PERF: Speed up reading of adjustments.

For a pipeline doing simple computations on USEquityPricing data, we
were spending ~60% of `run_pipeline` loading adjustments.  Almost all of
that time was spent in calls to `DatetimeIndex.get_loc` to find the
indices of adjustment `eff_date`s.

This optimizes the eff_date lookups by pre-populating a cache of
seconds-since-epoch timestamps that we expect to see, and falling back
to `np.searchsorted` on cache misses.

In testing, this reduces the time to compute a 1-year pipeline with 30
and 90 day moving averages from 3.1 seconds to 0.9 seconds.

zipline/data/_adjustments.pyx

@@ -21,6 +21,7 @@ from numpy import (
     uint32,
     zeros,
 )
+from numpy cimport int64_t, ndarray
 from pandas import Timestamp
 
 ctypedef object Timestamp_t
@@ -195,24 +196,30 @@ cpdef load_adjustments_from_sqlite(object adjustments_db,  # sqlite3.Connection
 
     cdef list results = [{} for column in columns]
     cdef dict asset_ixs = {}  # Cache sid lookups here.
+    cdef dict date_ixs = {}
     cdef:
+        int i
+        int dt
         int sid
         double ratio
         int eff_date
         int date_loc
         Py_ssize_t asset_ix
-        int i
         dict col_adjustments
 
+    cdef ndarray[int64_t, ndim=1] _dates_seconds = \
+        dates.values.astype('datetime64[s]').view(int)
+
+    # Pre-populate date index cache.
+    for i, dt in enumerate(_dates_seconds):
+        date_ixs[dt] = i
+
     # splits affect prices and volumes, volumes is the inverse
     for sid, ratio, eff_date in splits:
         if eff_date < start_date:
             continue
-        date_loc = dates.get_loc(
-            Timestamp(eff_date, unit='s', tz='UTC'),
-            # Get the first date **on or after** the effective date.
-            method='bfill',
-        )
+
+        date_loc = _lookup_dt(date_ixs, eff_date, _dates_seconds)
 
         if not PyDict_Contains(asset_ixs, sid):
             asset_ixs[sid] = assets.get_loc(sid)
@@ -239,11 +246,8 @@ cpdef load_adjustments_from_sqlite(object adjustments_db,  # sqlite3.Connection
     for sid, ratio, eff_date in mergers:
         if eff_date < start_date:
             continue
-        date_loc = dates.get_loc(
-            Timestamp(eff_date, unit='s', tz='UTC'),
-            # Get the first date **on or after** the effective date.
-            method='bfill',
-        )
+
+        date_loc = _lookup_dt(date_ixs, eff_date, _dates_seconds)
 
         if not PyDict_Contains(asset_ixs, sid):
             asset_ixs[sid] = assets.get_loc(sid)
@@ -262,11 +266,8 @@ cpdef load_adjustments_from_sqlite(object adjustments_db,  # sqlite3.Connection
     for sid, ratio, eff_date in dividends:
         if eff_date < start_date:
             continue
-        date_loc = dates.get_loc(
-            Timestamp(eff_date, unit='s', tz='UTC'),
-            # Get the first date **on or after** the effective date.
-            method='bfill',
-        )
+
+        date_loc = _lookup_dt(date_ixs, eff_date, _dates_seconds)
 
         if not PyDict_Contains(asset_ixs, sid):
             asset_ixs[sid] = assets.get_loc(sid)
@@ -282,3 +283,12 @@ cpdef load_adjustments_from_sqlite(object adjustments_db,  # sqlite3.Connection
                     col_adjustments[date_loc] = [adj]
 
     return results
+
+
+cdef _lookup_dt(dict dt_cache,
+                int dt,
+                ndarray[int64_t, ndim=1] fallback):
+
+    if not PyDict_Contains(dt_cache, dt):
+        dt_cache[dt] = fallback.searchsorted(dt, side='right')
+    return dt_cache[dt]