forked from quantopian/zipline
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtest_history.py
1825 lines (1563 loc) · 63.5 KB
/
test_history.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
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
#
# 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 textwrap import dedent
from nose_parameterized import parameterized
import numpy as np
from numpy import nan
import pandas as pd
from six import iteritems
from zipline import TradingAlgorithm
from zipline._protocol import handle_non_market_minutes, BarData
from zipline.assets import Asset
from zipline.errors import (
HistoryInInitialize,
HistoryWindowStartsBeforeData,
)
from zipline.finance.trading import SimulationParameters
from zipline.finance.asset_restrictions import NoRestrictions
from zipline.testing import (
create_minute_df_for_asset,
str_to_seconds,
MockDailyBarReader,
)
from zipline.testing.fixtures import (
WithCreateBarData,
WithDataPortal,
ZiplineTestCase,
alias,
)
OHLC = ['open', 'high', 'low', 'close']
OHLCP = OHLC + ['price']
ALL_FIELDS = OHLCP + ['volume']
class WithHistory(WithCreateBarData, WithDataPortal):
TRADING_START_DT = TRADING_ENV_MIN_DATE = START_DATE = pd.Timestamp(
'2014-01-03',
tz='UTC',
)
TRADING_END_DT = END_DATE = pd.Timestamp('2016-01-29', tz='UTC')
SPLIT_ASSET_SID = 4
DIVIDEND_ASSET_SID = 5
MERGER_ASSET_SID = 6
HALF_DAY_TEST_ASSET_SID = 7
SHORT_ASSET_SID = 8
# asset1:
# - 2014-03-01 (rounds up to TRADING_START_DT) to 2016-01-29.
# - every minute/day.
# asset2:
# - 2015-01-05 to 2015-12-31
# - every minute/day.
# asset3:
# - 2015-01-05 to 2015-12-31
# - trades every 10 minutes
# SPLIT_ASSET:
# - 2015-01-04 to 2015-12-31
# - trades every minute
# - splits on 2015-01-05 and 2015-01-06
# DIVIDEND_ASSET:
# - 2015-01-04 to 2015-12-31
# - trades every minute
# - dividends on 2015-01-05 and 2015-01-06
# MERGER_ASSET
# - 2015-01-04 to 2015-12-31
# - trades every minute
# - merger on 2015-01-05 and 2015-01-06
@classmethod
def init_class_fixtures(cls):
super(WithHistory, cls).init_class_fixtures()
cls.trading_days = cls.trading_calendar.sessions_in_range(
cls.TRADING_START_DT,
cls.TRADING_END_DT
)
cls.ASSET1 = cls.asset_finder.retrieve_asset(1)
cls.ASSET2 = cls.asset_finder.retrieve_asset(2)
cls.ASSET3 = cls.asset_finder.retrieve_asset(3)
cls.SPLIT_ASSET = cls.asset_finder.retrieve_asset(
cls.SPLIT_ASSET_SID,
)
cls.DIVIDEND_ASSET = cls.asset_finder.retrieve_asset(
cls.DIVIDEND_ASSET_SID,
)
cls.MERGER_ASSET = cls.asset_finder.retrieve_asset(
cls.MERGER_ASSET_SID,
)
cls.HALF_DAY_TEST_ASSET = cls.asset_finder.retrieve_asset(
cls.HALF_DAY_TEST_ASSET_SID,
)
cls.SHORT_ASSET = cls.asset_finder.retrieve_asset(
cls.SHORT_ASSET_SID,
)
@classmethod
def make_equity_info(cls):
jan_5_2015 = pd.Timestamp('2015-01-05', tz='UTC')
day_after_12312015 = pd.Timestamp('2016-01-04', tz='UTC')
return pd.DataFrame.from_dict(
{
1: {
'start_date': pd.Timestamp('2014-01-03', tz='UTC'),
'end_date': cls.TRADING_END_DT,
'symbol': 'ASSET1',
'exchange': "TEST",
},
2: {
'start_date': jan_5_2015,
'end_date': day_after_12312015,
'symbol': 'ASSET2',
'exchange': "TEST",
},
3: {
'start_date': jan_5_2015,
'end_date': day_after_12312015,
'symbol': 'ASSET3',
'exchange': "TEST",
},
cls.SPLIT_ASSET_SID: {
'start_date': jan_5_2015,
'end_date': day_after_12312015,
'symbol': 'SPLIT_ASSET',
'exchange': "TEST",
},
cls.DIVIDEND_ASSET_SID: {
'start_date': jan_5_2015,
'end_date': day_after_12312015,
'symbol': 'DIVIDEND_ASSET',
'exchange': "TEST",
},
cls.MERGER_ASSET_SID: {
'start_date': jan_5_2015,
'end_date': day_after_12312015,
'symbol': 'MERGER_ASSET',
'exchange': "TEST",
},
cls.HALF_DAY_TEST_ASSET_SID: {
'start_date': pd.Timestamp('2014-07-02', tz='UTC'),
'end_date': day_after_12312015,
'symbol': 'HALF_DAY_TEST_ASSET',
'exchange': "TEST",
},
cls.SHORT_ASSET_SID: {
'start_date': pd.Timestamp('2015-01-05', tz='UTC'),
'end_date': pd.Timestamp('2015-01-06', tz='UTC'),
'symbol': 'SHORT_ASSET',
'exchange': "TEST",
}
},
orient='index',
)
@classmethod
def make_splits_data(cls):
return pd.DataFrame([
{
'effective_date': str_to_seconds('2015-01-06'),
'ratio': 0.25,
'sid': cls.SPLIT_ASSET_SID,
},
{
'effective_date': str_to_seconds('2015-01-07'),
'ratio': 0.5,
'sid': cls.SPLIT_ASSET_SID,
},
])
@classmethod
def make_mergers_data(cls):
return pd.DataFrame([
{
'effective_date': str_to_seconds('2015-01-06'),
'ratio': 0.25,
'sid': cls.MERGER_ASSET_SID,
},
{
'effective_date': str_to_seconds('2015-01-07'),
'ratio': 0.5,
'sid': cls.MERGER_ASSET_SID,
}
])
@classmethod
def make_dividends_data(cls):
return pd.DataFrame([
{
# only care about ex date, the other dates don't matter here
'ex_date':
pd.Timestamp('2015-01-06', tz='UTC').to_datetime64(),
'record_date':
pd.Timestamp('2015-01-06', tz='UTC').to_datetime64(),
'declared_date':
pd.Timestamp('2015-01-06', tz='UTC').to_datetime64(),
'pay_date':
pd.Timestamp('2015-01-06', tz='UTC').to_datetime64(),
'amount': 2.0,
'sid': cls.DIVIDEND_ASSET_SID,
},
{
'ex_date':
pd.Timestamp('2015-01-07', tz='UTC').to_datetime64(),
'record_date':
pd.Timestamp('2015-01-07', tz='UTC').to_datetime64(),
'declared_date':
pd.Timestamp('2015-01-07', tz='UTC').to_datetime64(),
'pay_date':
pd.Timestamp('2015-01-07', tz='UTC').to_datetime64(),
'amount': 4.0,
'sid': cls.DIVIDEND_ASSET_SID,
}],
columns=[
'ex_date',
'record_date',
'declared_date',
'pay_date',
'amount',
'sid',
],
)
@classmethod
def make_adjustment_writer_equity_daily_bar_reader(cls):
return MockDailyBarReader()
def verify_regular_dt(self, idx, dt, mode, fields=None, assets=None):
if mode == 'daily':
freq = '1d'
else:
freq = '1m'
fields = fields if fields is not None else ALL_FIELDS
assets = assets if assets is not None else [self.ASSET2, self.ASSET3]
bar_data = self.create_bardata(
simulation_dt_func=lambda: dt,
)
check_internal_consistency(
bar_data, assets, fields, 10, freq
)
for field in fields:
for asset in assets:
asset_series = bar_data.history(asset, field, 10, freq)
base = MINUTE_FIELD_INFO[field] + 2
if idx < 9:
missing_count = 9 - idx
present_count = 9 - missing_count
if field in OHLCP:
if asset == self.ASSET2:
# asset2 should have some leading nans
np.testing.assert_array_equal(
np.full(missing_count, np.nan),
asset_series[0:missing_count]
)
# asset2 should also have some real values
np.testing.assert_array_equal(
np.array(range(base,
base + present_count + 1)),
asset_series[(9 - present_count):]
)
if asset == self.ASSET3:
# asset3 should be NaN the entire time
np.testing.assert_array_equal(
np.full(10, np.nan),
asset_series
)
elif field == 'volume':
if asset == self.ASSET2:
# asset2 should have some zeros (instead of nans)
np.testing.assert_array_equal(
np.zeros(missing_count),
asset_series[0:missing_count]
)
# and some real values
np.testing.assert_array_equal(
np.array(
range(base, base + present_count + 1)
) * 100,
asset_series[(9 - present_count):]
)
if asset == self.ASSET3:
# asset3 is all zeros, no volume yet
np.testing.assert_array_equal(
np.zeros(10),
asset_series
)
else:
# asset3 should have data every 10 minutes
# construct an array full of nans, put something in the
# right slot, and test for comparison
position_from_end = ((idx + 1) % 10) + 1
# asset3's baseline data is 9 NaNs, then 11, then 9 NaNs,
# then 21, etc. for idx 9 to 19, value_for_asset3 should
# be a baseline of 11 (then adjusted for the individual
# field), thus the rounding down to the nearest 10.
value_for_asset3 = (((idx + 1) // 10) * 10) + \
MINUTE_FIELD_INFO[field] + 1
if field in OHLC:
asset3_answer_key = np.full(10, np.nan)
asset3_answer_key[-position_from_end] = \
value_for_asset3
if asset == self.ASSET2:
np.testing.assert_array_equal(
np.array(
range(base + idx - 9, base + idx + 1)),
asset_series
)
if asset == self.ASSET3:
np.testing.assert_array_equal(
asset3_answer_key,
asset_series
)
elif field == 'volume':
asset3_answer_key = np.zeros(10)
asset3_answer_key[-position_from_end] = \
value_for_asset3 * 100
if asset == self.ASSET2:
np.testing.assert_array_equal(
np.array(
range(base + idx - 9, base + idx + 1)
) * 100,
asset_series
)
if asset == self.ASSET3:
np.testing.assert_array_equal(
asset3_answer_key,
asset_series
)
elif field == 'price':
# price is always forward filled
# asset2 has prices every minute, so it's easy
if asset == self.ASSET2:
# at idx 9, the data is 2 to 11
np.testing.assert_array_equal(
range(idx - 7, idx + 3),
asset_series
)
if asset == self.ASSET3:
first_part = asset_series[0:-position_from_end]
second_part = asset_series[-position_from_end:]
decile_count = ((idx + 1) // 10)
# in our test data, asset3 prices will be nine
# NaNs, then ten 11s, ten 21s, ten 31s...
if decile_count == 1:
np.testing.assert_array_equal(
np.full(len(first_part), np.nan),
first_part
)
np.testing.assert_array_equal(
np.array([11] * len(second_part)),
second_part
)
else:
np.testing.assert_array_equal(
np.array([decile_count * 10 - 9] *
len(first_part)),
first_part
)
np.testing.assert_array_equal(
np.array([decile_count * 10 + 1] *
len(second_part)),
second_part
)
def check_internal_consistency(bar_data, assets, fields, bar_count, freq):
if isinstance(assets, Asset):
asset_list = [assets]
else:
asset_list = assets
if isinstance(fields, str):
field_list = [fields]
else:
field_list = fields
multi_field_dict = {
asset: bar_data.history(asset, field_list, bar_count, freq)
for asset in asset_list
}
multi_asset_dict = {
field: bar_data.history(asset_list, field, bar_count, freq)
for field in fields
}
panel = bar_data.history(asset_list, field_list, bar_count, freq)
for field in field_list:
# make sure all the different query forms are internally
# consistent
for asset in asset_list:
series = bar_data.history(asset, field, bar_count, freq)
np.testing.assert_array_equal(
series,
multi_asset_dict[field][asset]
)
np.testing.assert_array_equal(
series,
multi_field_dict[asset][field]
)
np.testing.assert_array_equal(
series,
panel[field][asset]
)
# each minute's OHLCV data has a consistent offset for each field.
# for example, the open is always 1 higher than the close, the high
# is always 2 higher than the close, etc.
MINUTE_FIELD_INFO = {
'open': 1,
'high': 2,
'low': -1,
'close': 0,
'price': 0,
'volume': 0, # unused, later we'll multiply by 100
}
class MinuteEquityHistoryTestCase(WithHistory, ZiplineTestCase):
EQUITY_DAILY_BAR_SOURCE_FROM_MINUTE = True
DATA_PORTAL_FIRST_TRADING_DAY = alias('TRADING_START_DT')
@classmethod
def make_equity_minute_bar_data(cls):
data = {}
sids = {2, 5, cls.SHORT_ASSET_SID, cls.HALF_DAY_TEST_ASSET_SID}
for sid in sids:
asset = cls.asset_finder.retrieve_asset(sid)
data[sid] = create_minute_df_for_asset(
cls.trading_calendar,
asset.start_date,
asset.end_date,
start_val=2,
)
data[1] = create_minute_df_for_asset(
cls.trading_calendar,
pd.Timestamp('2014-01-03', tz='utc'),
pd.Timestamp('2016-01-29', tz='utc'),
start_val=2,
)
asset2 = cls.asset_finder.retrieve_asset(2)
data[asset2.sid] = create_minute_df_for_asset(
cls.trading_calendar,
asset2.start_date,
cls.trading_calendar.previous_session_label(asset2.end_date),
start_val=2,
minute_blacklist=[
pd.Timestamp('2015-01-08 14:31', tz='UTC'),
pd.Timestamp('2015-01-08 21:00', tz='UTC'),
],
)
# Start values are crafted so that the thousands place are equal when
# adjustments are applied correctly.
# The splits and mergers are defined as 4:1 then 2:1 ratios, so the
# prices approximate that adjustment by quartering and then halving
# the thousands place.
data[cls.MERGER_ASSET_SID] = data[cls.SPLIT_ASSET_SID] = pd.concat((
create_minute_df_for_asset(
cls.trading_calendar,
pd.Timestamp('2015-01-05', tz='UTC'),
pd.Timestamp('2015-01-05', tz='UTC'),
start_val=8000),
create_minute_df_for_asset(
cls.trading_calendar,
pd.Timestamp('2015-01-06', tz='UTC'),
pd.Timestamp('2015-01-06', tz='UTC'),
start_val=2000),
create_minute_df_for_asset(
cls.trading_calendar,
pd.Timestamp('2015-01-07', tz='UTC'),
pd.Timestamp('2015-01-07', tz='UTC'),
start_val=1000),
create_minute_df_for_asset(
cls.trading_calendar,
pd.Timestamp('2015-01-08', tz='UTC'),
pd.Timestamp('2015-01-08', tz='UTC'),
start_val=1000)
))
asset3 = cls.asset_finder.retrieve_asset(3)
data[3] = create_minute_df_for_asset(
cls.trading_calendar,
asset3.start_date,
asset3.end_date,
start_val=2,
interval=10,
)
return iteritems(data)
def test_history_in_initialize(self):
algo_text = dedent(
"""\
from zipline.api import history
def initialize(context):
history([1], 10, '1d', 'price')
def handle_data(context, data):
pass
"""
)
start = pd.Timestamp('2014-04-05', tz='UTC')
end = pd.Timestamp('2014-04-10', tz='UTC')
sim_params = SimulationParameters(
start_session=start,
end_session=end,
capital_base=float('1.0e5'),
data_frequency='minute',
emission_rate='daily',
trading_calendar=self.trading_calendar,
)
test_algo = TradingAlgorithm(
script=algo_text,
data_frequency='minute',
sim_params=sim_params,
env=self.env,
)
with self.assertRaises(HistoryInInitialize):
test_algo.initialize()
def test_daily_splits_and_mergers(self):
# self.SPLIT_ASSET and self.MERGER_ASSET had splits/mergers
# on 1/6 and 1/7
jan5 = pd.Timestamp('2015-01-05', tz='UTC')
for asset in [self.SPLIT_ASSET, self.MERGER_ASSET]:
# before any of the adjustments, 1/4 and 1/5
window1 = self.data_portal.get_history_window(
[asset],
self.trading_calendar.open_and_close_for_session(jan5)[1],
2,
'1d',
'close'
)[asset]
np.testing.assert_array_equal(np.array([np.nan, 8389]), window1)
# straddling the first event
window2 = self.data_portal.get_history_window(
[asset],
pd.Timestamp('2015-01-06 14:35', tz='UTC'),
2,
'1d',
'close'
)[asset]
# Value from 1/5 should be quartered
np.testing.assert_array_equal(
[2097.25,
# Split occurs. The value of the thousands place should
# match.
2004],
window2
)
# straddling both events!
window3 = self.data_portal.get_history_window(
[asset],
pd.Timestamp('2015-01-07 14:35', tz='UTC'),
3,
'1d',
'close'
)[asset]
np.testing.assert_array_equal(
[1048.625, 1194.50, 1004.0],
window3
)
# after last event
window4 = self.data_portal.get_history_window(
[asset],
pd.Timestamp('2015-01-08 14:40', tz='UTC'),
2,
'1d',
'close'
)[asset]
# should not be adjusted
np.testing.assert_array_equal([1389, 1009], window4)
def test_daily_dividends(self):
# self.DIVIDEND_ASSET had dividends on 1/6 and 1/7
jan5 = pd.Timestamp('2015-01-05', tz='UTC')
asset = self.DIVIDEND_ASSET
# before any of the dividends
window1 = self.data_portal.get_history_window(
[asset],
self.trading_calendar.open_and_close_for_session(jan5)[1],
2,
'1d',
'close'
)[asset]
np.testing.assert_array_equal(np.array([nan, 391]), window1)
# straddling the first event
window2 = self.data_portal.get_history_window(
[asset],
pd.Timestamp('2015-01-06 14:35', tz='UTC'),
2,
'1d',
'close'
)[asset]
np.testing.assert_array_equal(
[383.18, # 391 (last close) * 0.98 (first div)
# Dividend occurs prior.
396],
window2
)
# straddling both events!
window3 = self.data_portal.get_history_window(
[asset],
pd.Timestamp('2015-01-07 14:35', tz='UTC'),
3,
'1d',
'close'
)[asset]
np.testing.assert_array_equal(
[367.853, # 391 (last close) * 0.98 * 0.96 (both)
749.76, # 781 (last_close) * 0.96 (second div)
786], # no adjustment
window3
)
# after last event
window4 = self.data_portal.get_history_window(
[asset],
pd.Timestamp('2015-01-08 14:40', tz='UTC'),
2,
'1d',
'close'
)[asset]
# should not be adjusted, should be 787 to 791
np.testing.assert_array_equal([1171, 1181], window4)
def test_minute_before_assets_trading(self):
# since asset2 and asset3 both started trading on 1/5/2015, let's do
# some history windows that are completely before that
minutes = self.trading_calendar.minutes_for_session(
self.trading_calendar.previous_session_label(pd.Timestamp(
'2015-01-05', tz='UTC'
))
)[0:60]
for idx, minute in enumerate(minutes):
bar_data = self.create_bardata(
lambda: minute,
)
check_internal_consistency(
bar_data, [self.ASSET2, self.ASSET3], ALL_FIELDS, 10, '1m'
)
for field in ALL_FIELDS:
# OHLCP should be NaN
# Volume should be 0
asset2_series = bar_data.history(self.ASSET2, field, 10, '1m')
asset3_series = bar_data.history(self.ASSET3, field, 10, '1m')
if field == 'volume':
np.testing.assert_array_equal(np.zeros(10), asset2_series)
np.testing.assert_array_equal(np.zeros(10), asset3_series)
else:
np.testing.assert_array_equal(
np.full(10, np.nan),
asset2_series
)
np.testing.assert_array_equal(
np.full(10, np.nan),
asset3_series
)
@parameterized.expand([
('open_sid_2', 'open', 2),
('high_sid_2', 'high', 2),
('low_sid_2', 'low', 2),
('close_sid_2', 'close', 2),
('volume_sid_2', 'volume', 2),
('open_sid_3', 'open', 3),
('high_sid_3', 'high', 3),
('low_sid_3', 'low', 3),
('close_sid_3', 'close', 3),
('volume_sid_3', 'volume', 3),
])
def test_minute_regular(self, name, field, sid):
# asset2 and asset3 both started on 1/5/2015, but asset3 trades every
# 10 minutes
asset = self.env.asset_finder.retrieve_asset(sid)
minutes = self.trading_calendar.minutes_for_session(
pd.Timestamp('2015-01-05', tz='UTC')
)[0:60]
for idx, minute in enumerate(minutes):
self.verify_regular_dt(idx, minute, 'minute',
assets=[asset],
fields=[field])
def test_minute_midnight(self):
midnight = pd.Timestamp('2015-01-06', tz='UTC')
last_minute = self.trading_calendar.open_and_close_for_session(
self.trading_calendar.minute_to_session_label(
midnight,
direction="previous"
)
)[1]
midnight_bar_data = self.create_bardata(
lambda: midnight,
)
yesterday_bar_data = self.create_bardata(
lambda: last_minute
)
with handle_non_market_minutes(midnight_bar_data):
for field in ALL_FIELDS:
np.testing.assert_array_equal(
midnight_bar_data.history(self.ASSET2, field, 30, '1m'),
yesterday_bar_data.history(self.ASSET2, field, 30, '1m')
)
def test_minute_after_asset_stopped(self):
# SHORT_ASSET's last day was 2015-01-06
# get some history windows that straddle the end
minutes = self.trading_calendar.minutes_for_session(
pd.Timestamp('2015-01-07', tz='UTC')
)[0:60]
for idx, minute in enumerate(minutes):
bar_data = self.create_bardata(
lambda: minute
)
check_internal_consistency(
bar_data, self.SHORT_ASSET, ALL_FIELDS, 30, '1m'
)
# Reset data portal because it has advanced past next test date.
data_portal = self.make_data_portal()
# choose a window that contains the last minute of the asset
bar_data = BarData(
data_portal=data_portal,
simulation_dt_func=lambda: minutes[15],
data_frequency='minute',
restrictions=NoRestrictions(),
trading_calendar=self.trading_calendar,
)
# close high low open price volume
# 2015-01-06 20:47:00+00:00 768 770 767 769 768 76800
# 2015-01-06 20:48:00+00:00 769 771 768 770 769 76900
# 2015-01-06 20:49:00+00:00 770 772 769 771 770 77000
# 2015-01-06 20:50:00+00:00 771 773 770 772 771 77100
# 2015-01-06 20:51:00+00:00 772 774 771 773 772 77200
# 2015-01-06 20:52:00+00:00 773 775 772 774 773 77300
# 2015-01-06 20:53:00+00:00 774 776 773 775 774 77400
# 2015-01-06 20:54:00+00:00 775 777 774 776 775 77500
# 2015-01-06 20:55:00+00:00 776 778 775 777 776 77600
# 2015-01-06 20:56:00+00:00 777 779 776 778 777 77700
# 2015-01-06 20:57:00+00:00 778 780 777 779 778 77800
# 2015-01-06 20:58:00+00:00 779 781 778 780 779 77900
# 2015-01-06 20:59:00+00:00 780 782 779 781 780 78000
# 2015-01-06 21:00:00+00:00 781 783 780 782 781 78100
# 2015-01-07 14:31:00+00:00 NaN NaN NaN NaN NaN 0
# 2015-01-07 14:32:00+00:00 NaN NaN NaN NaN NaN 0
# 2015-01-07 14:33:00+00:00 NaN NaN NaN NaN NaN 0
# 2015-01-07 14:34:00+00:00 NaN NaN NaN NaN NaN 0
# 2015-01-07 14:35:00+00:00 NaN NaN NaN NaN NaN 0
# 2015-01-07 14:36:00+00:00 NaN NaN NaN NaN NaN 0
# 2015-01-07 14:37:00+00:00 NaN NaN NaN NaN NaN 0
# 2015-01-07 14:38:00+00:00 NaN NaN NaN NaN NaN 0
# 2015-01-07 14:39:00+00:00 NaN NaN NaN NaN NaN 0
# 2015-01-07 14:40:00+00:00 NaN NaN NaN NaN NaN 0
# 2015-01-07 14:41:00+00:00 NaN NaN NaN NaN NaN 0
# 2015-01-07 14:42:00+00:00 NaN NaN NaN NaN NaN 0
# 2015-01-07 14:43:00+00:00 NaN NaN NaN NaN NaN 0
# 2015-01-07 14:44:00+00:00 NaN NaN NaN NaN NaN 0
# 2015-01-07 14:45:00+00:00 NaN NaN NaN NaN NaN 0
# 2015-01-07 14:46:00+00:00 NaN NaN NaN NaN NaN 0
window = bar_data.history(self.SHORT_ASSET, ALL_FIELDS, 30, '1m')
# there should be 14 values and 16 NaNs/0s
for field in ALL_FIELDS:
if field == 'volume':
np.testing.assert_array_equal(
range(76800, 78101, 100),
window['volume'][0:14]
)
np.testing.assert_array_equal(
np.zeros(16),
window['volume'][-16:]
)
else:
np.testing.assert_array_equal(
np.array(range(768, 782)) + MINUTE_FIELD_INFO[field],
window[field][0:14]
)
np.testing.assert_array_equal(
np.full(16, np.nan),
window[field][-16:]
)
# now do a smaller window that is entirely contained after the asset
# ends
window = bar_data.history(self.SHORT_ASSET, ALL_FIELDS, 5, '1m')
for field in ALL_FIELDS:
if field == 'volume':
np.testing.assert_array_equal(np.zeros(5), window['volume'])
else:
np.testing.assert_array_equal(np.full(5, np.nan),
window[field])
def test_minute_splits_and_mergers(self):
# self.SPLIT_ASSET and self.MERGER_ASSET had splits/mergers
# on 1/6 and 1/7
jan5 = pd.Timestamp('2015-01-05', tz='UTC')
# the assets' close column starts at 2 on the first minute of
# 1/5, then goes up one per minute forever
for asset in [self.SPLIT_ASSET, self.MERGER_ASSET]:
# before any of the adjustments, last 10 minutes of jan 5
window1 = self.data_portal.get_history_window(
[asset],
self.trading_calendar.open_and_close_for_session(jan5)[1],
10,
'1m',
'close'
)[asset]
np.testing.assert_array_equal(
np.array(range(8380, 8390)), window1)
# straddling the first event
window2 = self.data_portal.get_history_window(
[asset],
pd.Timestamp('2015-01-06 14:35', tz='UTC'),
10,
'1m',
'close'
)[asset]
# five minutes from 1/5 should be halved
np.testing.assert_array_equal(
[2096.25,
2096.5,
2096.75,
2097,
2097.25,
# Split occurs. The value of the thousands place should
# match.
2000,
2001,
2002,
2003,
2004],
window2
)
# straddling both events!
window3 = self.data_portal.get_history_window(
[asset],
pd.Timestamp('2015-01-07 14:35', tz='UTC'),
400, # 5 minutes of 1/7, 390 of 1/6, and 5 minutes of 1/5
'1m',
'close'
)[asset]
# first five minutes should be 4385-4390, but eigthed
np.testing.assert_array_equal(
[1048.125, 1048.25, 1048.375, 1048.5, 1048.625],
window3[0:5]
)
# next 390 minutes should be 2000-2390, but halved
np.testing.assert_array_equal(
np.array(range(2000, 2390), dtype='float64') / 2,
window3[5:395]
)
# final 5 minutes should be 1000-1004
np.testing.assert_array_equal(range(1000, 1005), window3[395:])
# after last event
window4 = self.data_portal.get_history_window(
[asset],
pd.Timestamp('2015-01-07 14:40', tz='UTC'),
5,
'1m',
'close'
)[asset]
# should not be adjusted, should be 1005 to 1009
np.testing.assert_array_equal(range(1005, 1010), window4)
def test_minute_dividends(self):
# self.DIVIDEND_ASSET had dividends on 1/6 and 1/7
# before any of the dividends
window1 = self.data_portal.get_history_window(
[self.DIVIDEND_ASSET],
pd.Timestamp('2015-01-05 21:00', tz='UTC'),
10,
'1m',
'close'
)[self.DIVIDEND_ASSET]
np.testing.assert_array_equal(np.array(range(382, 392)), window1)
# straddling the first dividend
window2 = self.data_portal.get_history_window(
[self.DIVIDEND_ASSET],
pd.Timestamp('2015-01-06 14:35', tz='UTC'),
10,
'1m',
'close'
)[self.DIVIDEND_ASSET]
# first dividend is 2%, so the first five values should be 2% lower
# than before
np.testing.assert_array_almost_equal(
np.array(range(387, 392), dtype='float64') * 0.98,
window2[0:5]
)
# second half of window is unadjusted
np.testing.assert_array_equal(range(392, 397), window2[5:])
# straddling both dividends
window3 = self.data_portal.get_history_window(
[self.DIVIDEND_ASSET],
pd.Timestamp('2015-01-07 14:35', tz='UTC'),
400, # 5 minutes of 1/7, 390 of 1/6, and 5 minutes of 1/5