ENH: Compute engine architecture for FFC API.

This patch lays the groundwork for a compute engine designed to
facilitate construction of factor-based universe screening and portfolio
allocation.  It contains:

A new module, `zipline.modelling`, containing entities that can be used
to express computations as dependency graphs.  Each node in such a graph
is an instance of the base `Term` class, defined in
`zipline.modelling.term`.  Dependency graphs are executed by instances
of `FFCEngine`, defined in `zipline.modelling.engine`.

A new module, `zipline.data.ffc`, containing loaders and dataset
definitions for inputs to the modelling API.

New `TradingAlgorithm` api methods: `add_factor`, and `add_filter`.
These methods can only be called from `initialize`, and are used to
inform the algorithm that each day it should compute the given terms.
Computed factor results are made available through a new attribute of
the `data` object in `before_trading_start` and `handle_data`.  Computed
filter results control which assets are available in the factor matrix
on each day.

tests/test_algorithm.py

@@ -23,9 +23,10 @@ from unittest import TestCase
 import numpy as np
 import pandas as pd
 
+from zipline.api_support import ZiplineAPI
 from zipline.assets import AssetFinder
+from zipline.utils.control_flow import nullctx
 from zipline.utils.test_utils import (
-    nullctx,
     setup_logger,
     teardown_logger
 )
@@ -150,6 +151,26 @@ class TestMiscellaneousAPI(TestCase):
     def tearDown(self):
         teardown_logger(self)
 
+    def test_zipline_api_resolves_dynamically(self):
+        # Make a dummy algo.
+        algo = TradingAlgorithm(
+            initialize=lambda context: None,
+            handle_data=lambda context, data: None,
+            sim_params=self.sim_params,
+        )
+
+        # Verify that api methods get resolved dynamically by patching them out
+        # and then calling them
+        for method in algo.all_api_methods():
+            name = method.__name__
+            sentinel = object()
+
+            def fake_method(*args, **kwargs):
+                return sentinel
+            setattr(algo, name, fake_method)
+            with ZiplineAPI(algo):
+                self.assertIs(sentinel, getattr(zipline.api, name)())
+
     def test_get_environment(self):
         expected_env = {
             'arena': 'backtest',