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zipline/tests/test_algorithm.py
vikram-narayan d4ba48709d MAINT: use trading_calendars in zipline 
- remove zipline.utils.calendars
- update imports
- add trading-calendars to requirements
2018-07-02 11:10:49 -04:00

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#
# Copyright 2018 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 warnings
import datetime
from datetime import timedelta
from functools import partial
from textwrap import dedent
from copy import deepcopy
import logbook
import toolz
from logbook import TestHandler, WARNING
from nose_parameterized import parameterized
from six import iteritems, itervalues, string_types
from six.moves import range
from testfixtures import TempDirectory
import numpy as np
import pandas as pd
import pytz
from pandas.core.common import PerformanceWarning
from trading_calendars import get_calendar, register_calendar
import zipline.api
from zipline.api import FixedSlippage
from zipline.assets import Equity, Future, Asset
from zipline.assets.continuous_futures import ContinuousFuture
from zipline.assets.synthetic import (
make_jagged_equity_info,
make_simple_equity_info,
)
from zipline.errors import (
AccountControlViolation,
CannotOrderDelistedAsset,
IncompatibleSlippageModel,
RegisterTradingControlPostInit,
ScheduleFunctionInvalidCalendar,
SetCancelPolicyPostInit,
SymbolNotFound,
TradingControlViolation,
UnsupportedCancelPolicy,
UnsupportedDatetimeFormat,
)
from zipline.finance.commission import PerShare, PerTrade
from zipline.finance.execution import LimitOrder
from zipline.finance.order import ORDER_STATUS
from zipline.finance.trading import SimulationParameters
from zipline.finance.asset_restrictions import (
Restriction,
HistoricalRestrictions,
StaticRestrictions,
RESTRICTION_STATES,
)
from zipline.finance.controls import AssetDateBounds
from zipline.testing import (
FakeDataPortal,
create_daily_df_for_asset,
create_data_portal_from_trade_history,
create_minute_df_for_asset,
make_test_handler,
make_trade_data_for_asset_info,
parameter_space,
str_to_seconds,
to_utc,
)
from zipline.testing import RecordBatchBlotter
import zipline.testing.fixtures as zf
from zipline.test_algorithms import (
access_account_in_init,
access_portfolio_in_init,
api_algo,
api_get_environment_algo,
api_symbol_algo,
handle_data_api,
handle_data_noop,
initialize_api,
initialize_noop,
noop_algo,
record_float_magic,
record_variables,
call_with_kwargs,
call_without_kwargs,
call_with_bad_kwargs_current,
call_with_bad_kwargs_history,
bad_type_history_assets,
bad_type_history_fields,
bad_type_history_bar_count,
bad_type_history_frequency,
bad_type_history_assets_kwarg_list,
bad_type_current_assets,
bad_type_current_fields,
bad_type_can_trade_assets,
bad_type_is_stale_assets,
bad_type_history_assets_kwarg,
bad_type_history_fields_kwarg,
bad_type_history_bar_count_kwarg,
bad_type_history_frequency_kwarg,
bad_type_current_assets_kwarg,
bad_type_current_fields_kwarg,
call_with_bad_kwargs_get_open_orders,
call_with_good_kwargs_get_open_orders,
call_with_no_kwargs_get_open_orders,
empty_positions,
no_handle_data,
)
import zipline.test_algorithms as zta
from zipline.testing.predicates import assert_equal
from zipline.utils.api_support import ZiplineAPI
from zipline.utils.context_tricks import CallbackManager, nop_context
from zipline.utils.events import (
date_rules,
time_rules,
Always,
ComposedRule,
Never,
OncePerDay,
)
import zipline.utils.factory as factory
# Because test cases appear to reuse some resources.
_multiprocess_can_split_ = False
class TestRecord(zf.WithMakeAlgo, zf.ZiplineTestCase):
ASSET_FINDER_EQUITY_SIDS = (133,)
SIM_PARAMS_DATA_FREQUENCY = 'daily'
DATA_PORTAL_USE_MINUTE_DATA = False
def test_record_incr(self):
def initialize(self):
self.incr = 0
def handle_data(self, data):
self.incr += 1
self.record(incr=self.incr)
name = 'name'
self.record(name, self.incr)
zipline.api.record(name, self.incr, 'name2', 2, name3=self.incr)
output = self.run_algorithm(
initialize=initialize,
handle_data=handle_data,
)
np.testing.assert_array_equal(output['incr'].values,
range(1, len(output) + 1))
np.testing.assert_array_equal(output['name'].values,
range(1, len(output) + 1))
np.testing.assert_array_equal(output['name2'].values,
[2] * len(output))
np.testing.assert_array_equal(output['name3'].values,
range(1, len(output) + 1))
class TestMiscellaneousAPI(zf.WithMakeAlgo, zf.ZiplineTestCase):
START_DATE = pd.Timestamp('2006-01-03', tz='UTC')
END_DATE = pd.Timestamp('2006-01-04', tz='UTC')
SIM_PARAMS_DATA_FREQUENCY = 'minute'
sids = 1, 2
# FIXME: Pass a benchmark source instead of this.
BENCHMARK_SID = None
@classmethod
def make_equity_info(cls):
return pd.concat((
make_simple_equity_info(cls.sids, '2002-02-1', '2007-01-01'),
pd.DataFrame.from_dict(
{3: {'symbol': 'PLAY',
'start_date': '2002-01-01',
'end_date': '2004-01-01',
'exchange': 'TEST'},
4: {'symbol': 'PLAY',
'start_date': '2005-01-01',
'end_date': '2006-01-01',
'exchange': 'TEST'}},
orient='index',
),
))
@classmethod
def make_futures_info(cls):
return pd.DataFrame.from_dict(
{
5: {
'symbol': 'CLG06',
'root_symbol': 'CL',
'start_date': pd.Timestamp('2005-12-01', tz='UTC'),
'notice_date': pd.Timestamp('2005-12-20', tz='UTC'),
'expiration_date': pd.Timestamp('2006-01-20', tz='UTC'),
'exchange': 'TEST'
},
6: {
'root_symbol': 'CL',
'symbol': 'CLK06',
'start_date': pd.Timestamp('2005-12-01', tz='UTC'),
'notice_date': pd.Timestamp('2006-03-20', tz='UTC'),
'expiration_date': pd.Timestamp('2006-04-20', tz='UTC'),
'exchange': 'TEST',
},
7: {
'symbol': 'CLQ06',
'root_symbol': 'CL',
'start_date': pd.Timestamp('2005-12-01', tz='UTC'),
'notice_date': pd.Timestamp('2006-06-20', tz='UTC'),
'expiration_date': pd.Timestamp('2006-07-20', tz='UTC'),
'exchange': 'TEST',
},
8: {
'symbol': 'CLX06',
'root_symbol': 'CL',
'start_date': pd.Timestamp('2006-02-01', tz='UTC'),
'notice_date': pd.Timestamp('2006-09-20', tz='UTC'),
'expiration_date': pd.Timestamp('2006-10-20', tz='UTC'),
'exchange': 'TEST',
}
},
orient='index',
)
def test_cancel_policy_outside_init(self):
code = """
from zipline.api import cancel_policy, set_cancel_policy
def initialize(algo):
pass
def handle_data(algo, data):
set_cancel_policy(cancel_policy.NeverCancel())
"""
algo = self.make_algo(script=code)
with self.assertRaises(SetCancelPolicyPostInit):
algo.run()
def test_cancel_policy_invalid_param(self):
code = """
from zipline.api import set_cancel_policy
def initialize(algo):
set_cancel_policy("foo")
def handle_data(algo, data):
pass
"""
algo = self.make_algo(script=code)
with self.assertRaises(UnsupportedCancelPolicy):
algo.run()
def test_zipline_api_resolves_dynamically(self):
# Make a dummy algo.
algo = self.make_algo(
initialize=lambda context: None,
handle_data=lambda context, data: None,
)
# Verify that api methods get resolved dynamically by patching them out
# and then calling them
for method in algo.all_api_methods():
name = method.__name__
sentinel = object()
def fake_method(*args, **kwargs):
return sentinel
setattr(algo, name, fake_method)
with ZiplineAPI(algo):
self.assertIs(sentinel, getattr(zipline.api, name)())
def test_sid_datetime(self):
algo_text = """
from zipline.api import sid, get_datetime
def initialize(context):
pass
def handle_data(context, data):
aapl_dt = data.current(sid(1), "last_traded")
assert_equal(aapl_dt, get_datetime())
"""
self.run_algorithm(
script=algo_text,
namespace={'assert_equal': self.assertEqual},
)
def test_datetime_bad_params(self):
algo_text = """
from zipline.api import get_datetime
from pytz import timezone
def initialize(context):
pass
def handle_data(context, data):
get_datetime(timezone)
"""
algo = self.make_algo(script=algo_text)
with self.assertRaises(TypeError):
algo.run()
def test_get_environment(self):
expected_env = {
'arena': 'backtest',
'data_frequency': 'minute',
'start': pd.Timestamp('2006-01-03 14:31:00+0000', tz='utc'),
'end': pd.Timestamp('2006-01-04 21:00:00+0000', tz='utc'),
'capital_base': 100000.0,
'platform': 'zipline'
}
def initialize(algo):
self.assertEqual('zipline', algo.get_environment())
self.assertEqual(expected_env, algo.get_environment('*'))
def handle_data(algo, data):
pass
self.run_algorithm(initialize=initialize, handle_data=handle_data)
def test_get_open_orders(self):
def initialize(algo):
algo.minute = 0
def handle_data(algo, data):
if algo.minute == 0:
# Should be filled by the next minute
algo.order(algo.sid(1), 1)
# Won't be filled because the price is too low.
algo.order(algo.sid(2), 1, style=LimitOrder(0.01))
algo.order(algo.sid(2), 1, style=LimitOrder(0.01))
algo.order(algo.sid(2), 1, style=LimitOrder(0.01))
all_orders = algo.get_open_orders()
self.assertEqual(list(all_orders.keys()), [1, 2])
self.assertEqual(all_orders[1], algo.get_open_orders(1))
self.assertEqual(len(all_orders[1]), 1)
self.assertEqual(all_orders[2], algo.get_open_orders(2))
self.assertEqual(len(all_orders[2]), 3)
if algo.minute == 1:
# First order should have filled.
# Second order should still be open.
all_orders = algo.get_open_orders()
self.assertEqual(list(all_orders.keys()), [2])
self.assertEqual([], algo.get_open_orders(1))
orders_2 = algo.get_open_orders(2)
self.assertEqual(all_orders[2], orders_2)
self.assertEqual(len(all_orders[2]), 3)
for order_ in orders_2:
algo.cancel_order(order_)
all_orders = algo.get_open_orders()
self.assertEqual(all_orders, {})
algo.minute += 1
self.run_algorithm(initialize=initialize, handle_data=handle_data)
def test_schedule_function_custom_cal(self):
# run a simulation on the CME cal, and schedule a function
# using the NYSE cal
algotext = """
from zipline.api import (
schedule_function, get_datetime, time_rules, date_rules, calendars,
)
def initialize(context):
schedule_function(
func=log_nyse_open,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_open(),
calendar=calendars.US_EQUITIES,
)
schedule_function(
func=log_nyse_close,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_close(),
calendar=calendars.US_EQUITIES,
)
context.nyse_opens = []
context.nyse_closes = []
def log_nyse_open(context, data):
context.nyse_opens.append(get_datetime())
def log_nyse_close(context, data):
context.nyse_closes.append(get_datetime())
"""
algo = self.make_algo(
script=algotext,
trading_calendar=get_calendar("CME"),
)
algo.run()
nyse = get_calendar("NYSE")
for minute in algo.nyse_opens:
# each minute should be a nyse session open
session_label = nyse.minute_to_session_label(minute)
session_open = nyse.session_open(session_label)
self.assertEqual(session_open, minute)
for minute in algo.nyse_closes:
# each minute should be a minute before a nyse session close
session_label = nyse.minute_to_session_label(minute)
session_close = nyse.session_close(session_label)
self.assertEqual(session_close - timedelta(minutes=1), minute)
# Test that passing an invalid calendar parameter raises an error.
erroring_algotext = dedent(
"""
from zipline.api import schedule_function
from trading_calendars import get_calendar
def initialize(context):
schedule_function(func=my_func, calendar=get_calendar('NYSE'))
def my_func(context, data):
pass
"""
)
algo = self.make_algo(
script=erroring_algotext,
trading_calendar=get_calendar('CME'),
)
with self.assertRaises(ScheduleFunctionInvalidCalendar):
algo.run()
def test_schedule_function(self):
us_eastern = pytz.timezone('US/Eastern')
def incrementer(algo, data):
algo.func_called += 1
curdt = algo.get_datetime().tz_convert(pytz.utc)
self.assertEqual(
curdt,
us_eastern.localize(
datetime.datetime.combine(
curdt.date(),
datetime.time(9, 31)
),
),
)
def initialize(algo):
algo.func_called = 0
algo.days = 1
algo.date = None
algo.schedule_function(
func=incrementer,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_open(),
)
def handle_data(algo, data):
if not algo.date:
algo.date = algo.get_datetime().date()
if algo.date < algo.get_datetime().date():
algo.days += 1
algo.date = algo.get_datetime().date()
algo = self.make_algo(
initialize=initialize,
handle_data=handle_data,
)
algo.run()
self.assertEqual(algo.func_called, algo.days)
def test_event_context(self):
expected_data = []
collected_data_pre = []
collected_data_post = []
function_stack = []
def pre(data):
function_stack.append(pre)
collected_data_pre.append(data)
def post(data):
function_stack.append(post)
collected_data_post.append(data)
def initialize(context):
context.add_event(Always(), f)
context.add_event(Always(), g)
def handle_data(context, data):
function_stack.append(handle_data)
expected_data.append(data)
def f(context, data):
function_stack.append(f)
def g(context, data):
function_stack.append(g)
algo = self.make_algo(
initialize=initialize,
handle_data=handle_data,
create_event_context=CallbackManager(pre, post),
)
algo.run(self.data_portal)
self.assertEqual(len(expected_data), 780)
self.assertEqual(collected_data_pre, expected_data)
self.assertEqual(collected_data_post, expected_data)
self.assertEqual(
len(function_stack),
3900,
'Incorrect number of functions called: %s != 3900' %
len(function_stack),
)
expected_functions = [pre, handle_data, f, g, post] * 97530
for n, (f, g) in enumerate(zip(function_stack, expected_functions)):
self.assertEqual(
f,
g,
'function at position %d was incorrect, expected %s but got %s'
% (n, g.__name__, f.__name__),
)
@parameterized.expand([
('daily',),
('minute'),
])
def test_schedule_function_rule_creation(self, mode):
def nop(*args, **kwargs):
return None
self.sim_params.data_frequency = mode
algo = self.make_algo(
initialize=nop,
handle_data=nop,
sim_params=self.sim_params,
env=self.env,
)
# Schedule something for NOT Always.
# Compose two rules to ensure calendar is set properly.
algo.schedule_function(nop, time_rule=Never() & Always())
event_rule = algo.event_manager._events[1].rule
self.assertIsInstance(event_rule, OncePerDay)
self.assertEqual(event_rule.cal, algo.trading_calendar)
inner_rule = event_rule.rule
self.assertIsInstance(inner_rule, ComposedRule)
self.assertEqual(inner_rule.cal, algo.trading_calendar)
first = inner_rule.first
second = inner_rule.second
composer = inner_rule.composer
self.assertIsInstance(first, Always)
self.assertEqual(first.cal, algo.trading_calendar)
self.assertEqual(second.cal, algo.trading_calendar)
if mode == 'daily':
self.assertIsInstance(second, Always)
else:
self.assertIsInstance(second, ComposedRule)
self.assertIsInstance(second.first, Never)
self.assertEqual(second.first.cal, algo.trading_calendar)
self.assertIsInstance(second.second, Always)
self.assertEqual(second.second.cal, algo.trading_calendar)
self.assertIs(composer, ComposedRule.lazy_and)
def test_asset_lookup(self):
algo = self.make_algo()
# this date doesn't matter
start_session = pd.Timestamp("2000-01-01", tz="UTC")
# Test before either PLAY existed
algo.sim_params = algo.sim_params.create_new(
start_session,
pd.Timestamp('2001-12-01', tz='UTC')
)
with self.assertRaises(SymbolNotFound):
algo.symbol('PLAY')
with self.assertRaises(SymbolNotFound):
algo.symbols('PLAY')
# Test when first PLAY exists
algo.sim_params = algo.sim_params.create_new(
start_session,
pd.Timestamp('2002-12-01', tz='UTC')
)
list_result = algo.symbols('PLAY')
self.assertEqual(3, list_result[0])
# Test after first PLAY ends
algo.sim_params = algo.sim_params.create_new(
start_session,
pd.Timestamp('2004-12-01', tz='UTC')
)
self.assertEqual(3, algo.symbol('PLAY'))
# Test after second PLAY begins
algo.sim_params = algo.sim_params.create_new(
start_session,
pd.Timestamp('2005-12-01', tz='UTC')
)
self.assertEqual(4, algo.symbol('PLAY'))
# Test after second PLAY ends
algo.sim_params = algo.sim_params.create_new(
start_session,
pd.Timestamp('2006-12-01', tz='UTC')
)
self.assertEqual(4, algo.symbol('PLAY'))
list_result = algo.symbols('PLAY')
self.assertEqual(4, list_result[0])
# Test lookup SID
self.assertIsInstance(algo.sid(3), Equity)
self.assertIsInstance(algo.sid(4), Equity)
# Supplying a non-string argument to symbol()
# should result in a TypeError.
with self.assertRaises(TypeError):
algo.symbol(1)
with self.assertRaises(TypeError):
algo.symbol((1,))
with self.assertRaises(TypeError):
algo.symbol({1})
with self.assertRaises(TypeError):
algo.symbol([1])
with self.assertRaises(TypeError):
algo.symbol({'foo': 'bar'})
def test_future_symbol(self):
""" Tests the future_symbol API function.
"""
algo = self.make_algo()
algo.datetime = pd.Timestamp('2006-12-01', tz='UTC')
# Check that we get the correct fields for the CLG06 symbol
cl = algo.future_symbol('CLG06')
self.assertEqual(cl.sid, 5)
self.assertEqual(cl.symbol, 'CLG06')
self.assertEqual(cl.root_symbol, 'CL')
self.assertEqual(cl.start_date, pd.Timestamp('2005-12-01', tz='UTC'))
self.assertEqual(cl.notice_date, pd.Timestamp('2005-12-20', tz='UTC'))
self.assertEqual(cl.expiration_date,
pd.Timestamp('2006-01-20', tz='UTC'))
with self.assertRaises(SymbolNotFound):
algo.future_symbol('')
with self.assertRaises(SymbolNotFound):
algo.future_symbol('PLAY')
with self.assertRaises(SymbolNotFound):
algo.future_symbol('FOOBAR')
# Supplying a non-string argument to future_symbol()
# should result in a TypeError.
with self.assertRaises(TypeError):
algo.future_symbol(1)
with self.assertRaises(TypeError):
algo.future_symbol((1,))
with self.assertRaises(TypeError):
algo.future_symbol({1})
with self.assertRaises(TypeError):
algo.future_symbol([1])
with self.assertRaises(TypeError):
algo.future_symbol({'foo': 'bar'})
class TestSetSymbolLookupDate(zf.WithMakeAlgo, zf.ZiplineTestCase):
# January 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_DATE = pd.Timestamp('2006-01-03', tz='UTC')
END_DATE = pd.Timestamp('2006-01-06', tz='UTC')
SIM_PARAMS_START_DATE = pd.Timestamp('2006-01-04', tz='UTC')
SIM_PARAMS_DATA_FREQUENCY = 'daily'
DATA_PORTAL_USE_MINUTE_DATA = False
BENCHMARK_SID = 3
@classmethod
def make_equity_info(cls):
dates = pd.date_range(cls.START_DATE, cls.END_DATE)
assert len(dates) == 4, "Expected four dates."
# Two assets with the same ticker, ending on days[1] and days[3], plus
# a benchmark that spans the whole period.
cls.sids = [1, 2, 3]
cls.asset_starts = [dates[0], dates[2]]
cls.asset_ends = [dates[1], dates[3]]
return pd.DataFrame.from_records([
{'symbol': 'DUP',
'start_date': cls.asset_starts[0],
'end_date': cls.asset_ends[0],
'exchange': 'TEST',
'asset_name': 'FIRST'},
{'symbol': 'DUP',
'start_date': cls.asset_starts[1],
'end_date': cls.asset_ends[1],
'exchange': 'TEST',
'asset_name': 'SECOND'},
{'symbol': 'BENCH',
'start_date': cls.START_DATE,
'end_date': cls.END_DATE,
'exchange': 'TEST',
'asset_name': 'BENCHMARK'},
], index=cls.sids)
def test_set_symbol_lookup_date(self):
"""
Test the set_symbol_lookup_date API method.
"""
set_symbol_lookup_date = zipline.api.set_symbol_lookup_date
def initialize(context):
set_symbol_lookup_date(self.asset_ends[0])
self.assertEqual(zipline.api.symbol('DUP').sid, self.sids[0])
set_symbol_lookup_date(self.asset_ends[1])
self.assertEqual(zipline.api.symbol('DUP').sid, self.sids[1])
with self.assertRaises(UnsupportedDatetimeFormat):
set_symbol_lookup_date('foobar')
self.run_algorithm(initialize=initialize)
class TestRunTwice(zf.WithMakeAlgo, zf.ZiplineTestCase):
# January 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_DATE = pd.Timestamp('2006-01-03', tz='utc')
END_DATE = pd.Timestamp('2006-01-06', tz='utc')
ASSET_FINDER_EQUITY_SIDS = [0, 1, 133]
SIM_PARAMS_DATA_FREQUENCY = 'daily'
DATA_PORTAL_USE_MINUTE_DATA = False
# FIXME: Pass a benchmark source explicitly here.
BENCHMARK_SID = None
def test_run_twice(self):
algo = self.make_algo(script=zta.noop_algo)
res1 = algo.run()
# XXX: Calling run() twice only works if you pass a data portal
# explicitly on the second call, because we `del self.data_portal` at
# the end of an algorithm execution. Do we actually still care about
# the ability to double-run an algo?
res2 = algo.run(self.data_portal)
# There are some np.NaN values in the first row because there is not
# enough data to calculate the metric, e.g. beta.
res1 = res1.fillna(value=0)
res2 = res2.fillna(value=0)
np.testing.assert_array_equal(res1, res2)
class TestPositions(zf.WithMakeAlgo, zf.ZiplineTestCase):
START_DATE = pd.Timestamp('2006-01-03', tz='utc')
END_DATE = pd.Timestamp('2006-01-06', tz='utc')
SIM_PARAMS_CAPITAL_BASE = 1000
ASSET_FINDER_EQUITY_SIDS = (1, 133)
SIM_PARAMS_DATA_FREQUENCY = 'daily'
@classmethod
def make_equity_daily_bar_data(cls):
frame = pd.DataFrame(
{
'open': [90, 95, 100, 105],
'high': [90, 95, 100, 105],
'low': [90, 95, 100, 105],
'close': [90, 95, 100, 105],
'volume': 100,
},
index=cls.equity_daily_bar_days,
)
return ((sid, frame) for sid in cls.asset_finder.equities_sids)
@classmethod
def make_futures_info(cls):
return pd.DataFrame.from_dict(
{
1000: {
'symbol': 'CLF06',
'root_symbol': 'CL',
'start_date': cls.START_DATE,
'end_date': cls.END_DATE,
'auto_close_date': cls.END_DATE + cls.trading_calendar.day,
'exchange': 'CME',
'multiplier': 100,
},
},
orient='index',
)
@classmethod
def make_future_minute_bar_data(cls):
trading_calendar = cls.trading_calendars[Future]
sids = cls.asset_finder.futures_sids
minutes = trading_calendar.minutes_for_sessions_in_range(
cls.future_minute_bar_days[0],
cls.future_minute_bar_days[-1],
)
frame = pd.DataFrame(
{
'open': 2.0,
'high': 2.0,
'low': 2.0,
'close': 2.0,
'volume': 100,
},
index=minutes,
)
return ((sid, frame) for sid in sids)
def test_portfolio_exited_position(self):
# This test ensures ensures that 'phantom' positions do not appear in
# context.portfolio.positions in the case that a position has been
# entered and fully exited.
def initialize(context, sids):
context.ordered = False
context.exited = False
context.sids = sids
def handle_data(context, data):
if not context.ordered:
for s in context.sids:
context.order(context.sid(s), 1)
context.ordered = True
if not context.exited:
amounts = [pos.amount for pos
in itervalues(context.portfolio.positions)]
if (
len(amounts) > 0 and
all([(amount == 1) for amount in amounts])
):
for stock in context.portfolio.positions:
context.order(context.sid(stock), -1)
context.exited = True
# Should be 0 when all positions are exited.
context.record(num_positions=len(context.portfolio.positions))
result = self.run_algorithm(
initialize=initialize,
handle_data=handle_data,
sids=self.ASSET_FINDER_EQUITY_SIDS,
)
expected_position_count = [
0, # Before entering the first position
2, # After entering, exiting on this date
0, # After exiting
0,
]
for i, expected in enumerate(expected_position_count):
self.assertEqual(result.ix[i]['num_positions'], expected)
def test_noop_orders(self):
asset = self.asset_finder.retrieve_asset(1)
# Algorithm that tries to buy with extremely low stops/limits and tries
# to sell with extremely high versions of same. Should not end up with
# any positions for reasonable data.
def handle_data(algo, data):
########
# Buys #
########
# Buy with low limit, shouldn't trigger.
algo.order(asset, 100, limit_price=1)
# But with high stop, shouldn't trigger
algo.order(asset, 100, stop_price=10000000)
# Buy with high limit (should trigger) but also high stop (should
# prevent trigger).
algo.order(asset, 100, limit_price=10000000, stop_price=10000000)
# Buy with low stop (should trigger), but also low limit (should
# prevent trigger).
algo.order(asset, 100, limit_price=1, stop_price=1)
#########
# Sells #
#########
# Sell with high limit, shouldn't trigger.
algo.order(asset, -100, limit_price=1000000)
# Sell with low stop, shouldn't trigger.
algo.order(asset, -100, stop_price=1)
# Sell with low limit (should trigger), but also high stop (should
# prevent trigger).
algo.order(asset, -100, limit_price=1000000, stop_price=1000000)
# Sell with low limit (should trigger), but also low stop (should
# prevent trigger).
algo.order(asset, -100, limit_price=1, stop_price=1)
###################
# Rounding Checks #
###################
algo.order(asset, 100, limit_price=.00000001)
algo.order(asset, -100, stop_price=.00000001)
daily_stats = self.run_algorithm(handle_data=handle_data)
# Verify that positions are empty for all dates.
empty_positions = daily_stats.positions.map(lambda x: len(x) == 0)
self.assertTrue(empty_positions.all())
def test_position_weights(self):
sids = (1, 133, 1000)
equity_1, equity_133, future_1000 = \
self.asset_finder.retrieve_all(sids)
def initialize(algo, sids_and_amounts, *args, **kwargs):
algo.ordered = False
algo.sids_and_amounts = sids_and_amounts
algo.set_commission(
us_equities=PerTrade(0), us_futures=PerTrade(0),
)
algo.set_slippage(
us_equities=FixedSlippage(0),
us_futures=FixedSlippage(0),
)
def handle_data(algo, data):
if not algo.ordered:
for s, amount in algo.sids_and_amounts:
algo.order(algo.sid(s), amount)
algo.ordered = True
algo.record(
position_weights=algo.portfolio.current_portfolio_weights,
)
daily_stats = self.run_algorithm(
sids_and_amounts=zip(sids, [2, -1, 1]),
initialize=initialize,
handle_data=handle_data,
)
expected_position_weights = [
# No positions held on the first day.
pd.Series({}),
# Each equity's position value is its price times the number of
# shares held. In this example, we hold a long position in 2 shares
# of equity_1 so its weight is (95.0 * 2) = 190.0 divided by the
# total portfolio value. The total portfolio value is the sum of
# cash ($905.00) plus the value of all equity positions.
#
# For a futures contract, its weight is the unit price times number
# of shares held times the multiplier. For future_1000, this is
# (2.0 * 1 * 100) = 200.0 divided by total portfolio value.
pd.Series({
equity_1: 190.0 / (190.0 - 95.0 + 905.0),
equity_133: -95.0 / (190.0 - 95.0 + 905.0),
future_1000: 200.0 / (190.0 - 95.0 + 905.0),
}),
pd.Series({
equity_1: 200.0 / (200.0 - 100.0 + 905.0),
equity_133: -100.0 / (200.0 - 100.0 + 905.0),
future_1000: 200.0 / (200.0 - 100.0 + 905.0),
}),
pd.Series({
equity_1: 210.0 / (210.0 - 105.0 + 905.0),
equity_133: -105.0 / (210.0 - 105.0 + 905.0),
future_1000: 200.0 / (210.0 - 105.0 + 905.0),
}),
]
for i, expected in enumerate(expected_position_weights):
assert_equal(daily_stats.iloc[i]['position_weights'], expected)
class TestBeforeTradingStart(zf.WithMakeAlgo, zf.ZiplineTestCase):
START_DATE = pd.Timestamp('2016-01-06', tz='utc')
END_DATE = pd.Timestamp('2016-01-07', tz='utc')
SIM_PARAMS_CAPITAL_BASE = 10000
SIM_PARAMS_DATA_FREQUENCY = 'minute'
EQUITY_DAILY_BAR_LOOKBACK_DAYS = EQUITY_MINUTE_BAR_LOOKBACK_DAYS = 1
DATA_PORTAL_FIRST_TRADING_DAY = pd.Timestamp("2016-01-05", tz='UTC')
EQUITY_MINUTE_BAR_START_DATE = pd.Timestamp("2016-01-05", tz='UTC')
FUTURE_MINUTE_BAR_START_DATE = pd.Timestamp("2016-01-05", tz='UTC')
data_start = ASSET_FINDER_EQUITY_START_DATE = pd.Timestamp(
'2016-01-05',
tz='utc',
)
SPLIT_ASSET_SID = 3
ASSET_FINDER_EQUITY_SIDS = 1, 2, SPLIT_ASSET_SID
@classmethod
def make_equity_minute_bar_data(cls):
asset_minutes = \
cls.trading_calendar.minutes_in_range(
cls.data_start,
cls.END_DATE,
)
minutes_count = len(asset_minutes)
minutes_arr = np.arange(minutes_count) + 1
split_data = pd.DataFrame(
{
'open': minutes_arr + 1,
'high': minutes_arr + 2,
'low': minutes_arr - 1,
'close': minutes_arr,
'volume': 100 * minutes_arr,
},
index=asset_minutes,
)
split_data.iloc[780:] = split_data.iloc[780:] / 2.0
for sid in (1, 8554):
yield sid, create_minute_df_for_asset(
cls.trading_calendar,
cls.data_start,
cls.END_DATE,
)
yield 2, create_minute_df_for_asset(
cls.trading_calendar,
cls.data_start,
cls.END_DATE,
50,
)
yield cls.SPLIT_ASSET_SID, split_data
@classmethod
def make_splits_data(cls):
return pd.DataFrame.from_records([
{
'effective_date': str_to_seconds('2016-01-07'),
'ratio': 0.5,
'sid': cls.SPLIT_ASSET_SID,
}
])
@classmethod
def make_equity_daily_bar_data(cls):
for sid in cls.ASSET_FINDER_EQUITY_SIDS:
yield sid, create_daily_df_for_asset(
cls.trading_calendar,
cls.data_start,
cls.END_DATE,
)
def test_data_in_bts_minute(self):
algo_code = dedent("""
from zipline.api import record, sid
def initialize(context):
context.history_values = []
def before_trading_start(context, data):
record(the_price1=data.current(sid(1), "price"))
record(the_high1=data.current(sid(1), "high"))
record(the_price2=data.current(sid(2), "price"))
record(the_high2=data.current(sid(2), "high"))
context.history_values.append(data.history(
[sid(1), sid(2)],
["price", "high"],
60,
"1m"
))
def handle_data(context, data):
pass
""")
algo = self.make_algo(script=algo_code)
results = algo.run()
# fetching data at midnight gets us the previous market minute's data
self.assertEqual(390, results.iloc[0].the_price1)
self.assertEqual(392, results.iloc[0].the_high1)
# make sure that price is ffilled, but not other fields
self.assertEqual(350, results.iloc[0].the_price2)
self.assertTrue(np.isnan(results.iloc[0].the_high2))
# 10-minute history
# asset1 day1 price should be 331-390
np.testing.assert_array_equal(
range(331, 391), algo.history_values[0]["price"][1]
)
# asset1 day1 high should be 333-392
np.testing.assert_array_equal(
range(333, 393), algo.history_values[0]["high"][1]
)
# asset2 day1 price should be 19 300s, then 40 350s
np.testing.assert_array_equal(
[300] * 19, algo.history_values[0]["price"][2][0:19]
)
np.testing.assert_array_equal(
[350] * 40, algo.history_values[0]["price"][2][20:]
)
# asset2 day1 high should be all NaNs except for the 19th item
# = 2016-01-05 20:20:00+00:00
np.testing.assert_array_equal(
np.full(19, np.nan), algo.history_values[0]["high"][2][0:19]
)
self.assertEqual(352, algo.history_values[0]["high"][2][19])
np.testing.assert_array_equal(
np.full(40, np.nan), algo.history_values[0]["high"][2][20:]
)
def test_data_in_bts_daily(self):
algo_code = dedent("""
from zipline.api import record, sid
def initialize(context):
context.history_values = []
def before_trading_start(context, data):
record(the_price1=data.current(sid(1), "price"))
record(the_high1=data.current(sid(1), "high"))
record(the_price2=data.current(sid(2), "price"))
record(the_high2=data.current(sid(2), "high"))
context.history_values.append(data.history(
[sid(1), sid(2)],
["price", "high"],
1,
"1d",
))
def handle_data(context, data):
pass
""")
algo = self.make_algo(script=algo_code)
results = algo.run()
self.assertEqual(392, results.the_high1[0])
self.assertEqual(390, results.the_price1[0])
# nan because asset2 only trades every 50 minutes
self.assertTrue(np.isnan(results.the_high2[0]))
self.assertTrue(350, results.the_price2[0])
self.assertEqual(392, algo.history_values[0]["high"][1][0])
self.assertEqual(390, algo.history_values[0]["price"][1][0])
self.assertEqual(352, algo.history_values[0]["high"][2][0])
self.assertEqual(350, algo.history_values[0]["price"][2][0])
def test_portfolio_bts(self):
algo_code = dedent("""
from zipline.api import order, sid, record
def initialize(context):
context.ordered = False
context.hd_portfolio = context.portfolio
def before_trading_start(context, data):
bts_portfolio = context.portfolio
# Assert that the portfolio in BTS is the same as the last
# portfolio in handle_data
assert (context.hd_portfolio == bts_portfolio)
record(pos_value=bts_portfolio.positions_value)
def handle_data(context, data):
if not context.ordered:
order(sid(1), 1)
context.ordered = True
context.hd_portfolio = context.portfolio
""")
algo = self.make_algo(script=algo_code)
results = algo.run()
# Asset starts with price 1 on 1/05 and increases by 1 every minute.
# Simulation starts on 1/06, where the price in bts is 390, and
# positions_value is 0. On 1/07, price is 780, and after buying one
# share on the first bar of 1/06, positions_value is 780
self.assertEqual(results.pos_value.iloc[0], 0)
self.assertEqual(results.pos_value.iloc[1], 780)
def test_account_bts(self):
algo_code = dedent("""
from zipline.api import order, sid, record, set_slippage, slippage
def initialize(context):
context.ordered = False
context.hd_account = context.account
set_slippage(slippage.VolumeShareSlippage())
def before_trading_start(context, data):
bts_account = context.account
# Assert that the account in BTS is the same as the last account
# in handle_data
assert (context.hd_account == bts_account)
record(port_value=context.account.equity_with_loan)
def handle_data(context, data):
if not context.ordered:
order(sid(1), 1)
context.ordered = True
context.hd_account = context.account
""")
algo = self.make_algo(script=algo_code)
results = algo.run()
# Starting portfolio value is 10000. Order for the asset fills on the
# second bar of 1/06, where the price is 391, and costs the default
# commission of 0. On 1/07, the price is 780, and the increase in
# portfolio value is 780-392-0
self.assertEqual(results.port_value.iloc[0], 10000)
self.assertAlmostEqual(results.port_value.iloc[1],
10000 + 780 - 392 - 0,
places=2)
def test_portfolio_bts_with_overnight_split(self):
algo_code = dedent("""
from zipline.api import order, sid, record
def initialize(context):
context.ordered = False
context.hd_portfolio = context.portfolio
def before_trading_start(context, data):
bts_portfolio = context.portfolio
# Assert that the portfolio in BTS is the same as the last
# portfolio in handle_data, except for the positions
for k in bts_portfolio.__dict__:
if k != 'positions':
assert (context.hd_portfolio.__dict__[k]
== bts_portfolio.__dict__[k])
record(pos_value=bts_portfolio.positions_value)
record(pos_amount=bts_portfolio.positions[sid(3)].amount)
record(
last_sale_price=bts_portfolio.positions[sid(3)].last_sale_price
)
def handle_data(context, data):
if not context.ordered:
order(sid(3), 1)
context.ordered = True
context.hd_portfolio = context.portfolio
""")
results = self.run_algorithm(script=algo_code)
# On 1/07, positions value should by 780, same as without split
self.assertEqual(results.pos_value.iloc[0], 0)
self.assertEqual(results.pos_value.iloc[1], 780)
# On 1/07, after applying the split, 1 share becomes 2
self.assertEqual(results.pos_amount.iloc[0], 0)
self.assertEqual(results.pos_amount.iloc[1], 2)
# On 1/07, after applying the split, last sale price is halved
self.assertEqual(results.last_sale_price.iloc[0], 0)
self.assertEqual(results.last_sale_price.iloc[1], 390)
def test_account_bts_with_overnight_split(self):
algo_code = dedent("""
from zipline.api import order, sid, record, set_slippage, slippage
def initialize(context):
context.ordered = False
context.hd_account = context.account
set_slippage(slippage.VolumeShareSlippage())
def before_trading_start(context, data):
bts_account = context.account
# Assert that the account in BTS is the same as the last account
# in handle_data
assert (context.hd_account == bts_account)
record(port_value=bts_account.equity_with_loan)
def handle_data(context, data):
if not context.ordered:
order(sid(1), 1)
context.ordered = True
context.hd_account = context.account
""")
results = self.run_algorithm(script=algo_code)
# On 1/07, portfolio value is the same as without split
self.assertEqual(results.port_value.iloc[0], 10000)
self.assertAlmostEqual(results.port_value.iloc[1],
10000 + 780 - 392 - 0, places=2)
class TestAlgoScript(zf.WithMakeAlgo, zf.ZiplineTestCase):
START_DATE = pd.Timestamp('2006-01-03', tz='utc')
END_DATE = pd.Timestamp('2006-12-31', tz='utc')
SIM_PARAMS_DATA_FREQUENCY = 'daily'
DATA_PORTAL_USE_MINUTE_DATA = False
EQUITY_DAILY_BAR_LOOKBACK_DAYS = 5 # max history window length
STRING_TYPE_NAMES = [s.__name__ for s in string_types]
STRING_TYPE_NAMES_STRING = ', '.join(STRING_TYPE_NAMES)
ASSET_TYPE_NAME = Asset.__name__
CONTINUOUS_FUTURE_NAME = ContinuousFuture.__name__
ASSET_OR_STRING_TYPE_NAMES = ', '.join([ASSET_TYPE_NAME] +
STRING_TYPE_NAMES)
ASSET_OR_STRING_OR_CF_TYPE_NAMES = ', '.join([ASSET_TYPE_NAME,
CONTINUOUS_FUTURE_NAME] +
STRING_TYPE_NAMES)
ARG_TYPE_TEST_CASES = (
('history__assets', (bad_type_history_assets,
ASSET_OR_STRING_OR_CF_TYPE_NAMES,
True)),
('history__fields', (bad_type_history_fields,
STRING_TYPE_NAMES_STRING,
True)),
('history__bar_count', (bad_type_history_bar_count, 'int', False)),
('history__frequency', (bad_type_history_frequency,
STRING_TYPE_NAMES_STRING,
False)),
('current__assets', (bad_type_current_assets,
ASSET_OR_STRING_OR_CF_TYPE_NAMES,
True)),
('current__fields', (bad_type_current_fields,
STRING_TYPE_NAMES_STRING,
True)),
('is_stale__assets', (bad_type_is_stale_assets, 'Asset', True)),
('can_trade__assets', (bad_type_can_trade_assets, 'Asset', True)),
('history_kwarg__assets',
(bad_type_history_assets_kwarg,
ASSET_OR_STRING_OR_CF_TYPE_NAMES,
True)),
('history_kwarg_bad_list__assets',
(bad_type_history_assets_kwarg_list,
ASSET_OR_STRING_OR_CF_TYPE_NAMES,
True)),
('history_kwarg__fields',
(bad_type_history_fields_kwarg, STRING_TYPE_NAMES_STRING, True)),
('history_kwarg__bar_count',
(bad_type_history_bar_count_kwarg, 'int', False)),
('history_kwarg__frequency',
(bad_type_history_frequency_kwarg, STRING_TYPE_NAMES_STRING, False)),
('current_kwarg__assets',
(bad_type_current_assets_kwarg,
ASSET_OR_STRING_OR_CF_TYPE_NAMES,
True)),
('current_kwarg__fields',
(bad_type_current_fields_kwarg, STRING_TYPE_NAMES_STRING, True)),
)
sids = 0, 1, 3, 133
# FIXME: Pass a benchmark explicitly here.
BENCHMARK_SID = None
@classmethod
def make_equity_info(cls):
register_calendar("TEST", get_calendar("NYSE"), force=True)
data = make_simple_equity_info(
cls.sids,
cls.START_DATE,
cls.END_DATE,
)
data.loc[3, 'symbol'] = 'TEST'
return data
@classmethod
def make_equity_daily_bar_data(cls):
cal = cls.trading_calendars[Equity]
sessions = cal.sessions_in_range(cls.START_DATE, cls.END_DATE)
frame = pd.DataFrame({
'close': 10., 'high': 10.5, 'low': 9.5, 'open': 10., 'volume': 100,
}, index=sessions)
for sid in cls.sids:
yield sid, frame
def test_noop(self):
self.run_algorithm(
initialize=initialize_noop,
handle_data=handle_data_noop,
)
def test_noop_string(self):
self.run_algorithm(script=noop_algo)
def test_no_handle_data(self):
self.run_algorithm(script=no_handle_data)
def test_api_calls(self):
self.run_algorithm(
initialize=initialize_api,
handle_data=handle_data_api,
)
def test_api_calls_string(self):
self.run_algorithm(script=api_algo)
def test_api_get_environment(self):
platform = 'zipline'
algo = self.make_algo(
script=api_get_environment_algo,
platform=platform,
)
algo.run()
self.assertEqual(algo.environment, platform)
def test_api_symbol(self):
self.run_algorithm(script=api_symbol_algo)
def test_fixed_slippage(self):
# verify order -> transaction -> portfolio position.
# --------------
test_algo = self.make_algo(
script="""
from zipline.api import (slippage,
commission,
set_slippage,
set_commission,
order,
record,
sid)
def initialize(context):
model = slippage.FixedSlippage(spread=0.10)
set_slippage(model)
set_commission(commission.PerTrade(100.00))
context.count = 1
context.incr = 0
def handle_data(context, data):
if context.incr < context.count:
order(sid(0), -1000)
record(price=data.current(sid(0), "price"))
context.incr += 1""",
)
results = test_algo.run()
# flatten the list of txns
all_txns = [val for sublist in results["transactions"].tolist()
for val in sublist]
self.assertEqual(len(all_txns), 1)
txn = all_txns[0]
expected_spread = 0.05
expected_price = test_algo.recorded_vars["price"] - expected_spread
self.assertEqual(expected_price, txn['price'])
# make sure that the $100 commission was applied to our cash
# the txn was for -1000 shares at 9.95, means -9.95k. our capital_used
# for that day was therefore 9.95k, but after the $100 commission,
# it should be 9.85k.
self.assertEqual(9850, results.capital_used[1])
self.assertEqual(100, results["orders"].iloc[1][0]["commission"])
@parameterized.expand(
[
('no_minimum_commission', 0,),
('default_minimum_commission', 0,),
('alternate_minimum_commission', 2,),
]
)
def test_volshare_slippage(self, name, minimum_commission):
tempdir = TempDirectory()
try:
if name == "default_minimum_commission":
commission_line = "set_commission(commission.PerShare(0.02))"
else:
commission_line = \
"set_commission(commission.PerShare(0.02, " \
"min_trade_cost={0}))".format(minimum_commission)
# verify order -> transaction -> portfolio position.
# --------------
test_algo = self.make_algo(
script="""
from zipline.api import *
def initialize(context):
model = slippage.VolumeShareSlippage(
volume_limit=.3,
price_impact=0.05
)
set_slippage(model)
{0}
context.count = 2
context.incr = 0
def handle_data(context, data):
if context.incr < context.count:
# order small lots to be sure the
# order will fill in a single transaction
order(sid(0), 5000)
record(price=data.current(sid(0), "price"))
record(volume=data.current(sid(0), "volume"))
record(incr=context.incr)
context.incr += 1
""".format(commission_line),
)
trades = factory.create_daily_trade_source(
[0], self.sim_params, self.env, self.trading_calendar)
data_portal = create_data_portal_from_trade_history(
self.env.asset_finder, self.trading_calendar, tempdir,
self.sim_params, {0: trades})
results = test_algo.run(data_portal)
all_txns = [
val for sublist in results["transactions"].tolist()
for val in sublist]
self.assertEqual(len(all_txns), 67)
# all_orders are all the incremental versions of the
# orders as each new fill comes in.
all_orders = list(toolz.concat(results['orders']))
if minimum_commission == 0:
# for each incremental version of each order, the commission
# should be its filled amount * 0.02
for order_ in all_orders:
self.assertAlmostEqual(
order_["filled"] * 0.02,
order_["commission"]
)
else:
# the commission should be at least the min_trade_cost
for order_ in all_orders:
if order_["filled"] > 0:
self.assertAlmostEqual(
max(order_["filled"] * 0.02, minimum_commission),
order_["commission"]
)
else:
self.assertEqual(0, order_["commission"])
finally:
tempdir.cleanup()
def test_incorrectly_set_futures_slippage_model(self):
code = dedent(
"""
from zipline.api import set_slippage, slippage
class MySlippage(slippage.FutureSlippageModel):
def process_order(self, data, order):
return data.current(order.asset, 'price'), order.amount
def initialize(context):
set_slippage(MySlippage())
"""
)
test_algo = self.make_algo(script=code)
with self.assertRaises(IncompatibleSlippageModel):
# Passing a futures slippage model as the first argument, which is
# for setting equity models, should fail.
test_algo.run()
def test_algo_record_vars(self):
test_algo = self.make_algo(script=record_variables)
results = test_algo.run(self.data_portal)
for i in range(1, 252):
self.assertEqual(results.iloc[i-1]["incr"], i)
def test_algo_record_nan(self):
test_algo = self.make_algo(script=record_float_magic % 'nan')
results = test_algo.run()
for i in range(1, 252):
self.assertTrue(np.isnan(results.iloc[i-1]["data"]))
def test_batch_market_order_matches_multiple_manual_orders(self):
share_counts = pd.Series([50, 100])
multi_blotter = RecordBatchBlotter(self.SIM_PARAMS_DATA_FREQUENCY)
multi_test_algo = self.make_algo(
script=dedent("""\
from collections import OrderedDict
from six import iteritems
from zipline.api import sid, order
def initialize(context):
context.assets = [sid(0), sid(3)]
context.placed = False
def handle_data(context, data):
if not context.placed:
it = zip(context.assets, {share_counts})
for asset, shares in it:
order(asset, shares)
context.placed = True
""").format(share_counts=list(share_counts)),
blotter=multi_blotter,
)
multi_stats = multi_test_algo.run(self.data_portal)
self.assertFalse(multi_blotter.order_batch_called)
batch_blotter = RecordBatchBlotter(self.SIM_PARAMS_DATA_FREQUENCY)
batch_test_algo = self.make_algo(
script=dedent("""\
import pandas as pd
from zipline.api import sid, batch_market_order
def initialize(context):
context.assets = [sid(0), sid(3)]
context.placed = False
def handle_data(context, data):
if not context.placed:
orders = batch_market_order(pd.Series(
index=context.assets, data={share_counts}
))
assert len(orders) == 2, \
"len(orders) was %s but expected 2" % len(orders)
for o in orders:
assert o is not None, "An order is None"
context.placed = True
""").format(share_counts=list(share_counts)),
blotter=batch_blotter,
)
batch_stats = batch_test_algo.run()
self.assertTrue(batch_blotter.order_batch_called)
for stats in (multi_stats, batch_stats):
stats.orders = stats.orders.apply(
lambda orders: [toolz.dissoc(o, 'id') for o in orders]
)
stats.transactions = stats.transactions.apply(
lambda txns: [toolz.dissoc(txn, 'order_id') for txn in txns]
)
assert_equal(multi_stats, batch_stats)
def test_batch_market_order_filters_null_orders(self):
share_counts = [50, 0]
batch_blotter = RecordBatchBlotter(self.SIM_PARAMS_DATA_FREQUENCY)
batch_test_algo = self.make_algo(
script=dedent("""\
import pandas as pd
from zipline.api import sid, batch_market_order
def initialize(context):
context.assets = [sid(0), sid(3)]
context.placed = False
def handle_data(context, data):
if not context.placed:
orders = batch_market_order(pd.Series(
index=context.assets, data={share_counts}
))
assert len(orders) == 1, \
"len(orders) was %s but expected 1" % len(orders)
for o in orders:
assert o is not None, "An order is None"
context.placed = True
""").format(share_counts=share_counts),
blotter=batch_blotter,
)
batch_test_algo.run()
self.assertTrue(batch_blotter.order_batch_called)
def test_order_dead_asset(self):
# after asset 0 is dead
params = SimulationParameters(
start_session=pd.Timestamp("2007-01-03", tz='UTC'),
end_session=pd.Timestamp("2007-01-05", tz='UTC'),
trading_calendar=self.trading_calendar,
)
# order method shouldn't blow up
self.run_algorithm(
script="""
from zipline.api import order, sid
def initialize(context):
pass
def handle_data(context, data):
order(sid(0), 10)
""",
)
# order_value and order_percent should blow up
for order_str in ["order_value", "order_percent"]:
test_algo = self.make_algo(
script="""
from zipline.api import order_percent, order_value, sid
def initialize(context):
pass
def handle_data(context, data):
{0}(sid(0), 10)
""".format(order_str),
sim_params=params,
)
with self.assertRaises(CannotOrderDelistedAsset):
test_algo.run()
def test_portfolio_in_init(self):
"""
Test that accessing portfolio in init doesn't break.
"""
self.run_algorithm(script=access_portfolio_in_init)
def test_account_in_init(self):
"""
Test that accessing account in init doesn't break.
"""
self.run_algorithm(script=access_account_in_init)
def test_without_kwargs(self):
"""
Test that api methods on the data object can be called with positional
arguments.
"""
params = SimulationParameters(
start_session=pd.Timestamp("2006-01-10", tz='UTC'),
end_session=pd.Timestamp("2006-01-11", tz='UTC'),
trading_calendar=self.trading_calendar,
)
self.run_algorithm(sim_params=params, script=call_without_kwargs)
def test_good_kwargs(self):
"""
Test that api methods on the data object can be called with keyword
arguments.
"""
params = SimulationParameters(
start_session=pd.Timestamp("2006-01-10", tz='UTC'),
end_session=pd.Timestamp("2006-01-11", tz='UTC'),
trading_calendar=self.trading_calendar,
)
self.run_algorithm(script=call_with_kwargs, sim_params=params)
@parameterized.expand([('history', call_with_bad_kwargs_history),
('current', call_with_bad_kwargs_current)])
def test_bad_kwargs(self, name, algo_text):
"""
Test that api methods on the data object called with bad kwargs return
a meaningful TypeError that we create, rather than an unhelpful cython
error
"""
algo = self.make_algo(script=algo_text)
with self.assertRaises(TypeError) as cm:
algo.run()
self.assertEqual("%s() got an unexpected keyword argument 'blahblah'"
% name, cm.exception.args[0])
@parameterized.expand(ARG_TYPE_TEST_CASES)
def test_arg_types(self, name, inputs):
keyword = name.split('__')[1]
algo = self.make_algo(script=inputs[0])
with self.assertRaises(TypeError) as cm:
algo.run()
expected = "Expected %s argument to be of type %s%s" % (
keyword,
'or iterable of type ' if inputs[2] else '',
inputs[1]
)
self.assertEqual(expected, cm.exception.args[0])
def test_empty_asset_list_to_history(self):
params = SimulationParameters(
start_session=pd.Timestamp("2006-01-10", tz='UTC'),
end_session=pd.Timestamp("2006-01-11", tz='UTC'),
trading_calendar=self.trading_calendar,
)
self.run_algorithm(
script=dedent("""
def initialize(context):
pass
def handle_data(context, data):
data.history([], "price", 5, '1d')
"""),
sim_params=params,
)
@parameterized.expand(
[('bad_kwargs', call_with_bad_kwargs_get_open_orders),
('good_kwargs', call_with_good_kwargs_get_open_orders),
('no_kwargs', call_with_no_kwargs_get_open_orders)]
)
def test_get_open_orders_kwargs(self, name, script):
algo = self.make_algo(script=script)
if name == 'bad_kwargs':
with self.assertRaises(TypeError) as cm:
algo.run(self.data_portal)
self.assertEqual('Keyword argument `sid` is no longer '
'supported for get_open_orders. Use `asset` '
'instead.', cm.exception.args[0])
else:
algo.run(self.data_portal)
def test_empty_positions(self):
"""
Test that when we try context.portfolio.positions[stock] on a stock
for which we have no positions, we return a Position with values 0
(but more importantly, we don't crash) and don't save this Position
to the user-facing dictionary PositionTracker._positions_store
"""
results = self.run_algorithm(script=empty_positions)
num_positions = results.num_positions
amounts = results.amounts
self.assertTrue(all(num_positions == 0))
self.assertTrue(all(amounts == 0))
def test_schedule_function_time_rule_positionally_misplaced(self):
"""
Test that when a user specifies a time rule for the date_rule argument,
but no rule in the time_rule argument
(e.g. schedule_function(func, <time_rule>)), we assume that means
assign a time rule but no date rule
"""
sim_params = factory.create_simulation_parameters(
start=pd.Timestamp('2006-01-12', tz='UTC'),
end=pd.Timestamp('2006-01-13', tz='UTC'),
data_frequency='minute'
)
algocode = dedent("""
from zipline.api import time_rules, schedule_function
def do_at_open(context, data):
context.done_at_open.append(context.get_datetime())
def do_at_close(context, data):
context.done_at_close.append(context.get_datetime())
def initialize(context):
context.done_at_open = []
context.done_at_close = []
schedule_function(do_at_open, time_rules.market_open())
schedule_function(do_at_close, time_rules.market_close())
def handle_data(algo, data):
pass
""")
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("ignore", PerformanceWarning)
algo = self.make_algo(script=algocode, sim_params=sim_params)
algo.run()
self.assertEqual(len(w), 2)
for i, warning in enumerate(w):
self.assertIsInstance(warning.message, UserWarning)
self.assertEqual(
warning.message.args[0],
'Got a time rule for the second positional argument '
'date_rule. You should use keyword argument '
'time_rule= when calling schedule_function without '
'specifying a date_rule'
)
# The warnings come from line 13 and 14 in the algocode
self.assertEqual(warning.lineno, 13 + i)
self.assertEqual(
algo.done_at_open,
[pd.Timestamp('2006-01-12 14:31:00', tz='UTC'),
pd.Timestamp('2006-01-13 14:31:00', tz='UTC')]
)
self.assertEqual(
algo.done_at_close,
[pd.Timestamp('2006-01-12 20:59:00', tz='UTC'),
pd.Timestamp('2006-01-13 20:59:00', tz='UTC')]
)
class TestCapitalChanges(zf.WithMakeAlgo, zf.ZiplineTestCase):
START_DATE = pd.Timestamp('2006-01-03', tz='UTC')
END_DATE = pd.Timestamp('2006-01-09', tz='UTC')
# XXX: This suite only has daily data for sid 0 and only has minutely data
# for sid 1.
sids = ASSET_FINDER_EQUITY_SIDS = (0, 1)
DAILY_SID = 0
MINUTELY_SID = 1
# FIXME: Pass a benchmark source explicitly here.
BENCHMARK_SID = None
@classmethod
def make_equity_minute_bar_data(cls):
minutes = cls.trading_calendar.minutes_in_range(
cls.START_DATE,
cls.END_DATE,
)
closes = np.arange(100, 100 + len(minutes), 1)
opens = closes
highs = closes + 5
lows = closes - 5
frame = pd.DataFrame(
index=minutes,
data={
'open': opens,
'high': highs,
'low': lows,
'close': closes,
'volume': 10000,
},
)
yield cls.MINUTELY_SID, frame
@classmethod
def make_equity_daily_bar_data(cls):
days = cls.trading_calendar.sessions_in_range(
cls.START_DATE,
cls.END_DATE,
)
closes = np.arange(10.0, 10.0 + len(days), 1.0)
opens = closes
highs = closes + 0.5
lows = closes - 0.5
frame = pd.DataFrame(
index=days,
data={
'open': opens,
'high': highs,
'low': lows,
'close': closes,
'volume': 10000,
},
)
yield cls.DAILY_SID, frame
@parameterized.expand([
('target', 151000.0), ('delta', 50000.0)
])
def test_capital_changes_daily_mode(self, change_type, value):
capital_changes = {
pd.Timestamp('2006-01-06', tz='UTC'):
{'type': change_type, 'value': value}
}
algocode = """
from zipline.api import set_slippage, set_commission, slippage, commission, \
schedule_function, time_rules, order, sid
def initialize(context):
set_slippage(slippage.FixedSlippage(spread=0))
set_commission(commission.PerShare(0, 0))
schedule_function(order_stuff, time_rule=time_rules.market_open())
def order_stuff(context, data):
order(sid(0), 1000)
"""
algo = self.make_algo(
script=algocode,
capital_changes=capital_changes,
sim_params=SimulationParameters(
start_session=self.START_DATE,
end_session=self.END_DATE,
trading_calendar=self.nyse_calendar,
)
)
# We call get_generator rather than `run()` here because we care about
# the raw capital change packets.
gen = algo.get_generator()
results = list(gen)
cumulative_perf = \
[r['cumulative_perf'] for r in results if 'cumulative_perf' in r]
daily_perf = [r['daily_perf'] for r in results if 'daily_perf' in r]
capital_change_packets = \
[r['capital_change'] for r in results if 'capital_change' in r]
self.assertEqual(len(capital_change_packets), 1)
self.assertEqual(
capital_change_packets[0],
{'date': pd.Timestamp('2006-01-06', tz='UTC'),
'type': 'cash',
'target': 151000.0 if change_type == 'target' else None,
'delta': 50000.0})
# 1/03: price = 10, place orders
# 1/04: orders execute at price = 11, place orders
# 1/05: orders execute at price = 12, place orders
# 1/06: +50000 capital change,
# orders execute at price = 13, place orders
# 1/09: orders execute at price = 14, place orders
expected_daily = {}
expected_capital_changes = np.array([
0.0, 0.0, 0.0, 50000.0, 0.0
])
# Day 1, no transaction. Day 2, we transact, but the price of our stock
# does not change. Day 3, we start getting returns
expected_daily['returns'] = np.array([
0.0,
0.0,
# 1000 shares * gain of 1
(100000.0 + 1000.0) / 100000.0 - 1.0,
# 2000 shares * gain of 1, capital change of +50000
(151000.0 + 2000.0) / 151000.0 - 1.0,
# 3000 shares * gain of 1
(153000.0 + 3000.0) / 153000.0 - 1.0,
])
expected_daily['pnl'] = np.array([
0.0,
0.0,
1000.00, # 1000 shares * gain of 1
2000.00, # 2000 shares * gain of 1
3000.00, # 3000 shares * gain of 1
])
expected_daily['capital_used'] = np.array([
0.0,
-11000.0, # 1000 shares at price = 11
-12000.0, # 1000 shares at price = 12
-13000.0, # 1000 shares at price = 13
-14000.0, # 1000 shares at price = 14
])
expected_daily['ending_cash'] = \
np.array([100000.0] * 5) + \
np.cumsum(expected_capital_changes) + \
np.cumsum(expected_daily['capital_used'])
expected_daily['starting_cash'] = \
expected_daily['ending_cash'] - \
expected_daily['capital_used']
expected_daily['starting_value'] = np.array([
0.0,
0.0,
11000.0, # 1000 shares at price = 11
24000.0, # 2000 shares at price = 12
39000.0, # 3000 shares at price = 13
])
expected_daily['ending_value'] = \
expected_daily['starting_value'] + \
expected_daily['pnl'] - \
expected_daily['capital_used']
expected_daily['portfolio_value'] = \
expected_daily['ending_value'] + \
expected_daily['ending_cash']
stats = [
'returns', 'pnl', 'capital_used', 'starting_cash', 'ending_cash',
'starting_value', 'ending_value', 'portfolio_value'
]
expected_cumulative = {
'returns': np.cumprod(expected_daily['returns'] + 1) - 1,
'pnl': np.cumsum(expected_daily['pnl']),
'capital_used': np.cumsum(expected_daily['capital_used']),
'starting_cash':
np.repeat(expected_daily['starting_cash'][0:1], 5),
'ending_cash': expected_daily['ending_cash'],
'starting_value':
np.repeat(expected_daily['starting_value'][0:1], 5),
'ending_value': expected_daily['ending_value'],
'portfolio_value': expected_daily['portfolio_value'],
}
for stat in stats:
np.testing.assert_array_almost_equal(
np.array([perf[stat] for perf in daily_perf]),
expected_daily[stat],
err_msg='daily ' + stat,
)
np.testing.assert_array_almost_equal(
np.array([perf[stat] for perf in cumulative_perf]),
expected_cumulative[stat],
err_msg='cumulative ' + stat,
)
self.assertEqual(
algo.capital_change_deltas,
{pd.Timestamp('2006-01-06', tz='UTC'): 50000.0}
)
@parameterized.expand([
('interday_target', [('2006-01-04', 2388.0)]),
('interday_delta', [('2006-01-04', 1000.0)]),
('intraday_target', [('2006-01-04 17:00', 2184.0),
('2006-01-04 18:00', 2804.0)]),
('intraday_delta', [('2006-01-04 17:00', 500.0),
('2006-01-04 18:00', 500.0)]),
])
def test_capital_changes_minute_mode_daily_emission(self, change, values):
change_loc, change_type = change.split('_')
sim_params = SimulationParameters(
start_session=pd.Timestamp('2006-01-03', tz='UTC'),
end_session=pd.Timestamp('2006-01-05', tz='UTC'),
data_frequency='minute',
capital_base=1000.0,
trading_calendar=self.nyse_calendar,
)
capital_changes = {
pd.Timestamp(datestr, tz='UTC'): {
'type': change_type,
'value': value
}
for datestr, value in values
}
algocode = """
from zipline.api import set_slippage, set_commission, slippage, commission, \
schedule_function, time_rules, order, sid
def initialize(context):
set_slippage(slippage.FixedSlippage(spread=0))
set_commission(commission.PerShare(0, 0))
schedule_function(order_stuff, time_rule=time_rules.market_open())
def order_stuff(context, data):
order(sid(1), 1)
"""
algo = self.make_algo(
script=algocode,
sim_params=sim_params,
capital_changes=capital_changes
)
gen = algo.get_generator()
results = list(gen)
cumulative_perf = \
[r['cumulative_perf'] for r in results if 'cumulative_perf' in r]
daily_perf = [r['daily_perf'] for r in results if 'daily_perf' in r]
capital_change_packets = \
[r['capital_change'] for r in results if 'capital_change' in r]
self.assertEqual(len(capital_change_packets), len(capital_changes))
expected = [
{'date': pd.Timestamp(val[0], tz='UTC'),
'type': 'cash',
'target': val[1] if change_type == 'target' else None,
'delta': 1000.0 if len(values) == 1 else 500.0}
for val in values]
self.assertEqual(capital_change_packets, expected)
# 1/03: place orders at price = 100, execute at 101
# 1/04: place orders at price = 490, execute at 491,
# +500 capital change at 17:00 and 18:00 (intraday)
# or +1000 at 00:00 (interday),
# 1/05: place orders at price = 880, execute at 881
expected_daily = {}
expected_capital_changes = np.array([0.0, 1000.0, 0.0])
if change_loc == 'intraday':
# Fills at 491, +500 capital change comes at 638 (17:00) and
# 698 (18:00), ends day at 879
day2_return = (
(1388.0 + 149.0 + 147.0) / 1388.0 *
(2184.0 + 60.0 + 60.0) / 2184.0 *
(2804.0 + 181.0 + 181.0) / 2804.0 - 1.0
)
else:
# Fills at 491, ends day at 879, capital change +1000
day2_return = (2388.0 + 390.0 + 388.0) / 2388.0 - 1
expected_daily['returns'] = np.array([
# Fills at 101, ends day at 489
(1000.0 + 489 - 101) / 1000.0 - 1.0,
day2_return,
# Fills at 881, ends day at 1269
(3166.0 + 390.0 + 390.0 + 388.0) / 3166.0 - 1.0,
])
expected_daily['pnl'] = np.array([
388.0,
390.0 + 388.0,
390.0 + 390.0 + 388.0,
])
expected_daily['capital_used'] = np.array([
-101.0, -491.0, -881.0
])
expected_daily['ending_cash'] = \
np.array([1000.0] * 3) + \
np.cumsum(expected_capital_changes) + \
np.cumsum(expected_daily['capital_used'])
expected_daily['starting_cash'] = \
expected_daily['ending_cash'] - \
expected_daily['capital_used']
if change_loc == 'intraday':
# Capital changes come after day start
expected_daily['starting_cash'] -= expected_capital_changes
expected_daily['starting_value'] = np.array([
0.0, 489.0, 879.0 * 2
])
expected_daily['ending_value'] = \
expected_daily['starting_value'] + \
expected_daily['pnl'] - \
expected_daily['capital_used']
expected_daily['portfolio_value'] = \
expected_daily['ending_value'] + \
expected_daily['ending_cash']
stats = [
'returns', 'pnl', 'capital_used', 'starting_cash', 'ending_cash',
'starting_value', 'ending_value', 'portfolio_value'
]
expected_cumulative = {
'returns': np.cumprod(expected_daily['returns'] + 1) - 1,
'pnl': np.cumsum(expected_daily['pnl']),
'capital_used': np.cumsum(expected_daily['capital_used']),
'starting_cash':
np.repeat(expected_daily['starting_cash'][0:1], 3),
'ending_cash': expected_daily['ending_cash'],
'starting_value':
np.repeat(expected_daily['starting_value'][0:1], 3),
'ending_value': expected_daily['ending_value'],
'portfolio_value': expected_daily['portfolio_value'],
}
for stat in stats:
np.testing.assert_array_almost_equal(
np.array([perf[stat] for perf in daily_perf]),
expected_daily[stat]
)
np.testing.assert_array_almost_equal(
np.array([perf[stat] for perf in cumulative_perf]),
expected_cumulative[stat]
)
if change_loc == 'interday':
self.assertEqual(
algo.capital_change_deltas,
{pd.Timestamp('2006-01-04', tz='UTC'): 1000.0}
)
else:
self.assertEqual(
algo.capital_change_deltas,
{pd.Timestamp('2006-01-04 17:00', tz='UTC'): 500.0,
pd.Timestamp('2006-01-04 18:00', tz='UTC'): 500.0}
)
@parameterized.expand([
('interday_target', [('2006-01-04', 2388.0)]),
('interday_delta', [('2006-01-04', 1000.0)]),
('intraday_target', [('2006-01-04 17:00', 2184.0),
('2006-01-04 18:00', 2804.0)]),
('intraday_delta', [('2006-01-04 17:00', 500.0),
('2006-01-04 18:00', 500.0)]),
])
def test_capital_changes_minute_mode_minute_emission(self, change, values):
change_loc, change_type = change.split('_')
sim_params = SimulationParameters(
start_session=pd.Timestamp('2006-01-03', tz='UTC'),
end_session=pd.Timestamp('2006-01-05', tz='UTC'),
data_frequency='minute',
emission_rate='minute',
capital_base=1000.0,
trading_calendar=self.nyse_calendar,
)
capital_changes = {pd.Timestamp(val[0], tz='UTC'): {
'type': change_type, 'value': val[1]} for val in values}
algocode = """
from zipline.api import set_slippage, set_commission, slippage, commission, \
schedule_function, time_rules, order, sid
def initialize(context):
set_slippage(slippage.FixedSlippage(spread=0))
set_commission(commission.PerShare(0, 0))
schedule_function(order_stuff, time_rule=time_rules.market_open())
def order_stuff(context, data):
order(sid(1), 1)
"""
algo = self.make_algo(
script=algocode,
sim_params=sim_params,
capital_changes=capital_changes
)
gen = algo.get_generator()
results = list(gen)
cumulative_perf = \
[r['cumulative_perf'] for r in results if 'cumulative_perf' in r]
minute_perf = [r['minute_perf'] for r in results if 'minute_perf' in r]
daily_perf = [r['daily_perf'] for r in results if 'daily_perf' in r]
capital_change_packets = \
[r['capital_change'] for r in results if 'capital_change' in r]
self.assertEqual(len(capital_change_packets), len(capital_changes))
expected = [
{'date': pd.Timestamp(val[0], tz='UTC'),
'type': 'cash',
'target': val[1] if change_type == 'target' else None,
'delta': 1000.0 if len(values) == 1 else 500.0}
for val in values]
self.assertEqual(capital_change_packets, expected)
# 1/03: place orders at price = 100, execute at 101
# 1/04: place orders at price = 490, execute at 491,
# +500 capital change at 17:00 and 18:00 (intraday)
# or +1000 at 00:00 (interday),
# 1/05: place orders at price = 880, execute at 881
# Minute perfs are cumulative for the day
expected_minute = {}
capital_changes_after_start = np.array([0.0] * 1170)
if change_loc == 'intraday':
capital_changes_after_start[539:599] = 500.0
capital_changes_after_start[599:780] = 1000.0
expected_minute['pnl'] = np.array([0.0] * 1170)
expected_minute['pnl'][:2] = 0.0
expected_minute['pnl'][2:392] = 1.0
expected_minute['pnl'][392:782] = 2.0
expected_minute['pnl'][782:] = 3.0
for start, end in ((0, 390), (390, 780), (780, 1170)):
expected_minute['pnl'][start:end] = \
np.cumsum(expected_minute['pnl'][start:end])
expected_minute['capital_used'] = np.concatenate((
[0.0] * 1, [-101.0] * 389,
[0.0] * 1, [-491.0] * 389,
[0.0] * 1, [-881.0] * 389,
))
# +1000 capital changes comes before the day start if interday
day2adj = 0.0 if change_loc == 'intraday' else 1000.0
expected_minute['starting_cash'] = np.concatenate((
[1000.0] * 390,
# 101 spent on 1/03
[1000.0 - 101.0 + day2adj] * 390,
# 101 spent on 1/03, 491 on 1/04, +1000 capital change on 1/04
[1000.0 - 101.0 - 491.0 + 1000] * 390
))
expected_minute['ending_cash'] = \
expected_minute['starting_cash'] + \
expected_minute['capital_used'] + \
capital_changes_after_start
expected_minute['starting_value'] = np.concatenate((
[0.0] * 390,
[489.0] * 390,
[879.0 * 2] * 390
))
expected_minute['ending_value'] = \
expected_minute['starting_value'] + \
expected_minute['pnl'] - \
expected_minute['capital_used']
expected_minute['portfolio_value'] = \
expected_minute['ending_value'] + \
expected_minute['ending_cash']
expected_minute['returns'] = \
expected_minute['pnl'] / \
(expected_minute['starting_value'] +
expected_minute['starting_cash'])
# If the change is interday, we can just calculate the returns from
# the pnl, starting_value and starting_cash. If the change is intraday,
# the returns after the change have to be calculated from two
# subperiods
if change_loc == 'intraday':
# The last packet (at 1/04 16:59) before the first capital change
prev_subperiod_return = expected_minute['returns'][538]
# From 1/04 17:00 to 17:59
cur_subperiod_pnl = \
expected_minute['pnl'][539:599] - expected_minute['pnl'][538]
cur_subperiod_starting_value = \
np.array([expected_minute['ending_value'][538]] * 60)
cur_subperiod_starting_cash = \
np.array([expected_minute['ending_cash'][538] + 500] * 60)
cur_subperiod_returns = cur_subperiod_pnl / \
(cur_subperiod_starting_value + cur_subperiod_starting_cash)
expected_minute['returns'][539:599] = \
(cur_subperiod_returns + 1.0) * \
(prev_subperiod_return + 1.0) - \
1.0
# The last packet (at 1/04 17:59) before the second capital change
prev_subperiod_return = expected_minute['returns'][598]
# From 1/04 18:00 to 21:00
cur_subperiod_pnl = \
expected_minute['pnl'][599:780] - expected_minute['pnl'][598]
cur_subperiod_starting_value = \
np.array([expected_minute['ending_value'][598]] * 181)
cur_subperiod_starting_cash = \
np.array([expected_minute['ending_cash'][598] + 500] * 181)
cur_subperiod_returns = cur_subperiod_pnl / \
(cur_subperiod_starting_value + cur_subperiod_starting_cash)
expected_minute['returns'][599:780] = \
(cur_subperiod_returns + 1.0) * \
(prev_subperiod_return + 1.0) - \
1.0
# The last minute packet of each day
expected_daily = {
k: np.array([v[389], v[779], v[1169]])
for k, v in iteritems(expected_minute)
}
stats = [
'pnl', 'capital_used', 'starting_cash', 'ending_cash',
'starting_value', 'ending_value', 'portfolio_value', 'returns'
]
expected_cumulative = deepcopy(expected_minute)
# "Add" daily return from 1/03 to minute returns on 1/04 and 1/05
# "Add" daily return from 1/04 to minute returns on 1/05
expected_cumulative['returns'][390:] = \
(expected_cumulative['returns'][390:] + 1) * \
(expected_daily['returns'][0] + 1) - 1
expected_cumulative['returns'][780:] = \
(expected_cumulative['returns'][780:] + 1) * \
(expected_daily['returns'][1] + 1) - 1
# Add daily pnl/capital_used from 1/03 to 1/04 and 1/05
# Add daily pnl/capital_used from 1/04 to 1/05
expected_cumulative['pnl'][390:] += expected_daily['pnl'][0]
expected_cumulative['pnl'][780:] += expected_daily['pnl'][1]
expected_cumulative['capital_used'][390:] += \
expected_daily['capital_used'][0]
expected_cumulative['capital_used'][780:] += \
expected_daily['capital_used'][1]
# starting_cash, starting_value are same as those of the first daily
# packet
expected_cumulative['starting_cash'] = \
np.repeat(expected_daily['starting_cash'][0:1], 1170)
expected_cumulative['starting_value'] = \
np.repeat(expected_daily['starting_value'][0:1], 1170)
# extra cumulative packet per day from the daily packet
for stat in stats:
for i in (390, 781, 1172):
expected_cumulative[stat] = np.insert(
expected_cumulative[stat],
i,
expected_cumulative[stat][i-1]
)
for stat in stats:
np.testing.assert_array_almost_equal(
np.array([perf[stat] for perf in minute_perf]),
expected_minute[stat]
)
np.testing.assert_array_almost_equal(
np.array([perf[stat] for perf in daily_perf]),
expected_daily[stat]
)
np.testing.assert_array_almost_equal(
np.array([perf[stat] for perf in cumulative_perf]),
expected_cumulative[stat]
)
if change_loc == 'interday':
self.assertEqual(
algo.capital_change_deltas,
{pd.Timestamp('2006-01-04', tz='UTC'): 1000.0}
)
else:
self.assertEqual(
algo.capital_change_deltas,
{pd.Timestamp('2006-01-04 17:00', tz='UTC'): 500.0,
pd.Timestamp('2006-01-04 18:00', tz='UTC'): 500.0}
)
class TestGetDatetime(zf.WithMakeAlgo, zf.ZiplineTestCase):
SIM_PARAMS_DATA_FREQUENCY = 'minute'
START_DATE = to_utc('2014-01-02 9:31')
END_DATE = to_utc('2014-01-03 9:31')
ASSET_FINDER_EQUITY_SIDS = 0, 1
# FIXME: Pass a benchmark source explicitly here.
BENCHMARK_SID = None
@parameterized.expand(
[
('default', None,),
('utc', 'UTC',),
('us_east', 'US/Eastern',),
]
)
def test_get_datetime(self, name, tz):
algo = dedent(
"""
import pandas as pd
from zipline.api import get_datetime
def initialize(context):
context.tz = {tz} or 'UTC'
context.first_bar = True
def handle_data(context, data):
dt = get_datetime({tz})
if dt.tz.zone != context.tz:
raise ValueError("Mismatched Zone")
if context.first_bar:
if dt.tz_convert("US/Eastern").hour != 9:
raise ValueError("Mismatched Hour")
elif dt.tz_convert("US/Eastern").minute != 31:
raise ValueError("Mismatched Minute")
context.first_bar = False
""".format(tz=repr(tz))
)
algo = self.make_algo(script=algo)
algo.run()
self.assertFalse(algo.first_bar)
class TestTradingControls(zf.WithMakeAlgo,
zf.ZiplineTestCase):
START_DATE = pd.Timestamp('2006-01-03', tz='utc')
END_DATE = pd.Timestamp('2006-01-06', tz='utc')
sid = 133
sids = ASSET_FINDER_EQUITY_SIDS = 133, 134
SIM_PARAMS_DATA_FREQUENCY = 'daily'
DATA_PORTAL_USE_MINUTE_DATA = True
@classmethod
def init_class_fixtures(cls):
super(TestTradingControls, cls).init_class_fixtures()
cls.asset = cls.asset_finder.retrieve_asset(cls.sid)
cls.another_asset = cls.asset_finder.retrieve_asset(134)
def _check_algo(self,
algo,
expected_order_count,
expected_exc):
with self.assertRaises(expected_exc) if expected_exc else nop_context:
algo.run()
self.assertEqual(algo.order_count, expected_order_count)
def check_algo_succeeds(self, algo, order_count=4):
# Default for order_count assumes one order per handle_data call.
self._check_algo(algo, order_count, None)
def check_algo_fails(self, algo, order_count):
self._check_algo(algo,
order_count,
TradingControlViolation)
def test_set_max_position_size(self):
def initialize(self, asset, max_shares, max_notional):
self.set_slippage(FixedSlippage())
self.order_count = 0
self.set_max_position_size(asset=asset,
max_shares=max_shares,
max_notional=max_notional)
# Buy one share four times. Should be fine.
def handle_data(algo, data):
algo.order(algo.sid(self.sid), 1)
algo.order_count += 1
algo = self.make_algo(
asset=self.asset,
max_shares=10,
max_notional=500.0,
initialize=initialize,
handle_data=handle_data,
)
self.check_algo_succeeds(algo)
# Buy three shares four times. Should bail on the fourth before it's
# placed.
def handle_data(algo, data):
algo.order(algo.sid(self.sid), 3)
algo.order_count += 1
algo = self.make_algo(
asset=self.asset,
max_shares=10,
max_notional=500.0,
initialize=initialize,
handle_data=handle_data,
)
self.check_algo_fails(algo, 3)
# Buy three shares four times. Should bail due to max_notional on the
# third attempt.
def handle_data(algo, data):
algo.order(algo.sid(self.sid), 3)
algo.order_count += 1
algo = self.make_algo(
asset=self.asset,
max_shares=10,
max_notional=67.0,
initialize=initialize,
handle_data=handle_data,
)
self.check_algo_fails(algo, 2)
# Set the trading control to a different sid, then BUY ALL THE THINGS!.
# Should continue normally.
def handle_data(algo, data):
algo.order(algo.sid(self.sid), 10000)
algo.order_count += 1
algo = self.make_algo(
asset=self.another_asset,
max_shares=10,
max_notional=67.0,
initialize=initialize,
handle_data=handle_data,
)
self.check_algo_succeeds(algo)
# Set the trading control sid to None, then BUY ALL THE THINGS!. Should
# fail because setting sid to None makes the control apply to all sids.
def handle_data(algo, data):
algo.order(algo.sid(self.sid), 10000)
algo.order_count += 1
algo = self.make_algo(
max_shares=10,
max_notional=61.0,
asset=None,
initialize=initialize,
handle_data=handle_data,
)
self.check_algo_fails(algo, 0)
def test_set_asset_restrictions(self):
def initialize(algo, sid, restrictions, on_error):
algo.order_count = 0
algo.set_asset_restrictions(restrictions, on_error)
def handle_data(algo, data):
algo.could_trade = data.can_trade(algo.sid(self.sid))
algo.order(algo.sid(self.sid), 100)
algo.order_count += 1
# Set HistoricalRestrictions for one sid for the entire simulation,
# and fail.
rlm = HistoricalRestrictions([
Restriction(
self.sid,
self.sim_params.start_session,
RESTRICTION_STATES.FROZEN)
])
algo = self.make_algo(
sid=self.sid,
restrictions=rlm,
on_error='fail',
initialize=initialize,
handle_data=handle_data,
)
self.check_algo_fails(algo, 0)
self.assertFalse(algo.could_trade)
# Set StaticRestrictions for one sid and fail.
rlm = StaticRestrictions([self.sid])
algo = self.make_algo(
sid=self.sid,
restrictions=rlm,
on_error='fail',
initialize=initialize,
handle_data=handle_data,
)
self.check_algo_fails(algo, 0)
self.assertFalse(algo.could_trade)
# just log an error on the violation if we choose not to fail.
algo = self.make_algo(
sid=self.sid,
restrictions=rlm,
on_error='log',
initialize=initialize,
handle_data=handle_data,
)
with make_test_handler(self) as log_catcher:
self.check_algo_succeeds(algo)
logs = [r.message for r in log_catcher.records]
self.assertIn("Order for 100 shares of Equity(133 [A]) at "
"2006-01-03 21:00:00+00:00 violates trading constraint "
"RestrictedListOrder({})", logs)
self.assertFalse(algo.could_trade)
# set the restricted list to exclude the sid, and succeed
rlm = HistoricalRestrictions([
Restriction(
sid,
self.sim_params.start_session,
RESTRICTION_STATES.FROZEN) for sid in [134, 135, 136]
])
algo = self.make_algo(
sid=self.sid,
restrictions=rlm,
on_error='fail',
initialize=initialize,
handle_data=handle_data,
)
self.check_algo_succeeds(algo)
self.assertTrue(algo.could_trade)
@parameterized.expand([
('order_first_restricted_sid', 0),
('order_second_restricted_sid', 1)
])
def test_set_multiple_asset_restrictions(self, name, to_order_idx):
def initialize(algo, restrictions1, restrictions2, on_error):
algo.order_count = 0
algo.set_asset_restrictions(restrictions1, on_error)
algo.set_asset_restrictions(restrictions2, on_error)
def handle_data(algo, data):
algo.could_trade1 = data.can_trade(algo.sid(self.sids[0]))
algo.could_trade2 = data.can_trade(algo.sid(self.sids[1]))
algo.order(algo.sid(self.sids[to_order_idx]), 100)
algo.order_count += 1
rl1 = StaticRestrictions([self.sids[0]])
rl2 = StaticRestrictions([self.sids[1]])
algo = self.make_algo(
restrictions1=rl1,
restrictions2=rl2,
initialize=initialize,
handle_data=handle_data,
on_error='fail',
)
self.check_algo_fails(algo, 0)
self.assertFalse(algo.could_trade1)
self.assertFalse(algo.could_trade2)
def test_set_do_not_order_list(self):
def initialize(self, restricted_list):
self.order_count = 0
self.set_do_not_order_list(restricted_list, on_error='fail')
def handle_data(algo, data):
algo.could_trade = data.can_trade(algo.sid(self.sid))
algo.order(algo.sid(self.sid), 100)
algo.order_count += 1
rlm = [self.sid]
algo = self.make_algo(
restricted_list=rlm,
initialize=initialize,
handle_data=handle_data,
)
self.check_algo_fails(algo, 0)
self.assertFalse(algo.could_trade)
def test_set_max_order_size(self):
def initialize(algo, asset, max_shares, max_notional):
algo.order_count = 0
algo.set_max_order_size(asset=asset,
max_shares=max_shares,
max_notional=max_notional)
# Buy one share.
def handle_data(algo, data):
algo.order(algo.sid(self.sid), 1)
algo.order_count += 1
algo = self.make_algo(
initialize=initialize,
handle_data=handle_data,
asset=self.asset,
max_shares=10,
max_notional=500.0,
)
self.check_algo_succeeds(algo)
# Buy 1, then 2, then 3, then 4 shares. Bail on the last attempt
# because we exceed shares.
def handle_data(algo, data):
algo.order(algo.sid(self.sid), algo.order_count + 1)
algo.order_count += 1
algo = self.make_algo(
initialize=initialize,
handle_data=handle_data,
asset=self.asset,
max_shares=3,
max_notional=500.0,
)
self.check_algo_fails(algo, 3)
# Buy 1, then 2, then 3, then 4 shares. Bail on the last attempt
# because we exceed notional.
def handle_data(algo, data):
algo.order(algo.sid(self.sid), algo.order_count + 1)
algo.order_count += 1
algo = self.make_algo(
initialize=initialize,
handle_data=handle_data,
asset=self.asset,
max_shares=10,
max_notional=40.0,
)
self.check_algo_fails(algo, 3)
# Set the trading control to a different sid, then BUY ALL THE THINGS!.
# Should continue normally.
def handle_data(algo, data):
algo.order(algo.sid(self.sid), 10000)
algo.order_count += 1
algo = self.make_algo(
initialize=initialize,
handle_data=handle_data,
asset=self.another_asset,
max_shares=1,
max_notional=1.0,
)
self.check_algo_succeeds(algo)
# Set the trading control sid to None, then BUY ALL THE THINGS!.
# Should fail because not specifying a sid makes the trading control
# apply to all sids.
def handle_data(algo, data):
algo.order(algo.sid(self.sid), 10000)
algo.order_count += 1
algo = self.make_algo(
initialize=initialize,
handle_data=handle_data,
asset=None,
max_shares=1,
max_notional=1.0,
)
self.check_algo_fails(algo, 0)
def test_set_max_order_count(self):
def initialize(algo, count):
algo.order_count = 0
algo.set_max_order_count(count)
def handle_data(algo, data):
for i in range(5):
algo.order(self.asset, 1)
algo.order_count += 1
algo = self.make_algo(
count=3,
initialize=initialize,
handle_data=handle_data,
)
with self.assertRaises(TradingControlViolation):
algo.run()
self.assertEqual(algo.order_count, 3)
def test_set_max_order_count_minutely(self):
sim_params = self.make_simparams(data_frequency='minute')
def initialize(algo, max_orders_per_day):
algo.minute_count = 0
algo.order_count = 0
algo.set_max_order_count(max_orders_per_day)
# Order 5 times twice in a single day, and set a max order count of
# 9. The last order of the second batch should fail.
def handle_data(algo, data):
if algo.minute_count == 0 or algo.minute_count == 100:
for i in range(5):
algo.order(self.asset, 1)
algo.order_count += 1
algo.minute_count += 1
algo = self.make_algo(
initialize=initialize,
handle_data=handle_data,
max_orders_per_day=9,
sim_params=sim_params,
)
with self.assertRaises(TradingControlViolation):
algo.run()
self.assertEqual(algo.order_count, 9)
# Set a limit of 5 orders per day, and order 5 times in the first
# minute of each day. This should succeed because the counter gets
# reset each day.
def handle_data(algo, data):
if (algo.minute_count % 390) == 0:
for i in range(5):
algo.order(self.asset, 1)
algo.order_count += 1
algo.minute_count += 1
algo = self.make_algo(
initialize=initialize,
handle_data=handle_data,
max_orders_per_day=5,
sim_params=sim_params,
)
algo.run()
# 5 orders per day times 4 days.
self.assertEqual(algo.order_count, 20)
def test_long_only(self):
def initialize(algo):
algo.order_count = 0
algo.set_long_only()
# Sell immediately -> fail immediately.
def handle_data(algo, data):
algo.order(algo.sid(self.sid), -1)
algo.order_count += 1
algo = self.make_algo(initialize=initialize, handle_data=handle_data)
self.check_algo_fails(algo, 0)
# Buy on even days, sell on odd days. Never takes a short position, so
# should succeed.
def handle_data(algo, data):
if (algo.order_count % 2) == 0:
algo.order(algo.sid(self.sid), 1)
else:
algo.order(algo.sid(self.sid), -1)
algo.order_count += 1
algo = self.make_algo(initialize=initialize, handle_data=handle_data)
self.check_algo_succeeds(algo)
# Buy on first three days, then sell off holdings. Should succeed.
def handle_data(algo, data):
amounts = [1, 1, 1, -3]
algo.order(algo.sid(self.sid), amounts[algo.order_count])
algo.order_count += 1
algo = self.make_algo(initialize=initialize, handle_data=handle_data)
self.check_algo_succeeds(algo)
# Buy on first three days, then sell off holdings plus an extra share.
# Should fail on the last sale.
def handle_data(algo, data):
amounts = [1, 1, 1, -4]
algo.order(algo.sid(self.sid), amounts[algo.order_count])
algo.order_count += 1
algo = self.make_algo(initialize=initialize, handle_data=handle_data)
self.check_algo_fails(algo, 3)
def test_register_post_init(self):
def initialize(algo):
algo.initialized = True
def handle_data(algo, data):
with self.assertRaises(RegisterTradingControlPostInit):
algo.set_max_position_size(self.sid, 1, 1)
with self.assertRaises(RegisterTradingControlPostInit):
algo.set_max_order_size(self.sid, 1, 1)
with self.assertRaises(RegisterTradingControlPostInit):
algo.set_max_order_count(1)
with self.assertRaises(RegisterTradingControlPostInit):
algo.set_long_only()
self.run_algorithm(initialize=initialize, handle_data=handle_data)
class TestAssetDateBounds(zf.WithMakeAlgo, zf.ZiplineTestCase):
START_DATE = pd.Timestamp('2014-01-02', tz='UTC')
END_DATE = pd.Timestamp('2014-01-03', tz='UTC')
SIM_PARAMS_START_DATE = END_DATE # Only run for one day.
SIM_PARAMS_DATA_FREQUENCY = 'daily'
DATA_PORTAL_USE_MINUTE_DATA = False
BENCHMARK_SID = 3
@classmethod
def make_equity_info(cls):
T = partial(pd.Timestamp, tz='UTC')
return pd.DataFrame.from_records([
{'sid': 1,
'symbol': 'OLD',
'start_date': T('1990'),
'end_date': T('1991'),
'exchange': 'TEST'},
{'sid': 2,
'symbol': 'NEW',
'start_date': T('2017'),
'end_date': T('2018'),
'exchange': 'TEST'},
{'sid': 3,
'symbol': 'GOOD',
'start_date': cls.START_DATE,
'end_date': cls.END_DATE,
'exchange': 'TEST'},
])
def test_asset_date_bounds(self):
def initialize(algo):
algo.ran = False
algo.register_trading_control(AssetDateBounds(on_error='fail'))
def handle_data(algo, data):
# This should work because sid 3 is valid during the algo lifetime.
algo.order(algo.sid(3), 1)
# Sid already expired.
with self.assertRaises(TradingControlViolation):
algo.order(algo.sid(1), 1)
# Sid doesn't exist yet.
with self.assertRaises(TradingControlViolation):
algo.order(algo.sid(2), 1)
algo.ran = True
algo = self.make_algo(initialize=initialize, handle_data=handle_data)
algo.run()
self.assertTrue(algo.ran)
class TestAccountControls(zf.WithMakeAlgo,
zf.ZiplineTestCase):
START_DATE = pd.Timestamp('2006-01-03', tz='utc')
END_DATE = pd.Timestamp('2006-01-06', tz='utc')
sidint, = ASSET_FINDER_EQUITY_SIDS = (133,)
BENCHMARK_SID = None
SIM_PARAMS_DATA_FREQUENCY = 'daily'
DATA_PORTAL_USE_MINUTE_DATA = False
@classmethod
def make_equity_daily_bar_data(cls):
frame = pd.DataFrame(data={
'close': [10., 10., 11., 11.],
'open': [10., 10., 11., 11.],
'low': [9.5, 9.5, 10.45, 10.45],
'high': [10.5, 10.5, 11.55, 11.55],
'volume': [100, 100, 100, 300],
}, index=cls.equity_daily_bar_days)
yield cls.sidint, frame
def _check_algo(self, algo, expected_exc):
with self.assertRaises(expected_exc) if expected_exc else nop_context:
algo.run()
def check_algo_succeeds(self, algo):
# Default for order_count assumes one order per handle_data call.
self._check_algo(algo, None)
def check_algo_fails(self, algo):
self._check_algo(algo, AccountControlViolation)
def test_set_max_leverage(self):
def initialize(algo, max_leverage):
algo.set_max_leverage(max_leverage=max_leverage)
def handle_data(algo, data):
algo.order(algo.sid(self.sidint), 1)
algo.record(latest_time=algo.get_datetime())
# Set max leverage to 0 so buying one share fails.
algo = self.make_algo(
initialize=initialize,
handle_data=handle_data,
max_leverage=0,
)
self.check_algo_fails(algo)
self.assertEqual(
algo.recorded_vars['latest_time'],
pd.Timestamp('2006-01-04 21:00:00', tz='UTC'),
)
# Set max leverage to 1 so buying one share passes
def handle_data(algo, data):
algo.order(algo.sid(self.sidint), 1)
algo = self.make_algo(
initialize=initialize,
handle_data=handle_data,
max_leverage=1,
)
self.check_algo_succeeds(algo)
def test_set_min_leverage(self):
def initialize(algo, min_leverage, grace_period):
algo.set_min_leverage(
min_leverage=min_leverage, grace_period=grace_period
)
def handle_data(algo, data):
algo.order_target_percent(algo.sid(self.sidint), .5)
algo.record(latest_time=algo.get_datetime())
# Helper for not having to pass init/handle_data at each callsite.
def make_algo(min_leverage, grace_period):
return self.make_algo(
initialize=initialize,
handle_data=handle_data,
min_leverage=min_leverage,
grace_period=grace_period,
)
# Set min leverage to 1.
# The algorithm will succeed because it doesn't run for more
# than 10 days.
offset = pd.Timedelta('10 days')
algo = make_algo(min_leverage=1, grace_period=offset)
self.check_algo_succeeds(algo)
# The algorithm will fail because it doesn't reach a min leverage of 1
# after 1 day.
offset = pd.Timedelta('1 days')
algo = make_algo(min_leverage=1, grace_period=offset)
self.check_algo_fails(algo)
self.assertEqual(
algo.recorded_vars['latest_time'],
pd.Timestamp('2006-01-04 21:00:00', tz='UTC'),
)
# Increase the offset to 2 days, and the algorithm fails a day later
offset = pd.Timedelta('2 days')
algo = make_algo(min_leverage=1, grace_period=offset)
self.check_algo_fails(algo)
self.assertEqual(
algo.recorded_vars['latest_time'],
pd.Timestamp('2006-01-05 21:00:00', tz='UTC'),
)
# Set the min_leverage to .0001 and the algorithm succeeds.
algo = make_algo(min_leverage=.0001, grace_period=offset)
self.check_algo_succeeds(algo)
class TestFuturesAlgo(zf.WithMakeAlgo, zf.ZiplineTestCase):
START_DATE = pd.Timestamp('2016-01-06', tz='utc')
END_DATE = pd.Timestamp('2016-01-07', tz='utc')
FUTURE_MINUTE_BAR_START_DATE = pd.Timestamp('2016-01-05', tz='UTC')
SIM_PARAMS_DATA_FREQUENCY = 'minute'
TRADING_CALENDAR_STRS = ('us_futures',)
TRADING_CALENDAR_PRIMARY_CAL = 'us_futures'
BENCHMARK_SID = None
@classmethod
def make_futures_info(cls):
return pd.DataFrame.from_dict(
{
1: {
'symbol': 'CLG16',
'root_symbol': 'CL',
'start_date': pd.Timestamp('2015-12-01', tz='UTC'),
'notice_date': pd.Timestamp('2016-01-20', tz='UTC'),
'expiration_date': pd.Timestamp('2016-02-19', tz='UTC'),
'auto_close_date': pd.Timestamp('2016-01-18', tz='UTC'),
'exchange': 'TEST',
},
},
orient='index',
)
def test_futures_history(self):
algo_code = dedent(
"""
from datetime import time
from zipline.api import (
date_rules,
get_datetime,
schedule_function,
sid,
time_rules,
)
def initialize(context):
context.history_values = []
schedule_function(
make_history_call,
date_rules.every_day(),
time_rules.market_open(),
)
schedule_function(
check_market_close_time,
date_rules.every_day(),
time_rules.market_close(),
)
def make_history_call(context, data):
# Ensure that the market open is 6:31am US/Eastern.
open_time = get_datetime().tz_convert('US/Eastern').time()
assert open_time == time(6, 31)
context.history_values.append(
data.history(sid(1), 'close', 5, '1m'),
)
def check_market_close_time(context, data):
# Ensure that this function is called at 4:59pm US/Eastern.
# By default, `market_close()` uses an offset of 1 minute.
close_time = get_datetime().tz_convert('US/Eastern').time()
assert close_time == time(16, 59)
"""
)
algo = self.make_algo(
script=algo_code,
trading_calendar=get_calendar('us_futures'),
)
algo.run()
# Assert that we were able to retrieve history data for minutes outside
# of the 6:31am US/Eastern to 5:00pm US/Eastern futures open times.
np.testing.assert_array_equal(
algo.history_values[0].index,
pd.date_range(
'2016-01-06 6:27',
'2016-01-06 6:31',
freq='min',
tz='US/Eastern',
),
)
np.testing.assert_array_equal(
algo.history_values[1].index,
pd.date_range(
'2016-01-07 6:27',
'2016-01-07 6:31',
freq='min',
tz='US/Eastern',
),
)
# Expected prices here are given by the range values created by the
# default `make_future_minute_bar_data` method.
np.testing.assert_array_equal(
algo.history_values[0].values, list(map(float, range(2196, 2201))),
)
np.testing.assert_array_equal(
algo.history_values[1].values, list(map(float, range(3636, 3641))),
)
@staticmethod
def algo_with_slippage(slippage_model):
return dedent(
"""
from zipline.api import (
commission,
order,
set_commission,
set_slippage,
sid,
slippage,
get_datetime,
)
def initialize(context):
commission_model = commission.PerFutureTrade(0)
set_commission(us_futures=commission_model)
slippage_model = slippage.{model}
set_slippage(us_futures=slippage_model)
context.ordered = False
def handle_data(context, data):
if not context.ordered:
order(sid(1), 10)
context.ordered = True
context.order_price = data.current(sid(1), 'price')
"""
).format(model=slippage_model)
def test_fixed_future_slippage(self):
algo_code = self.algo_with_slippage('FixedSlippage(spread=0.10)')
algo = self.make_algo(
script=algo_code,
trading_calendar=get_calendar('us_futures'),
)
results = algo.run()
# Flatten the list of transactions.
all_txns = [
val for sublist in results['transactions'].tolist()
for val in sublist
]
self.assertEqual(len(all_txns), 1)
txn = all_txns[0]
# Add 1 to the expected price because the order does not fill until the
# bar after the price is recorded.
expected_spread = 0.05
expected_price = (algo.order_price + 1) + expected_spread
self.assertEqual(txn['price'], expected_price)
self.assertEqual(results['orders'][0][0]['commission'], 0.0)
def test_volume_contract_slippage(self):
algo_code = self.algo_with_slippage(
'VolumeShareSlippage(volume_limit=0.05, price_impact=0.1)',
)
algo = self.make_algo(
script=algo_code,
trading_calendar=get_calendar('us_futures'),
)
results = algo.run()
# There should be no commissions.
self.assertEqual(results['orders'][0][0]['commission'], 0.0)
# Flatten the list of transactions.
all_txns = [
val for sublist in results['transactions'].tolist()
for val in sublist
]
# With a volume limit of 0.05, and a total volume of 100 contracts
# traded per minute, we should require 2 transactions to order 10
# contracts.
self.assertEqual(len(all_txns), 2)
for i, txn in enumerate(all_txns):
# Add 1 to the order price because the order does not fill until
# the bar after the price is recorded.
order_price = algo.order_price + i + 1
expected_impact = order_price * 0.1 * (0.05 ** 2)
expected_price = order_price + expected_impact
self.assertEqual(txn['price'], expected_price)
class TestAnalyzeAPIMethod(zf.WithMakeAlgo, zf.ZiplineTestCase):
START_DATE = pd.Timestamp('2016-01-05', tz='utc')
END_DATE = pd.Timestamp('2016-01-05', tz='utc')
SIM_PARAMS_DATA_FREQUENCY = 'daily'
DATA_PORTAL_USE_MINUTE_DATA = False
def test_analyze_called(self):
self.perf_ref = None
def initialize(context):
pass
def handle_data(context, data):
pass
def analyze(context, perf):
self.perf_ref = perf
algo = self.make_algo(
initialize=initialize, handle_data=handle_data, analyze=analyze,
)
results = algo.run()
self.assertIs(results, self.perf_ref)
class TestOrderCancelation(zf.WithMakeAlgo, zf.ZiplineTestCase):
START_DATE = pd.Timestamp('2016-01-05', tz='utc')
END_DATE = pd.Timestamp('2016-01-07', tz='utc')
ASSET_FINDER_EQUITY_SIDS = (1,)
ASSET_FINDER_EQUITY_SYMBOLS = ('ASSET1',)
BENCHMARK_SID = None
code = dedent(
"""
from zipline.api import (
sid, order, set_slippage, slippage, VolumeShareSlippage,
set_cancel_policy, cancel_policy, EODCancel
)
def initialize(context):
set_slippage(
slippage.VolumeShareSlippage(
volume_limit=1,
price_impact=0
)
)
{0}
context.ordered = False
def handle_data(context, data):
if not context.ordered:
order(sid(1), {1})
context.ordered = True
""",
)
@classmethod
def make_equity_minute_bar_data(cls):
asset_minutes = \
cls.trading_calendar.minutes_for_sessions_in_range(
cls.START_DATE,
cls.END_DATE,
)
minutes_count = len(asset_minutes)
minutes_arr = np.arange(1, 1 + minutes_count)
# normal test data, but volume is pinned at 1 share per minute
yield 1, pd.DataFrame(
{
'open': minutes_arr + 1,
'high': minutes_arr + 2,
'low': minutes_arr - 1,
'close': minutes_arr,
'volume': np.full(minutes_count, 1.0),
},
index=asset_minutes,
)
@classmethod
def make_equity_daily_bar_data(cls):
yield 1, pd.DataFrame(
{
'open': np.full(3, 1, dtype=np.float64),
'high': np.full(3, 1, dtype=np.float64),
'low': np.full(3, 1, dtype=np.float64),
'close': np.full(3, 1, dtype=np.float64),
'volume': np.full(3, 1, dtype=np.float64),
},
index=cls.equity_daily_bar_days,
)
def prep_algo(self,
cancelation_string,
data_frequency="minute",
amount=1000,
minute_emission=False):
code = self.code.format(cancelation_string, amount)
return self.make_algo(
script=code,
sim_params=self.make_simparams(
data_frequency=data_frequency,
emission_rate='minute' if minute_emission else 'daily',
)
)
@parameter_space(
direction=[1, -1],
minute_emission=[True, False],
)
def test_eod_order_cancel_minute(self, direction, minute_emission):
"""
Test that EOD order cancel works in minute mode for both shorts and
longs, and both daily emission and minute emission
"""
# order 1000 shares of asset1. the volume is only 1 share per bar,
# so the order should be cancelled at the end of the day.
algo = self.prep_algo(
"set_cancel_policy(cancel_policy.EODCancel())",
amount=np.copysign(1000, direction),
minute_emission=minute_emission
)
log_catcher = TestHandler()
with log_catcher:
results = algo.run()
for daily_positions in results.positions:
self.assertEqual(1, len(daily_positions))
self.assertEqual(
np.copysign(389, direction),
daily_positions[0]["amount"],
)
self.assertEqual(1, results.positions[0][0]["sid"])
# should be an order on day1, but no more orders afterwards
np.testing.assert_array_equal([1, 0, 0],
list(map(len, results.orders)))
# should be 389 txns on day 1, but no more afterwards
np.testing.assert_array_equal([389, 0, 0],
list(map(len, results.transactions)))
the_order = results.orders[0][0]
self.assertEqual(ORDER_STATUS.CANCELLED, the_order["status"])
self.assertEqual(np.copysign(389, direction), the_order["filled"])
warnings = [record for record in log_catcher.records if
record.level == WARNING]
self.assertEqual(1, len(warnings))
if direction == 1:
self.assertEqual(
"Your order for 1000 shares of ASSET1 has been partially "
"filled. 389 shares were successfully purchased. "
"611 shares were not filled by the end of day and "
"were canceled.",
str(warnings[0].message)
)
elif direction == -1:
self.assertEqual(
"Your order for -1000 shares of ASSET1 has been partially "
"filled. 389 shares were successfully sold. "
"611 shares were not filled by the end of day and "
"were canceled.",
str(warnings[0].message)
)
def test_default_cancelation_policy(self):
algo = self.prep_algo("")
log_catcher = TestHandler()
with log_catcher:
results = algo.run()
# order stays open throughout simulation
np.testing.assert_array_equal([1, 1, 1],
list(map(len, results.orders)))
# one txn per minute. 389 the first day (since no order until the
# end of the first minute). 390 on the second day. 221 on the
# the last day, sum = 1000.
np.testing.assert_array_equal([389, 390, 221],
list(map(len, results.transactions)))
self.assertFalse(log_catcher.has_warnings)
def test_eod_order_cancel_daily(self):
# in daily mode, EODCancel does nothing.
algo = self.prep_algo(
"set_cancel_policy(cancel_policy.EODCancel())",
"daily"
)
log_catcher = TestHandler()
with log_catcher:
results = algo.run()
# order stays open throughout simulation
np.testing.assert_array_equal([1, 1, 1],
list(map(len, results.orders)))
# one txn per day
np.testing.assert_array_equal([0, 1, 1],
list(map(len, results.transactions)))
self.assertFalse(log_catcher.has_warnings)
class TestDailyEquityAutoClose(zf.WithMakeAlgo, zf.ZiplineTestCase):
"""
Tests if delisted equities are properly removed from a portfolio holding
positions in said equities.
"""
# January 2015
# 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_DATE = pd.Timestamp('2015-01-05', tz='UTC')
END_DATE = pd.Timestamp('2015-01-13', tz='UTC')
SIM_PARAMS_DATA_FREQUENCY = 'daily'
DATA_PORTAL_USE_MINUTE_DATA = False
BENCHMARK_SID = None
@classmethod
def init_class_fixtures(cls):
super(TestDailyEquityAutoClose, cls).init_class_fixtures()
cls.assets = (
cls.asset_finder.retrieve_all(cls.asset_finder.equities_sids)
)
@classmethod
def make_equity_info(cls):
cls.test_days = cls.trading_calendar.sessions_in_range(
cls.START_DATE, cls.END_DATE,
)
assert len(cls.test_days) == 7, "Number of days in test changed!"
cls.first_asset_expiration = cls.test_days[2]
# Assets start on start date and delist every two days:
#
# start_date end_date auto_close_date
# 0 2015-01-05 2015-01-07 2015-01-09
# 1 2015-01-05 2015-01-09 2015-01-13
# 2 2015-01-05 2015-01-13 2015-01-15
cls.asset_info = make_jagged_equity_info(
num_assets=3,
start_date=cls.test_days[0],
first_end=cls.first_asset_expiration,
frequency=cls.trading_calendar.day,
periods_between_ends=2,
auto_close_delta=2 * cls.trading_calendar.day,
)
return cls.asset_info
@classmethod
def make_equity_daily_bar_data(cls):
cls.daily_data = make_trade_data_for_asset_info(
dates=cls.test_days,
asset_info=cls.asset_info,
price_start=10,
price_step_by_sid=10,
price_step_by_date=1,
volume_start=100,
volume_step_by_sid=100,
volume_step_by_date=10,
)
return cls.daily_data.items()
def daily_prices_on_tick(self, row):
return [
trades.iloc[row].close for trades in itervalues(self.daily_data)
]
def final_daily_price(self, asset):
return self.daily_data[asset.sid].loc[asset.end_date].close
def default_initialize(self):
"""
Initialize function shared between test algos.
"""
def initialize(context):
context.ordered = False
context.set_commission(PerShare(0, 0))
context.set_slippage(FixedSlippage(spread=0))
context.num_positions = []
context.cash = []
return initialize
def default_handle_data(self, assets, order_size):
"""
Handle data function shared between test algos.
"""
def handle_data(context, data):
if not context.ordered:
for asset in assets:
context.order(asset, order_size)
context.ordered = True
context.cash.append(context.portfolio.cash)
context.num_positions.append(len(context.portfolio.positions))
return handle_data
@parameter_space(
order_size=[10, -10],
capital_base=[0, 100000],
__fail_fast=True,
)
def test_daily_delisted_equities(self,
order_size,
capital_base):
"""
Make sure that after an equity gets delisted, our portfolio holds the
correct number of equities and correct amount of cash.
"""
assets = self.assets
final_prices = {
asset.sid: self.final_daily_price(asset)
for asset in assets
}
# Prices at which we expect our orders to be filled.
initial_fill_prices = self.daily_prices_on_tick(1)
cost_basis = sum(initial_fill_prices) * order_size
# Last known prices of assets that will be auto-closed.
fp0 = final_prices[0]
fp1 = final_prices[1]
algo = self.make_algo(
initialize=self.default_initialize(),
handle_data=self.default_handle_data(assets, order_size),
sim_params=self.make_simparams(
capital_base=capital_base,
data_frequency='daily',
),
)
output = algo.run()
initial_cash = capital_base
after_fills = initial_cash - cost_basis
after_first_auto_close = after_fills + fp0 * (order_size)
after_second_auto_close = after_first_auto_close + fp1 * (order_size)
# Day 1: Order 10 shares of each equity; there are 3 equities.
# Day 2: Order goes through at the day 2 price of each equity.
# Day 3: End date of Equity 0.
# Day 4: Nothing happens.
# Day 5: End date of Equity 1. Auto close of equity 0.
# Add cash == (fp0 * size).
# Day 6: Nothing happens.
# Day 7: End date of Equity 2 and auto-close date of Equity 1.
# Add cash equal to (fp1 * size).
expected_cash = [
initial_cash,
after_fills,
after_fills,
after_fills,
after_first_auto_close,
after_first_auto_close,
after_second_auto_close,
]
expected_num_positions = [0, 3, 3, 3, 2, 2, 1]
# Check expected cash.
self.assertEqual(expected_cash, list(output['ending_cash']))
# The cash recorded by the algo should be behind by a day from the
# computed ending cash.
expected_cash.insert(3, after_fills)
self.assertEqual(algo.cash, expected_cash[:-1])
# Check expected long/short counts.
# We have longs if order_size > 0.
# We have shorts if order_size < 0.
if order_size > 0:
self.assertEqual(
expected_num_positions,
list(output['longs_count']),
)
self.assertEqual(
[0] * len(self.test_days),
list(output['shorts_count']),
)
else:
self.assertEqual(
expected_num_positions,
list(output['shorts_count']),
)
self.assertEqual(
[0] * len(self.test_days),
list(output['longs_count']),
)
# The number of positions recorded by the algo should be behind by a
# day from the computed long/short counts.
expected_num_positions.insert(3, 3)
self.assertEqual(algo.num_positions, expected_num_positions[:-1])
# Check expected transactions.
# We should have a transaction of order_size shares per sid.
transactions = output['transactions']
initial_fills = transactions.iloc[1]
self.assertEqual(len(initial_fills), len(assets))
last_minute_of_session = \
self.trading_calendar.session_close(self.test_days[1])
for asset, txn in zip(assets, initial_fills):
self.assertDictContainsSubset(
{
'amount': order_size,
'commission': None,
'dt': last_minute_of_session,
'price': initial_fill_prices[asset],
'sid': asset,
},
txn,
)
# This will be a UUID.
self.assertIsInstance(txn['order_id'], str)
def transactions_for_date(date):
return transactions.iloc[self.test_days.get_loc(date)]
# We should have exactly one auto-close transaction on the close date
# of asset 0.
(first_auto_close_transaction,) = transactions_for_date(
assets[0].auto_close_date
)
self.assertEqual(
first_auto_close_transaction,
{
'amount': -order_size,
'commission': None,
'dt': self.trading_calendar.session_close(
assets[0].auto_close_date,
),
'price': fp0,
'sid': assets[0],
'order_id': None, # Auto-close txns emit Nones for order_id.
},
)
(second_auto_close_transaction,) = transactions_for_date(
assets[1].auto_close_date
)
self.assertEqual(
second_auto_close_transaction,
{
'amount': -order_size,
'commission': None,
'dt': self.trading_calendar.session_close(
assets[1].auto_close_date,
),
'price': fp1,
'sid': assets[1],
'order_id': None, # Auto-close txns emit Nones for order_id.
},
)
def test_cancel_open_orders(self):
"""
Test that any open orders for an equity that gets delisted are
canceled. Unless an equity is auto closed, any open orders for that
equity will persist indefinitely.
"""
assets = self.assets
first_asset_end_date = assets[0].end_date
first_asset_auto_close_date = assets[0].auto_close_date
def initialize(context):
pass
def handle_data(context, data):
# The only order we place in this test should never be filled.
assert (
context.portfolio.cash == context.portfolio.starting_cash
)
today_session = self.trading_calendar.minute_to_session_label(
context.get_datetime()
)
day_after_auto_close = self.trading_calendar.next_session_label(
first_asset_auto_close_date,
)
if today_session == first_asset_end_date:
# Equity 0 will no longer exist tomorrow, so this order will
# never be filled.
assert len(context.get_open_orders()) == 0
context.order(context.sid(0), 10)
assert len(context.get_open_orders()) == 1
elif today_session == first_asset_auto_close_date:
# We do not cancel open orders until the end of the auto close
# date, so our open order should still exist at this point.
assert len(context.get_open_orders()) == 1
elif today_session == day_after_auto_close:
assert len(context.get_open_orders()) == 0
algo = self.make_algo(
initialize=initialize,
handle_data=handle_data,
sim_params=self.make_simparams(
data_frequency='daily',
),
)
results = algo.run()
orders = results['orders']
def orders_for_date(date):
return orders.iloc[self.test_days.get_loc(date)]
original_open_orders = orders_for_date(first_asset_end_date)
assert len(original_open_orders) == 1
last_close_for_asset = \
algo.trading_calendar.session_close(first_asset_end_date)
self.assertDictContainsSubset(
{
'amount': 10,
'commission': 0.0,
'created': last_close_for_asset,
'dt': last_close_for_asset,
'sid': assets[0],
'status': ORDER_STATUS.OPEN,
'filled': 0,
},
original_open_orders[0],
)
orders_after_auto_close = orders_for_date(first_asset_auto_close_date)
assert len(orders_after_auto_close) == 1
self.assertDictContainsSubset(
{
'amount': 10,
'commission': 0.0,
'created': last_close_for_asset,
'dt': algo.trading_calendar.session_close(
first_asset_auto_close_date,
),
'sid': assets[0],
'status': ORDER_STATUS.CANCELLED,
'filled': 0,
},
orders_after_auto_close[0],
)
# NOTE: This suite is almost the same as TestDailyEquityAutoClose, except it
# uses minutely data instead of daily data, and the auto_close_date for
# equities is one day after their end_date instead of two.
class TestMinutelyEquityAutoClose(zf.WithMakeAlgo,
zf.ZiplineTestCase):
# January 2015
# 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_DATE = pd.Timestamp('2015-01-05', tz='UTC')
END_DATE = pd.Timestamp('2015-01-13', tz='UTC')
BENCHMARK_SID = None
@classmethod
def init_class_fixtures(cls):
super(TestMinutelyEquityAutoClose, cls).init_class_fixtures()
cls.assets = (
cls.asset_finder.retrieve_all(cls.asset_finder.equities_sids)
)
@classmethod
def make_equity_info(cls):
cls.test_days = cls.trading_calendar.sessions_in_range(
cls.START_DATE, cls.END_DATE,
)
cls.test_minutes = cls.trading_calendar.minutes_for_sessions_in_range(
cls.START_DATE, cls.END_DATE,
)
cls.first_asset_expiration = cls.test_days[2]
# Assets start on start date and delist every two days:
#
# start_date end_date auto_close_date
# 0 2015-01-05 2015-01-07 2015-01-09
# 1 2015-01-05 2015-01-09 2015-01-13
# 2 2015-01-05 2015-01-13 2015-01-15
cls.asset_info = make_jagged_equity_info(
num_assets=3,
start_date=cls.test_days[0],
first_end=cls.first_asset_expiration,
frequency=cls.trading_calendar.day,
periods_between_ends=2,
auto_close_delta=1 * cls.trading_calendar.day,
)
return cls.asset_info
# XXX: This test suite uses inconsistent data for minutely and daily bars.
@classmethod
def make_equity_minute_bar_data(cls):
cls.minute_data = make_trade_data_for_asset_info(
dates=cls.test_minutes,
asset_info=cls.asset_info,
price_start=10,
price_step_by_sid=10,
price_step_by_date=1,
volume_start=100,
volume_step_by_sid=100,
volume_step_by_date=10,
)
return cls.minute_data.items()
def minute_prices_on_tick(self, row):
return [
trades.iloc[row].close for trades in itervalues(self.minute_data)
]
def final_minute_price(self, asset):
return self.minute_data[asset.sid].loc[
self.trading_calendar.session_close(asset.end_date)
].close
def default_initialize(self):
"""
Initialize function shared between test algos.
"""
def initialize(context):
context.ordered = False
context.set_commission(PerShare(0, 0))
context.set_slippage(FixedSlippage(spread=0))
context.num_positions = []
context.cash = []
return initialize
def default_handle_data(self, assets, order_size):
"""
Handle data function shared between test algos.
"""
def handle_data(context, data):
if not context.ordered:
for asset in assets:
context.order(asset, order_size)
context.ordered = True
context.cash.append(context.portfolio.cash)
context.num_positions.append(len(context.portfolio.positions))
return handle_data
def test_minutely_delisted_equities(self):
assets = self.assets
final_prices = {
asset.sid: self.final_minute_price(asset)
for asset in assets
}
backtest_minutes = self.minute_data[0].index.tolist()
order_size = 10
capital_base = 100000
algo = self.make_algo(
initialize=self.default_initialize(),
handle_data=self.default_handle_data(assets, order_size),
sim_params=self.make_simparams(
capital_base=capital_base,
data_frequency='minute',
)
)
output = algo.run()
initial_fill_prices = self.minute_prices_on_tick(1)
cost_basis = sum(initial_fill_prices) * order_size
# Last known prices of assets that will be auto-closed.
fp0 = final_prices[0]
fp1 = final_prices[1]
initial_cash = capital_base
after_fills = initial_cash - cost_basis
after_first_auto_close = after_fills + fp0 * (order_size)
after_second_auto_close = after_first_auto_close + fp1 * (order_size)
expected_cash = [initial_cash]
expected_position_counts = [0]
# We have the rest of the first sim day, plus the second, third and
# fourth days' worth of minutes with cash spent.
expected_cash.extend([after_fills] * (389 + 390 + 390 + 390))
expected_position_counts.extend([3] * (389 + 390 + 390 + 390))
# We then have two days with the cash refunded from asset 0.
expected_cash.extend([after_first_auto_close] * (390 + 390))
expected_position_counts.extend([2] * (390 + 390))
# We then have one day with cash refunded from asset 1.
expected_cash.extend([after_second_auto_close] * 390)
expected_position_counts.extend([1] * 390)
# Check list lengths first to avoid expensive comparison
self.assertEqual(len(algo.cash), len(expected_cash))
# TODO find more efficient way to compare these lists
self.assertEqual(algo.cash, expected_cash)
self.assertEqual(
list(output['ending_cash']),
[
after_fills,
after_fills,
after_fills,
after_first_auto_close,
after_first_auto_close,
after_second_auto_close,
after_second_auto_close,
],
)
self.assertEqual(algo.num_positions, expected_position_counts)
self.assertEqual(
list(output['longs_count']),
[3, 3, 3, 2, 2, 1, 1],
)
# Check expected transactions.
# We should have a transaction of order_size shares per sid.
transactions = output['transactions']
# Note that the transactions appear on the first day rather than the
# second in minute mode, because the fills happen on the second tick of
# the backtest, which is still on the first day in minute mode.
initial_fills = transactions.iloc[0]
self.assertEqual(len(initial_fills), len(assets))
for asset, txn in zip(assets, initial_fills):
self.assertDictContainsSubset(
{
'amount': order_size,
'commission': None,
'dt': backtest_minutes[1],
'price': initial_fill_prices[asset],
'sid': asset,
},
txn,
)
# This will be a UUID.
self.assertIsInstance(txn['order_id'], str)
def transactions_for_date(date):
return transactions.iloc[self.test_days.get_loc(date)]
# We should have exactly one auto-close transaction on the close date
# of asset 0.
(first_auto_close_transaction,) = transactions_for_date(
assets[0].auto_close_date
)
self.assertEqual(
first_auto_close_transaction,
{
'amount': -order_size,
'commission': None,
'dt': algo.trading_calendar.session_close(
assets[0].auto_close_date,
),
'price': fp0,
'sid': assets[0],
'order_id': None, # Auto-close txns emit Nones for order_id.
},
)
(second_auto_close_transaction,) = transactions_for_date(
assets[1].auto_close_date
)
self.assertEqual(
second_auto_close_transaction,
{
'amount': -order_size,
'commission': None,
'dt': algo.trading_calendar.session_close(
assets[1].auto_close_date,
),
'price': fp1,
'sid': assets[1],
'order_id': None, # Auto-close txns emit Nones for order_id.
},
)
class TestOrderAfterDelist(zf.WithMakeAlgo, zf.ZiplineTestCase):
start = pd.Timestamp('2016-01-05', tz='utc')
day_1 = pd.Timestamp('2016-01-06', tz='utc')
day_4 = pd.Timestamp('2016-01-11', tz='utc')
end = pd.Timestamp('2016-01-15', tz='utc')
# FIXME: Pass a benchmark source here.
BENCHMARK_SID = None
@classmethod
def make_equity_info(cls):
return pd.DataFrame.from_dict(
{
# Asset whose auto close date is after its end date.
1: {
'start_date': cls.start,
'end_date': cls.day_1,
'auto_close_date': cls.day_4,
'symbol': "ASSET1",
'exchange': "TEST",
},
# Asset whose auto close date is before its end date.
2: {
'start_date': cls.start,
'end_date': cls.day_4,
'auto_close_date': cls.day_1,
'symbol': 'ASSET2',
'exchange': 'TEST',
},
},
orient='index',
)
# XXX: This suite doesn't use the data in its DataPortal; it uses a
# FakeDataPortal with different mock data.
def init_instance_fixtures(self):
super(TestOrderAfterDelist, self).init_instance_fixtures()
self.data_portal = FakeDataPortal(self.env)
@parameterized.expand([
('auto_close_after_end_date', 1),
('auto_close_before_end_date', 2),
])
def test_order_in_quiet_period(self, name, sid):
asset = self.asset_finder.retrieve_asset(sid)
algo_code = dedent("""
from zipline.api import (
sid,
order,
order_value,
order_percent,
order_target,
order_target_percent,
order_target_value
)
def initialize(context):
pass
def handle_data(context, data):
order(sid({sid}), 1)
order_value(sid({sid}), 100)
order_percent(sid({sid}), 0.5)
order_target(sid({sid}), 50)
order_target_percent(sid({sid}), 0.5)
order_target_value(sid({sid}), 50)
""").format(sid=sid)
# run algo from 1/6 to 1/7
algo = self.make_algo(
script=algo_code,
sim_params=SimulationParameters(
start_session=pd.Timestamp("2016-01-06", tz='UTC'),
end_session=pd.Timestamp("2016-01-07", tz='UTC'),
trading_calendar=self.trading_calendar,
data_frequency="minute"
)
)
with make_test_handler(self) as log_catcher:
algo.run()
warnings = [r for r in log_catcher.records
if r.level == logbook.WARNING]
# one warning per order on the second day
self.assertEqual(6 * 390, len(warnings))
for w in warnings:
expected_message = (
'Cannot place order for ASSET{sid}, as it has de-listed. '
'Any existing positions for this asset will be liquidated '
'on {date}.'.format(sid=sid, date=asset.auto_close_date)
)
self.assertEqual(expected_message, w.message)
class AlgoInputValidationTestCase(zf.WithMakeAlgo,
zf.ZiplineTestCase):
def test_reject_passing_both_api_methods_and_script(self):
script = dedent(
"""
def initialize(context):
pass
def handle_data(context, data):
pass
def before_trading_start(context, data):
pass
def analyze(context, results):
pass
"""
)
for method in ('initialize',
'handle_data',
'before_trading_start',
'analyze'):
with self.assertRaises(ValueError):
self.make_algo(
script=script,
**{method: lambda *args, **kwargs: None}
)
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