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Refactoring of TradingEnvironment to isolate the global state: index symbol and exchange timezone. Parameters that define the simulation (start, end, and capital base) were put in a new class, SimulationParameters.

Browse Source 
Global state for the financial simulation environment is accessed through the
zipline.finance.trading module, which now contains a module variable:
environment.

Parameters are passed into an algorithm as a keyword argument, sim_params.
SimulationParameters creates a trading day index for the test period that
can be used to find trading days, calculate distance between trading days,
and other common operations. The sim params index is just selected from the
global state.

================

Details:

    - adding delorean to the requirements.
    - made index symbol a parameter for loading the benchmark data. changed
    messagepack storage to be symbol specific.
    - ported risk, performance, algorithm, transforms, batch transforms
    and associated tests to use simulation parameters and global environment
    - factory and sim factory use global state and sim params
    - factory method parameter names now reflect the class expected
This commit is contained in:
fawce
2013-02-12 16:27:03 -05:00
committed by Eddie Hebert
parent 8f37b794e5
commit 2c7355a0dc
23 changed files with 576 additions and 475 deletions
Download Patch File Download Diff File Expand all files Collapse all files
tests/test_algorithm.py
+47 -17
View File
@@ -29,17 +29,17 @@ from zipline.transforms import MovingAverage
class TestRecordAlgorithm(TestCase):
def setUp(self):
self.trading_environment = factory.create_trading_environment()
self.sim_params = factory.create_simulation_parameters()
trade_history = factory.create_trade_history(
133,
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(days=1),
self.trading_environment
self.sim_params
)
self.source = SpecificEquityTrades(event_list=trade_history)
self.df_source, self.df = \
factory.create_test_df_source(self.trading_environment)
factory.create_test_df_source(self.sim_params)
def test_record_incr(self):
algo = RecordAlgorithm()
@@ -51,7 +51,7 @@ class TestRecordAlgorithm(TestCase):
class TestTransformAlgorithm(TestCase):
def setUp(self):
setup_logger(self)
self.trading_environment = factory.create_trading_environment()
self.sim_params = factory.create_simulation_parameters()
setup_logger(self)
trade_history = factory.create_trade_history(
@@ -59,35 +59,48 @@ class TestTransformAlgorithm(TestCase):
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(days=1),
self.trading_environment
self.sim_params
)
self.source = SpecificEquityTrades(event_list=trade_history)
self.df_source, self.df = \
factory.create_test_df_source(self.trading_environment)
factory.create_test_df_source(self.sim_params)
self.panel_source, self.panel = \
factory.create_test_panel_source(self.trading_environment)
factory.create_test_panel_source(self.sim_params)
def test_source_as_input(self):
algo = TestRegisterTransformAlgorithm(sids=[133])
algo = TestRegisterTransformAlgorithm(
self.sim_params,
sids=[133]
)
algo.run(self.source)
self.assertEqual(len(algo.sources), 1)
assert isinstance(algo.sources[0], SpecificEquityTrades)
def test_multi_source_as_input_no_start_end(self):
algo = TestRegisterTransformAlgorithm(sids=[133])
algo = TestRegisterTransformAlgorithm(
self.sim_params,
sids=[133]
)
with self.assertRaises(AssertionError):
algo.run([self.source, self.df_source])
def test_multi_source_as_input(self):
algo = TestRegisterTransformAlgorithm(sids=[0, 1, 133])
algo = TestRegisterTransformAlgorithm(
self.sim_params,
sids=[0, 1, 133]
)
algo.run([self.source, self.df_source],
start=self.df.index[0], end=self.df.index[-1])
self.assertEqual(len(algo.sources), 2)
def test_df_as_input(self):
algo = TestRegisterTransformAlgorithm(sids=[0, 1])
algo = TestRegisterTransformAlgorithm(
self.sim_params,
sids=[0, 1]
)
algo.run(self.df)
assert isinstance(algo.sources[0], DataFrameSource)
@@ -97,14 +110,22 @@ class TestTransformAlgorithm(TestCase):
assert isinstance(algo.sources[0], DataPanelSource)
def test_run_twice(self):
algo = TestRegisterTransformAlgorithm(sids=[0, 1])
algo = TestRegisterTransformAlgorithm(
self.sim_params,
sids=[0, 1]
)
res1 = algo.run(self.df)
res2 = algo.run(self.df)
np.testing.assert_array_equal(res1, res2)
def test_transform_registered(self):
algo = TestRegisterTransformAlgorithm(sids=[133])
algo = TestRegisterTransformAlgorithm(
self.sim_params,
sids=[133]
)
algo.run(self.source)
assert 'mavg' in algo.registered_transforms
assert algo.registered_transforms['mavg']['args'] == (['price'],)
@@ -113,15 +134,24 @@ class TestTransformAlgorithm(TestCase):
assert algo.registered_transforms['mavg']['class'] is MovingAverage
def test_data_frequency_setting(self):
algo = TestRegisterTransformAlgorithm(data_frequency='daily')
algo = TestRegisterTransformAlgorithm(
self.sim_params,
data_frequency='daily'
)
self.assertEqual(algo.data_frequency, 'daily')
self.assertEqual(algo.annualizer, 250)
algo = TestRegisterTransformAlgorithm(data_frequency='minute')
algo = TestRegisterTransformAlgorithm(
self.sim_params,
data_frequency='minute'
)
self.assertEqual(algo.data_frequency, 'minute')
self.assertEqual(algo.annualizer, 250 * 6 * 60)
algo = TestRegisterTransformAlgorithm(data_frequency='minute',
annualizer=10)
algo = TestRegisterTransformAlgorithm(
self.sim_params,
data_frequency='minute',
annualizer=10
)
self.assertEqual(algo.data_frequency, 'minute')
self.assertEqual(algo.annualizer, 10)