_data=handle_data,
+                data_frequency=data_frequency,
+                data=panel,
+                environ={'ZIPLINE_ROOT': root},
+            )
+            check_panels()

tests/test_examples.py

@@ -21,8 +21,9 @@
 
 from zipline import examples
 from zipline.data.bundles import register, unregister
-from zipline.testing import test_resource_path
-from zipline.testing.fixtures import WithTmpDir, ZiplineTestCase
+from zipline.testing import test_resource_path, copy_market_data
+from zipline.testing.fixtures import WithTmpDir, ZiplineTestCase, \
+    WithTradingEnvironment
 from zipline.testing.predicates import assert_equal
 from zipline.utils.cache import dataframe_cache
 
@@ -53,6 +54,9 @@ def init_class_fixtures(cls):
             serialization='pickle',
         )
 
+        copy_market_data(WithTradingEnvironment.MARKET_DATA_DIR,
+                         cls.tmpdir.getpath('example_data/root'))
+
     @parameterized.expand(examples.EXAMPLE_MODULES)
     def test_example(self, example_name):
         actual_perf = examples.run_example(

zipline/data/loader.py

@@ -53,13 +53,13 @@ def last_modified_time(path):
     return pd.Timestamp(os.path.getmtime(path), unit='s', tz='UTC')
 
 
-def get_data_filepath(name):
+def get_data_filepath(name, environ=None):
     """
     Returns a handle to data file.
 
     Creates containing directory, if needed.
     """
-    dr = data_root()
+    dr = data_root(environ)
 
     if not os.path.exists(dr):
         os.makedirs(dr)
@@ -91,7 +91,8 @@ def has_data_for_dates(series_or_df, first_date, last_date):
     return (first <= first_date) and (last >= last_date)
 
 
-def load_market_data(trading_day=None, trading_days=None, bm_symbol='^GSPC'):
+def load_market_data(trading_day=None, trading_days=None, bm_symbol='^GSPC',
+                     environ=None):
     """
     Load benchmark returns and treasury yield curves for the given calendar and
     benchmark symbol.
@@ -162,19 +163,22 @@ def load_market_data(trading_day=None, trading_days=None, bm_symbol='^GSPC'):
         # We need the trading_day to figure out the close prior to the first
         # date so that we can compute returns for the first date.
         trading_day,
+        environ,
     )
     tc = ensure_treasury_data(
         bm_symbol,
         first_date,
         last_date,
         now,
+        environ,
     )
     benchmark_returns = br[br.index.slice_indexer(first_date, last_date)]
     treasury_curves = tc[tc.index.slice_indexer(first_date, last_date)]
     return benchmark_returns, treasury_curves
 
 
-def ensure_benchmark_data(symbol, first_date, last_date, now, trading_day):
+def ensure_benchmark_data(symbol, first_date, last_date, now, trading_day,
+                          environ=None):
     """
     Ensure we have benchmark data for `symbol` from `first_date` to `last_date`
 
@@ -204,7 +208,8 @@ def ensure_benchmark_data(symbol, first_date, last_date, now, trading_day):
     path.
     """
     filename = get_benchmark_filename(symbol)
-    data = _load_cached_data(filename, first_date, last_date, now, 'benchmark')
+    data = _load_cached_data(filename, first_date, last_date, now, 'benchmark',
+                             environ)
     if data is not None:
         return data
 
@@ -218,7 +223,7 @@ def ensure_benchmark_data(symbol, first_date, last_date, now, trading_day):
             first_date - trading_day,
             last_date,
         )
-        data.to_csv(get_data_filepath(filename))
+        data.to_csv(get_data_filepath(filename, environ))
     except (OSError, IOError, HTTPError):
         logger.exception('failed to cache the new benchmark returns')
         raise
@@ -227,7 +232,7 @@ def ensure_benchmark_data(symbol, first_date, last_date, now, trading_day):
     return data
 
 
-def ensure_treasury_data(symbol, first_date, last_date, now):
+def ensure_treasury_data(symbol, first_date, last_date, now, environ=None):
     """
     Ensure we have treasury data from treasury module associated with
     `symbol`.
@@ -259,7 +264,8 @@ def ensure_treasury_data(symbol, first_date, last_date, now):
     )
     first_date = max(first_date, loader_module.earliest_possible_date())
 
-    data = _load_cached_data(filename, first_date, last_date, now, 'treasury')
+    data = _load_cached_data(filename, first_date, last_date, now, 'treasury',
+                             environ)
     if data is not None:
         return data
 
@@ -269,22 +275,23 @@ def ensure_treasury_data(symbol, first_date, last_date, now):
 
     try:
         data = loader_module.get_treasury_data(first_date, last_date)
-        data.to_csv(get_data_filepath(filename))
+        data.to_csv(get_data_filepath(filename, environ))
     except (OSError, IOError, HTTPError):
         logger.exception('failed to cache treasury data')
     if not has_data_for_dates(data, first_date, last_date):
         logger.warn("Still don't have expected data after redownload!")
     return data
 
 
-def _load_cached_data(filename, first_date, last_date, now, resource_name):
+def _load_cached_data(filename, first_date, last_date, now, resource_name,
+                      environ=None):
     if resource_name == 'benchmark':
         from_csv = pd.Series.from_csv
     else:
         from_csv = pd.DataFrame.from_csv
 
     # Path for the cache.
-    path = get_data_filepath(filename)
+    path = get_data_filepath(filename, environ)
 
     # If the path does not exist, it means the first download has not happened
     # yet, so don't try to read from 'path'.

zipline/finance/trading.py

@@ -12,6 +12,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+from functools import partial
 
 import logbook
 import pandas as pd
@@ -86,11 +87,12 @@ def __init__(
         trading_calendar=None,
         asset_db_path=':memory:',
         future_chain_predicates=CHAIN_PREDICATES,
+        environ=None,
     ):
 
         self.bm_symbol = bm_symbol
         if not load:
-            load = load_market_data
+            load = partial(load_market_data, environ=environ)
 
         if not trading_calendar:
             trading_calendar = get_calendar("NYSE")

zipline/testing/__init__.py

@@ -16,6 +16,7 @@
     check_allclose,
     check_arrays,
     chrange,
+    copy_market_data,
     create_daily_df_for_asset,
     create_data_portal,
     create_data_portal_from_trade_history,

zipline/testing/core.py

@@ -29,6 +29,7 @@
 from zipline.assets import AssetFinder, AssetDBWriter
 from zipline.assets.synthetic import make_simple_equity_info
 from zipline.data.data_portal import DataPortal
+from zipline.data.loader import get_benchmark_filename, INDEX_MAPPING
 from zipline.data.minute_bars import (
     BcolzMinuteBarReader,
     BcolzMinuteBarWriter,
@@ -52,6 +53,7 @@
 from zipline.utils.input_validation import expect_dimensions
 from zipline.utils.numpy_utils import as_column, isnat
 from zipline.utils.pandas_utils import timedelta_to_integral_seconds
+from zipline.utils.paths import ensure_directory
 from zipline.utils.sentinel import sentinel
 
 import numpy as np
@@ -1490,6 +1492,19 @@ def patched_read_csv(filepath_or_buffer, *args, **kwargs):
         yield
 
 
+def copy_market_data(src_market_data_dir, dest_root_dir):
+    symbol = '^GSPC'
+    filenames = (get_benchmark_filename(symbol), INDEX_MAPPING[symbol][1])
+
+    ensure_directory(os.path.join(dest_root_dir, 'data'))
+
+    for filename in filenames:
+        shutil.copyfile(
+            os.path.join(src_market_data_dir, filename),
+            os.path.join(dest_root_dir, 'data', filename)
+        )
+
+
 @curry
 def ensure_doctest(f, name=None):
     """Ensure that an object gets doctested. This is useful for instances

zipline/utils/run_algo.py

@@ -129,7 +129,7 @@ def _run(handle_data,
                 "invalid url %r, must begin with 'sqlite:///'" %
                 str(bundle_data.asset_finder.engine.url),
             )
-        env = TradingEnvironment(asset_db_path=connstr)
+        env = TradingEnvironment(asset_db_path=connstr, environ=environ)
         first_trading_day =\
             bundle_data.equity_minute_bar_reader.first_trading_day
         data = DataPortal(
@@ -152,7 +152,7 @@ def choose_loader(column):
                 "No PipelineLoader registered for column %s." % column
             )
     else:
-        env = None
+        env = TradingEnvironment(environ=environ)
         choose_loader = None
 
     perf = TradingAlgorithm(