From 3e1ac4f19aa7891cde07adf4cbea49f57b75e2b6 Mon Sep 17 00:00:00 2001
From: Eddie Hebert <ehebert@quantopian.com>
Date: Sat, 4 May 2013 19:56:30 -0400
Subject: [PATCH] TST: Add tests to verify risk calcualtions from streamed
 events.

So that we can verify the risk metrics as they are calculated.
Work towards being able to hand verify risk calculations.
---
 tests/test_events_through_risk.py | 163 ++++++++++++++++++++++++++++++
 1 file changed, 163 insertions(+)
 create mode 100644 tests/test_events_through_risk.py

diff --git a/tests/test_events_through_risk.py b/tests/test_events_through_risk.py
new file mode 100644
index 00000000..ed3ddf80
--- /dev/null
+++ b/tests/test_events_through_risk.py
@@ -0,0 +1,163 @@
+#
+# 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
+
+import numpy as np
+
+from zipline.finance.trading import SimulationParameters
+from zipline.algorithm import TradingAlgorithm
+from zipline.protocol import (
+    Event,
+    DATASOURCE_TYPE
+)
+
+
+class BuyAndHoldAlgorithm(TradingAlgorithm):
+
+    SID_TO_BUY_AND_HOLD = 1
+
+    def initialize(self):
+        self.holding = False
+
+    def handle_data(self, data):
+        if not self.holding:
+            self.order(self.SID_TO_BUY_AND_HOLD, 100)
+            self.holding = True
+
+
+class TestEventsThroughRisk(unittest.TestCase):
+
+    def test_daily_buy_and_hold(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=5,
+            hour=0,
+            minute=0,
+            tzinfo=pytz.utc)
+
+        sim_params = SimulationParameters(
+            period_start=start_date,
+            period_end=end_date,
+            emission_rate='daily'
+        )
+
+        algo = BuyAndHoldAlgorithm(
+            sim_params=sim_params,
+            data_frequency='daily')
+
+        first_date = datetime.datetime(2006, 1, 3, tzinfo=pytz.utc)
+        second_date = datetime.datetime(2006, 1, 4, tzinfo=pytz.utc)
+        third_date = datetime.datetime(2006, 1, 5, tzinfo=pytz.utc)
+
+        trade_bar_data = [
+            Event({
+                'open_price': 10,
+                'close_price': 15,
+                'price': 15,
+                'volume': 1000,
+                'sid': 1,
+                'dt': first_date,
+                'source_id': 'test-trade-source',
+                'type': DATASOURCE_TYPE.TRADE
+            }),
+            Event({
+                'open_price': 15,
+                'close_price': 20,
+                'price': 20,
+                'volume': 2000,
+                'sid': 1,
+                'dt': second_date,
+                'source_id': 'test_list',
+                'type': DATASOURCE_TYPE.TRADE
+            }),
+            Event({
+                'open_price': 20,
+                'close_price': 15,
+                'price': 15,
+                'volume': 1000,
+                'sid': 1,
+                'dt': third_date,
+                'source_id': 'test_list',
+                'type': DATASOURCE_TYPE.TRADE
+            }),
+        ]
+        benchmark_data = [
+            Event({
+                'returns': 0.1,
+                'dt': first_date,
+                'source_id': 'test-benchmark-source',
+                'type': DATASOURCE_TYPE.BENCHMARK
+            }),
+            Event({
+                'returns': 0.2,
+                'dt': second_date,
+                'source_id': 'test-benchmark-source',
+                'type': DATASOURCE_TYPE.BENCHMARK
+            }),
+            Event({
+                'returns': 0.4,
+                'dt': third_date,
+                'source_id': 'test-benchmark-source',
+                'type': DATASOURCE_TYPE.BENCHMARK
+            }),
+        ]
+
+        algo.benchmark_return_source = benchmark_data
+        algo.sources = list([trade_bar_data])
+        gen = algo._create_generator(sim_params)
+
+        # TODO: Hand derive these results.
+        #       Currently, the output from the time of this writing to
+        #       at least be an early warning against changes.
+        expected_algorithm_returns = {
+            first_date: 0.0,
+            second_date: -0.000350,
+            third_date: -0.050018
+        }
+
+        # TODO: Hand derive these results.
+        #       Currently, the output from the time of this writing to
+        #       at least be an early warning against changes.
+        expected_sharpe = {
+            first_date: np.nan,
+            second_date: -1.630920,
+            third_date: -1.016842,
+        }
+
+        for bar in gen:
+            current_dt = algo.get_datetime()
+            crm = algo.perf_tracker.cumulative_risk_metrics
+
+            np.testing.assert_almost_equal(
+                expected_algorithm_returns[current_dt],
+                crm.algorithm_returns[-1],
+                decimal=6)
+
+            np.testing.assert_almost_equal(
+                expected_sharpe[current_dt],
+                crm.sharpe[-1],
+                decimal=6)