TST: Add answer key indexes for cumulative risk metrics.

Add indexes for Sharpe, returns and other values needed for
reading answers for cumulative risk metrics.

Prepare for unit test and matching change of implementation.

tests/risk/answer_key.py

@@ -12,10 +12,13 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import datetime
 import hashlib
 import os
 
 import numpy as np
+import pandas as pd
+import pytz
 import xlrd
 import requests
 
@@ -151,6 +154,11 @@ class AnswerKey(object):
     INDEXES = {
         'RETURNS': DataIndex('Sim Period', 'D', 4, 255),
 
+        'BENCHMARK': {
+            'Dates': DataIndex('s_p', 'A', 4, 254, value_type='date'),
+            'Returns': DataIndex('s_p', 'H', 4, 254)
+        },
+
         # Below matches the inconsistent capitalization in spreadsheet
         'BENCHMARK_PERIOD_RETURNS': {
             'Monthly': DataIndex('s_p', 'P', 8, 19),
@@ -213,7 +221,16 @@ class AnswerKey(object):
             '3-Month': DataIndex('Sim Period', 'AY', 25, 34),
             '6-month': DataIndex('Sim Period', 'AZ', 28, 34),
             'year': DataIndex('Sim Period', 'BA', 34, 34),
-        }
+        },
+
+        'ALGORITHM_RETURN_VALUES': DataIndex(
+            'Sim Cumulative', 'D', 4, 254),
+
+        'ALGORITHM_CUMULATIVE_VOLATILITY': DataIndex(
+            'Sim Cumulative', 'N', 4, 254),
+
+        'ALGORITHM_CUMULATIVE_SHARPE': DataIndex(
+            'Sim Cumulative', 'O', 4, 254)
     }
 
     def __init__(self):
@@ -221,6 +238,8 @@ class AnswerKey(object):
 
         self.sheets = {}
         self.sheets['Sim Period'] = self.workbook.sheet_by_name('Sim Period')
+        self.sheets['Sim Cumulative'] = self.workbook.sheet_by_name(
+            'Sim Cumulative')
         self.sheets['s_p'] = self.workbook.sheet_by_name('s_p')
 
         for name, index in self.INDEXES.items():
@@ -254,3 +273,17 @@ class AnswerKey(object):
     def get_values(self, data_index):
         value_parser = self.value_type_to_value_func[data_index.value_type]
         return map(value_parser, self.get_raw_values(data_index))
+
+
+ANSWER_KEY = AnswerKey()
+
+BENCHMARK_DATES = ANSWER_KEY.BENCHMARK['Dates']
+BENCHMARK_RETURNS = ANSWER_KEY.BENCHMARK['Returns']
+BENCHMARK = pd.Series(
+    dict(zip((datetime.datetime(*x, tzinfo=pytz.UTC) for x in BENCHMARK_DATES),
+             BENCHMARK_RETURNS)))
+ALGORITHM_RETURNS = pd.Series(
+    dict(zip((datetime.datetime(*x, tzinfo=pytz.UTC) for x in BENCHMARK_DATES),
+             ANSWER_KEY.ALGORITHM_RETURN_VALUES)))
+RETURNS_DATA = pd.DataFrame({'Benchmark Returns': BENCHMARK,
+                             'Algorithm Returns': ALGORITHM_RETURNS})