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catalyst/tests/test_algorithm.py
T
Stewart Douglas 4e2039c9b0 ENH: Coerce user input with API method decorator 
Previously we have capitalized input strings at different levels in
our code: in the user-facing API methods and in the asset finder.
This commit moves input string capitalization exclusively to the API
method to which the string was supplied. Specifically, the string is
capitalized by a preprocess API method decorator. The preprocess
decorator passes the input string to the newly defined
ensure_upper_case() method, which returns a TypeError if the argument
supplied is not a string.

ensure_upper_case() is defined in a new file, zipline/utils/input_validation.py.
The existing expect_types() method is also moved there.

Various tests in tests/test_assets.py are modified to account for the
fact that the asset finder method lookup_symol() no longer capitalizes
its supplied argument.
2015-10-08 15:41:33 -04:00

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#
# Copyright 2014 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 datetime
from datetime import timedelta
from mock import MagicMock
from nose_parameterized import parameterized
from six.moves import range, map
from textwrap import dedent
from unittest import TestCase
import numpy as np
import pandas as pd
from zipline.utils.api_support import ZiplineAPI
from zipline.utils.control_flow import nullctx
from zipline.utils.test_utils import (
setup_logger,
teardown_logger
)
import zipline.utils.factory as factory
import zipline.utils.simfactory as simfactory
from zipline.errors import (
OrderDuringInitialize,
RegisterTradingControlPostInit,
TradingControlViolation,
AccountControlViolation,
SymbolNotFound,
RootSymbolNotFound,
UnsupportedDatetimeFormat,
)
from zipline.test_algorithms import (
access_account_in_init,
access_portfolio_in_init,
AmbitiousStopLimitAlgorithm,
EmptyPositionsAlgorithm,
InvalidOrderAlgorithm,
RecordAlgorithm,
TestAlgorithm,
TestOrderAlgorithm,
TestOrderInstantAlgorithm,
TestOrderPercentAlgorithm,
TestOrderStyleForwardingAlgorithm,
TestOrderValueAlgorithm,
TestRegisterTransformAlgorithm,
TestTargetAlgorithm,
TestTargetPercentAlgorithm,
TestTargetValueAlgorithm,
SetLongOnlyAlgorithm,
SetAssetDateBoundsAlgorithm,
SetMaxPositionSizeAlgorithm,
SetMaxOrderCountAlgorithm,
SetMaxOrderSizeAlgorithm,
SetDoNotOrderListAlgorithm,
SetMaxLeverageAlgorithm,
api_algo,
api_get_environment_algo,
api_symbol_algo,
call_all_order_methods,
call_order_in_init,
handle_data_api,
handle_data_noop,
initialize_api,
initialize_noop,
noop_algo,
record_float_magic,
record_variables,
)
import zipline.utils.events
from zipline.utils.test_utils import (
assert_single_position,
drain_zipline,
to_utc,
)
from zipline.sources import (SpecificEquityTrades,
DataFrameSource,
DataPanelSource,
RandomWalkSource)
from zipline.assets import Equity
from zipline.finance.execution import LimitOrder
from zipline.finance.trading import SimulationParameters
from zipline.utils.api_support import set_algo_instance
from zipline.utils.events import DateRuleFactory, TimeRuleFactory
from zipline.algorithm import TradingAlgorithm
from zipline.protocol import DATASOURCE_TYPE
from zipline.finance.trading import TradingEnvironment
from zipline.finance.commission import PerShare
# Because test cases appear to reuse some resources.
_multiprocess_can_split_ = False
class TestRecordAlgorithm(TestCase):
@classmethod
def setUpClass(cls):
cls.env = TradingEnvironment()
cls.env.write_data(equities_identifiers=[133])
@classmethod
def tearDownClass(cls):
del cls.env
def setUp(self):
self.sim_params = factory.create_simulation_parameters(num_days=4,
env=self.env)
trade_history = factory.create_trade_history(
133,
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(days=1),
self.sim_params,
self.env
)
self.source = SpecificEquityTrades(event_list=trade_history,
env=self.env)
self.df_source, self.df = \
factory.create_test_df_source(self.sim_params, self.env)
def test_record_incr(self):
algo = RecordAlgorithm(sim_params=self.sim_params, env=self.env)
output = algo.run(self.source)
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(TestCase):
@classmethod
def setUpClass(cls):
cls.sids = [1, 2]
cls.env = TradingEnvironment()
metadata = {3: {'symbol': 'PLAY',
'asset_type': 'equity',
'start_date': '2002-01-01',
'end_date': '2004-01-01'},
4: {'symbol': 'PLAY',
'asset_type': 'equity',
'start_date': '2005-01-01',
'end_date': '2006-01-01'}}
futures_metadata = {
5: {
'symbol': 'CLG06',
'root_symbol': 'CL',
'asset_type': 'future',
'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')},
6: {
'root_symbol': 'CL',
'symbol': 'CLK06',
'asset_type': 'future',
'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')},
7: {
'symbol': 'CLQ06',
'root_symbol': 'CL',
'asset_type': 'future',
'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')},
8: {
'symbol': 'CLX06',
'root_symbol': 'CL',
'asset_type': 'future',
'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')}
}
cls.env.write_data(equities_identifiers=cls.sids,
equities_data=metadata,
futures_data=futures_metadata)
@classmethod
def tearDownClass(cls):
del cls.env
def setUp(self):
setup_logger(self)
self.sim_params = factory.create_simulation_parameters(
num_days=2,
data_frequency='minute',
emission_rate='minute',
env=self.env,
)
self.source = factory.create_minutely_trade_source(
self.sids,
sim_params=self.sim_params,
concurrent=True,
env=self.env,
)
def tearDown(self):
teardown_logger(self)
def test_zipline_api_resolves_dynamically(self):
# Make a dummy algo.
algo = TradingAlgorithm(
initialize=lambda context: None,
handle_data=lambda context, data: None,
sim_params=self.sim_params,
)
# 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_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
algo = TradingAlgorithm(initialize=initialize,
handle_data=handle_data,
sim_params=self.sim_params,
env=self.env)
algo.run(self.source)
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
algo = TradingAlgorithm(initialize=initialize,
handle_data=handle_data,
sim_params=self.sim_params,
env=self.env)
algo.run(self.source)
def test_schedule_function(self):
date_rules = DateRuleFactory
time_rules = TimeRuleFactory
def incrementer(algo, data):
algo.func_called += 1
self.assertEqual(
algo.get_datetime().time(),
datetime.time(hour=14, minute=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 = TradingAlgorithm(
initialize=initialize,
handle_data=handle_data,
sim_params=self.sim_params,
env=self.env,
)
algo.run(self.source)
self.assertEqual(algo.func_called, algo.days)
@parameterized.expand([
('daily',),
('minute'),
])
def test_schedule_funtion_rule_creation(self, mode):
def nop(*args, **kwargs):
return None
self.sim_params.data_frequency = mode
algo = TradingAlgorithm(
initialize=nop,
handle_data=nop,
sim_params=self.sim_params,
env=self.env,
)
# Schedule something for NOT Always.
algo.schedule_function(nop, time_rule=zipline.utils.events.Never())
event_rule = algo.event_manager._events[1].rule
self.assertIsInstance(event_rule, zipline.utils.events.OncePerDay)
inner_rule = event_rule.rule
self.assertIsInstance(inner_rule, zipline.utils.events.ComposedRule)
first = inner_rule.first
second = inner_rule.second
composer = inner_rule.composer
self.assertIsInstance(first, zipline.utils.events.Always)
if mode == 'daily':
self.assertIsInstance(second, zipline.utils.events.Always)
else:
self.assertIsInstance(second, zipline.utils.events.Never)
self.assertIs(composer, zipline.utils.events.ComposedRule.lazy_and)
def test_asset_lookup(self):
algo = TradingAlgorithm(env=self.env)
# Test before either PLAY existed
algo.sim_params.period_end = 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.period_end = 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.period_end = pd.Timestamp('2004-12-01', tz='UTC')
self.assertEqual(3, algo.symbol('PLAY'))
# Test after second PLAY begins
algo.sim_params.period_end = pd.Timestamp('2005-12-01', tz='UTC')
self.assertEqual(4, algo.symbol('PLAY'))
# Test after second PLAY ends
algo.sim_params.period_end = 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 = TradingAlgorithm(env=self.env)
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'})
def test_future_chain(self):
""" Tests the future_chain API function.
"""
algo = TradingAlgorithm(env=self.env)
algo.datetime = pd.Timestamp('2006-12-01', tz='UTC')
# Check that the fields of the FutureChain object are set correctly
cl = algo.future_chain('CL')
self.assertEqual(cl.root_symbol, 'CL')
self.assertEqual(cl.as_of_date, algo.datetime)
# Check the fields are set correctly if an as_of_date is supplied
as_of_date = pd.Timestamp('1952-08-11', tz='UTC')
cl = algo.future_chain('CL', as_of_date=as_of_date)
self.assertEqual(cl.root_symbol, 'CL')
self.assertEqual(cl.as_of_date, as_of_date)
cl = algo.future_chain('CL', as_of_date='1952-08-11')
self.assertEqual(cl.root_symbol, 'CL')
self.assertEqual(cl.as_of_date, as_of_date)
# Check that weird capitalization is corrected
cl = algo.future_chain('cL')
self.assertEqual(cl.root_symbol, 'CL')
cl = algo.future_chain('cl')
self.assertEqual(cl.root_symbol, 'CL')
# Check that invalid root symbols raise RootSymbolNotFound
with self.assertRaises(RootSymbolNotFound):
algo.future_chain('CLZ')
with self.assertRaises(RootSymbolNotFound):
algo.future_chain('')
# Check that invalid dates raise UnsupportedDatetimeFormat
with self.assertRaises(UnsupportedDatetimeFormat):
algo.future_chain('CL', 'my_finger_slipped')
with self.assertRaises(UnsupportedDatetimeFormat):
algo.future_chain('CL', '2015-09-')
# Supplying a non-string argument to future_chain()
# should result in a TypeError.
with self.assertRaises(TypeError):
algo.future_chain(1)
with self.assertRaises(TypeError):
algo.future_chain((1,))
with self.assertRaises(TypeError):
algo.future_chain({1})
with self.assertRaises(TypeError):
algo.future_chain([1])
with self.assertRaises(TypeError):
algo.future_chain({'foo': 'bar'})
def test_set_symbol_lookup_date(self):
"""
Test the set_symbol_lookup_date API method.
"""
# Note we start sid enumeration at i+3 so as not to
# collide with sids [1, 2] added in the setUp() method.
dates = pd.date_range('2013-01-01', freq='2D', periods=2, tz='UTC')
# Create two assets with the same symbol but different
# non-overlapping date ranges.
metadata = pd.DataFrame.from_records(
[
{
'sid': i + 3,
'symbol': 'DUP',
'start_date': date.value,
'end_date': (date + timedelta(days=1)).value,
}
for i, date in enumerate(dates)
]
)
env = TradingEnvironment()
env.write_data(equities_df=metadata)
algo = TradingAlgorithm(env=env)
# Set the period end to a date after the period end
# dates for our assets.
algo.sim_params.period_end = pd.Timestamp('2015-01-01', tz='UTC')
# With no symbol lookup date set, we will use the period end date
# for the as_of_date, resulting here in the asset with the earlier
# start date being returned.
result = algo.symbol('DUP')
self.assertEqual(result.symbol, 'DUP')
# By first calling set_symbol_lookup_date, the relevant asset
# should be returned by lookup_symbol
for i, date in enumerate(dates):
algo.set_symbol_lookup_date(date)
result = algo.symbol('DUP')
self.assertEqual(result.symbol, 'DUP')
self.assertEqual(result.sid, i + 3)
with self.assertRaises(UnsupportedDatetimeFormat):
algo.set_symbol_lookup_date('foobar')
class TestTransformAlgorithm(TestCase):
@classmethod
def setUpClass(cls):
futures_metadata = {3: {'asset_type': 'future',
'contract_multiplier': 10}}
cls.env = TradingEnvironment()
cls.env.write_data(equities_identifiers=[0, 1, 133],
futures_data=futures_metadata)
@classmethod
def tearDownClass(cls):
del cls.env
def setUp(self):
setup_logger(self)
self.sim_params = factory.create_simulation_parameters(num_days=4,
env=self.env)
trade_history = factory.create_trade_history(
133,
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(days=1),
self.sim_params,
self.env
)
self.source = SpecificEquityTrades(
event_list=trade_history,
env=self.env,
)
self.df_source, self.df = \
factory.create_test_df_source(self.sim_params, self.env)
self.panel_source, self.panel = \
factory.create_test_panel_source(self.sim_params, self.env)
def tearDown(self):
teardown_logger(self)
def test_source_as_input(self):
algo = TestRegisterTransformAlgorithm(
sim_params=self.sim_params,
env=self.env,
sids=[133]
)
algo.run(self.source)
self.assertEqual(len(algo.sources), 1)
assert isinstance(algo.sources[0], SpecificEquityTrades)
def test_invalid_order_parameters(self):
algo = InvalidOrderAlgorithm(
sids=[133],
sim_params=self.sim_params,
env=self.env,
)
algo.run(self.source)
def test_multi_source_as_input(self):
sim_params = SimulationParameters(
self.df.index[0],
self.df.index[-1],
env=self.env,
)
algo = TestRegisterTransformAlgorithm(
sim_params=sim_params,
sids=[0, 1],
env=self.env,
)
algo.run([self.source, self.df_source], overwrite_sim_params=False)
self.assertEqual(len(algo.sources), 2)
def test_df_as_input(self):
algo = TestRegisterTransformAlgorithm(
sim_params=self.sim_params,
env=self.env,
)
algo.run(self.df)
assert isinstance(algo.sources[0], DataFrameSource)
def test_panel_as_input(self):
algo = TestRegisterTransformAlgorithm(
sim_params=self.sim_params,
env=self.env,
sids=[0, 1])
panel = self.panel.copy()
panel.items = pd.Index(map(Equity, panel.items))
algo.run(panel)
assert isinstance(algo.sources[0], DataPanelSource)
def test_df_of_assets_as_input(self):
algo = TestRegisterTransformAlgorithm(
sim_params=self.sim_params,
env=TradingEnvironment(), # new env without assets
)
df = self.df.copy()
df.columns = pd.Index(map(Equity, df.columns))
algo.run(df)
assert isinstance(algo.sources[0], DataFrameSource)
def test_panel_of_assets_as_input(self):
algo = TestRegisterTransformAlgorithm(
sim_params=self.sim_params,
env=TradingEnvironment(), # new env without assets
sids=[0, 1])
algo.run(self.panel)
assert isinstance(algo.sources[0], DataPanelSource)
def test_run_twice(self):
algo1 = TestRegisterTransformAlgorithm(
sim_params=self.sim_params,
sids=[0, 1]
)
res1 = algo1.run(self.df)
# Create a new trading algorithm, which will
# use the newly instantiated environment.
algo2 = TestRegisterTransformAlgorithm(
sim_params=self.sim_params,
sids=[0, 1]
)
res2 = algo2.run(self.df)
np.testing.assert_array_equal(res1, res2)
def test_data_frequency_setting(self):
self.sim_params.data_frequency = 'daily'
algo = TestRegisterTransformAlgorithm(
sim_params=self.sim_params,
env=self.env,
)
self.assertEqual(algo.sim_params.data_frequency, 'daily')
self.sim_params.data_frequency = 'minute'
algo = TestRegisterTransformAlgorithm(
sim_params=self.sim_params,
env=self.env,
)
self.assertEqual(algo.sim_params.data_frequency, 'minute')
@parameterized.expand([
(TestOrderAlgorithm,),
(TestOrderValueAlgorithm,),
(TestTargetAlgorithm,),
(TestOrderPercentAlgorithm,),
(TestTargetPercentAlgorithm,),
(TestTargetValueAlgorithm,),
])
def test_order_methods(self, algo_class):
algo = algo_class(
sim_params=self.sim_params,
env=self.env,
)
algo.run(self.df)
@parameterized.expand([
(TestOrderAlgorithm,),
(TestOrderValueAlgorithm,),
(TestTargetAlgorithm,),
(TestOrderPercentAlgorithm,),
(TestTargetValueAlgorithm,),
])
def test_order_methods_for_future(self, algo_class):
algo = algo_class(
sim_params=self.sim_params,
env=self.env,
)
algo.run(self.df)
def test_order_method_style_forwarding(self):
method_names_to_test = ['order',
'order_value',
'order_percent',
'order_target',
'order_target_percent',
'order_target_value']
for name in method_names_to_test:
# Don't supply an env so the TradingAlgorithm builds a new one for
# each method
algo = TestOrderStyleForwardingAlgorithm(
sim_params=self.sim_params,
instant_fill=False,
method_name=name
)
algo.run(self.df)
def test_order_instant(self):
algo = TestOrderInstantAlgorithm(sim_params=self.sim_params,
env=self.env,
instant_fill=True)
algo.run(self.df)
def test_minute_data(self):
source = RandomWalkSource(freq='minute',
start=pd.Timestamp('2000-1-3',
tz='UTC'),
end=pd.Timestamp('2000-1-4',
tz='UTC'))
self.sim_params.data_frequency = 'minute'
algo = TestOrderInstantAlgorithm(sim_params=self.sim_params,
env=self.env,
instant_fill=True)
algo.run(source)
class TestPositions(TestCase):
def setUp(self):
setup_logger(self)
self.env = TradingEnvironment()
self.sim_params = factory.create_simulation_parameters(num_days=4,
env=self.env)
self.env.write_data(equities_identifiers=[1, 133])
trade_history = factory.create_trade_history(
1,
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(days=1),
self.sim_params,
self.env
)
self.source = SpecificEquityTrades(
event_list=trade_history,
env=self.env,
)
self.df_source, self.df = \
factory.create_test_df_source(self.sim_params, self.env)
def tearDown(self):
teardown_logger(self)
def test_empty_portfolio(self):
algo = EmptyPositionsAlgorithm(sim_params=self.sim_params,
env=self.env)
daily_stats = algo.run(self.df)
expected_position_count = [
0, # Before entering the first position
1, # After entering, exiting on this date
0, # After exiting
0,
]
for i, expected in enumerate(expected_position_count):
self.assertEqual(daily_stats.ix[i]['num_positions'],
expected)
def test_noop_orders(self):
algo = AmbitiousStopLimitAlgorithm(sid=1,
sim_params=self.sim_params,
env=self.env)
daily_stats = algo.run(self.source)
# Verify that possitions are empty for all dates.
empty_positions = daily_stats.positions.map(lambda x: len(x) == 0)
self.assertTrue(empty_positions.all())
class TestAlgoScript(TestCase):
@classmethod
def setUpClass(cls):
cls.env = TradingEnvironment()
cls.env.write_data(
equities_identifiers=[0, 1, 133]
)
@classmethod
def tearDownClass(cls):
del cls.env
def setUp(self):
days = 251
# Note that create_simulation_parameters creates
# a new TradingEnvironment
self.sim_params = factory.create_simulation_parameters(num_days=days,
env=self.env)
setup_logger(self)
trade_history = factory.create_trade_history(
133,
[10.0] * days,
[100] * days,
timedelta(days=1),
self.sim_params,
self.env
)
self.source = SpecificEquityTrades(
sids=[133],
event_list=trade_history,
env=self.env,
)
self.df_source, self.df = \
factory.create_test_df_source(self.sim_params, self.env)
self.zipline_test_config = {
'sid': 0,
}
def tearDown(self):
teardown_logger(self)
def test_noop(self):
algo = TradingAlgorithm(initialize=initialize_noop,
handle_data=handle_data_noop)
algo.run(self.df)
def test_noop_string(self):
algo = TradingAlgorithm(script=noop_algo)
algo.run(self.df)
def test_api_calls(self):
algo = TradingAlgorithm(initialize=initialize_api,
handle_data=handle_data_api)
algo.run(self.df)
def test_api_calls_string(self):
algo = TradingAlgorithm(script=api_algo)
algo.run(self.df)
def test_api_get_environment(self):
platform = 'zipline'
# Use sid not already in test database.
metadata = {3: {'symbol': 'TEST',
'asset_type': 'equity'}}
algo = TradingAlgorithm(script=api_get_environment_algo,
equities_metadata=metadata,
platform=platform)
algo.run(self.df)
self.assertEqual(algo.environment, platform)
def test_api_symbol(self):
# Use sid not already in test database.
metadata = {3: {'symbol': 'TEST',
'asset_type': 'equity'}}
algo = TradingAlgorithm(script=api_symbol_algo,
equities_metadata=metadata)
algo.run(self.df)
def test_fixed_slippage(self):
# verify order -> transaction -> portfolio position.
# --------------
test_algo = TradingAlgorithm(
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[0].price)
context.incr += 1""",
sim_params=self.sim_params,
env=self.env,
)
set_algo_instance(test_algo)
self.zipline_test_config['algorithm'] = test_algo
self.zipline_test_config['trade_count'] = 200
# this matches the value in the algotext initialize
# method, and will be used inside assert_single_position
# to confirm we have as many transactions as orders we
# placed.
self.zipline_test_config['order_count'] = 1
zipline = simfactory.create_test_zipline(
**self.zipline_test_config)
output, _ = assert_single_position(self, zipline)
# confirm the slippage and commission on a sample
# transaction
recorded_price = output[1]['daily_perf']['recorded_vars']['price']
transaction = output[1]['daily_perf']['transactions'][0]
self.assertEqual(100.0, transaction['commission'])
expected_spread = 0.05
expected_commish = 0.10
expected_price = recorded_price - expected_spread - expected_commish
self.assertEqual(expected_price, transaction['price'])
def test_volshare_slippage(self):
# verify order -> transaction -> portfolio position.
# --------------
test_algo = TradingAlgorithm(
script="""
from zipline.api import *
def initialize(context):
model = slippage.VolumeShareSlippage(
volume_limit=.3,
price_impact=0.05
)
set_slippage(model)
set_commission(commission.PerShare(0.02))
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[0].price)
record(volume=data[0].volume)
record(incr=context.incr)
context.incr += 1
""",
sim_params=self.sim_params,
env=self.env,
)
set_algo_instance(test_algo)
self.zipline_test_config['algorithm'] = test_algo
self.zipline_test_config['trade_count'] = 100
# 67 will be used inside assert_single_position
# to confirm we have as many transactions as expected.
# The algo places 2 trades of 5000 shares each. The trade
# events have volume ranging from 100 to 950. The volume cap
# of 0.3 limits the trade volume to a range of 30 - 316 shares.
# The spreadsheet linked below calculates the total position
# size over each bar, and predicts 67 txns will be required
# to fill the two orders. The number of bars and transactions
# differ because some bars result in multiple txns. See
# spreadsheet for details:
# https://www.dropbox.com/s/ulrk2qt0nrtrigb/Volume%20Share%20Worksheet.xlsx
self.zipline_test_config['expected_transactions'] = 67
zipline = simfactory.create_test_zipline(
**self.zipline_test_config)
output, _ = assert_single_position(self, zipline)
# confirm the slippage and commission on a sample
# transaction
per_share_commish = 0.02
perf = output[1]
transaction = perf['daily_perf']['transactions'][0]
commish = transaction['amount'] * per_share_commish
self.assertEqual(commish, transaction['commission'])
self.assertEqual(2.029, transaction['price'])
def test_algo_record_vars(self):
test_algo = TradingAlgorithm(
script=record_variables,
sim_params=self.sim_params,
env=self.env,
)
set_algo_instance(test_algo)
self.zipline_test_config['algorithm'] = test_algo
self.zipline_test_config['trade_count'] = 200
zipline = simfactory.create_test_zipline(
**self.zipline_test_config)
output, _ = drain_zipline(self, zipline)
self.assertEqual(len(output), 252)
incr = []
for o in output[:200]:
incr.append(o['daily_perf']['recorded_vars']['incr'])
np.testing.assert_array_equal(incr, range(1, 201))
def test_algo_record_allow_mock(self):
"""
Test that values from "MagicMock"ed methods can be passed to record.
Relevant for our basic/validation and methods like history, which
will end up returning a MagicMock instead of a DataFrame.
"""
test_algo = TradingAlgorithm(
script=record_variables,
sim_params=self.sim_params,
)
set_algo_instance(test_algo)
test_algo.record(foo=MagicMock())
def _algo_record_float_magic_should_pass(self, var_type):
test_algo = TradingAlgorithm(
script=record_float_magic % var_type,
sim_params=self.sim_params,
env=self.env,
)
set_algo_instance(test_algo)
self.zipline_test_config['algorithm'] = test_algo
self.zipline_test_config['trade_count'] = 200
zipline = simfactory.create_test_zipline(
**self.zipline_test_config)
output, _ = drain_zipline(self, zipline)
self.assertEqual(len(output), 252)
incr = []
for o in output[:200]:
incr.append(o['daily_perf']['recorded_vars']['data'])
np.testing.assert_array_equal(incr, [np.nan] * 200)
def test_algo_record_nan(self):
self._algo_record_float_magic_should_pass('nan')
def test_order_methods(self):
"""
Only test that order methods can be called without error.
Correct filling of orders is tested in zipline.
"""
test_algo = TradingAlgorithm(
script=call_all_order_methods,
sim_params=self.sim_params,
env=self.env,
)
set_algo_instance(test_algo)
self.zipline_test_config['algorithm'] = test_algo
self.zipline_test_config['trade_count'] = 200
zipline = simfactory.create_test_zipline(
**self.zipline_test_config)
output, _ = drain_zipline(self, zipline)
def test_order_in_init(self):
"""
Test that calling order in initialize
will raise an error.
"""
with self.assertRaises(OrderDuringInitialize):
test_algo = TradingAlgorithm(
script=call_order_in_init,
sim_params=self.sim_params,
env=self.env,
)
set_algo_instance(test_algo)
test_algo.run(self.source)
def test_portfolio_in_init(self):
"""
Test that accessing portfolio in init doesn't break.
"""
test_algo = TradingAlgorithm(
script=access_portfolio_in_init,
sim_params=self.sim_params,
env=self.env,
)
set_algo_instance(test_algo)
self.zipline_test_config['algorithm'] = test_algo
self.zipline_test_config['trade_count'] = 1
zipline = simfactory.create_test_zipline(
**self.zipline_test_config)
output, _ = drain_zipline(self, zipline)
def test_account_in_init(self):
"""
Test that accessing account in init doesn't break.
"""
test_algo = TradingAlgorithm(
script=access_account_in_init,
sim_params=self.sim_params,
env=self.env,
)
set_algo_instance(test_algo)
self.zipline_test_config['algorithm'] = test_algo
self.zipline_test_config['trade_count'] = 1
zipline = simfactory.create_test_zipline(
**self.zipline_test_config)
output, _ = drain_zipline(self, zipline)
class TestHistory(TestCase):
def setUp(self):
setup_logger(self)
def tearDown(self):
teardown_logger(self)
@classmethod
def setUpClass(cls):
cls._start = pd.Timestamp('1991-01-01', tz='UTC')
cls._end = pd.Timestamp('1991-01-15', tz='UTC')
cls.env = TradingEnvironment()
cls.sim_params = factory.create_simulation_parameters(
data_frequency='minute',
env=cls.env
)
cls.env.write_data(equities_identifiers=[0, 1])
@classmethod
def tearDownClass(cls):
del cls.env
@property
def source(self):
return RandomWalkSource(start=self._start, end=self._end)
def test_history(self):
history_algo = """
from zipline.api import history, add_history
def initialize(context):
add_history(10, '1d', 'price')
def handle_data(context, data):
df = history(10, '1d', 'price')
"""
algo = TradingAlgorithm(
script=history_algo,
sim_params=self.sim_params,
env=self.env,
)
output = algo.run(self.source)
self.assertIsNot(output, None)
def test_history_without_add(self):
def handle_data(algo, data):
algo.history(1, '1m', 'price')
algo = TradingAlgorithm(
initialize=lambda _: None,
handle_data=handle_data,
sim_params=self.sim_params,
env=self.env,
)
algo.run(self.source)
self.assertIsNotNone(algo.history_container)
self.assertEqual(algo.history_container.buffer_panel.window_length, 1)
def test_add_history_in_handle_data(self):
def handle_data(algo, data):
algo.add_history(1, '1m', 'price')
algo = TradingAlgorithm(
initialize=lambda _: None,
handle_data=handle_data,
sim_params=self.sim_params,
env=self.env,
)
algo.run(self.source)
self.assertIsNotNone(algo.history_container)
self.assertEqual(algo.history_container.buffer_panel.window_length, 1)
class TestGetDatetime(TestCase):
@classmethod
def setUpClass(cls):
cls.env = TradingEnvironment()
cls.env.write_data(equities_identifiers=[0, 1])
@classmethod
def tearDownClass(cls):
del cls.env
def setUp(self):
setup_logger(self)
def tearDown(self):
teardown_logger(self)
@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):
if context.first_bar:
dt = get_datetime({tz})
if dt.tz.zone != context.tz:
raise ValueError("Mismatched Zone")
elif 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))
)
start = to_utc('2014-01-02 9:31')
end = to_utc('2014-01-03 9:31')
source = RandomWalkSource(
start=start,
end=end,
)
sim_params = factory.create_simulation_parameters(
data_frequency='minute',
env=self.env,
)
algo = TradingAlgorithm(
script=algo,
sim_params=sim_params,
env=self.env,
)
algo.run(source)
self.assertFalse(algo.first_bar)
class TestTradingControls(TestCase):
@classmethod
def setUpClass(cls):
cls.sid = 133
cls.env = TradingEnvironment()
cls.env.write_data(equities_identifiers=[cls.sid])
@classmethod
def tearDownClass(cls):
del cls.env
def setUp(self):
self.sim_params = factory.create_simulation_parameters(num_days=4,
env=self.env)
self.trade_history = factory.create_trade_history(
self.sid,
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(days=1),
self.sim_params,
self.env
)
self.source = SpecificEquityTrades(
event_list=self.trade_history,
env=self.env,
)
def _check_algo(self,
algo,
handle_data,
expected_order_count,
expected_exc):
algo._handle_data = handle_data
with self.assertRaises(expected_exc) if expected_exc else nullctx():
algo.run(self.source)
self.assertEqual(algo.order_count, expected_order_count)
self.source.rewind()
def check_algo_succeeds(self, algo, handle_data, order_count=4):
# Default for order_count assumes one order per handle_data call.
self._check_algo(algo, handle_data, order_count, None)
def check_algo_fails(self, algo, handle_data, order_count):
self._check_algo(algo,
handle_data,
order_count,
TradingControlViolation)
def test_set_max_position_size(self):
# 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 = SetMaxPositionSizeAlgorithm(sid=self.sid,
max_shares=10,
max_notional=500.0,
sim_params=self.sim_params,
env=self.env)
self.check_algo_succeeds(algo, handle_data)
# 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 = SetMaxPositionSizeAlgorithm(sid=self.sid,
max_shares=10,
max_notional=500.0,
sim_params=self.sim_params,
env=self.env)
self.check_algo_fails(algo, handle_data, 3)
# Buy two 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 = SetMaxPositionSizeAlgorithm(sid=self.sid,
max_shares=10,
max_notional=61.0,
sim_params=self.sim_params,
env=self.env)
self.check_algo_fails(algo, handle_data, 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 = SetMaxPositionSizeAlgorithm(sid=self.sid + 1,
max_shares=10,
max_notional=61.0,
sim_params=self.sim_params,
env=self.env)
self.check_algo_succeeds(algo, handle_data)
# 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 = SetMaxPositionSizeAlgorithm(max_shares=10, max_notional=61.0,
sim_params=self.sim_params,
env=self.env)
self.check_algo_fails(algo, handle_data, 0)
def test_set_do_not_order_list(self):
# set the restricted list to be the sid, and fail.
algo = SetDoNotOrderListAlgorithm(
sid=self.sid,
restricted_list=[self.sid],
sim_params=self.sim_params,
env=self.env,
)
def handle_data(algo, data):
algo.order(algo.sid(self.sid), 100)
algo.order_count += 1
self.check_algo_fails(algo, handle_data, 0)
# set the restricted list to exclude the sid, and succeed
algo = SetDoNotOrderListAlgorithm(
sid=self.sid,
restricted_list=[134, 135, 136],
sim_params=self.sim_params,
env=self.env,
)
def handle_data(algo, data):
algo.order(algo.sid(self.sid), 100)
algo.order_count += 1
self.check_algo_succeeds(algo, handle_data)
def test_set_max_order_size(self):
# Buy one share.
def handle_data(algo, data):
algo.order(algo.sid(self.sid), 1)
algo.order_count += 1
algo = SetMaxOrderSizeAlgorithm(sid=self.sid,
max_shares=10,
max_notional=500.0,
sim_params=self.sim_params,
env=self.env)
self.check_algo_succeeds(algo, handle_data)
# 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 = SetMaxOrderSizeAlgorithm(sid=self.sid,
max_shares=3,
max_notional=500.0,
sim_params=self.sim_params,
env=self.env)
self.check_algo_fails(algo, handle_data, 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 = SetMaxOrderSizeAlgorithm(sid=self.sid,
max_shares=10,
max_notional=40.0,
sim_params=self.sim_params,
env=self.env)
self.check_algo_fails(algo, handle_data, 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 = SetMaxOrderSizeAlgorithm(sid=self.sid + 1,
max_shares=1,
max_notional=1.0,
sim_params=self.sim_params,
env=self.env)
self.check_algo_succeeds(algo, handle_data)
# 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 = SetMaxOrderSizeAlgorithm(max_shares=1,
max_notional=1.0,
sim_params=self.sim_params,
env=self.env)
self.check_algo_fails(algo, handle_data, 0)
def test_set_max_order_count(self):
# Override the default setUp to use six-hour intervals instead of full
# days so we can exercise trading-session rollover logic.
trade_history = factory.create_trade_history(
self.sid,
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(hours=6),
self.sim_params,
self.env
)
self.source = SpecificEquityTrades(event_list=trade_history,
env=self.env)
def handle_data(algo, data):
for i in range(5):
algo.order(algo.sid(self.sid), 1)
algo.order_count += 1
algo = SetMaxOrderCountAlgorithm(3, sim_params=self.sim_params,
env=self.env)
self.check_algo_fails(algo, handle_data, 3)
# Second call to handle_data is the same day as the first, so the last
# order of the second call should fail.
algo = SetMaxOrderCountAlgorithm(9, sim_params=self.sim_params,
env=self.env)
self.check_algo_fails(algo, handle_data, 9)
# Only ten orders are placed per day, so this should pass even though
# in total more than 20 orders are placed.
algo = SetMaxOrderCountAlgorithm(10, sim_params=self.sim_params,
env=self.env)
self.check_algo_succeeds(algo, handle_data, order_count=20)
def test_long_only(self):
# Sell immediately -> fail immediately.
def handle_data(algo, data):
algo.order(algo.sid(self.sid), -1)
algo.order_count += 1
algo = SetLongOnlyAlgorithm(sim_params=self.sim_params, env=self.env)
self.check_algo_fails(algo, handle_data, 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 = SetLongOnlyAlgorithm(sim_params=self.sim_params, env=self.env)
self.check_algo_succeeds(algo, handle_data)
# 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 = SetLongOnlyAlgorithm(sim_params=self.sim_params, env=self.env)
self.check_algo_succeeds(algo, handle_data)
# 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 = SetLongOnlyAlgorithm(sim_params=self.sim_params, env=self.env)
self.check_algo_fails(algo, handle_data, 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()
algo = TradingAlgorithm(initialize=initialize,
handle_data=handle_data,
sim_params=self.sim_params,
env=self.env)
algo.run(self.source)
self.source.rewind()
def test_asset_date_bounds(self):
# Run the algorithm with a sid that ends far in the future
temp_env = TradingEnvironment()
df_source, _ = factory.create_test_df_source(self.sim_params, temp_env)
metadata = {0: {'start_date': '1990-01-01',
'end_date': '2020-01-01'}}
algo = SetAssetDateBoundsAlgorithm(
equities_metadata=metadata,
sim_params=self.sim_params,
env=temp_env,
)
algo.run(df_source)
# Run the algorithm with a sid that has already ended
temp_env = TradingEnvironment()
df_source, _ = factory.create_test_df_source(self.sim_params, temp_env)
metadata = {0: {'start_date': '1989-01-01',
'end_date': '1990-01-01'}}
algo = SetAssetDateBoundsAlgorithm(
equities_metadata=metadata,
sim_params=self.sim_params,
env=temp_env,
)
with self.assertRaises(TradingControlViolation):
algo.run(df_source)
# Run the algorithm with a sid that has not started
temp_env = TradingEnvironment()
df_source, _ = factory.create_test_df_source(self.sim_params, temp_env)
metadata = {0: {'start_date': '2020-01-01',
'end_date': '2021-01-01'}}
algo = SetAssetDateBoundsAlgorithm(
equities_metadata=metadata,
sim_params=self.sim_params,
env=temp_env,
)
with self.assertRaises(TradingControlViolation):
algo.run(df_source)
# Run the algorithm with a sid that starts on the first day and
# ends on the last day of the algorithm's parameters (*not* an error).
temp_env = TradingEnvironment()
df_source, _ = factory.create_test_df_source(self.sim_params, temp_env)
metadata = {0: {'start_date': '2006-01-03',
'end_date': '2006-01-06'}}
algo = SetAssetDateBoundsAlgorithm(
equities_metadata=metadata,
sim_params=self.sim_params,
env=temp_env,
)
algo.run(df_source)
class TestAccountControls(TestCase):
@classmethod
def setUpClass(cls):
cls.sidint = 133
cls.env = TradingEnvironment()
cls.env.write_data(
equities_identifiers=[cls.sidint]
)
@classmethod
def tearDownClass(cls):
del cls.env
def setUp(self):
self.sim_params = factory.create_simulation_parameters(
num_days=4, env=self.env
)
self.trade_history = factory.create_trade_history(
self.sidint,
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(days=1),
self.sim_params,
self.env,
)
self.source = SpecificEquityTrades(
event_list=self.trade_history,
env=self.env,
)
def _check_algo(self,
algo,
handle_data,
expected_exc):
algo._handle_data = handle_data
with self.assertRaises(expected_exc) if expected_exc else nullctx():
algo.run(self.source)
self.source.rewind()
def check_algo_succeeds(self, algo, handle_data):
# Default for order_count assumes one order per handle_data call.
self._check_algo(algo, handle_data, None)
def check_algo_fails(self, algo, handle_data):
self._check_algo(algo,
handle_data,
AccountControlViolation)
def test_set_max_leverage(self):
# Set max leverage to 0 so buying one share fails.
def handle_data(algo, data):
algo.order(algo.sid(self.sidint), 1)
algo = SetMaxLeverageAlgorithm(0, sim_params=self.sim_params,
env=self.env)
self.check_algo_fails(algo, handle_data)
# Set max leverage to 1 so buying one share passes
def handle_data(algo, data):
algo.order(algo.sid(self.sidint), 1)
algo = SetMaxLeverageAlgorithm(1, sim_params=self.sim_params,
env=self.env)
self.check_algo_succeeds(algo, handle_data)
class TestClosePosAlgo(TestCase):
def setUp(self):
self.env = TradingEnvironment()
self.days = self.env.trading_days[:4]
self.panel = pd.Panel({1: pd.DataFrame({
'price': [1, 1, 2, 4], 'volume': [1e9, 1e9, 1e9, 0],
'type': [DATASOURCE_TYPE.TRADE,
DATASOURCE_TYPE.TRADE,
DATASOURCE_TYPE.TRADE,
DATASOURCE_TYPE.CLOSE_POSITION]},
index=self.days)
})
self.no_close_panel = pd.Panel({1: pd.DataFrame({
'price': [1, 1, 2, 4], 'volume': [1e9, 1e9, 1e9, 1e9],
'type': [DATASOURCE_TYPE.TRADE,
DATASOURCE_TYPE.TRADE,
DATASOURCE_TYPE.TRADE,
DATASOURCE_TYPE.TRADE]},
index=self.days)
})
def test_close_position_equity(self):
metadata = {1: {'symbol': 'TEST',
'asset_type': 'equity',
'end_date': self.days[3]}}
self.env.write_data(equities_data=metadata)
algo = TestAlgorithm(sid=1, amount=1, order_count=1,
commission=PerShare(0),
env=self.env)
data = DataPanelSource(self.panel)
# Check results
expected_positions = [0, 1, 1, 0]
expected_pnl = [0, 0, 1, 2]
results = algo.run(data)
self.check_algo_positions(results, expected_positions)
self.check_algo_pnl(results, expected_pnl)
def test_close_position_future(self):
metadata = {1: {'symbol': 'TEST',
'asset_type': 'future',
}}
self.env.write_data(futures_data=metadata)
algo = TestAlgorithm(sid=1, amount=1, order_count=1,
commission=PerShare(0),
env=self.env)
data = DataPanelSource(self.panel)
# Check results
expected_positions = [0, 1, 1, 0]
expected_pnl = [0, 0, 1, 2]
results = algo.run(data)
self.check_algo_pnl(results, expected_pnl)
self.check_algo_positions(results, expected_positions)
def test_auto_close_future(self):
metadata = {1: {'symbol': 'TEST',
'asset_type': 'future',
'auto_close_date': self.env.trading_days[4]}}
self.env.write_data(futures_data=metadata)
algo = TestAlgorithm(sid=1, amount=1, order_count=1,
commission=PerShare(0),
env=self.env)
data = DataPanelSource(self.no_close_panel)
# Check results
results = algo.run(data)
expected_positions = [0, 1, 1, 0]
self.check_algo_positions(results, expected_positions)
expected_pnl = [0, 0, 1, 2]
self.check_algo_pnl(results, expected_pnl)
def check_algo_pnl(self, results, expected_pnl):
np.testing.assert_array_almost_equal(results.pnl, expected_pnl)
def check_algo_positions(self, results, expected_positions):
for i, amount in enumerate(results.positions):
if amount:
actual_position = amount[0]['amount']
else:
actual_position = 0
self.assertEqual(
actual_position, expected_positions[i],
"position for day={0} not equal, actual={1}, expected={2}".
format(i, actual_position, expected_positions[i]))
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