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ENH: Session bar reader resampled from minute data

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Implement a `SessionBarReader` which uses a minute bar reader as a
backing source, resampling the minute bars into the box around the
corresponding session data.

Also, add future/CME test cases to resample suite.
This commit is contained in:
Eddie Hebert
2016-08-18 11:37:42 -04:00
parent 42d61d7e4f
commit 4a017ef63b
3 changed files with 204 additions and 11 deletions
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tests/data/test_resample.py
+148 -4
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@@ -23,12 +23,14 @@ from six import iteritems
from zipline.data.resample import (
minute_to_session,
DailyHistoryAggregator
DailyHistoryAggregator,
MinuteResampleSessionBarReader,
)
from zipline.testing.fixtures import (
WithEquityMinuteBarData,
WithBcolzEquityMinuteBarReader,
WithBcolzFutureMinuteBarReader,
ZiplineTestCase,
)
@@ -56,6 +58,26 @@ NYSE_MINUTES = OrderedDict((
))
CME_MINUTES = OrderedDict((
('day_0_front', pd.date_range('2016-03-15 18:01',
'2016-03-15 18:03',
freq='min',
tz='US/Eastern').tz_convert('UTC')),
('day_0_back', pd.date_range('2016-03-16 17:58',
'2016-03-16 18:00',
freq='min',
tz='US/Eastern').tz_convert('UTC')),
('day_1_front', pd.date_range('2016-03-16 18:01',
'2016-03-16 18:03',
freq='min',
tz='US/Eastern').tz_convert('UTC')),
('day_1_back', pd.date_range('2016-03-17 17:58',
'2016-03-17 18:00',
freq='min',
tz='US/Eastern').tz_convert('UTC')),
))
SCENARIOS = OrderedDict((
('none_missing', array([
[101.5, 101.9, 101.1, 101.3, 1001],
@@ -103,6 +125,24 @@ for sid, combos in _EQUITY_CASES:
for s, m in combos]
EQUITY_CASES[sid] = pd.concat(frames)
_FUTURE_CASES = (
(1001, (('none_missing', 'day_0_front'),
('none_missing', 'day_0_back'))),
(1002, (('missing_first', 'day_0_front'),
('none_missing', 'day_0_back'))),
(1003, (('missing_last', 'day_0_back'),
('missing_first', 'day_1_front'))),
)
FUTURE_CASES = OrderedDict()
for sid, combos in _FUTURE_CASES:
frames = [DataFrame(SCENARIOS[s], columns=OHLCV).
set_index(CME_MINUTES[m])
for s, m in combos]
FUTURE_CASES[sid] = pd.concat(frames)
EXPECTED_AGGREGATION = {
1: DataFrame({
'open': [101.5, 101.5, 101.5, 101.5, 101.5, 101.5],
@@ -126,18 +166,50 @@ EXPECTED_AGGREGATION = {
'close': [107.3, 108.3, 108.3, nan, 103.3, 102.3],
'volume': [1007, 2015, 2015, 0, 1003, 2005],
}, columns=OHLCV),
1001: DataFrame({
'open': [101.5, 101.5, 101.5, 101.5, 101.5, 101.5],
'high': [101.9, 103.9, 103.9, 103.9, 103.9, 103.9],
'low': [101.1, 101.1, 101.1, 101.1, 101.1, 101.1],
'close': [101.3, 103.3, 102.3, 101.3, 103.3, 102.3],
'volume': [1001, 2004, 3006, 4007, 5010, 6012],
}, columns=OHLCV),
1002: DataFrame({
'open': [nan, 103.5, 103.5, 103.5, 103.5, 103.5],
'high': [nan, 103.9, 103.9, 103.9, 103.9, 103.9],
'low': [nan, 103.1, 102.1, 101.1, 101.1, 101.1],
'close': [nan, 103.3, 102.3, 101.3, 103.3, 102.3],
'volume': [0, 1003, 2005, 3006, 4009, 5011],
}, columns=OHLCV),
# Equity 3 straddles two days.
1003: DataFrame({
'open': [107.5, 107.5, 107.5, nan, 103.5, 103.5],
'high': [107.9, 108.9, 108.9, nan, 103.9, 103.9],
'low': [107.1, 107.1, 107.1, nan, 103.1, 102.1],
'close': [107.3, 108.3, 108.3, nan, 103.3, 102.3],
'volume': [1007, 2015, 2015, 0, 1003, 2005],
}, columns=OHLCV),
}
EXPECTED_SESSIONS = {
1: DataFrame([EXPECTED_AGGREGATION[1].iloc[-1].values],
columns=OHLCV,
index=['2016-03-15']),
index=pd.to_datetime(['2016-03-15'], utc=True)),
2: DataFrame([EXPECTED_AGGREGATION[2].iloc[-1].values],
columns=OHLCV,
index=['2016-03-15']),
index=pd.to_datetime(['2016-03-15'], utc=True)),
3: DataFrame(EXPECTED_AGGREGATION[3].iloc[[2, 5]].values,
columns=OHLCV,
index=['2016-03-15', '2016-03-16']),
index=pd.to_datetime(['2016-03-15', '2016-03-16'], utc=True)),
1001: DataFrame([EXPECTED_AGGREGATION[1001].iloc[-1].values],
columns=OHLCV,
index=pd.to_datetime(['2016-03-16'], utc=True)),
1002: DataFrame([EXPECTED_AGGREGATION[1002].iloc[-1].values],
columns=OHLCV,
index=pd.to_datetime(['2016-03-16'], utc=True)),
1003: DataFrame(EXPECTED_AGGREGATION[1003].iloc[[2, 5]].values,
columns=OHLCV,
index=pd.to_datetime(['2016-03-16', '2016-03-17'],
utc=True))
}
@@ -383,3 +455,75 @@ class TestMinuteToSession(WithEquityMinuteBarData,
assert_almost_equal(expected.values,
result.values,
err_msg='sid={0}'.format(sid))
class TestResampleSessionBars(WithBcolzFutureMinuteBarReader,
ZiplineTestCase):
TRADING_CALENDAR_STRS = ('CME',)
TRADING_CALENDAR_PRIMARY_CAL = 'CME'
ASSET_FINDER_FUTURE_SIDS = 1001, 1002, 1003
START_DATE = pd.Timestamp('2016-03-16', tz='UTC')
END_DATE = pd.Timestamp('2016-03-17', tz='UTC')
NUM_SESSIONS = 2
@classmethod
def make_future_minute_bar_data(cls):
for sid in cls.ASSET_FINDER_FUTURE_SIDS:
frame = FUTURE_CASES[sid]
yield sid, frame
def test_resample(self):
calendar = self.trading_calendar
session_bar_reader = MinuteResampleSessionBarReader(
calendar,
self.bcolz_future_minute_bar_reader
)
for sid in self.ASSET_FINDER_FUTURE_SIDS:
case_frame = FUTURE_CASES[sid]
first = case_frame.index[0]
last = case_frame.index[-1]
result = session_bar_reader.load_raw_arrays(
['open', 'high', 'low', 'close', 'volume'],
first, last, [sid])
assert_almost_equal(result, EXPECTED_SESSIONS[sid],
err_msg="sid={0}".format(sid))
def test_sessions(self):
calendar = self.trading_calendar
session_bar_reader = MinuteResampleSessionBarReader(
calendar,
self.bcolz_future_minute_bar_reader
)
sessions = session_bar_reader.sessions
self.assertEqual(self.NUM_SESSIONS, len(sessions))
self.assertEqual(self.START_DATE, sessions[0])
self.assertEqual(self.END_DATE, sessions[-1])
def test_last_available_dt(self):
calendar = self.trading_calendar
session_bar_reader = MinuteResampleSessionBarReader(
calendar,
self.bcolz_future_minute_bar_reader
)
self.assertEqual(self.END_DATE, session_bar_reader.last_available_dt)
def test_spot_price(self):
calendar = self.trading_calendar
session_bar_reader = MinuteResampleSessionBarReader(
calendar,
self.bcolz_future_minute_bar_reader
)
for sid in self.ASSET_FINDER_FUTURE_SIDS:
expected = EXPECTED_SESSIONS[sid]
for dt_str, values in expected.iterrows():
dt = pd.Timestamp(dt_str, tz='UTC')
for col in OHLCV:
result = session_bar_reader.spot_price(sid, dt, col)
assert_almost_equal(values[col], result,
err_msg="sid={0} col={1} dt={2}".
format(sid, col, dt))