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TST: test history() in before_trading_start()
This commit is contained in:
Stewart Douglas
@@ -22,6 +22,7 @@ from nose_parameterized import parameterized
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import numpy as np
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import pandas as pd
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from pandas.util.testing import assert_frame_equal
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from pandas.tseries.tools import normalize_date
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from zipline.history import history
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from zipline.history.history_container import HistoryContainer
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@@ -557,6 +558,310 @@ def handle_data(context, data):
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self.assertEquals(139.36946942498648, last_prices[oldest_dt])
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self.assertEquals(180.15661995395106, last_prices[newest_dt])
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@parameterized.expand([
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('daily',),
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('minute',),
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])
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def test_history_in_bts_price_days(self, data_freq):
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"""
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Test calling history() in before_trading_start()
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with daily price bars.
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"""
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algo_text = """
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from zipline.api import history
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def initialize(context):
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context.first_bts_call = True
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def before_trading_start(context, data):
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if not context.first_bts_call:
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prices_bts = history(bar_count=3, frequency='1d', field='price')
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context.prices_bts = prices_bts
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context.first_bts_call = False
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def handle_data(context, data):
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prices_hd = history(bar_count=3, frequency='1d', field='price')
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context.prices_hd = prices_hd
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""".strip()
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# March 2006
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# Su Mo Tu We Th Fr Sa
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# 1 2 3 4
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# 5 6 7 8 9 10 11
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# 12 13 14 15 16 17 18
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# 19 20 21 22 23 24 25
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# 26 27 28 29 30 31
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start = pd.Timestamp('2006-03-20', tz='UTC')
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end = pd.Timestamp('2006-03-22', tz='UTC')
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sim_params = factory.create_simulation_parameters(
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start=start, end=end, data_frequency=data_freq)
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test_algo = TradingAlgorithm(
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script=algo_text,
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data_frequency=data_freq,
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sim_params=sim_params,
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env=TestHistoryAlgo.env,
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)
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source = RandomWalkSource(start=start, end=end, freq=data_freq)
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output = test_algo.run(source)
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self.assertIsNotNone(output)
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# Get the prices recorded by history() within handle_data()
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prices_hd = test_algo.prices_hd[0]
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# Get the prices recorded by history() within BTS
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prices_bts = test_algo.prices_bts[0]
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# before_trading_start() is timestamp'd to midnight prior to
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# the day's trading. Since no equity trades occur at midnight,
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# the price recorded for this time is forward filled from the
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# last trade - typically ~4pm the previous day. This results
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# in the OHLCV data recorded by history() in BTS lagging
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# that recorded by history in handle_data().
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# The trace of the pricing data from history() called within
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# handle_data() vs. BTS in the above algo is as follows:
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# When called within handle_data()
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# ---------------------------------
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# 2006-03-20 21:00:00 139.369469
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# 2006-03-21 21:00:00 180.156620
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# 2006-03-22 21:00:00 221.344654
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# When called within BTS
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# ---------------------------------
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# 2006-03-17 21:00:00 NaN
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# 2006-03-20 21:00:00 139.369469
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# 2006-03-22 00:00:00 180.156620
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# Get relevant Timestamps for the history() call within handle_data()
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oldest_hd_dt = pd.Timestamp(
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'2006-03-20 4:00 PM', tz='US/Eastern').tz_convert('UTC')
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penultimate_hd_dt = pd.Timestamp(
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'2006-03-21 4:00 PM', tz='US/Eastern').tz_convert('UTC')
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# Get relevant Timestamps for the history() call within BTS
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penultimate_bts_dt = pd.Timestamp(
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'2006-03-20 4:00 PM', tz='US/Eastern').tz_convert('UTC')
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newest_bts_dt = normalize_date(pd.Timestamp(
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'2006-03-22 04:00 PM', tz='US/Eastern').tz_convert('UTC'))
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if data_freq == 'daily':
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# If we're dealing with daily data, then we record
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# canonicalized timestamps, so make conversion here:
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oldest_hd_dt = normalize_date(oldest_hd_dt)
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penultimate_hd_dt = normalize_date(penultimate_hd_dt)
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penultimate_bts_dt = normalize_date(penultimate_bts_dt)
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self.assertEquals(prices_hd[oldest_hd_dt],
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prices_bts[penultimate_bts_dt])
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self.assertEquals(prices_hd[penultimate_hd_dt],
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prices_bts[newest_bts_dt])
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def test_history_in_bts_price_minutes(self):
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"""
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Test calling history() in before_trading_start()
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with minutely price bars.
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"""
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algo_text = """
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from zipline.api import history
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def initialize(context):
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context.first_bts_call = True
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def before_trading_start(context, data):
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if not context.first_bts_call:
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price_bts = history(bar_count=1, frequency='1m', field='price')
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context.price_bts = price_bts
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context.first_bts_call = False
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def handle_data(context, data):
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pass
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""".strip()
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# March 2006
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# Su Mo Tu We Th Fr Sa
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# 1 2 3 4
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# 5 6 7 8 9 10 11
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# 12 13 14 15 16 17 18
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# 19 20 21 22 23 24 25
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# 26 27 28 29 30 31
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start = pd.Timestamp('2006-03-20', tz='UTC')
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end = pd.Timestamp('2006-03-22', tz='UTC')
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sim_params = factory.create_simulation_parameters(
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start=start, end=end)
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test_algo = TradingAlgorithm(
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script=algo_text,
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data_frequency='minute',
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sim_params=sim_params,
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env=TestHistoryAlgo.env,
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)
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source = RandomWalkSource(start=start, end=end)
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output = test_algo.run(source)
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self.assertIsNotNone(output)
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# Get the prices recorded by history() within BTS
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price_bts_0 = test_algo.price_bts[0]
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price_bts_1 = test_algo.price_bts[1]
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# The prices recorded by history() in BTS should
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# be the closing price of the previous day, which are:
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#
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# sid | close on 2006-03-21
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# ----------------------------
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# 0 | 180.15661995395106
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# 1 | 578.41665003444723
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# These are not 'real' price values. They are the product of
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# RandonWalkSource, which produces random walk OHLCV timeseries. For a
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# given seed these values are deterministc.
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self.assertEquals(180.15661995395106, price_bts_0.ix[0])
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self.assertEquals(578.41665003444723, price_bts_1.ix[0])
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@parameterized.expand([
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('daily',),
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('minute',),
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])
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def test_history_in_bts_volume_days(self, data_freq):
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"""
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Test calling history() in before_trading_start()
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with daily volume bars.
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"""
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algo_text = """
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from zipline.api import history
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def initialize(context):
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context.first_bts_call = True
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def before_trading_start(context, data):
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if not context.first_bts_call:
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volume_bts = history(bar_count=2, frequency='1d', field='volume')
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context.volume_bts = volume_bts
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context.first_bts_call = False
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def handle_data(context, data):
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volume_hd = history(bar_count=2, frequency='1d', field='volume')
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context.volume_hd = volume_hd
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""".strip()
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# March 2006
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# Su Mo Tu We Th Fr Sa
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# 1 2 3 4
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# 5 6 7 8 9 10 11
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# 12 13 14 15 16 17 18
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# 19 20 21 22 23 24 25
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# 26 27 28 29 30 31
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start = pd.Timestamp('2006-03-20', tz='UTC')
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end = pd.Timestamp('2006-03-22', tz='UTC')
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sim_params = factory.create_simulation_parameters(
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start=start, end=end, data_frequency=data_freq)
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test_algo = TradingAlgorithm(
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script=algo_text,
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data_frequency=data_freq,
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sim_params=sim_params,
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env=TestHistoryAlgo.env,
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)
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source = RandomWalkSource(start=start, end=end, freq=data_freq)
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output = test_algo.run(source)
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self.assertIsNotNone(output)
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# Get the volume recorded by history() within handle_data()
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volume_hd_0 = test_algo.volume_hd[0]
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volume_hd_1 = test_algo.volume_hd[1]
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# Get the volume recorded by history() within BTS
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volume_bts_0 = test_algo.volume_bts[0]
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volume_bts_1 = test_algo.volume_bts[1]
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penultimate_hd_dt = pd.Timestamp(
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'2006-03-21 4:00 PM', tz='US/Eastern').tz_convert('UTC')
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# Midnight of the day on which BTS is invoked.
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newest_bts_dt = normalize_date(pd.Timestamp(
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'2006-03-22 04:00 PM', tz='US/Eastern').tz_convert('UTC'))
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if data_freq == 'daily':
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# If we're dealing with daily data, then we record
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# canonicalized timestamps, so make conversion here:
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penultimate_hd_dt = normalize_date(penultimate_hd_dt)
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# When history() is called in BTS, its 'current' volume value
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# should equal the sum of the previous day.
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self.assertEquals(volume_hd_0[penultimate_hd_dt],
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volume_bts_0[newest_bts_dt])
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self.assertEquals(volume_hd_1[penultimate_hd_dt],
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volume_bts_1[newest_bts_dt])
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def test_history_in_bts_volume_minutes(self):
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"""
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Test calling history() in before_trading_start()
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with minutely volume bars.
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"""
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algo_text = """
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from zipline.api import history
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def initialize(context):
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context.first_bts_call = True
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def before_trading_start(context, data):
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if not context.first_bts_call:
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volume_bts = history(bar_count=2, frequency='1m', field='volume')
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context.volume_bts = volume_bts
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context.first_bts_call = False
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def handle_data(context, data):
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pass
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""".strip()
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# March 2006
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# Su Mo Tu We Th Fr Sa
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# 1 2 3 4
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# 5 6 7 8 9 10 11
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# 12 13 14 15 16 17 18
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# 19 20 21 22 23 24 25
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# 26 27 28 29 30 31
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start = pd.Timestamp('2006-03-20', tz='UTC')
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end = pd.Timestamp('2006-03-22', tz='UTC')
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sim_params = factory.create_simulation_parameters(
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start=start, end=end)
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test_algo = TradingAlgorithm(
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script=algo_text,
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data_frequency='minute',
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sim_params=sim_params,
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env=TestHistoryAlgo.env,
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)
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source = RandomWalkSource(start=start, end=end)
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output = test_algo.run(source)
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self.assertIsNotNone(output)
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# Get the volumes recorded for sid 0 by history() within BTS
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volume_bts_0 = test_algo.volume_bts[0]
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# Get the volumes recorded for sid 1 by history() within BTS
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volume_bts_1 = test_algo.volume_bts[1]
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# The values recorded on 2006-03-22 by history() in BTS
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# should equal the final volume values for the trading
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# day 2006-03-21:
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# 0 1
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# 2006-03-21 20:59:00 215548 439908
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# 2006-03-21 21:00:00 985645 664313
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#
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# Note: These are not 'real' volume values. They are the product of
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# RandonWalkSource, which produces random walk OHLCV timeseries. For a
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# given seed these values are deterministc.
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self.assertEquals(215548, volume_bts_0.ix[0])
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self.assertEquals(985645, volume_bts_0.ix[1])
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self.assertEquals(439908, volume_bts_1.ix[0])
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self.assertEquals(664313, volume_bts_1.ix[1])
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def test_basic_history_one_day(self):
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algo_text = """
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from zipline.api import history, add_history
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