diff --git a/appveyor.yml b/appveyor.yml
index f3812a39..721d9b4b 100644
--- a/appveyor.yml
+++ b/appveyor.yml
@@ -72,6 +72,13 @@ install:
   - sed -i "s/scipy==.*/scipy==%SCIPY_VERSION%/" etc/requirements.txt
 
   - conda info -a
+  - conda install conda=4.1.11 conda-build=1.21.11 anaconda-client=1.5.1 --yes -q
+  # https://blog.ionelmc.ro/2014/12/21/compiling-python-extensions-on-windows/ for 64bit C compilation
+  - ps: copy .\ci\appveyor\vcvars64.bat "C:\Program Files (x86)\Microsoft Visual Studio 10.0\VC\bin\amd64"
+  - "%CMD_IN_ENV% python .\\ci\\make_conda_packages.py"
+
+  # test that we can conda install zipline in a new env
+  - conda create -n installenv --yes -q --use-local python=%PYTHON_VERSION% numpy=%NUMPY_VERSION% zipline -c quantopian -c https://conda.anaconda.org/quantopian/label/ci
 
   - ps: $env:BCOLZ_VERSION=(sls "bcolz==(.*)" .\etc\requirements.txt -ca).matches.groups[1].value
   - ps: $env:NUMEXPR_VERSION=(sls "numexpr==(.*)" .\etc\requirements.txt -ca).matches.groups[1].value
@@ -88,11 +95,9 @@ install:
   - pip freeze | sort
 
 test_script:
-  - nosetests -e zipline.utils.numpy_utils -x
+  - nosetests -e zipline.utils.numpy_utils
+  - flake8 zipline tests
 
 branches:
   only:
     - master
-
-on_finish:
-  - ps: $blockRdp = $true; iex ((new-object net.webclient).DownloadString('https://raw.githubusercontent.com/appveyor/ci/master/scripts/enable-rdp.ps1'))
diff --git a/tests/risk/test_risk_period.py b/tests/risk/test_risk_period.py
index 7c0967ca..3a563b4d 100644
--- a/tests/risk/test_risk_period.py
+++ b/tests/risk/test_risk_period.py
@@ -64,11 +64,6 @@ class TestRisk(WithTradingEnvironment, ZiplineTestCase):
             treasury_curves=self.env.treasury_curves,
         )
 
-    @classmethod
-    def init_class_fixtures(cls):
-        cls.TRADING_CALENDAR_PRIMARY_CAL = 'NYSE'
-        super(TestRisk, cls).init_class_fixtures()
-
     def test_factory(self):
         returns = [0.1] * 100
         r_objects = factory.create_returns_from_list(returns, self.sim_params)
@@ -393,18 +388,18 @@ class TestRisk(WithTradingEnvironment, ZiplineTestCase):
             pd.Timestamp("1991-01-01", tz='UTC')
         )
 
-        # 2008 and 2012 were leap years
+        # 1992 and 1996 were leap years
         total_days = 365 * 5 + 2
         end_session = start_session + datetime.timedelta(days=total_days)
-        sim_params = SimulationParameters(
+        sim_params90s = SimulationParameters(
             start_session=start_session,
             end_session=end_session,
             trading_calendar=self.trading_calendar,
         )
 
-        returns = factory.create_returns_from_range(sim_params)
+        returns = factory.create_returns_from_range(sim_params90s)
         returns = returns[:-10]  # truncate the returns series to end mid-month
-        metrics = risk.RiskReport(returns, sim_params,
+        metrics = risk.RiskReport(returns, sim_params90s,
                                   trading_calendar=self.trading_calendar,
                                   treasury_curves=self.env.treasury_curves,
                                   benchmark_returns=self.env.benchmark_returns)
diff --git a/zipline/data/loader.py b/zipline/data/loader.py
index 02519272..8e736c15 100644
--- a/zipline/data/loader.py
+++ b/zipline/data/loader.py
@@ -21,6 +21,7 @@ from pandas_datareader.data import DataReader
 import pytz
 from six import iteritems
 from six.moves.urllib_error import HTTPError
+import zipline
 
 from .benchmarks import get_benchmark_returns
 from . import treasuries, treasuries_can
@@ -43,6 +44,9 @@ INDEX_MAPPING = {
     (treasuries, 'treasury_curves.csv', 'www.federalreserve.gov'),
 }
 
+zipline_dir = os.path.dirname(zipline.__file__)
+MARKET_DATA_DIR = os.path.join(zipline_dir, 'resources', 'market_data')
+
 ONE_HOUR = pd.Timedelta(hours=1)
 
 
@@ -194,51 +198,23 @@ def ensure_benchmark_data(symbol, first_date, last_date, now, trading_day):
         A trading day delta.  Used to find the day before first_date so we can
         get the close of the day prior to first_date.
 
-    We attempt to download data unless we already have data stored at the data
-    cache for `symbol` whose first entry is before or on `first_date` and whose
-    last entry is on or after `last_date`.
+    We attempt to download data unless we already have data stored in source or
+    in the data cache for `symbol` whose first entry is before or on
+    `first_date` and whose last entry is on or after `last_date`.
 
     If we perform a download and the cache criteria are not satisfied, we wait
     at least one hour before attempting a redownload.  This is determined by
     comparing the current time to the result of os.path.getmtime on the cache
     path.
     """
-    path = get_data_filepath(get_benchmark_filename(symbol))
+    filename = get_benchmark_filename(symbol)
+    data = _load_cached_data(filename, first_date, last_date, now, 'benchmark')
+    if data is not None:
+        return data
 
-    # If the path does not exist, it means the first download has not happened
-    # yet, so don't try to read from 'path'.
-    if os.path.exists(path):
-        try:
-            data = pd.Series.from_csv(path).tz_localize('UTC')
-            if has_data_for_dates(data, first_date, last_date):
-                return data
-
-            # Don't re-download if we've successfully downloaded and written a
-            # file in the last hour.
-            last_download_time = last_modified_time(path)
-            if (now - last_download_time) <= ONE_HOUR:
-                logger.warn(
-                    "Refusing to download new benchmark data because a "
-                    "download succeeded at %s." % last_download_time
-                )
-                return data
-
-        except (OSError, IOError, ValueError) as e:
-            # These can all be raised by various versions of pandas on various
-            # classes of malformed input.  Treat them all as cache misses.
-            logger.info(
-                "Loading data for {path} failed with error [{error}].".format(
-                    path=path, error=e,
-                )
-            )
-    logger.info(
-        "Cache at {path} does not have data from {start} to {end}.\n"
-        "Downloading benchmark data for '{symbol}'.",
-        start=first_date,
-        end=last_date,
-        symbol=symbol,
-        path=path,
-    )
+    # If no cached data was found or it was missing any dates then download the
+    # necessary data.
+    logger.info('Downloading benchmark data for {symbol!r}.', symbol=symbol)
 
     try:
         data = get_benchmark_returns(
@@ -246,7 +222,7 @@ def ensure_benchmark_data(symbol, first_date, last_date, now, trading_day):
             first_date - trading_day,
             last_date,
         )
-        data.to_csv(path)
+        data.to_csv(get_data_filepath(filename))
     except (OSError, IOError, HTTPError):
         logger.exception('failed to cache the new benchmark returns')
         raise
@@ -255,14 +231,14 @@ def ensure_benchmark_data(symbol, first_date, last_date, now, trading_day):
     return data
 
 
-def ensure_treasury_data(bm_symbol, first_date, last_date, now):
+def ensure_treasury_data(symbol, first_date, last_date, now):
     """
     Ensure we have treasury data from treasury module associated with
-    `bm_symbol`.
+    `symbol`.
 
     Parameters
     ----------
-    bm_symbol : str
+    symbol : str
         Benchmark symbol for which we're loading associated treasury curves.
     first_date : pd.Timestamp
         First date required to be in the cache.
@@ -273,9 +249,9 @@ def ensure_treasury_data(bm_symbol, first_date, last_date, now):
         re-download data that isn't available due to scheduling quirks or other
         failures.
 
-    We attempt to download data unless we already have data stored in the cache
-    for `module_name` whose first entry is before or on `first_date` and whose
-    last entry is on or after `last_date`.
+    We attempt to download data unless we already have data stored in source or
+    in the cache for `module_name` whose first entry is before or on
+    `first_date` and whose last entry is on or after `last_date`.
 
     If we perform a download and the cache criteria are not satisfied, we wait
     at least one hour before attempting a redownload.  This is determined by
@@ -283,16 +259,58 @@ def ensure_treasury_data(bm_symbol, first_date, last_date, now):
     path.
     """
     loader_module, filename, source = INDEX_MAPPING.get(
-        bm_symbol, INDEX_MAPPING['^GSPC']
+        symbol, INDEX_MAPPING['^GSPC'],
     )
     first_date = max(first_date, loader_module.earliest_possible_date())
+
+    data = _load_cached_data(filename, first_date, last_date, now, 'treasury')
+    if data is not None:
+        return data
+
+    # If no cached data was found or it was missing any dates then download the
+    # necessary data.
+    logger.info('Downloading treasury data for {symbol!r}.', symbol=symbol)
+
+    try:
+        data = loader_module.get_treasury_data(first_date, last_date)
+        data.to_csv(get_data_filepath(filename))
+    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):
+    if resource_name == 'benchmark':
+        from_csv = pd.Series.from_csv
+    else:
+        from_csv = pd.DataFrame.from_csv
+
+    # First try to retrieve the data from a static csv file in source.
+    source_path = os.path.join(MARKET_DATA_DIR, filename)
+    try:
+        data = from_csv(source_path).tz_localize('UTC')
+        if has_data_for_dates(data, first_date, last_date):
+            return data
+    except (OSError, IOError, ValueError) as e:
+        # These can all be raised by various versions of pandas on various
+        # classes of malformed input.
+        logger.info(
+            'Loading data from source path {path!r} failed with error '
+            '[{error}].',
+            path=source_path,
+            error=e,
+        )
+
+    # If the data in source was missing any dates then check the cache.
     path = get_data_filepath(filename)
 
     # If the path does not exist, it means the first download has not happened
     # yet, so don't try to read from 'path'.
     if os.path.exists(path):
         try:
-            data = pd.DataFrame.from_csv(path).tz_localize('UTC')
+            data = from_csv(path).tz_localize('UTC')
             if has_data_for_dates(data, first_date, last_date):
                 return data
 
@@ -301,8 +319,10 @@ def ensure_treasury_data(bm_symbol, first_date, last_date, now):
             last_download_time = last_modified_time(path)
             if (now - last_download_time) <= ONE_HOUR:
                 logger.warn(
-                    "Refusing to download new treasury data because a "
-                    "download succeeded at %s." % last_download_time
+                    "Refusing to download new {resource} data because a "
+                    "download succeeded at {time}.",
+                    resource=resource_name,
+                    time=last_download_time,
                 )
                 return data
 
@@ -310,19 +330,18 @@ def ensure_treasury_data(bm_symbol, first_date, last_date, now):
             # These can all be raised by various versions of pandas on various
             # classes of malformed input.  Treat them all as cache misses.
             logger.info(
-                "Loading data for {path} failed with error [{error}].".format(
-                    path=path, error=e,
-                )
+                "Loading data for {path} failed with error [{error}].",
+                path=path,
+                error=e,
             )
 
-    try:
-        data = loader_module.get_treasury_data(first_date, last_date)
-        data.to_csv(path)
-    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
+    logger.info(
+        "Cache at {path} does not have data from {start} to {end}.\n",
+        start=first_date,
+        end=last_date,
+        path=path,
+    )
+    return None
 
 
 def _load_raw_yahoo_data(indexes=None, stocks=None, start=None, end=None):
diff --git a/zipline/resources/market_data/^GSPC_benchmark.csv b/zipline/resources/market_data/^GSPC_benchmark.csv
new file mode 100644
index 00000000..53a87717
--- /dev/null
+++ b/zipline/resources/market_data/^GSPC_benchmark.csv
@@ -0,0 +1,6865 @@
+1990-01-02 00:00:00+00:00,0.0177985515189
+1990-01-03 00:00:00+00:00,-0.00258553753184
+1990-01-04 00:00:00+00:00,-0.00861299173227
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diff --git a/zipline/resources/market_data/treasury_curves.csv b/zipline/resources/market_data/treasury_curves.csv
new file mode 100644
index 00000000..5baa5da8
--- /dev/null
+++ b/zipline/resources/market_data/treasury_curves.csv
@@ -0,0 +1,6817 @@
+Time Period,1month,3month,6month,1year,2year,3year,5year,7year,10year,20year,30year
+1990-01-02 00:00:00+00:00,,0.0783,0.0789,0.0781,0.0787,0.079,0.0787,0.0798,0.0794,,0.08
+1990-01-03 00:00:00+00:00,,0.0789,0.0794,0.0785,0.0794,0.0796,0.0792,0.0804,0.0799,,0.0804
+1990-01-04 00:00:00+00:00,,0.0784,0.079,0.0782,0.0792,0.0793,0.0791,0.0802,0.0798,,0.0804
+1990-01-05 00:00:00+00:00,,0.0779,0.0785,0.0779,0.079,0.0794,0.0792,0.0803,0.0799,,0.0806
+1990-01-08 00:00:00+00:00,,0.0779,0.0788,0.0781,0.079,0.0795,0.0792,0.0805,0.0802,,0.0809
+1990-01-09 00:00:00+00:00,,0.078,0.0782,0.0778,0.0791,0.0794,0.0792,0.0805,0.0802,,0.081
+1990-01-10 00:00:00+00:00,,0.0775,0.0778,0.0777,0.0791,0.0795,0.0792,0.08,0.0803,,0.0811
+1990-01-11 00:00:00+00:00,,0.078,0.078,0.0777,0.0791,0.0795,0.0794,0.0801,0.0804,,0.0811
+1990-01-12 00:00:00+00:00,,0.0774,0.0781,0.0776,0.0793,0.0798,0.0799,0.0807,0.081,,0.0817
+1990-01-16 00:00:00+00:00,,0.0789,0.0799,0.0792,0.081,0.0813,0.0811,0.0818,0.082,,0.0825
+1990-01-17 00:00:00+00:00,,0.0797,0.0797,0.0791,0.0809,0.0811,0.0811,0.0817,0.0819,,0.0825
+1990-01-18 00:00:00+00:00,,0.0804,0.0808,0.0805,0.0825,0.0828,0.0827,0.0831,0.0832,,0.0835
+1990-01-19 00:00:00+00:00,,0.08,0.0801,0.08,0.082,0.0823,0.082,0.0824,0.0826,,0.0829
+1990-01-22 00:00:00+00:00,,0.0799,0.0799,0.0798,0.0818,0.082,0.0819,0.0825,0.0827,,0.0831
+1990-01-23 00:00:00+00:00,,0.0793,0.0797,0.0797,0.0818,0.082,0.0818,0.0823,0.0826,,0.0829
+1990-01-24 00:00:00+00:00,,0.0793,0.0799,0.08,0.082,0.0829,0.0828,0.0834,0.0838,,0.0841
+1990-01-25 00:00:00+00:00,,0.0795,0.0801,0.0803,0.0824,0.0832,0.0831,0.0839,0.0842,,0.0846
+1990-01-26 00:00:00+00:00,,0.0793,0.0804,0.0807,0.0828,0.0838,0.0838,0.0845,0.0849,,0.0855
+1990-01-29 00:00:00+00:00,,0.08,0.0809,0.0808,0.083,0.0839,0.0839,0.0845,0.085,,0.0854
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+1990-01-31 00:00:00+00:00,,0.08,0.0813,0.0808,0.0828,0.0836,0.0835,0.0839,0.0843,,0.0846
+1990-02-01 00:00:00+00:00,,0.0802,0.0813,0.0809,0.0828,0.0835,0.0835,0.0838,0.0842,,0.0844
+1990-02-02 00:00:00+00:00,,0.0807,0.0817,0.0813,0.0837,0.0843,0.0842,0.0846,0.085,,0.0851
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+1990-02-09 00:00:00+00:00,,0.0802,0.0809,0.0803,0.0825,0.0824,0.0829,0.0834,0.0831,,0.0836
+1990-02-12 00:00:00+00:00,,0.0799,0.0811,0.0806,0.0832,0.0832,0.0837,0.0841,0.084,,0.0843
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+1990-02-14 00:00:00+00:00,,0.0787,0.08,0.0803,0.0827,0.0827,0.0834,0.0836,0.0836,,0.0841
+1990-02-15 00:00:00+00:00,,0.0797,0.0808,0.0811,0.0835,0.0835,0.084,0.0844,0.0843,,0.0847
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+1990-02-20 00:00:00+00:00,,0.0806,0.082,0.0824,0.0852,0.0852,0.0858,0.0863,0.0862,,0.0866
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+1990-03-09 00:00:00+00:00,,0.0826,0.0833,0.0846,0.0873,0.0871,0.0868,0.0871,0.0865,,0.0863
+1990-03-12 00:00:00+00:00,,0.0826,0.0833,0.0844,0.0871,0.087,0.0867,0.087,0.0863,,0.0862
+1990-03-13 00:00:00+00:00,,0.0826,0.0834,0.0848,0.0877,0.0877,0.0875,0.0878,0.0873,,0.0872
+1990-03-14 00:00:00+00:00,,0.0823,0.0831,0.0841,0.0869,0.087,0.0867,0.0871,0.0865,,0.0861
+1990-03-15 00:00:00+00:00,,0.0821,0.0832,0.0841,0.0871,0.087,0.0869,0.0873,0.0866,,0.0863
+1990-03-16 00:00:00+00:00,,0.0819,0.0829,0.0836,0.0864,0.0862,0.0861,0.0864,0.0859,,0.0855
+1990-03-19 00:00:00+00:00,,0.0822,0.0832,0.0838,0.0868,0.0866,0.0865,0.0866,0.0859,,0.0854
+1990-03-20 00:00:00+00:00,,0.0825,0.0835,0.0838,0.0868,0.0865,0.0861,0.0862,0.0854,,0.0847
+1990-03-21 00:00:00+00:00,,0.0823,0.0834,0.0839,0.0869,0.0866,0.0862,0.0863,0.0855,,0.0847
+1990-03-22 00:00:00+00:00,,0.0817,0.0827,0.0832,0.0862,0.086,0.0856,0.0861,0.0853,,0.0849
+1990-03-23 00:00:00+00:00,,0.0815,0.0826,0.0829,0.0861,0.0859,0.0854,0.0859,0.0852,,0.0848
+1990-03-26 00:00:00+00:00,,0.0813,0.0828,0.083,0.0861,0.0859,0.0852,0.0858,0.0851,,0.0847
+1990-03-27 00:00:00+00:00,,0.0817,0.083,0.0835,0.0859,0.086,0.0855,0.0859,0.0852,,0.0848
+1990-03-28 00:00:00+00:00,,0.0812,0.0827,0.0833,0.0857,0.086,0.0853,0.0858,0.0851,,0.0847
+1990-03-29 00:00:00+00:00,,0.081,0.0828,0.0836,0.0862,0.0865,0.086,0.0866,0.086,,0.0858
+1990-03-30 00:00:00+00:00,,0.0807,0.0824,0.0835,0.0864,0.0869,0.0865,0.087,0.0865,,0.0863
+1990-04-02 00:00:00+00:00,,0.0805,0.0823,0.0834,0.0862,0.0867,0.0865,0.0869,0.0865,,0.0863
+1990-04-03 00:00:00+00:00,,0.0809,0.0825,0.0832,0.0861,0.0865,0.0864,0.0869,0.0863,,0.0861
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+1990-04-05 00:00:00+00:00,,0.0802,0.0818,0.0828,0.0854,0.086,0.0857,0.0863,0.0857,,0.0852
+1990-04-06 00:00:00+00:00,,0.0802,0.0816,0.0826,0.0852,0.0858,0.0855,0.0861,0.0856,,0.0852
+1990-04-09 00:00:00+00:00,,0.0809,0.0823,0.0829,0.0854,0.086,0.0858,0.0865,0.0859,,0.0855
+1990-04-10 00:00:00+00:00,,0.0805,0.0821,0.0829,0.0854,0.0859,0.0858,0.0865,0.086,,0.0857
+1990-04-11 00:00:00+00:00,,0.0803,0.082,0.0828,0.0856,0.0862,0.086,0.0863,0.0863,,0.0858
+1990-04-12 00:00:00+00:00,,0.0802,0.082,0.0829,0.0857,0.0863,0.0862,0.0865,0.0864,,0.086
+1990-04-16 00:00:00+00:00,,0.0801,0.0818,0.0829,0.0861,0.0866,0.0865,0.0869,0.0868,,0.0864
+1990-04-17 00:00:00+00:00,,0.0803,0.0823,0.0838,0.087,0.0875,0.0873,0.0878,0.0877,,0.0874
+1990-04-18 00:00:00+00:00,,0.0808,0.083,0.0849,0.0879,0.0884,0.0884,0.0888,0.0886,,0.0885
+1990-04-19 00:00:00+00:00,,0.0806,0.0828,0.0846,0.0881,0.0886,0.0884,0.0888,0.0887,,0.0886
+1990-04-20 00:00:00+00:00,,0.0795,0.0824,0.0844,0.0882,0.0888,0.0888,0.0895,0.0895,,0.0893
+1990-04-23 00:00:00+00:00,,0.08,0.0831,0.085,0.0887,0.0891,0.0892,0.0897,0.0898,,0.0896
+1990-04-24 00:00:00+00:00,,0.0803,0.0836,0.0855,0.0892,0.0898,0.0896,0.0901,0.09,,0.0898
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+1990-05-02 00:00:00+00:00,,0.0817,0.0849,0.0863,0.0905,0.0911,0.091,0.0912,0.0909,,0.0905
+1990-05-03 00:00:00+00:00,,0.0815,0.0845,0.0862,0.0898,0.0905,0.0904,0.0905,0.0904,,0.0899
+1990-05-04 00:00:00+00:00,,0.0802,0.0825,0.0843,0.087,0.0877,0.088,0.0883,0.0884,,0.0883
+1990-05-07 00:00:00+00:00,,0.0809,0.0826,0.0844,0.0874,0.0882,0.0883,0.0886,0.0887,,0.0885
+1990-05-08 00:00:00+00:00,,0.0806,0.0822,0.084,0.0868,0.0873,0.0879,0.0883,0.0884,,0.0881
+1990-05-09 00:00:00+00:00,,0.0801,0.082,0.084,0.0875,0.0879,0.0887,0.0892,0.0888,,0.089
+1990-05-10 00:00:00+00:00,,0.0797,0.0815,0.0833,0.0869,0.0873,0.088,0.0886,0.0882,,0.0881
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+1990-06-01 00:00:00+00:00,,0.0794,0.0801,0.0806,0.0833,0.0836,0.0838,0.0847,0.0844,,0.0843
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+2017-03-29 00:00:00+00:00,0.0076,0.0078,0.0092,0.0104,0.0126,0.0153,0.0193,0.0221,0.0239,0.0274,0.0299
diff --git a/zipline/testing/fixtures.py b/zipline/testing/fixtures.py
index 66e5dd3d..febf4e32 100644
--- a/zipline/testing/fixtures.py
+++ b/zipline/testing/fixtures.py
@@ -1,3 +1,4 @@
+import os
 import sqlite3
 from unittest import TestCase
 
@@ -18,6 +19,11 @@ from ..data.data_portal import (
     DEFAULT_MINUTE_HISTORY_PREFETCH,
     DEFAULT_DAILY_HISTORY_PREFETCH,
 )
+from ..data.loader import (
+    get_benchmark_filename,
+    INDEX_MAPPING,
+    MARKET_DATA_DIR,
+)
 from ..data.resample import (
     minute_frame_to_session_frame,
     MinuteResampleSessionBarReader
@@ -484,7 +490,50 @@ class WithTradingEnvironment(WithAssetFinder,
 
     @classmethod
     def make_load_function(cls):
-        return None
+        def load(*args, **kwargs):
+            symbol = '^GSPC'
+
+            filename = get_benchmark_filename(symbol)
+            source_path = os.path.join(MARKET_DATA_DIR, filename)
+            benchmark_returns = \
+                pd.Series.from_csv(source_path).tz_localize('UTC')
+
+            filename = INDEX_MAPPING[symbol][1]
+            source_path = os.path.join(MARKET_DATA_DIR, filename)
+            treasury_curves = \
+                pd.DataFrame.from_csv(source_path).tz_localize('UTC')
+
+            # The TradingEnvironment ordinarily uses cached benchmark returns
+            # and treasury curves data, but when running the zipline tests this
+            # cache is not always updated to include the appropriate dates
+            # required by both the futures and equity calendars. In order to
+            # create more reliable and consistent data throughout the entirety
+            # of the tests, we read static benchmark returns and treasury curve
+            # csv files from source. If a test using the TradingEnvironment
+            # fixture attempts to run outside of the static date range of the
+            # csv files, raise an exception warning the user to either update
+            # the csv files in source or to use a date range within the current
+            # bounds.
+            static_start_date = benchmark_returns.index[0].date()
+            static_end_date = benchmark_returns.index[-1].date()
+            warning_message = (
+                'The TradingEnvironment fixture uses static data between '
+                '{static_start} and {static_end}. To use a start and end date '
+                'of {given_start} and {given_end} you will have to update the '
+                'files in {resource_dir} to include the missing dates.'.format(
+                    static_start=static_start_date,
+                    static_end=static_end_date,
+                    given_start=cls.START_DATE.date(),
+                    given_end=cls.END_DATE.date(),
+                    resource_dir=MARKET_DATA_DIR,
+                )
+            )
+            if cls.START_DATE.date() < static_start_date or \
+                    cls.END_DATE.date() > static_end_date:
+                raise Warning(warning_message)
+
+            return benchmark_returns, treasury_curves
+        return load
 
     @classmethod
     def make_trading_environment(cls):