MAINT: Ensure that test sources only provide market days.

Instead of using all calendar days between start and end in test
sources, use the trading calendar for test sources.

Needed for an incoming refactoring of market open and close,
where the opens and closes are indexed by market days.

zipline/finance/risk/cumulative.py

@@ -149,11 +149,9 @@ class RiskMetricsCumulative(object):
             hour=0, minute=0, second=0, microsecond=0
         )
 
-        all_trading_days = trading.environment.trading_days
-        mask = ((all_trading_days >= self.start_date) &
-                (all_trading_days <= self.end_date))
-
-        self.trading_days = all_trading_days[mask]
+        self.trading_days = trading.environment.days_in_range(
+            self.start_date,
+            self.end_date)
 
         last_day = normalize_date(sim_params.period_end)
         if last_day not in self.trading_days:

zipline/finance/trading.py

@@ -162,6 +162,11 @@ class TradingEnvironment(object):
 
         return None
 
+    def days_in_range(self, start, end):
+        mask = ((self.trading_days >= start) &
+                (self.trading_days <= end))
+        return self.trading_days[mask]
+
     def next_open_and_close(self, start_date):
         """
         Given the start_date, returns the next open and close of

zipline/utils/factory.py

@@ -323,24 +323,23 @@ def create_test_df_source(sim_params=None, bars='daily'):
     if sim_params:
         index = sim_params.trading_days
     else:
+        if trading.environment is None:
+            trading.environment = trading.TradingEnvironment()
+
         start = pd.datetime(1990, 1, 3, 0, 0, 0, 0, pytz.utc)
         end = pd.datetime(1990, 1, 8, 0, 0, 0, 0, pytz.utc)
-        index = pd.DatetimeIndex(
-            start=start,
-            end=end,
-            freq=freq
-        )
-        if bars == 'minute':
-            new_index = []
-            for i in index:
-                market_open = i.replace(hour=14,
-                                        minute=31)
-                market_close = i.replace(hour=21,
-                                         minute=0)
 
-                if i >= market_open and i <= market_close:
-                    new_index.append(i)
-            index = new_index
+        days = trading.environment.days_in_range(start, end)
+
+        if bars == 'daily':
+            index = days
+        if bars == 'minute':
+            index = pd.DatetimeIndex([], freq=freq)
+
+            for day in days:
+                day_index = trading.environment.market_minutes_for_day(day)
+                index = index.append(day_index)
+
     x = np.arange(1, len(index) + 1)
 
     df = pd.DataFrame(x, index=index, columns=[0])
@@ -355,7 +354,11 @@ def create_test_panel_source(sim_params=None):
     end = sim_params.last_close \
         if sim_params else pd.datetime(1990, 1, 8, 0, 0, 0, 0, pytz.utc)
 
-    index = pd.DatetimeIndex(start=start, end=end, freq=pd.datetools.day)
+    if trading.environment is None:
+        trading.environment = trading.TradingEnvironment()
+
+    index = trading.environment.days_in_range(start, end)
+
     price = np.arange(0, len(index))
     volume = np.ones(len(index)) * 1000
     arbitrary = np.ones(len(index))
@@ -376,7 +379,10 @@ def create_test_panel_ohlc_source(sim_params=None):
     end = sim_params.last_close \
         if sim_params else pd.datetime(1990, 1, 8, 0, 0, 0, 0, pytz.utc)
 
-    index = pd.DatetimeIndex(start=start, end=end, freq=pd.datetools.day)
+    if trading.environment is None:
+        trading.environment = trading.TradingEnvironment()
+
+    index = trading.environment.days_in_range(start, end)
     price = np.arange(0, len(index)) + 100
     high = price * 1.05
     low = price * 0.95