/
test_data_portal.py
589 lines (525 loc) · 22 KB
/
test_data_portal.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
#
# Copyright 2016 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 collections import OrderedDict
from numpy import array, append, nan, full
from numpy.testing import assert_almost_equal
import pandas as pd
from pandas.tslib import Timedelta
from six import iteritems
from zipline.assets import Equity, Future
from zipline.data.data_portal import HISTORY_FREQUENCIES, OHLCV_FIELDS
from zipline.data.minute_bars import (
FUTURES_MINUTES_PER_DAY,
US_EQUITIES_MINUTES_PER_DAY,
)
from zipline.testing import parameter_space
from zipline.testing.fixtures import (
ZiplineTestCase,
WithTradingSessions,
WithDataPortal,
alias,
)
from zipline.testing.predicates import assert_equal
from zipline.utils.numpy_utils import float64_dtype
class DataPortalTestBase(WithDataPortal,
WithTradingSessions):
ASSET_FINDER_EQUITY_SIDS = (1, 2, 3)
DIVIDEND_ASSET_SID = 3
START_DATE = pd.Timestamp('2016-08-01')
END_DATE = pd.Timestamp('2016-08-08')
TRADING_CALENDAR_STRS = ('NYSE', 'us_futures')
EQUITY_DAILY_BAR_SOURCE_FROM_MINUTE = True
# Since the future with sid 10001 has a tick size of 0.0001, its prices
# should be rounded out to 4 decimal places. To test that this rounding
# occurs correctly, store its prices out to 5 decimal places by using a
# multiplier of 100,000 when writing its values.
OHLC_RATIOS_PER_SID = {10001: 100000}
@classmethod
def make_root_symbols_info(self):
return pd.DataFrame({
'root_symbol': ['BAR', 'BUZ'],
'root_symbol_id': [1, 2],
'exchange': ['CME', 'CME'],
})
@classmethod
def make_futures_info(cls):
trading_sessions = cls.trading_sessions['us_futures']
return pd.DataFrame({
'sid': [10000, 10001],
'root_symbol': ['BAR', 'BUZ'],
'symbol': ['BARA', 'BUZZ'],
'start_date': [trading_sessions[1], trading_sessions[0]],
'end_date': [cls.END_DATE, cls.END_DATE],
# TODO: Make separate from 'end_date'
'notice_date': [cls.END_DATE, cls.END_DATE],
'expiration_date': [cls.END_DATE, cls.END_DATE],
'tick_size': [0.01, 0.0001],
'multiplier': [500, 50000],
'exchange': ['CME', 'CME'],
})
@classmethod
def make_equity_minute_bar_data(cls):
trading_calendar = cls.trading_calendars[Equity]
# No data on first day.
dts = trading_calendar.minutes_for_session(cls.trading_days[0])
dfs = []
dfs.append(pd.DataFrame(
{
'open': full(len(dts), nan),
'high': full(len(dts), nan),
'low': full(len(dts), nan),
'close': full(len(dts), nan),
'volume': full(len(dts), 0),
},
index=dts))
dts = trading_calendar.minutes_for_session(cls.trading_days[1])
dfs.append(pd.DataFrame(
{
'open': append(100.5, full(len(dts) - 1, nan)),
'high': append(100.9, full(len(dts) - 1, nan)),
'low': append(100.1, full(len(dts) - 1, nan)),
'close': append(100.3, full(len(dts) - 1, nan)),
'volume': append(1000, full(len(dts) - 1, nan)),
},
index=dts))
dts = trading_calendar.minutes_for_session(cls.trading_days[2])
dfs.append(pd.DataFrame(
{
'open': [nan, 103.50, 102.50, 104.50, 101.50, nan],
'high': [nan, 103.90, 102.90, 104.90, 101.90, nan],
'low': [nan, 103.10, 102.10, 104.10, 101.10, nan],
'close': [nan, 103.30, 102.30, 104.30, 101.30, nan],
'volume': [0, 1003, 1002, 1004, 1001, 0]
},
index=dts[:6]
))
dts = trading_calendar.minutes_for_session(cls.trading_days[3])
dfs.append(pd.DataFrame(
{
'open': full(len(dts), nan),
'high': full(len(dts), nan),
'low': full(len(dts), nan),
'close': full(len(dts), nan),
'volume': full(len(dts), 0),
},
index=dts))
asset1_df = pd.concat(dfs)
yield 1, asset1_df
asset2_df = pd.DataFrame(
{
'open': 1.0055,
'high': 1.0059,
'low': 1.0051,
'close': 1.0055,
'volume': 100,
},
index=asset1_df.index,
)
yield 2, asset2_df
yield cls.DIVIDEND_ASSET_SID, asset2_df.copy()
@classmethod
def make_future_minute_bar_data(cls):
trading_calendar = cls.trading_calendars[Future]
trading_sessions = cls.trading_sessions['us_futures']
# No data on first day, future asset intentionally not on the same
# dates as equities, so that cross-wiring of results do not create a
# false positive.
dts = trading_calendar.minutes_for_session(trading_sessions[1])
dfs = []
dfs.append(pd.DataFrame(
{
'open': full(len(dts), nan),
'high': full(len(dts), nan),
'low': full(len(dts), nan),
'close': full(len(dts), nan),
'volume': full(len(dts), 0),
},
index=dts))
dts = trading_calendar.minutes_for_session(trading_sessions[2])
dfs.append(pd.DataFrame(
{
'open': append(200.5, full(len(dts) - 1, nan)),
'high': append(200.9, full(len(dts) - 1, nan)),
'low': append(200.1, full(len(dts) - 1, nan)),
'close': append(200.3, full(len(dts) - 1, nan)),
'volume': append(2000, full(len(dts) - 1, nan)),
},
index=dts))
dts = trading_calendar.minutes_for_session(trading_sessions[3])
dfs.append(pd.DataFrame(
{
'open': [nan, 203.50, 202.50, 204.50, 201.50, nan],
'high': [nan, 203.90, 202.90, 204.90, 201.90, nan],
'low': [nan, 203.10, 202.10, 204.10, 201.10, nan],
'close': [nan, 203.30, 202.30, 204.30, 201.30, nan],
'volume': [0, 2003, 2002, 2004, 2001, 0]
},
index=dts[:6]
))
dts = trading_calendar.minutes_for_session(trading_sessions[4])
dfs.append(pd.DataFrame(
{
'open': full(len(dts), nan),
'high': full(len(dts), nan),
'low': full(len(dts), nan),
'close': full(len(dts), nan),
'volume': full(len(dts), 0),
},
index=dts))
asset10000_df = pd.concat(dfs)
yield 10000, asset10000_df
missing_dts = trading_calendar.minutes_for_session(trading_sessions[0])
asset10001_df = pd.DataFrame(
{
'open': 1.00549,
'high': 1.00591,
'low': 1.00507,
'close': 1.0055,
'volume': 100,
},
index=missing_dts.append(asset10000_df.index),
)
yield 10001, asset10001_df
@classmethod
def make_dividends_data(cls):
return pd.DataFrame([
{
# only care about ex date, the other dates don't matter here
'ex_date':
cls.trading_days[2].to_datetime64(),
'record_date':
cls.trading_days[2].to_datetime64(),
'declared_date':
cls.trading_days[2].to_datetime64(),
'pay_date':
cls.trading_days[2].to_datetime64(),
'amount': 0.5,
'sid': cls.DIVIDEND_ASSET_SID,
}],
columns=[
'ex_date',
'record_date',
'declared_date',
'pay_date',
'amount',
'sid'],
)
def test_get_last_traded_equity_minute(self):
trading_calendar = self.trading_calendars[Equity]
# Case: Missing data at front of data set, and request dt is before
# first value.
dts = trading_calendar.minutes_for_session(self.trading_days[0])
asset = self.asset_finder.retrieve_asset(1)
self.assertTrue(pd.isnull(
self.data_portal.get_last_traded_dt(
asset, dts[0], 'minute')))
# Case: Data on requested dt.
dts = trading_calendar.minutes_for_session(self.trading_days[2])
self.assertEqual(dts[1],
self.data_portal.get_last_traded_dt(
asset, dts[1], 'minute'))
# Case: No data on dt, but data occuring before dt.
self.assertEqual(dts[4],
self.data_portal.get_last_traded_dt(
asset, dts[5], 'minute'))
def test_get_last_traded_future_minute(self):
asset = self.asset_finder.retrieve_asset(10000)
trading_calendar = self.trading_calendars[Future]
# Case: Missing data at front of data set, and request dt is before
# first value.
dts = trading_calendar.minutes_for_session(self.trading_days[0])
self.assertTrue(pd.isnull(
self.data_portal.get_last_traded_dt(
asset, dts[0], 'minute')))
# Case: Data on requested dt.
dts = trading_calendar.minutes_for_session(self.trading_days[3])
self.assertEqual(dts[1],
self.data_portal.get_last_traded_dt(
asset, dts[1], 'minute'))
# Case: No data on dt, but data occuring before dt.
self.assertEqual(dts[4],
self.data_portal.get_last_traded_dt(
asset, dts[5], 'minute'))
def test_get_last_traded_dt_equity_daily(self):
# Case: Missing data at front of data set, and request dt is before
# first value.
asset = self.asset_finder.retrieve_asset(1)
self.assertTrue(pd.isnull(
self.data_portal.get_last_traded_dt(
asset, self.trading_days[0], 'daily')))
# Case: Data on requested dt.
self.assertEqual(self.trading_days[1],
self.data_portal.get_last_traded_dt(
asset, self.trading_days[1], 'daily'))
# Case: No data on dt, but data occuring before dt.
self.assertEqual(self.trading_days[2],
self.data_portal.get_last_traded_dt(
asset, self.trading_days[3], 'daily'))
def test_get_spot_value_equity_minute(self):
trading_calendar = self.trading_calendars[Equity]
asset = self.asset_finder.retrieve_asset(1)
dts = trading_calendar.minutes_for_session(self.trading_days[2])
# Case: Get data on exact dt.
dt = dts[1]
expected = OrderedDict({
'open': 103.5,
'high': 103.9,
'low': 103.1,
'close': 103.3,
'volume': 1003,
'price': 103.3
})
result = [self.data_portal.get_spot_value(asset,
field,
dt,
'minute')
for field in expected.keys()]
assert_almost_equal(array(list(expected.values())), result)
# Case: Get data on empty dt, return nan or most recent data for price.
dt = dts[100]
expected = OrderedDict({
'open': nan,
'high': nan,
'low': nan,
'close': nan,
'volume': 0,
'price': 101.3
})
result = [self.data_portal.get_spot_value(asset,
field,
dt,
'minute')
for field in expected.keys()]
assert_almost_equal(array(list(expected.values())), result)
def test_get_spot_value_future_minute(self):
trading_calendar = self.trading_calendars[Future]
asset = self.asset_finder.retrieve_asset(10000)
dts = trading_calendar.minutes_for_session(self.trading_days[3])
# Case: Get data on exact dt.
dt = dts[1]
expected = OrderedDict({
'open': 203.5,
'high': 203.9,
'low': 203.1,
'close': 203.3,
'volume': 2003,
'price': 203.3
})
result = [self.data_portal.get_spot_value(asset,
field,
dt,
'minute')
for field in expected.keys()]
assert_almost_equal(array(list(expected.values())), result)
# Case: Get data on empty dt, return nan or most recent data for price.
dt = dts[100]
expected = OrderedDict({
'open': nan,
'high': nan,
'low': nan,
'close': nan,
'volume': 0,
'price': 201.3
})
result = [self.data_portal.get_spot_value(asset,
field,
dt,
'minute')
for field in expected.keys()]
assert_almost_equal(array(list(expected.values())), result)
def test_get_spot_value_multiple_assets(self):
equity = self.asset_finder.retrieve_asset(1)
future = self.asset_finder.retrieve_asset(10000)
trading_calendar = self.trading_calendars[Future]
dts = trading_calendar.minutes_for_session(self.trading_days[3])
# We expect the outputs to be lists of spot values.
expected = pd.DataFrame(
{
equity: [nan, nan, nan, nan, 0, 101.3],
future: [203.5, 203.9, 203.1, 203.3, 2003, 203.3],
},
index=['open', 'high', 'low', 'close', 'volume', 'price'],
)
result = [
self.data_portal.get_spot_value(
assets=[equity, future],
field=field,
dt=dts[1],
data_frequency='minute',
)
for field in expected.index
]
assert_almost_equal(expected.values.tolist(), result)
@parameter_space(data_frequency=['daily', 'minute'],
field=['close', 'price'])
def test_get_adjustments(self, data_frequency, field):
asset = self.asset_finder.retrieve_asset(self.DIVIDEND_ASSET_SID)
calendar = self.trading_calendars[Equity]
day = calendar.day
dividend_date = self.trading_days[2]
prev_day_price = 1.005
dividend_amount = 0.5 # see self.make_dividends_data
ratio = 1.0 - dividend_amount / prev_day_price
cases = OrderedDict([
((dividend_date - day, dividend_date - day), 1.0),
((dividend_date - day, dividend_date), ratio),
((dividend_date - day, dividend_date + day), ratio),
((dividend_date, dividend_date), 1.0),
((dividend_date, dividend_date + day), 1.0),
((dividend_date + day, dividend_date + day), 1.0),
])
for (dt, perspective_dt), expected in iteritems(cases):
if data_frequency == 'minute':
dt = calendar.session_open(dt)
perspective_dt = calendar.session_open(perspective_dt)
val = self.data_portal.get_adjustments(
asset,
field,
dt,
perspective_dt,
)[0]
assert_almost_equal(val, expected,
err_msg="at dt={} perspective={}"
.format(dt, perspective_dt))
def test_bar_count_for_simple_transforms(self):
# July 2015
# Su Mo Tu We Th Fr Sa
# 1 2 3 4
# 5 6 7 8 9 10 11
# 12 13 14 15 16 17 18
# 19 20 21 22 23 24 25
# 26 27 28 29 30 31
# half an hour into july 9, getting a 4-"day" window should get us
# all the minutes of 7/6, 7/7, 7/8, and 31 minutes of 7/9
july_9_dt = self.trading_calendar.open_and_close_for_session(
pd.Timestamp("2015-07-09", tz='UTC')
)[0] + Timedelta("30 minutes")
self.assertEqual(
(3 * 390) + 31,
self.data_portal._get_minute_count_for_transform(july_9_dt, 4)
)
# November 2015
# Su Mo Tu We Th Fr Sa
# 1 2 3 4 5 6 7
# 8 9 10 11 12 13 14
# 15 16 17 18 19 20 21
# 22 23 24 25 26 27 28
# 29 30
# nov 26th closed
# nov 27th was an early close
# half an hour into nov 30, getting a 4-"day" window should get us
# all the minutes of 11/24, 11/25, 11/27 (half day!), and 31 minutes
# of 11/30
nov_30_dt = self.trading_calendar.open_and_close_for_session(
pd.Timestamp("2015-11-30", tz='UTC')
)[0] + Timedelta("30 minutes")
self.assertEqual(
390 + 390 + 210 + 31,
self.data_portal._get_minute_count_for_transform(nov_30_dt, 4)
)
def test_get_last_traded_dt_minute(self):
minutes = self.nyse_calendar.minutes_for_session(
self.trading_days[2])
equity = self.asset_finder.retrieve_asset(1)
result = self.data_portal.get_last_traded_dt(equity,
minutes[3],
'minute')
self.assertEqual(minutes[3], result,
"Asset 1 had a trade on third minute, so should "
"return that as the last trade on that dt.")
result = self.data_portal.get_last_traded_dt(equity,
minutes[5],
'minute')
self.assertEqual(minutes[4], result,
"Asset 1 had a trade on fourth minute, so should "
"return that as the last trade on the fifth.")
future = self.asset_finder.retrieve_asset(10000)
calendar = self.trading_calendars[Future]
minutes = calendar.minutes_for_session(self.trading_days[3])
result = self.data_portal.get_last_traded_dt(future,
minutes[3],
'minute')
self.assertEqual(minutes[3], result,
"Asset 10000 had a trade on the third minute, so "
"return that as the last trade on that dt.")
result = self.data_portal.get_last_traded_dt(future,
minutes[5],
'minute')
self.assertEqual(minutes[4], result,
"Asset 10000 had a trade on fourth minute, so should "
"return that as the last trade on the fifth.")
def test_get_empty_splits(self):
splits = self.data_portal.get_splits([], self.trading_days[2])
self.assertEqual([], splits)
@parameter_space(frequency=HISTORY_FREQUENCIES, field=OHLCV_FIELDS)
def test_price_rounding(self, frequency, field):
equity = self.asset_finder.retrieve_asset(2)
future = self.asset_finder.retrieve_asset(10001)
cf = self.data_portal.asset_finder.create_continuous_future(
'BUZ', 0, 'calendar', None,
)
minutes = self.nyse_calendar.minutes_for_session(self.trading_days[0])
if frequency == '1m':
minute = minutes[0]
expected_equity_volume = 100
expected_future_volume = 100
data_frequency = 'minute'
else:
minute = minutes[0].normalize()
expected_equity_volume = 100 * US_EQUITIES_MINUTES_PER_DAY
expected_future_volume = 100 * FUTURES_MINUTES_PER_DAY
data_frequency = 'daily'
# Equity prices should be floored to three decimal places.
expected_equity_values = {
'open': 1.005,
'high': 1.005,
'low': 1.005,
'close': 1.005,
'volume': expected_equity_volume,
}
# Futures prices should be rounded to four decimal places.
expected_future_values = {
'open': 1.0055,
'high': 1.0059,
'low': 1.0051,
'close': 1.0055,
'volume': expected_future_volume,
}
result = self.data_portal.get_history_window(
assets=[equity, future, cf],
end_dt=minute,
bar_count=1,
frequency=frequency,
field=field,
data_frequency=data_frequency,
)
expected_result = pd.DataFrame(
{
equity: expected_equity_values[field],
future: expected_future_values[field],
cf: expected_future_values[field],
},
index=[minute],
dtype=float64_dtype,
)
assert_equal(result, expected_result)
class TestDataPortal(DataPortalTestBase,
ZiplineTestCase):
DATA_PORTAL_LAST_AVAILABLE_SESSION = None
DATA_PORTAL_LAST_AVAILABLE_MINUTE = None
class TestDataPortalExplicitLastAvailable(DataPortalTestBase,
ZiplineTestCase):
DATA_PORTAL_LAST_AVAILABLE_SESSION = alias('START_DATE')
DATA_PORTAL_LAST_AVAILABLE_MINUTE = alias('END_DATE')