def setUpClass(cls):
random.seed(0)
cls.sids = (1, 2, 3)
minute_sim_ps = factory.create_simulation_parameters(
num_days=3,
sids=cls.sids,
data_frequency='minute',
emission_rate='minute',
)
daily_sim_ps = factory.create_simulation_parameters(
num_days=30,
sids=cls.sids,
data_frequency='daily',
emission_rate='daily',
)
cls.sim_and_source = {
'minute': (minute_sim_ps, factory.create_minutely_trade_source(
cls.sids,
trade_count=45,
sim_params=minute_sim_ps,
)),
'daily': (daily_sim_ps, factory.create_trade_source(
cls.sids,
trade_count=90,
trade_time_increment=timedelta(days=1),
sim_params=daily_sim_ps,
)),
}
def init_class_fixtures(cls):
super(SecurityListTestCase, cls).init_class_fixtures()
# this is ugly, but we need to create two different
# TradingEnvironment/DataPortal pairs
cls.start = pd.Timestamp(list(LEVERAGED_ETFS.keys())[0])
end = pd.Timestamp('2015-02-17', tz='utc')
cls.extra_knowledge_date = pd.Timestamp('2015-01-27', tz='utc')
cls.trading_day_before_first_kd = pd.Timestamp('2015-01-23', tz='utc')
symbols = ['AAPL', 'GOOG', 'BZQ', 'URTY', 'JFT']
cls.env = cls.enter_class_context(tmp_trading_env(
equities=pd.DataFrame.from_records([{
'start_date': cls.start,
'end_date': end,
'symbol': symbol,
'exchange': "TEST",
} for symbol in symbols]),
load=cls.make_load_function(),
))
cls.sim_params = factory.create_simulation_parameters(
start=cls.start,
num_days=4,
trading_calendar=cls.trading_calendar
)
cls.sim_params2 = sp2 = factory.create_simulation_parameters(
start=cls.trading_day_before_first_kd, num_days=4
)
cls.env2 = cls.enter_class_context(tmp_trading_env(
equities=pd.DataFrame.from_records([{
'start_date': sp2.start_session,
'end_date': sp2.end_session,
'symbol': symbol,
'exchange': "TEST",
} for symbol in symbols]),
load=cls.make_load_function(),
))
cls.tempdir = cls.enter_class_context(tmp_dir())
cls.tempdir2 = cls.enter_class_context(tmp_dir())
cls.data_portal = create_data_portal(
asset_finder=cls.env.asset_finder,
tempdir=cls.tempdir,
sim_params=cls.sim_params,
sids=range(0, 5),
trading_calendar=cls.trading_calendar,
)
cls.data_portal2 = create_data_portal(
asset_finder=cls.env2.asset_finder,
tempdir=cls.tempdir2,
sim_params=cls.sim_params2,
sids=range(0, 5),
trading_calendar=cls.trading_calendar,
)
def setUpClass(cls):
random.seed(0)
cls.sids = (1, 2, 3)
minute_sim_ps = factory.create_simulation_parameters(
num_days=3, data_frequency="minute", emission_rate="minute"
)
daily_sim_ps = factory.create_simulation_parameters(num_days=30, data_frequency="daily", emission_rate="daily")
cls.sim_and_source = {
"minute": (minute_sim_ps, factory.create_minutely_trade_source(cls.sids, sim_params=minute_sim_ps)),
"daily": (
daily_sim_ps,
factory.create_trade_source(cls.sids, trade_time_increment=timedelta(days=1), sim_params=daily_sim_ps),
),
}
def test_minutely_fetcher(self):
self.responses.add(
self.responses.GET,
'https://fake.urls.com/aapl_minute_csv_data.csv',
body=AAPL_MINUTE_CSV_DATA,
content_type='text/csv',
)
sim_params = factory.create_simulation_parameters(
start=pd.Timestamp("2006-01-03", tz='UTC'),
end=pd.Timestamp("2006-01-10", tz='UTC'),
emission_rate="minute",
data_frequency="minute"
)
test_algo = TradingAlgorithm(
script="""
from zipline.api import fetch_csv, record, sid
def initialize(context):
fetch_csv('https://fake.urls.com/aapl_minute_csv_data.csv')
def handle_data(context, data):
record(aapl_signal=data.current(sid(24), "signal"))
""", sim_params=sim_params, data_frequency="minute", env=self.env)
# manually setting data portal and getting generator because we need
# the minutely emission packets here. TradingAlgorithm.run() only
# returns daily packets.
test_algo.data_portal = FetcherDataPortal(self.env,
self.trading_calendar)
gen = test_algo.get_generator()
perf_packets = list(gen)
signal = [result["minute_perf"]["recorded_vars"]["aapl_signal"] for
result in perf_packets if "minute_perf" in result]
self.assertEqual(6 * 390, len(signal))
# csv data is:
# symbol,date,signal
# aapl,1/4/06 5:31AM, 1
# aapl,1/4/06 11:30AM, 2
# aapl,1/5/06 5:31AM, 1
# aapl,1/5/06 11:30AM, 3
# aapl,1/9/06 5:31AM, 1
# aapl,1/9/06 11:30AM, 4 for dates 1/3 to 1/10
# 2 signals per day, only last signal is taken. So we expect
# 390 bars of signal NaN on 1/3
# 390 bars of signal 2 on 1/4
# 390 bars of signal 3 on 1/5
# 390 bars of signal 3 on 1/6 (forward filled)
# 390 bars of signal 4 on 1/9
# 390 bars of signal 4 on 1/9 (forward filled)
np.testing.assert_array_equal([np.NaN] * 390, signal[0:390])
np.testing.assert_array_equal([2] * 390, signal[390:780])
np.testing.assert_array_equal([3] * 780, signal[780:1560])
np.testing.assert_array_equal([4] * 780, signal[1560:])
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.perf_tracker.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_algo_without_rl_violation_after_delete(self):
sim_params = factory.create_simulation_parameters(
start=self.extra_knowledge_date,
num_days=4,
)
equities = pd.DataFrame.from_records([{
'symbol': 'BZQ',
'start_date': sim_params.start_session,
'end_date': sim_params.end_session,
'exchange': "TEST",
}])
with TempDirectory() as new_tempdir, \
security_list_copy(), \
tmp_trading_env(equities=equities) as env:
# add a delete statement removing bzq
# write a new delete statement file to disk
add_security_data([], ['BZQ'])
data_portal = create_data_portal(
env.asset_finder,
new_tempdir,
sim_params,
range(0, 5),
trading_calendar=self.trading_calendar,
)
algo = RestrictedAlgoWithoutCheck(
symbol='BZQ', sim_params=sim_params, env=env
)
algo.run(data_portal)
def setUp(self):
setup_logger(self)
sim_params = factory.create_simulation_parameters(
start=datetime(1990, 1, 1, tzinfo=pytz.utc),
end=datetime(1990, 3, 30, tzinfo=pytz.utc))
self.source, self.panel = \
factory.create_test_panel_ohlc_source(sim_params)
def test_blotter_processes_splits(self):
sim_params = factory.create_simulation_parameters()
blotter = Blotter()
blotter.set_date(sim_params.period_start)
# set up two open limit orders with very low limit prices,
# one for sid 1 and one for sid 2
blotter.order(1, 100, LimitOrder(10))
blotter.order(2, 100, LimitOrder(10))
# send in a split for sid 2
split_event = factory.create_split(2, 0.33333,
sim_params.period_start +
timedelta(days=1))
blotter.process_split(split_event)
for sid in [1, 2]:
order_lists = blotter.open_orders[sid]
self.assertIsNotNone(order_lists)
self.assertEqual(1, len(order_lists))
aapl_order = blotter.open_orders[1][0].to_dict()
fls_order = blotter.open_orders[2][0].to_dict()
# make sure the aapl order didn't change
self.assertEqual(100, aapl_order['amount'])
self.assertEqual(10, aapl_order['limit'])
self.assertEqual(1, aapl_order['sid'])
# make sure the fls order did change
# to 300 shares at 3.33
self.assertEqual(300, fls_order['amount'])
self.assertEqual(3.33, fls_order['limit'])
self.assertEqual(2, fls_order['sid'])
def test_lse_algorithm(self):
lse = trading.TradingEnvironment(
bm_symbol='^FTSE',
exchange_tz='Europe/London'
)
with lse:
sim_params = factory.create_simulation_parameters(
start=datetime(2012, 5, 1, tzinfo=pytz.utc),
end=datetime(2012, 6, 30, tzinfo=pytz.utc)
)
algo = TestAlgo(self, sim_params=sim_params)
trade_source = factory.create_daily_trade_source(
[8229],
200,
sim_params
)
algo.set_sources([trade_source])
gen = algo.get_generator()
results = list(gen)
self.assertEqual(len(results), 42)
# May 7, 2012 was an LSE holiday, confirm the 4th trading
# day was May 8.
self.assertEqual(results[4]['daily_perf']['period_open'],
datetime(2012, 5, 8, 8, 30, tzinfo=pytz.utc))
def run(self, identity, data, strategy):
''' Wrapper of zipline run() method. Use the configuration set so far
to build up the trading environment and launch the system '''
engine = TradingEngine(identity,
self.configuration['modules'],
strategy)
#NOTE This method does not change anything
#engine.set_sources([DataLiveSource(data_tmp)])
#TODO A new command line parameter ? only minutely and daily
# (and hourly normaly) Use filter parameter of datasource ?
#engine.set_data_frequency(self.configuration['frequency'])
engine.is_live = self.configuration['live']
# Running simulation with it
#FIXME crash if trading one day that is not a trading day
with self.context:
sim_params = create_simulation_parameters(
capital_base=strategy['manager']['cash'],
start=self.configuration['index'][0],
end=self.configuration['index'][-1])
daily_stats = engine.trade(data, sim_params=sim_params)
return Analyze(
results=daily_stats,
metrics=engine.risk_report,
configuration=self.configuration)
def test_history_daily_data_1m_window(self):
algo_text = """
from zipline.api import history, add_history
def initialize(context):
add_history(bar_count=1, frequency='1m', field='price')
def handle_data(context, data):
prices = history(bar_count=3, frequency='1d', field='price')
""".strip()
start = pd.Timestamp('2006-03-20', tz='UTC')
end = pd.Timestamp('2006-03-30', tz='UTC')
sim_params = factory.create_simulation_parameters(
start=start, end=end)
with self.assertRaises(IncompatibleHistoryFrequency):
algo = TradingAlgorithm(
script=algo_text,
data_frequency='daily',
sim_params=sim_params
)
source = RandomWalkSource(start=start, end=end)
algo.run(source)
def test_algo_with_rl_violation(self):
sim_params = factory.create_simulation_parameters(
start=list(LEVERAGED_ETFS.keys())[0], num_days=4)
trade_history = factory.create_trade_history(
'BZQ',
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(days=1),
sim_params
)
self.source = SpecificEquityTrades(event_list=trade_history)
algo = RestrictedAlgoWithoutCheck(symbol='BZQ', sim_params=sim_params)
with self.assertRaises(TradingControlViolation) as ctx:
algo.run(self.source)
self.check_algo_exception(algo, ctx, 0)
# repeat with a symbol from a different lookup date
trade_history = factory.create_trade_history(
'JFT',
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(days=1),
sim_params
)
self.source = SpecificEquityTrades(event_list=trade_history)
algo = RestrictedAlgoWithoutCheck(symbol='JFT', sim_params=sim_params)
with self.assertRaises(TradingControlViolation) as ctx:
algo.run(self.source)
self.check_algo_exception(algo, ctx, 0)
def test_fetcher_in_before_trading_start(self):
self.responses.add(
self.responses.GET,
'https://fake.urls.com/fetcher_nflx_data.csv',
body=NFLX_DATA,
content_type='text/csv',
)
sim_params = factory.create_simulation_parameters(
start=pd.Timestamp("2013-06-13", tz='UTC'),
end=pd.Timestamp("2013-11-15", tz='UTC'),
data_frequency="minute"
)
results = self.run_algo("""
from zipline.api import fetch_csv, record, symbol
def initialize(context):
fetch_csv('https://fake.urls.com/fetcher_nflx_data.csv',
date_column = 'Settlement Date',
date_format = '%m/%d/%y')
context.stock = symbol('NFLX')
def before_trading_start(context, data):
record(Short_Interest = data.current(context.stock, 'dtc'))
""", sim_params=sim_params, data_frequency="minute")
values = results["Short_Interest"]
np.testing.assert_array_equal(values[0:33], np.full(33, np.nan))
np.testing.assert_array_almost_equal(values[33:44], [1.690317] * 11)
np.testing.assert_array_almost_equal(values[44:55], [2.811858] * 11)
np.testing.assert_array_almost_equal(values[55:64], [2.50233] * 9)
np.testing.assert_array_almost_equal(values[64:75], [2.550829] * 11)
np.testing.assert_array_almost_equal(values[75:], [2.64484] * 35)
def test_fetcher_universe_non_security_return(self):
self.responses.add(
self.responses.GET,
'https://fake.urls.com/bad_fetcher_universe_data.csv',
body=NON_ASSET_FETCHER_UNIVERSE_DATA,
content_type='text/csv',
)
sim_params = factory.create_simulation_parameters(
start=pd.Timestamp("2006-01-09", tz='UTC'),
end=pd.Timestamp("2006-01-10", tz='UTC')
)
self.run_algo(
"""
from zipline.api import fetch_csv
def initialize(context):
fetch_csv(
'https://fake.urls.com/bad_fetcher_universe_data.csv',
date_format='%m/%d/%Y'
)
def handle_data(context, data):
if len(data.fetcher_assets) > 0:
raise Exception("Shouldn't be any assets in fetcher_assets!")
""",
sim_params=sim_params,
)
def test_fetcher_bad_data(self):
self.responses.add(
self.responses.GET,
'https://fake.urls.com/fetcher_nflx_data.csv',
body=NFLX_DATA,
content_type='text/csv',
)
sim_params = factory.create_simulation_parameters(
start=pd.Timestamp("2013-06-12", tz='UTC'),
end=pd.Timestamp("2013-06-14", tz='UTC'),
data_frequency="minute"
)
results = self.run_algo("""
from zipline.api import fetch_csv, symbol
import numpy as np
def initialize(context):
fetch_csv('https://fake.urls.com/fetcher_nflx_data.csv',
date_column = 'Settlement Date',
date_format = '%m/%d/%y')
context.nflx = symbol('NFLX')
context.aapl = symbol('AAPL')
def handle_data(context, data):
assert np.isnan(data.current(context.nflx, 'invalid_column'))
assert np.isnan(data.current(context.aapl, 'invalid_column'))
assert np.isnan(data.current(context.aapl, 'dtc'))
""", sim_params=sim_params, data_frequency="minute")
self.assertEqual(3, len(results))
def test_minutely_emissions_generate_performance_stats_for_last_day(self):
params = factory.create_simulation_parameters(num_days=1)
params.data_frequency = 'minute'
params.emission_rate = 'minute'
algo = NoopAlgorithm()
algo.run(source=[], sim_params=params)
self.assertEqual(algo.perf_tracker.day_count, 1.0)
def test_algo_with_rl_violation_cumulative(self):
"""
Add a new restriction, run a test long after both
knowledge dates, make sure stock from original restriction
set is still disallowed.
"""
sim_params = factory.create_simulation_parameters(
start=list(
LEVERAGED_ETFS.keys())[0] + timedelta(days=7), num_days=4)
with security_list_copy():
add_security_data(['AAPL'], [])
trade_history = factory.create_trade_history(
'BZQ',
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(days=1),
sim_params,
env=self.env,
)
self.source = SpecificEquityTrades(event_list=trade_history,
env=self.env)
algo = RestrictedAlgoWithoutCheck(
symbol='BZQ', sim_params=sim_params, env=self.env)
with self.assertRaises(TradingControlViolation) as ctx:
algo.run(self.source)
self.check_algo_exception(algo, ctx, 0)
def setUpClass(cls):
setup_logger(cls)
cls.env = trading.TradingEnvironment()
cls.sim_params = factory.create_simulation_parameters(
start=pd.Timestamp("2006-01-05", tz='UTC'),
end=pd.Timestamp("2006-01-06", tz='UTC')
)
cls.env.write_data(equities_data={
24: {
'start_date': cls.sim_params.trading_days[0],
'end_date': cls.env.next_trading_day(
cls.sim_params.trading_days[-1]
)
},
25: {
'start_date': cls.sim_params.trading_days[0],
'end_date': cls.env.next_trading_day(
cls.sim_params.trading_days[-1]
)
}
})
cls.tempdir = TempDirectory()
assets = {
24: pd.DataFrame({
"open": [50, 50],
"high": [50, 50],
"low": [50, 50],
"close": [50, 50],
"volume": [100, 400],
"day": [day.value for day in cls.sim_params.trading_days]
}),
25: pd.DataFrame({
"open": [50, 50],
"high": [50, 50],
"low": [50, 50],
"close": [50, 50],
"volume": [100, 400],
"day": [day.value for day in cls.sim_params.trading_days]
})
}
path = os.path.join(cls.tempdir.path, "tempdata.bcolz")
DailyBarWriterFromDataFrames(assets).write(
path,
cls.sim_params.trading_days,
assets
)
equity_daily_reader = BcolzDailyBarReader(path)
cls.data_portal = DataPortal(
cls.env,
equity_daily_reader=equity_daily_reader,
)
def test_factory_daily(self):
sim_params = factory.create_simulation_parameters()
trade_source = factory.create_daily_trade_source([133], 200, sim_params)
prev = None
for trade in trade_source:
if prev:
self.assertTrue(trade.dt > prev.dt)
prev = trade
def setUp(self):
self.sim_params = factory.create_simulation_parameters(num_days=4)
trade_history = factory.create_trade_history(
133, [10.0, 10.0, 11.0, 11.0], [100, 100, 100, 300], timedelta(days=1), self.sim_params
)
self.source = SpecificEquityTrades(event_list=trade_history)
self.df_source, self.df = factory.create_test_df_source(self.sim_params)
def setUp(self):
self.sim_params = factory.create_simulation_parameters()
setup_logger(self)
trade_history = factory.create_trade_history(
133, [10.0, 10.0, 11.0, 11.0], [100, 100, 100, 300], timedelta(days=1), self.sim_params
)
self.source = SpecificEquityTrades(event_list=trade_history)
def setUp(self):
self.sim_params = factory.create_simulation_parameters(
start=datetime(1990, 1, 1, tzinfo=pytz.utc),
end=datetime(1990, 1, 8, tzinfo=pytz.utc)
)
setup_logger(self)
self.source, self.df = \
factory.create_test_df_source(self.sim_params)
def test_minutely_emissions_generate_performance_stats_for_last_day(self):
params = factory.create_simulation_parameters(num_days=1,
data_frequency='minute',
emission_rate='minute')
with patch.object(BenchmarkSource, "get_value",
self.fake_minutely_benchmark):
algo = NoopAlgorithm(sim_params=params, env=self.env)
algo.run(FakeDataPortal(self.env))
self.assertEqual(len(algo.perf_tracker.sim_params.sessions), 1)
def setUp(self):
self.sids = range(90)
self.env = TradingEnvironment()
self.env.write_data(equities_identifiers=self.sids)
self.sim_params = factory.create_simulation_parameters(
start=datetime(1990, 1, 1, tzinfo=pytz.utc),
end=datetime(1990, 1, 8, tzinfo=pytz.utc),
env=self.env,
)
def setUpClass(cls):
start = datetime.datetime(
year=2006, month=1, day=1, tzinfo=pytz.utc)
end = datetime.datetime(
year=2006, month=1, day=10, tzinfo=pytz.utc)
cls.period = pd.date_range(start, end)
sim_params = create_simulation_parameters(start=start, end=end)
sim_params.capital_base = 200
perf = cls.create_mock_perf()
cls.metrics = RiskReport(perf, sim_params)
def test_basic_history_positional_args(self):
"""
Ensure that positional args work.
"""
algo_text = """
import copy
from zipline.api import history, add_history
def initialize(context):
add_history(2, '1d', 'price')
def handle_data(context, data):
prices = history(2, '1d', 'price')
context.last_prices = copy.deepcopy(prices)
""".strip()
# March 2006
# 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 = pd.Timestamp('2006-03-20', tz='UTC')
end = pd.Timestamp('2006-03-21', tz='UTC')
sim_params = factory.create_simulation_parameters(
start=start, end=end)
test_algo = TradingAlgorithm(
script=algo_text,
data_frequency='minute',
sim_params=sim_params
)
source = RandomWalkSource(start=start,
end=end)
output = test_algo.run(source)
self.assertIsNotNone(output)
last_prices = test_algo.last_prices[0]
oldest_dt = pd.Timestamp(
'2006-03-20 4:00 PM', tz='US/Eastern').tz_convert('UTC')
newest_dt = pd.Timestamp(
'2006-03-21 4:00 PM', tz='US/Eastern').tz_convert('UTC')
self.assertEquals(oldest_dt, last_prices.index[0])
self.assertEquals(newest_dt, last_prices.index[-1])
self.assertEquals(139.36946942498648, last_prices[oldest_dt])
self.assertEquals(180.15661995395106, last_prices[newest_dt])
def setUp(self):
setup_logger(self)
sids = [1, 2]
self.sim_params = factory.create_simulation_parameters(num_days=2,
sids=sids)
self.source = factory.create_minutely_trade_source(
sids,
trade_count=100,
sim_params=self.sim_params,
concurrent=True,
)
def setUp(self):
start = pd.datetime(1990, 1, 3, 0, 0, 0, 0, pytz.utc)
end = pd.datetime(1990, 1, 8, 0, 0, 0, 0, pytz.utc)
self.sim_params = factory.create_simulation_parameters(
start=start,
end=end,
)
self.sim_params.emission_rate = 'daily'
self.sim_params.data_frequency = 'minute'
setup_logger(self)
self.source, self.df = \
factory.create_test_df_source(bars='minute')
def setUp(self):
self.sim_params = factory.create_simulation_parameters(num_days=4)
self.sidint = 133
self.trade_history = factory.create_trade_history(
self.sidint,
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(days=1),
self.sim_params
)
self.source = SpecificEquityTrades(event_list=self.trade_history)
def test_fetcher_universe(self, name, data, column_name):
# Patching fetch_url here rather than using responses because (a) it's
# easier given the paramaterization, and (b) there are enough tests
# using responses that the fetch_url code is getting a good workout so
# we don't have to use it in every test.
with patch('zipline.sources.requests_csv.PandasRequestsCSV.fetch_url',
new=lambda *a, **k: data):
sim_params = factory.create_simulation_parameters(
start=pd.Timestamp("2006-01-09", tz='UTC'),
end=pd.Timestamp("2006-01-11", tz='UTC')
)
algocode = """
from pandas import Timestamp
from pandas.tseries.tools import normalize_date
from zipline.api import fetch_csv, record, sid, get_datetime
def initialize(context):
fetch_csv(
'https://dl.dropbox.com/u/16705795/dtoc_history.csv',
date_format='%m/%d/%Y'{token}
)
context.expected_sids = {{
Timestamp('2006-01-09 00:00:00+0000', tz='UTC'):[24, 3766, 5061],
Timestamp('2006-01-10 00:00:00+0000', tz='UTC'):[24, 3766, 5061],
Timestamp('2006-01-11 00:00:00+0000', tz='UTC'):[24, 3766, 5061, 14848]
}}
context.bar_count = 0
def handle_data(context, data):
expected = context.expected_sids[normalize_date(get_datetime())]
actual = data.fetcher_assets
for stk in expected:
if stk not in actual:
raise Exception(
"{{stk}} is missing on dt={{dt}}".format(
stk=stk, dt=get_datetime()))
record(sid_count=len(actual))
record(bar_count=context.bar_count)
context.bar_count += 1
"""
replacement = ""
if column_name:
replacement = ",symbol_column='%s'\n" % column_name
real_algocode = algocode.format(token=replacement)
results = self.run_algo(real_algocode, sim_params=sim_params)
self.assertEqual(len(results), 3)
self.assertEqual(3, results["sid_count"].iloc[0])
self.assertEqual(3, results["sid_count"].iloc[1])
self.assertEqual(4, results["sid_count"].iloc[2])
def test_algo_with_rl_violation_after_add(self):
try:
add_security_data(['AAPL'], [])
sim_params = factory.create_simulation_parameters(
start=self.trading_day_before_first_kd, num_days=4)
trade_history = factory.create_trade_history(
'AAPL', [10.0, 10.0, 11.0, 11.0], [100, 100, 100, 300],
timedelta(days=1), sim_params)
self.source = SpecificEquityTrades(event_list=trade_history)
algo = RestrictedAlgoWithoutCheck(sid='AAPL',
sim_params=sim_params)
with self.assertRaises(TradingControlViolation) as ctx:
algo.run(self.source)
self.check_algo_exception(algo, ctx, 2)
finally:
remove_security_data_directory()
def setUp(self):
setup_logger(self)
self.sim_params = factory.create_simulation_parameters(num_days=4)
setup_logger(self)
trade_history = factory.create_trade_history(1,
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(days=1),
self.sim_params)
self.source = SpecificEquityTrades(event_list=trade_history)
self.df_source, self.df = \
factory.create_test_df_source(self.sim_params)
self.panel_source, self.panel = \
factory.create_test_panel_source(self.sim_params)
def setUp(self):
days = 251
self.sim_params = factory.create_simulation_parameters(num_days=days)
setup_logger(self)
trade_history = factory.create_trade_history(133, [10.0] * days,
[100] * days,
timedelta(days=1),
self.sim_params)
self.source = SpecificEquityTrades(event_list=trade_history)
self.df_source, self.df = \
factory.create_test_df_source(self.sim_params)
self.zipline_test_config = {
'sid': 0,
}
def test_algo_with_rl_violation_after_knowledge_date(self):
sim_params = factory.create_simulation_parameters(
start=list(
LEVERAGED_ETFS.keys())[0] + timedelta(days=7), num_days=5)
trade_history = factory.create_trade_history(
'BZQ',
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(days=1),
sim_params
)
self.source = SpecificEquityTrades(event_list=trade_history)
algo = RestrictedAlgoWithoutCheck(sid='BZQ', sim_params=sim_params)
with self.assertRaises(TradingControlViolation) as ctx:
algo.run(self.source)
self.check_algo_exception(algo, ctx, 0)
def test_set_portfolio(self):
"""
Are we protected against overwriting an algo's portfolio?
"""
# Simulation
# ----------
self.zipline_test_config['algorithm'] = \
SetPortfolioAlgorithm(
self.zipline_test_config['sid'],
sim_params=factory.create_simulation_parameters(),
env=self.env
)
zipline = simfactory.create_test_zipline(**self.zipline_test_config)
with self.assertRaises(AttributeError):
output, _ = drain_zipline(self, zipline)
def test_algo_without_rl_violation_via_check(self):
sim_params = factory.create_simulation_parameters(start=list(
LEVERAGED_ETFS.keys())[0],
num_days=4,
env=self.env)
trade_history = factory.create_trade_history('BZQ',
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(days=1),
sim_params,
env=self.env)
self.source = SpecificEquityTrades(event_list=trade_history,
env=self.env)
algo = RestrictedAlgoWithCheck(symbol='BZQ',
sim_params=sim_params,
env=self.env)
algo.run(self.source)
def test_algo_with_rl_violation_cumulative(self):
"""
Add a new restriction, run a test long after both
knowledge dates, make sure stock from original restriction
set is still disallowed.
"""
sim_params = factory.create_simulation_parameters(
start=list(
LEVERAGED_ETFS.keys())[0] + timedelta(days=7), num_days=4)
with security_list_copy():
add_security_data(['AAPL'], [])
algo = RestrictedAlgoWithoutCheck(
symbol='BZQ', sim_params=sim_params, env=self.env)
with self.assertRaises(TradingControlViolation) as ctx:
algo.run(self.data_portal)
self.check_algo_exception(algo, ctx, 0)
def build_backtest_environment(star_date, end_date,capital_base=100000):
trading_calendar = get_calendar("NYSE")
sim_params = create_simulation_parameters(capital_base=capital_base,
data_frequency='daily',
trading_calendar=trading_calendar,
start=pd.Timestamp(pd.to_datetime(star_date)).tz_localize('US/Eastern'),
end=pd.Timestamp(pd.to_datetime(end_date)).tz_localize('US/Eastern')
)
bundle = bundles.load('quandl')
prefix, connstr = re.split(r'sqlite:///', str(bundle.asset_finder.engine.url), maxsplit=1, )
env = TradingEnvironment(asset_db_path=connstr, environ=os.environ)
data = DataPortal(
env.asset_finder, trading_calendar,
first_trading_day=bundle.equity_minute_bar_reader.first_trading_day,
equity_minute_reader=bundle.equity_minute_bar_reader,
equity_daily_reader=bundle.equity_daily_bar_reader,
adjustment_reader=bundle.adjustment_reader,
)
return data,env,bundle,sim_params
def run_batchtransform(window_length=10):
sim_params = factory.create_simulation_parameters(
start=datetime(1990, 1, 1, tzinfo=pytz.utc),
end=datetime(1995, 1, 8, tzinfo=pytz.utc)
)
source, df = factory.create_test_df_source(sim_params)
return_price_class = ReturnPriceBatchTransform(
refresh_period=1,
window_length=window_length,
clean_nans=False
)
for raw_event in source:
raw_event['datetime'] = raw_event.dt
event = {0: raw_event}
return_price_class.handle_data(event)
batch_transform,
ReturnPriceBatchTransform)
def test_algo_without_rl_violation_after_delete(self):
with security_list_copy():
# add a delete statement removing bzq
# write a new delete statement file to disk
add_security_data([], ['BZQ'])
sim_params = factory.create_simulation_parameters(
start=self.extra_knowledge_date, num_days=3)
trade_history = factory.create_trade_history(
'BZQ',
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(days=1),
sim_params
)
self.source = SpecificEquityTrades(event_list=trade_history)
algo = RestrictedAlgoWithoutCheck(
sid='BZQ', sim_params=sim_params)
algo.run(self.source)
def test_algo_with_rl_violation(self):
sim_params = factory.create_simulation_parameters(
start=list(LEVERAGED_ETFS.keys())[0], num_days=4)
trade_history = factory.create_trade_history(
'BZQ',
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(days=1),
sim_params
)
self.source = SpecificEquityTrades(event_list=trade_history)
self.df_source, self.df = \
factory.create_test_df_source(sim_params)
algo = RestrictedAlgoWithoutCheck(sid='BZQ', sim_params=sim_params)
with self.assertRaises(TradingControlViolation) as ctx:
algo.run(self.source)
self.check_algo_exception(algo, ctx, 0)
# repeat with a symbol from a different lookup date
trade_history = factory.create_trade_history(
'JFT',
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(days=1),
sim_params
)
self.source = SpecificEquityTrades(event_list=trade_history)
self.df_source, self.df = \
factory.create_test_df_source(sim_params)
algo = RestrictedAlgoWithoutCheck(sid='JFT', sim_params=sim_params)
with self.assertRaises(TradingControlViolation) as ctx:
algo.run(self.source)
self.check_algo_exception(algo, ctx, 0)
def test_fetcher_universe_non_security_return(self):
sim_params = factory.create_simulation_parameters(
start=pd.Timestamp("2006-01-09", tz='UTC'),
end=pd.Timestamp("2006-01-10", tz='UTC'))
self.run_algo(
"""
from zipline.api import fetch_csv
def initialize(context):
fetch_csv(
'https://fake.urls.com/bad_fetcher_universe_data.csv',
date_format='%m/%d/%Y'
)
def handle_data(context, data):
if len(data.fetcher_assets) > 0:
raise Exception("Shouldn't be any assets in fetcher_assets!")
""",
sim_params=sim_params,
)
def test_fetcher_universe_minute(self):
sim_params = factory.create_simulation_parameters(
start=pd.Timestamp("2006-01-09", tz='UTC'),
end=pd.Timestamp("2006-01-11", tz='UTC'),
data_frequency="minute")
results = self.run_algo("""
from pandas import Timestamp
from zipline.api import fetch_csv, record, get_datetime
def initialize(context):
fetch_csv(
'https://fake.urls.com/fetcher_universe_data.csv',
date_format='%m/%d/%Y'
)
context.expected_sids = {
Timestamp('2006-01-09 00:00:00+0000', tz='UTC'):[24, 3766, 5061],
Timestamp('2006-01-10 00:00:00+0000', tz='UTC'):[24, 3766, 5061],
Timestamp('2006-01-11 00:00:00+0000', tz='UTC'):[24, 3766, 5061, 14848]
}
context.bar_count = 0
def handle_data(context, data):
expected = context.expected_sids[get_datetime().replace(hour=0, minute=0)]
actual = data.fetcher_assets
for stk in expected:
if stk not in actual:
raise Exception("{stk} is missing".format(stk=stk))
record(sid_count=len(actual))
record(bar_count=context.bar_count)
context.bar_count += 1
""",
sim_params=sim_params,
data_frequency="minute")
self.assertEqual(3, len(results))
self.assertEqual(3, results["sid_count"].iloc[0])
self.assertEqual(3, results["sid_count"].iloc[1])
self.assertEqual(4, results["sid_count"].iloc[2])
def test_blotter_processes_splits(self):
sim_params = factory.create_simulation_parameters(
start=self.start,
end=self.end)
blotter = SimulationBlotter(sim_params, equity_slippage=FixedSlippage())
# set up two open limit orders with very low limit prices,
# one for sid 1 and one for sid 2
asset1 = self.asset_finder.retrieve_asset(1)
asset2 = self.asset_finder.retrieve_asset(2)
asset133 = self.asset_finder.retrieve_asset(133)
blotter.order(asset1, 100, LimitOrder(10, asset=asset1))
blotter.order(asset2, 100, LimitOrder(10, asset=asset2))
# send in splits for assets 133 and 2. We have no open orders for
# asset 133 so it should be ignored.
blotter.process_splits([(asset133, 0.5), (asset2, 0.3333)])
for asset in [asset1, asset2]:
order_lists = blotter.open_orders[asset]
self.assertIsNotNone(order_lists)
self.assertEqual(1, len(order_lists))
asset1_order = blotter.open_orders[1][0]
asset2_order = blotter.open_orders[2][0]
# make sure the asset1 order didn't change
self.assertEqual(100, asset1_order.amount)
self.assertEqual(10, asset1_order.limit)
self.assertEqual(1, asset1_order.asset)
# make sure the asset2 order did change
# to 300 shares at 3.33
self.assertEqual(300, asset2_order.amount)
self.assertEqual(3.33, asset2_order.limit)
self.assertEqual(2, asset2_order.asset)
