def run_date_sort(self, event_stream, expected, source_ids):
"""
Take a list of events, their source_ids, and an expected sorting.
Assert that date_sort's output agrees with expected.
"""
sort_out = date_sort(event_stream, source_ids)
for m1, m2 in izip_longest(sort_out, expected):
assert m1 == m2
def run_date_sort(self, event_stream, expected, source_ids):
"""
Take a list of events, their source_ids, and an expected sorting.
Assert that date_sort's output agrees with expected.
"""
sort_out = date_sort(event_stream, source_ids)
for m1, m2 in izip_longest(sort_out, expected):
assert m1 == m2
def date_sorted_sources(*sources):
"""
Takes an iterable of sources, generating namestrings and
piping their output into date_sort.
"""
for source in sources:
assert iter(source), "Source %s not iterable" % source
assert hasattr(source, 'get_hash'), "No get_hash"
# Get name hashes to pass to date_sort.
names = [source.get_hash() for source in sources]
# Convert the list of generators into a flat stream by pulling
# one element at a time from each.
stream_in = roundrobin(sources, names)
# Guarantee the flat stream will be sorted by date, using
# source_id as tie-breaker, which is fully deterministic (given
# deterministic string representation for all args/kwargs)
return date_sort(stream_in, names)
def test_tracker(self, parameter_comment, days_to_delete):
"""
@days_to_delete - configures which days in the data set we should
remove, used for ensuring that we still return performance messages
even when there is no data.
"""
# This date range covers Columbus day,
# however Columbus day is not a market holiday
#
# October 2008
# Su Mo Tu We Th Fr Sa
# 1 2 3 4
# 5 6 7 8 9 10 11
# 12 13 14 15 16 17 18
# 19 20 21 22 23 24 25
# 26 27 28 29 30 31
start_dt = datetime.datetime(year=2008,
month=10,
day=9,
tzinfo=pytz.utc)
end_dt = datetime.datetime(year=2008,
month=10,
day=16,
tzinfo=pytz.utc)
trade_count = 6
sid = 133
price = 10.1
price_list = [price] * trade_count
volume = [100] * trade_count
trade_time_increment = datetime.timedelta(days=1)
benchmark_returns, treasury_curves = \
factory.load_market_data()
trading_environment = TradingEnvironment(
benchmark_returns,
treasury_curves,
period_start=start_dt,
period_end=end_dt
)
trade_history = factory.create_trade_history(
sid,
price_list,
volume,
trade_time_increment,
trading_environment,
source_id="factory1"
)
sid2 = 134
price2 = 12.12
price2_list = [price2] * trade_count
trade_history2 = factory.create_trade_history(
sid2,
price2_list,
volume,
trade_time_increment,
trading_environment,
source_id="factory2"
)
# 'middle' start of 3 depends on number of days == 7
middle = 3
# First delete from middle
if days_to_delete.middle:
del trade_history[middle:(middle + days_to_delete.middle)]
del trade_history2[middle:(middle + days_to_delete.middle)]
# Delete start
if days_to_delete.start:
del trade_history[:days_to_delete.start]
del trade_history2[:days_to_delete.start]
# Delete from end
if days_to_delete.end:
del trade_history[-days_to_delete.end:]
del trade_history2[-days_to_delete.end:]
trade_history.extend(trade_history2)
trading_environment.first_open = \
trading_environment.calculate_first_open()
trading_environment.last_close = \
trading_environment.calculate_last_close()
trading_environment.capital_base = 1000.0
trading_environment.frame_index = [
'sid',
'volume',
'dt',
'price',
'changed']
perf_tracker = perf.PerformanceTracker(
trading_environment
)
# date_sort requires 'DONE' messages from each source
events = itertools.chain(trade_history,
[ndict({
'source_id': 'factory1',
'dt': 'DONE',
'type': DATASOURCE_TYPE.TRADE
}),
ndict({
'source_id': 'factory2',
'dt': 'DONE',
'type': DATASOURCE_TYPE.TRADE
})])
events = date_sort(events, ('factory1', 'factory2'))
events = itertools.chain(events,
[ndict({'dt': 'DONE'})])
events = [self.event_with_txn(event, trade_history[0].dt)
for event in events]
perf_messages = \
[msg for date, snapshot in
perf_tracker.transform(
itertools.groupby(events, attrgetter('dt')))
for event in snapshot
for msg in event.perf_messages]
#we skip two trades, to test case of None transaction
txn_count = len(trade_history) - 2
self.assertEqual(perf_tracker.txn_count, txn_count)
cumulative_pos = perf_tracker.cumulative_performance.positions[sid]
expected_size = txn_count / 2 * -25
self.assertEqual(cumulative_pos.amount, expected_size)
self.assertEqual(perf_tracker.last_close,
perf_tracker.cumulative_risk_metrics.end_date)
self.assertEqual(len(perf_messages),
trading_environment.days_in_period)
def test_tracker(self, start_dt):
trade_count = 100
sid = 133
price = 10.1
price_list = [price] * trade_count
volume = [100] * trade_count
trade_time_increment = datetime.timedelta(days=1)
trading_environment, start_dt, end_dt = self.create_env(start_dt)
trade_history = factory.create_trade_history(
sid,
price_list,
volume,
trade_time_increment,
trading_environment,
source_id="factory1"
)
sid2 = 134
price2 = 12.12
price2_list = [price2] * trade_count
trade_history2 = factory.create_trade_history(
sid2,
price2_list,
volume,
trade_time_increment,
trading_environment,
source_id="factory2"
)
trade_history.extend(trade_history2)
trading_environment.period_start = trade_history[0].dt
trading_environment.period_end = trade_history[-1].dt
trading_environment.first_open = \
trading_environment.calculate_first_open()
trading_environment.last_close = \
trading_environment.calculate_last_close()
trading_environment.capital_base = 1000.0
trading_environment.frame_index = [
'sid',
'volume',
'dt',
'price',
'changed']
perf_tracker = perf.PerformanceTracker(
trading_environment
)
# date_sort requires 'DONE' messages from each source
events = itertools.chain(trade_history,
[ndict({
'source_id': 'factory1',
'dt': 'DONE',
'type': DATASOURCE_TYPE.TRADE
}),
ndict({
'source_id': 'factory2',
'dt': 'DONE',
'type': DATASOURCE_TYPE.TRADE
})])
events = date_sort(events, ('factory1', 'factory2'))
events = itertools.chain(events,
[ndict({'dt': 'DONE'})])
events = [self.event_with_txn(event, trading_environment)
for event in events]
list(perf_tracker.transform(
itertools.groupby(events, attrgetter('dt'))))
#we skip two trades, to test case of None transaction
txn_count = len(trade_history) - 2
self.assertEqual(perf_tracker.txn_count, txn_count)
cumulative_pos = perf_tracker.cumulative_performance.positions[sid]
expected_size = txn_count / 2 * -25
self.assertEqual(cumulative_pos.amount, expected_size)
self.assertEqual(perf_tracker.last_close,
perf_tracker.cumulative_risk_metrics.end_date)