MAINT: pull the data query bounds logic into a helper

zipline/pipeline/loaders/blaze/core.py

@@ -127,7 +127,6 @@ from __future__ import division, absolute_import
 from abc import ABCMeta, abstractproperty
 from collections import namedtuple, defaultdict
 from copy import copy
-from datetime import timedelta
 from functools import partial
 from itertools import count
 import warnings
@@ -161,7 +160,7 @@ import toolz.curried.operator as op
 from zipline.pipeline.data.dataset import DataSet, Column
 from zipline.pipeline.loaders.utils import (
     check_data_query_args,
-    normalize_data_query_time,
+    normalize_data_query_bounds,
     normalize_timestamp_to_query_time,
 )
 from zipline.lib.adjusted_array import AdjustedArray
@@ -834,20 +833,12 @@ class BlazeLoader(dict):
 
         data_query_time = self._data_query_time
         data_query_tz = self._data_query_tz
-        if data_query_time is not None:
-            lower_dt = normalize_data_query_time(
-                dates[0] - timedelta(days=1),
-                data_query_time,
-                data_query_tz,
-            )
-            upper_dt = normalize_data_query_time(
-                dates[-1],
-                data_query_time,
-                data_query_tz,
-            )
-        else:
-            lower_dt = dates[0] - timedelta(days=1)
-            upper_dt = dates[-1]
+        lower_dt, upper_dt = normalize_data_query_bounds(
+            dates[0],
+            dates[-1],
+            data_query_time,
+            data_query_tz,
+        )
 
         def where(e):
             """Create the query to run against the resources.

zipline/pipeline/loaders/blaze/earnings.py

@@ -1,5 +1,3 @@
-from datetime import timedelta
-
 from datashape import istabular
 import pandas as pd
 from toolz import valmap
@@ -15,7 +13,7 @@ from zipline.pipeline.loaders.base import PipelineLoader
 from zipline.pipeline.loaders.earnings import EarningsCalendarLoader
 from zipline.pipeline.loaders.utils import (
     check_data_query_args,
-    normalize_data_query_time,
+    normalize_data_query_bounds,
     normalize_timestamp_to_query_time,
 )
 from zipline.utils.input_validation import ensure_timezone, optionally
@@ -100,20 +98,12 @@ class BlazeEarningsCalendarLoader(PipelineLoader):
     def load_adjusted_array(self, columns, dates, assets, mask):
         data_query_time = self._data_query_time
         data_query_tz = self._data_query_tz
-        if data_query_time is not None:
-            lower_dt = normalize_data_query_time(
-                dates[0] - timedelta(days=1),
-                data_query_time,
-                data_query_tz,
-            )
-            upper_dt = normalize_data_query_time(
-                dates[-1],
-                data_query_time,
-                data_query_tz,
-            )
-        else:
-            lower_dt = dates[0] - timedelta(days=1)
-            upper_dt = dates[-1]
+        lower_dt, upper_dt = normalize_data_query_bounds(
+            dates[0],
+            dates[-1],
+            data_query_time,
+            data_query_tz,
+        )
 
         raw = ffill_query_in_range(
             self._expr,

zipline/pipeline/loaders/utils.py

@@ -125,6 +125,40 @@ def normalize_data_query_time(dt, time, tz):
     ).tz_convert('utc')
 
 
+def normalize_data_query_bounds(lower, upper, time, tz):
+    """Adjust the first and last dates in the requested datetime index based on
+    the provided query time and tz.
+
+    lower : pd.Timestamp
+        The lower date requested.
+    upper : pd.Timestamp
+        The upper date requested.
+    time : datetime.time
+        The time of day to use as the cutoff point for new data. Data points
+        that you learn about after this time will become available to your
+        algorithm on the next trading day.
+    tz : tzinfo
+        The timezone to normalize your dates to before comparing against
+        `time`.
+    """
+    # Subtract one day to grab things that happened on the first day we are
+    # requesting. This doesn't need to be a trading day, we are only adding
+    # a lower bound to limit the amount of in memory filtering that needs
+    # to happen.
+    lower -= datetime.timedelta(days=1)
+    if time is not None:
+        return normalize_data_query_time(
+            lower,
+            time,
+            tz,
+        ), normalize_data_query_time(
+            upper,
+            time,
+            tz,
+        )
+    return lower, upper
+
+
 def normalize_timestamp_to_query_time(df,
                                       time,
                                       tz,