def test_empty_pipeline(self):
# For ensuring we call before_trading_start.
count = [0]
def initialize(context):
pipeline = attach_pipeline(Pipeline(), 'test')
vwap = VWAP(window_length=10)
pipeline.add(vwap, 'vwap')
# Nothing should have prices less than 0.
pipeline.set_screen(vwap < 0)
def handle_data(context, data):
pass
def before_trading_start(context, data):
context.results = pipeline_output('test')
self.assertTrue(context.results.empty)
count[0] += 1
algo = TradingAlgorithm(
initialize=initialize,
handle_data=handle_data,
before_trading_start=before_trading_start,
data_frequency='daily',
get_pipeline_loader=lambda column: self.pipeline_loader,
start=self.dates[0],
end=self.dates[-1],
env=self.env,
)
algo.run(
FakeDataPortal(),
overwrite_sim_params=False,
)
self.assertTrue(count[0] > 0)
def test_before_trading_start(self, test_name, num_days, freq,
emission_rate):
params = factory.create_simulation_parameters(
num_days=num_days,
data_frequency=freq,
emission_rate=emission_rate)
def fake_benchmark(self, dt):
return 0.01
with patch.object(BenchmarkSource, "get_value",
self.fake_minutely_benchmark):
algo = BeforeTradingAlgorithm(sim_params=params)
algo.run(FakeDataPortal())
self.assertEqual(algo.perf_tracker.day_count, num_days)
self.assertTrue(
params.trading_days.equals(
pd.DatetimeIndex(algo.before_trading_at)),
"Expected %s but was %s." %
(params.trading_days, algo.before_trading_at))
def test_before_trading_start(self, test_name, num_days, freq,
emission_rate):
params = factory.create_simulation_parameters(
num_days=num_days,
data_frequency=freq,
emission_rate=emission_rate)
def fake_benchmark(self, dt):
return 0.01
with patch.object(BenchmarkSource, "get_value",
self.fake_minutely_benchmark):
algo = BeforeTradingAlgorithm(sim_params=params, env=self.env)
algo.run(FakeDataPortal(self.env))
self.assertEqual(len(algo.sim_params.sessions), num_days)
bts_minutes = days_at_time(params.sessions, time(8, 45),
"US/Eastern")
self.assertTrue(
bts_minutes.equals(pd.DatetimeIndex(algo.before_trading_at)),
"Expected %s but was %s." %
(params.sessions, algo.before_trading_at))
def test_handle_adjustment(self, set_screen):
AAPL, MSFT, BRK_A = assets = self.AAPL, self.MSFT, self.BRK_A
window_lengths = [1, 2, 5, 10]
vwaps = self.compute_expected_vwaps(window_lengths)
def vwap_key(length):
return "vwap_%d" % length
def initialize(context):
pipeline = Pipeline()
context.vwaps = []
for length in vwaps:
name = vwap_key(length)
factor = VWAP(window_length=length)
context.vwaps.append(factor)
pipeline.add(factor, name=name)
filter_ = (USEquityPricing.close.latest > 300)
pipeline.add(filter_, 'filter')
if set_screen:
pipeline.set_screen(filter_)
attach_pipeline(pipeline, 'test')
def handle_data(context, data):
today = get_datetime()
results = pipeline_output('test')
expect_over_300 = {
AAPL: today < self.AAPL_split_date,
MSFT: False,
BRK_A: True,
}
for asset in assets:
should_pass_filter = expect_over_300[asset]
if set_screen and not should_pass_filter:
self.assertNotIn(asset, results.index)
continue
asset_results = results.loc[asset]
self.assertEqual(asset_results['filter'], should_pass_filter)
for length in vwaps:
computed = results.loc[asset, vwap_key(length)]
expected = vwaps[length][asset].loc[today]
# Only having two places of precision here is a bit
# unfortunate.
assert_almost_equal(computed, expected, decimal=2)
# Do the same checks in before_trading_start
before_trading_start = handle_data
algo = TradingAlgorithm(
initialize=initialize,
handle_data=handle_data,
before_trading_start=before_trading_start,
data_frequency='daily',
get_pipeline_loader=lambda column: self.pipeline_loader,
start=self.dates[max(window_lengths)],
end=self.dates[-1],
env=self.env,
)
algo.run(
FakeDataPortal(),
# Yes, I really do want to use the start and end dates I passed to
# TradingAlgorithm.
overwrite_sim_params=False,
)
