PERF: Deterministically GC pipeline results.

Any DataFrame that's had `.loc` or `.iloc `called on it participates in
a cycle, which means they're not immediately garbage collected when they
go out of scope.  This matters for pipeline results because they consume
multiple megabytes per column, which means that a pipeline result with
many columns can hold take up over 100MB.  By manually breaking
DataFrame cycles, we can ensure that we never hold multiple pipeline
results in memory at once.

zipline/utils/cache.py

@@ -1,7 +1,7 @@
 """
 Caching utilities for zipline
 """
-from collections import namedtuple, MutableMapping
+from collections import MutableMapping
 import errno
 import os
 import pickle
@@ -20,7 +20,7 @@ class Expired(Exception):
     """
 
 
-class CachedObject(namedtuple("_CachedObject", "value expires")):
+class CachedObject(object):
     """
     A simple struct for maintaining a cached object with an expiration date.
 
@@ -47,6 +47,9 @@ class CachedObject(namedtuple("_CachedObject", "value expires")):
         ...
     Expired: 2014-01-01 00:00:00+00:00
     """
+    def __init__(self, value, expires):
+        self._value = value
+        self._expires = expires
 
     def unwrap(self, dt):
         """
@@ -62,9 +65,13 @@ class CachedObject(namedtuple("_CachedObject", "value expires")):
         Expired
             Raised when `dt` is greater than self.expires.
         """
-        if dt > self.expires:
-            raise Expired(self.expires)
-        return self.value
+        if dt > self._expires:
+            raise Expired(self._expires)
+        return self._value
+
+    def _unsafe_get_value(self):
+        """You almost certainly shouldn't use this."""
+        return self._value
 
 
 class ExpiringCache(object):