MAINT: Remove references to minute risk.

The minutely calculation of risk metrics had been removed with a
previous patch, remove vestigial references.

Remove a test which tested the behavior of updating the second minute of
a day.

Remove the logic that changed the datetime index of the risk metrics
depending on emission rate, now only trading_days are needed.

Remove `returns_frequency` parameter since both minute and daily
data frequency always use daily returns.

tests/risk/test_minute_risk.py

@@ -1,58 +0,0 @@
-#
-# Copyright 2013 Quantopian, Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# 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 unittest
-import datetime
-import pytz
-
-from zipline.finance.trading import SimulationParameters
-from zipline.finance import risk
-
-
-class TestMinuteRisk(unittest.TestCase):
-
-    def setUp(self):
-
-        start_date = datetime.datetime(
-            year=2006,
-            month=1,
-            day=3,
-            hour=0,
-            minute=0,
-            tzinfo=pytz.utc)
-        end_date = datetime.datetime(
-            year=2006, month=1, day=3, tzinfo=pytz.utc)
-
-        self.sim_params = SimulationParameters(
-            period_start=start_date,
-            period_end=end_date
-        )
-        self.sim_params.emission_rate = 'minute'
-
-    def test_minute_risk(self):
-
-        risk_metrics = risk.RiskMetricsCumulative(self.sim_params)
-
-        first_dt = self.sim_params.first_open
-        second_dt = self.sim_params.first_open + datetime.timedelta(minutes=1)
-        account = {'leverage': 0.0}
-
-        risk_metrics.update(first_dt, 1.0, 2.0, account)
-
-        self.assertEquals(1, len(risk_metrics.metrics.alpha.valid()))
-
-        risk_metrics.update(second_dt, 3.0, 4.0, account)
-
-        self.assertEquals(2, len(risk_metrics.metrics.alpha.valid()))

zipline/finance/performance/tracker.py

@@ -125,7 +125,6 @@ class PerformanceTracker(object):
 
             self.cumulative_risk_metrics = \
                 risk.RiskMetricsCumulative(self.sim_params,
-                                           returns_frequency='daily',
                                            create_first_day_stats=True)
 
             self.minute_performance = PerformancePeriod(

zipline/finance/risk/cumulative.py

@@ -92,15 +92,8 @@ class RiskMetricsCumulative(object):
     )
 
     def __init__(self, sim_params,
-                 returns_frequency=None,
                  create_first_day_stats=False,
                  account=None):
-        """
-        - @returns_frequency allows for configuration of the whether
-        the benchmark and algorithm returns are in units of minutes or days,
-        if `None` defaults to the `emission_rate` in `sim_params`.
-        """
-
         self.treasury_curves = trading.environment.treasury_curves
         self.start_date = sim_params.period_start.replace(
             hour=0, minute=0, second=0, microsecond=0
@@ -130,15 +123,7 @@ class RiskMetricsCumulative(object):
 
         self.create_first_day_stats = create_first_day_stats
 
-        if returns_frequency is None:
-            returns_frequency = self.sim_params.emission_rate
-
-        self.returns_frequency = returns_frequency
-
-        if returns_frequency == 'daily':
-            cont_index = self.get_daily_index()
-        elif returns_frequency == 'minute':
-            cont_index = self.get_minute_index(sim_params)
+        cont_index = self.trading_days
 
         self.cont_index = cont_index
         self.cont_len = len(self.cont_index)
@@ -185,24 +170,6 @@ class RiskMetricsCumulative(object):
 
         self.num_trading_days = 0
 
-    def get_minute_index(self, sim_params):
-        """
-        Stitches together multiple days worth of business minutes into
-        one continous index.
-        """
-        trading_minutes = None
-        for day in self.trading_days:
-            minutes_for_day = trading.environment.market_minutes_for_day(day)
-            if trading_minutes is None:
-                # Create container for all minutes on first iteration
-                trading_minutes = minutes_for_day
-            else:
-                trading_minutes = trading_minutes.union(minutes_for_day)
-        return trading_minutes
-
-    def get_daily_index(self):
-        return self.trading_days
-
     def update(self, dt, algorithm_returns, benchmark_returns, account):
         # Keep track of latest dt for use in to_dict and other methods
         # that report current state.