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zipline/tests/test_algorithm.py
twiecki 3eb810ad97 ENH: Add simulated random trade source. 
This adds a new data source that emits events
with certain user-specified frequency (minute
or daily).

This allows users to backtest and debug an
algorithm in minute mode to provide a cleaner
path towards Quantopian.
2014-03-22 21:22:22 -04:00

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Python
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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.
from unittest import TestCase
from datetime import timedelta
import numpy as np
import pandas as pd
from mock import MagicMock
from zipline.utils.test_utils import setup_logger
import zipline.utils.factory as factory
import zipline.utils.simfactory as simfactory
from zipline.test_algorithms import (TestRegisterTransformAlgorithm,
RecordAlgorithm,
TestOrderAlgorithm,
TestOrderInstantAlgorithm,
TestOrderValueAlgorithm,
TestTargetAlgorithm,
TestOrderPercentAlgorithm,
TestTargetPercentAlgorithm,
TestTargetValueAlgorithm,
EmptyPositionsAlgorithm,
initialize_noop,
handle_data_noop,
initialize_api,
handle_data_api,
noop_algo,
api_algo,
api_symbol_algo,
call_all_order_methods,
record_variables,
record_float_magic
)
from zipline.utils.test_utils import drain_zipline, assert_single_position
from zipline.sources import (SpecificEquityTrades,
DataFrameSource,
DataPanelSource,
RandomWalkSource)
from zipline.transforms import MovingAverage
from zipline.finance.trading import SimulationParameters
from zipline.utils.api_support import set_algo_instance
from zipline.algorithm import TradingAlgorithm
class TestRecordAlgorithm(TestCase):
def setUp(self):
self.sim_params = factory.create_simulation_parameters(num_days=4)
trade_history = factory.create_trade_history(
133,
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(days=1),
self.sim_params
)
self.source = SpecificEquityTrades(event_list=trade_history)
self.df_source, self.df = \
factory.create_test_df_source(self.sim_params)
def test_record_incr(self):
algo = RecordAlgorithm(
sim_params=self.sim_params,
data_frequency='daily')
output = algo.run(self.source)
np.testing.assert_array_equal(output['incr'].values,
range(1, len(output) + 1))
class TestTransformAlgorithm(TestCase):
def setUp(self):
setup_logger(self)
self.sim_params = factory.create_simulation_parameters(num_days=4)
setup_logger(self)
trade_history = factory.create_trade_history(
133,
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(days=1),
self.sim_params
)
self.source = SpecificEquityTrades(event_list=trade_history)
self.df_source, self.df = \
factory.create_test_df_source(self.sim_params)
self.panel_source, self.panel = \
factory.create_test_panel_source(self.sim_params)
def test_source_as_input(self):
algo = TestRegisterTransformAlgorithm(
sim_params=self.sim_params,
sids=[133]
)
algo.run(self.source)
self.assertEqual(len(algo.sources), 1)
assert isinstance(algo.sources[0], SpecificEquityTrades)
def test_multi_source_as_input_no_start_end(self):
algo = TestRegisterTransformAlgorithm(
sids=[133]
)
with self.assertRaises(AssertionError):
algo.run([self.source, self.df_source])
def test_multi_source_as_input(self):
sim_params = SimulationParameters(
self.df.index[0],
self.df.index[-1]
)
algo = TestRegisterTransformAlgorithm(
sim_params=sim_params,
sids=[0, 1, 133]
)
algo.run([self.source, self.df_source])
self.assertEqual(len(algo.sources), 2)
def test_df_as_input(self):
algo = TestRegisterTransformAlgorithm(
sim_params=self.sim_params,
sids=[0, 1]
)
algo.run(self.df)
assert isinstance(algo.sources[0], DataFrameSource)
def test_panel_as_input(self):
algo = TestRegisterTransformAlgorithm(
sim_params=self.sim_params,
sids=[0, 1])
algo.run(self.panel)
assert isinstance(algo.sources[0], DataPanelSource)
def test_run_twice(self):
algo = TestRegisterTransformAlgorithm(
sim_params=self.sim_params,
sids=[0, 1]
)
res1 = algo.run(self.df)
res2 = algo.run(self.df)
np.testing.assert_array_equal(res1, res2)
def test_transform_registered(self):
algo = TestRegisterTransformAlgorithm(
sim_params=self.sim_params,
sids=[133]
)
algo.run(self.source)
assert 'mavg' in algo.registered_transforms
assert algo.registered_transforms['mavg']['args'] == (['price'],)
assert algo.registered_transforms['mavg']['kwargs'] == \
{'window_length': 2, 'market_aware': True}
assert algo.registered_transforms['mavg']['class'] is MovingAverage
def test_data_frequency_setting(self):
algo = TestRegisterTransformAlgorithm(
sim_params=self.sim_params,
data_frequency='daily'
)
self.assertEqual(algo.data_frequency, 'daily')
self.assertEqual(algo.annualizer, 250)
algo = TestRegisterTransformAlgorithm(
sim_params=self.sim_params,
data_frequency='minute'
)
self.assertEqual(algo.data_frequency, 'minute')
self.assertEqual(algo.annualizer, 250 * 6 * 60)
algo = TestRegisterTransformAlgorithm(
sim_params=self.sim_params,
data_frequency='minute',
annualizer=10
)
self.assertEqual(algo.data_frequency, 'minute')
self.assertEqual(algo.annualizer, 10)
def test_order_methods(self):
AlgoClasses = [TestOrderAlgorithm,
TestOrderValueAlgorithm,
TestTargetAlgorithm,
TestOrderPercentAlgorithm,
TestTargetPercentAlgorithm,
TestTargetValueAlgorithm]
for AlgoClass in AlgoClasses:
algo = AlgoClass(
sim_params=self.sim_params,
data_frequency='daily'
)
algo.run(self.df)
def test_order_instant(self):
algo = TestOrderInstantAlgorithm(sim_params=self.sim_params,
data_frequency='daily',
instant_fill=True)
algo.run(self.df)
def test_minute_data(self):
source = RandomWalkSource(freq='minute',
start=pd.Timestamp('2000-1-1',
tz='UTC'),
end=pd.Timestamp('2000-1-1',
tz='UTC'))
algo = TestOrderInstantAlgorithm(sim_params=self.sim_params,
data_frequency='minute',
instant_fill=True)
algo.run(source)
class TestPositions(TestCase):
def setUp(self):
setup_logger(self)
self.sim_params = factory.create_simulation_parameters(num_days=4)
setup_logger(self)
trade_history = factory.create_trade_history(
1,
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(days=1),
self.sim_params
)
self.source = SpecificEquityTrades(event_list=trade_history)
self.df_source, self.df = \
factory.create_test_df_source(self.sim_params)
self.panel_source, self.panel = \
factory.create_test_panel_source(self.sim_params)
def test_empty_portfolio(self):
algo = EmptyPositionsAlgorithm(sim_params=self.sim_params,
data_frequency='daily')
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)
class TestAlgoScript(TestCase):
def setUp(self):
days = 251
self.sim_params = factory.create_simulation_parameters(num_days=days)
setup_logger(self)
trade_history = factory.create_trade_history(
133,
[10.0] * days,
[100] * days,
timedelta(days=1),
self.sim_params
)
self.source = SpecificEquityTrades(event_list=trade_history)
self.df_source, self.df = \
factory.create_test_df_source(self.sim_params)
self.zipline_test_config = {
'sid': 0,
}
def test_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_symbol(self):
algo = TradingAlgorithm(script=api_symbol_algo)
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)
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(0, -1000)
record(price=data[0].price)
context.incr += 1""",
sim_params=self.sim_params,
)
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
# self.zipline_test_config['transforms'] = \
# test_algo.transform_visitor.transforms.values()
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(0, 5000)
record(price=data[0].price)
record(volume=data[0].volume)
record(incr=context.incr)
context.incr += 1
""",
sim_params=self.sim_params,
)
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
# self.zipline_test_config['transforms'] = \
# test_algo.transform_visitor.transforms.values()
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,
)
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,
)
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,
)
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)
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