mirror of
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126 lines
4.3 KiB
Python
126 lines
4.3 KiB
Python
#
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# Copyright 2014 Quantopian, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import pandas as pd
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from zipline.gens.sim_engine import BEFORE_TRADING_START_BAR
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from zipline.finance.asset_restrictions import NoRestrictions
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from zipline.finance import metrics
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from zipline.finance.trading import SimulationParameters
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from zipline.gens.tradesimulation import AlgorithmSimulator
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from zipline.testing.core import parameter_space
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import zipline.testing.fixtures as zf
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class TestBeforeTradingStartTiming(zf.WithMakeAlgo,
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zf.WithTradingSessions,
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zf.ZiplineTestCase):
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ASSET_FINDER_EQUITY_SIDS = (1,)
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BENCHMARK_SID = 1
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# These dates are chosen to cross a DST transition.
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# March 2016
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# Su Mo Tu We Th Fr Sa
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# 1 2 3 4 5
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# 6 7 8 9 10 11 12
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# 13 14 15 16 17 18 19
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# 20 21 22 23 24 25 26
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# 27 28 29 30 31
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START_DATE = pd.Timestamp('2016-03-10', tz='UTC')
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END_DATE = pd.Timestamp('2016-03-15', tz='UTC')
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@parameter_space(
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num_sessions=[1, 2, 3],
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data_frequency=['daily', 'minute'],
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emission_rate=['daily', 'minute'],
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__fail_fast=True,
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)
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def test_before_trading_start_runs_at_8_45(self,
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num_sessions,
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data_frequency,
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emission_rate):
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bts_times = []
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def initialize(algo, data):
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pass
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def before_trading_start(algo, data):
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bts_times.append(algo.get_datetime())
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sim_params = SimulationParameters(
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# start at index 1 so we have an extra day to calculate benchmark
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# returns.
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start_session=self.nyse_sessions[1],
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end_session=self.nyse_sessions[num_sessions],
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data_frequency=data_frequency,
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emission_rate=emission_rate,
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trading_calendar=self.trading_calendar,
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)
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self.run_algorithm(
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before_trading_start=before_trading_start,
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sim_params=sim_params,
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)
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self.assertEqual(len(bts_times), num_sessions)
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expected_times = [
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pd.Timestamp('2016-03-11 8:45', tz='US/Eastern').tz_convert('UTC'),
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pd.Timestamp('2016-03-14 8:45', tz='US/Eastern').tz_convert('UTC'),
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pd.Timestamp('2016-03-15 8:45', tz='US/Eastern').tz_convert('UTC'),
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]
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self.assertEqual(bts_times, expected_times[:num_sessions])
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class BeforeTradingStartsOnlyClock(object):
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def __init__(self, bts_minute):
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self.bts_minute = bts_minute
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def __iter__(self):
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yield self.bts_minute, BEFORE_TRADING_START_BAR
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class TestBeforeTradingStartSimulationDt(zf.WithMakeAlgo, zf.ZiplineTestCase):
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SIM_PARAMS_DATA_FREQUENCY = 'daily'
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DATA_PORTAL_USE_MINUTE_DATA = False
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def test_bts_simulation_dt(self):
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code = """
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def initialize(context):
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pass
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"""
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algo = self.make_algo(script=code, metrics=metrics.load('none'))
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algo.metrics_tracker = algo._create_metrics_tracker()
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benchmark_source = algo._create_benchmark_source()
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algo.metrics_tracker.handle_start_of_simulation(benchmark_source)
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dt = pd.Timestamp("2016-08-04 9:13:14", tz='US/Eastern')
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algo_simulator = AlgorithmSimulator(
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algo,
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self.sim_params,
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self.data_portal,
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BeforeTradingStartsOnlyClock(dt),
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benchmark_source,
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NoRestrictions(),
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None
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)
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# run through the algo's simulation
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list(algo_simulator.transform())
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# since the clock only ever emitted a single before_trading_start
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# event, we can check that the simulation_dt was properly set
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self.assertEqual(dt, algo_simulator.simulation_dt)
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