Filtering#

This section contains 8 examples for Filtering using the onetick-py.
Each example is a self-contained script that can be run against the OneTick Cloud sample databases.

# onetick-py WebAPI configuration for OneTick Cloud
import os
os.environ['OTP_WEBAPI'] = '1'
os.environ['OTP_HTTP_ADDRESS'] = 'https://rest.cloud.onetick.com'
os.environ['OTP_ACCESS_TOKEN_URL'] = 'https://cloud-auth.parent.onetick.com/realms/OMD/protocol/openid-connect/token'
os.environ['OTP_CLIENT_ID'] = '__FILL_IN__'
os.environ['OTP_CLIENT_SECRET'] = '__FILL_IN__'

Adding Multiple Filters#

Applying multiple filters to the dataset as two separate operations.

import onetick.py as otp

data = otp.DataSource(db='US_COMP_SAMPLE', tick_type='TRD')
data = data[['PRICE', 'SIZE', 'EXCHANGE']]
data['TRADED_VALUE'] = data['PRICE'] * data['SIZE']

data = data.where(data['EXCHANGE'] == 'N')
data = data.where(data['SIZE'] > 100)

result = otp.run(data,
                 start=otp.dt(2024, 1, 3, 9, 30),
                 end=otp.dt(2024, 1, 3, 9, 40),
                 timezone='America/New_York',
                 symbols='CSCO')
result
Time PRICE SIZE EXCHANGE TRADED_VALUE
0 2024-01-03 09:30:02.371760827 50.13 127 N 6366.51
1 2024-01-03 09:30:19.325065423 50.13 337 N 16893.81
2 2024-01-03 09:30:19.701084742 50.15 400 N 20060.00
3 2024-01-03 09:30:20.787037891 50.14 107 N 5364.98
4 2024-01-03 09:30:23.436860708 50.14 600 N 30084.00
... ... ... ... ... ...
54 2024-01-03 09:38:28.029695573 50.08 200 N 10016.00
55 2024-01-03 09:39:01.147191601 50.09 312 N 15628.08
56 2024-01-03 09:39:04.654739241 50.08 200 N 10016.00
57 2024-01-03 09:39:17.427491120 50.08 211 N 10566.88
58 2024-01-03 09:39:22.549900523 50.10 200 N 10020.00

59 rows × 5 columns

Filtering on a Single Trade Condition#

Filtering that a trade condition is present in the COND field of the US_COMP_SAMPLE database.

import onetick.py as otp

data = otp.DataSource(db='US_COMP_SAMPLE', tick_type='TRD')
data, _= data[data['COND'].str.contains('I')]
data = data[:100]
result = otp.run(data,
                 start=otp.dt(2024, 1, 3),
                 end=otp.dt(2024, 1, 4),
                 timezone='America/New_York',
                 symbols='CSCO')
result
Time COND CORR DELETED_TIME EXCHANGE OMDSEQ PARTICIPANT_TIME PRICE SEQ_NUM SIZE SOURCE STOP_STOCK TICKER TICK_STATUS TRADE_ID TRF TRF_TIME TTE
0 2024-01-03 04:00:00.019128245 @ TI 0 1969-12-31 19:00:00 K 0 2024-01-03 04:00:00.000052000 50.46 1496 1 N CSCO 0 1 1969-12-31 19:00:00 0
1 2024-01-03 04:00:00.905488831 @ TI 0 1969-12-31 19:00:00 P 0 2024-01-03 04:00:00.905146127 50.05 1531 5 N CSCO 0 1 1969-12-31 19:00:00 0
2 2024-01-03 04:00:01.278537460 @FTI 0 1969-12-31 19:00:00 Q 0 2024-01-03 04:00:01.278518875 50.29 1540 13 N CSCO 0 1 1969-12-31 19:00:00 1
3 2024-01-03 04:00:01.278746794 @FTI 0 1969-12-31 19:00:00 P 1 2024-01-03 04:00:01.278404559 50.29 1541 13 N CSCO 0 2 1969-12-31 19:00:00 1
4 2024-01-03 04:00:03.090763650 @FTI 0 1969-12-31 19:00:00 Q 0 2024-01-03 04:00:03.090746743 50.29 1565 1 N CSCO 0 2 1969-12-31 19:00:00 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
95 2024-01-03 06:42:37.790073044 @FTI 0 1969-12-31 19:00:00 P 0 2024-01-03 06:42:37.789729308 50.12 43556 1 N CSCO 0 39 1969-12-31 19:00:00 1
96 2024-01-03 06:42:37.804968776 @FTI 0 1969-12-31 19:00:00 Q 0 2024-01-03 06:42:37.804953171 50.17 43557 10 N CSCO 0 54 1969-12-31 19:00:00 1
97 2024-01-03 06:42:37.805831496 @FTI 0 1969-12-31 19:00:00 Q 0 2024-01-03 06:42:37.805816609 50.17 43558 5 N CSCO 0 55 1969-12-31 19:00:00 1
98 2024-01-03 06:42:37.816687662 @FTI 0 1969-12-31 19:00:00 Q 0 2024-01-03 06:42:37.816672176 50.17 43559 5 N CSCO 0 56 1969-12-31 19:00:00 1
99 2024-01-03 06:42:38.014732702 @FTI 0 1969-12-31 19:00:00 Q 0 2024-01-03 06:42:38.014717674 50.19 43562 5 N CSCO 0 57 1969-12-31 19:00:00 1

100 rows × 18 columns

Filtering on Multiple Trade Conditions#

Filtering that any of the specified trade conditions are present in the COND field of the US_COMP database, by using character_present().

import onetick.py as otp

data = otp.DataSource(db='US_COMP_SAMPLE', tick_type='TRD')
data = data.character_present(data['COND'], 'O6TUHILNRWZ47QMBCGPV')
data = data[:100]
result = otp.run(data,
                 start=otp.dt(2024, 1, 3),
                 end=otp.dt(2024, 1, 4),
                 timezone='America/New_York',
                 symbols='CSCO')
result
Time COND CORR DELETED_TIME EXCHANGE OMDSEQ PARTICIPANT_TIME PRICE SEQ_NUM SIZE SOURCE STOP_STOCK TICKER TICK_STATUS TRADE_ID TRF TRF_TIME TTE
0 2024-01-03 04:00:00.019128245 @ TI 0 1969-12-31 19:00:00 K 0 2024-01-03 04:00:00.000052000 50.46 1496 1 N CSCO 0 1 1969-12-31 19:00:00 0
1 2024-01-03 04:00:00.905488831 @ TI 0 1969-12-31 19:00:00 P 0 2024-01-03 04:00:00.905146127 50.05 1531 5 N CSCO 0 1 1969-12-31 19:00:00 0
2 2024-01-03 04:00:01.278537460 @FTI 0 1969-12-31 19:00:00 Q 0 2024-01-03 04:00:01.278518875 50.29 1540 13 N CSCO 0 1 1969-12-31 19:00:00 1
3 2024-01-03 04:00:01.278746794 @FTI 0 1969-12-31 19:00:00 P 1 2024-01-03 04:00:01.278404559 50.29 1541 13 N CSCO 0 2 1969-12-31 19:00:00 1
4 2024-01-03 04:00:03.090763650 @FTI 0 1969-12-31 19:00:00 Q 0 2024-01-03 04:00:03.090746743 50.29 1565 1 N CSCO 0 2 1969-12-31 19:00:00 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
95 2024-01-03 06:10:45.228483994 @ T 0 1969-12-31 19:00:00 P 3 2024-01-03 06:10:45.228132116 50.20 36285 788 N CSCO 0 35 1969-12-31 19:00:00 0
96 2024-01-03 06:10:52.079452806 @ T 0 1969-12-31 19:00:00 P 0 2024-01-03 06:10:52.079105701 50.20 36303 100 N CSCO 0 36 1969-12-31 19:00:00 0
97 2024-01-03 06:10:52.079454393 @ T 0 1969-12-31 19:00:00 P 1 2024-01-03 06:10:52.079105701 50.20 36304 900 N CSCO 0 37 1969-12-31 19:00:00 0
98 2024-01-03 06:31:08.521306739 @FTI 0 1969-12-31 19:00:00 P 0 2024-01-03 06:31:08.520961545 50.15 39749 1 N CSCO 0 38 1969-12-31 19:00:00 1
99 2024-01-03 06:42:03.828154778 @ TI 0 1969-12-31 19:00:00 K 0 2024-01-03 06:42:03.827956000 50.17 43183 1 N CSCO 0 10 1969-12-31 19:00:00 0

100 rows × 18 columns

Filtering on Excluding Multiple Trade Conditions#

Filtering that any of the specified trade conditions are not present in the COND field of the US_COMP_SAMPLE database.
Using character_present() with parameter discard_on_match=True.

import onetick.py as otp

data = otp.DataSource(db='US_COMP_SAMPLE', tick_type='TRD')
data = data.character_present(data['COND'], 'O6TUHILNRWZ47QMBCGPV', discard_on_match=True)
data = data[:100]
result = otp.run(data,
                 start=otp.dt(2024, 1, 3),
                 end=otp.dt(2024, 1, 4),
                 timezone='America/New_York',
                 symbols='CSCO')
result
Time COND CORR DELETED_TIME EXCHANGE OMDSEQ PARTICIPANT_TIME PRICE SEQ_NUM SIZE SOURCE STOP_STOCK TICKER TICK_STATUS TRADE_ID TRF TRF_TIME TTE
0 2024-01-03 09:30:00.127459523 @ 0 1969-12-31 19:00:00 V 0 2024-01-03 09:30:00.065044730 50.170 169181 100 N CSCO 0 13 1969-12-31 19:00:00.000000000 0
1 2024-01-03 09:30:00.280877749 @ 0 1969-12-31 19:00:00 D 0 2024-01-03 09:30:00.280542269 50.002 169295 200 N CSCO 0 94 Q 2024-01-03 09:30:00.280841375 0
2 2024-01-03 09:30:00.607073913 @F 0 1969-12-31 19:00:00 Z 4 2024-01-03 09:30:00.606878000 50.090 170006 124 N CSCO 0 63 1969-12-31 19:00:00.000000000 1
3 2024-01-03 09:30:00.888302226 @ 0 1969-12-31 19:00:00 Q 4 2024-01-03 09:30:00.888283518 50.090 171782 109 N CSCO 0 360 1969-12-31 19:00:00.000000000 0
4 2024-01-03 09:30:00.889018624 @F 0 1969-12-31 19:00:00 U 0 2024-01-03 09:30:00.888812693 50.090 171787 100 N CSCO 0 17 1969-12-31 19:00:00.000000000 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
95 2024-01-03 09:30:04.203519590 @F 0 1969-12-31 19:00:00 Q 0 2024-01-03 09:30:04.203504691 50.220 185111 100 N CSCO 0 537 1969-12-31 19:00:00.000000000 1
96 2024-01-03 09:30:04.203522132 @F 0 1969-12-31 19:00:00 Q 1 2024-01-03 09:30:04.203504691 50.220 185112 138 N CSCO 0 538 1969-12-31 19:00:00.000000000 1
97 2024-01-03 09:30:04.203653496 @ 0 1969-12-31 19:00:00 Q 2 2024-01-03 09:30:04.203638794 50.220 185114 100 N CSCO 0 539 1969-12-31 19:00:00.000000000 0
98 2024-01-03 09:30:04.203816597 @F 0 1969-12-31 19:00:00 Z 4 2024-01-03 09:30:04.203620000 50.220 185121 296 N CSCO 0 109 1969-12-31 19:00:00.000000000 1
99 2024-01-03 09:30:04.203826441 @ 0 1969-12-31 19:00:00 Q 5 2024-01-03 09:30:04.203810447 50.220 185122 100 N CSCO 0 540 1969-12-31 19:00:00.000000000 0

100 rows × 18 columns

Filtering on Specific Time Ranges#

Applying a filter on the Data Source based on specific time periods per day, using time_filter().

import onetick.py as otp

data = otp.DataSource(db='US_COMP_SAMPLE', tick_type='TRD')
data = data.time_filter(start_time='09:33:00', end_time='09:35:00')
data = data[:1000]
result = otp.run(data,
                 start=otp.dt(2024, 1, 3, 9, 30),
                 end=otp.dt(2024, 1, 3, 9, 40),
                 timezone='America/New_York',
                 symbols='CSCO')
result
Time COND CORR DELETED_TIME EXCHANGE OMDSEQ PARTICIPANT_TIME PRICE SEQ_NUM SIZE SOURCE STOP_STOCK TICKER TICK_STATUS TRADE_ID TRF TRF_TIME TTE
0 2024-01-03 09:33:00.084705868 @ I 0 1969-12-31 19:00:00 Z 0 2024-01-03 09:33:00.084511000 50.05 262862 1 N CSCO 0 305 1969-12-31 19:00:00.000000000 0
1 2024-01-03 09:33:00.230210508 @ I 0 1969-12-31 19:00:00 Z 0 2024-01-03 09:33:00.230021000 50.05 262931 2 N CSCO 0 306 1969-12-31 19:00:00.000000000 0
2 2024-01-03 09:33:00.320146067 @ I 0 1969-12-31 19:00:00 Z 0 2024-01-03 09:33:00.319957000 50.05 262947 1 N CSCO 0 307 1969-12-31 19:00:00.000000000 0
3 2024-01-03 09:33:00.441710628 @ I 0 1969-12-31 19:00:00 Z 0 2024-01-03 09:33:00.441521000 50.05 262984 1 N CSCO 0 308 1969-12-31 19:00:00.000000000 0
4 2024-01-03 09:33:00.444156451 @F I 0 1969-12-31 19:00:00 Q 0 2024-01-03 09:33:00.444135893 50.05 262986 2 N CSCO 0 1142 1969-12-31 19:00:00.000000000 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
780 2024-01-03 09:34:59.152722398 @F 0 1969-12-31 19:00:00 P 4 2024-01-03 09:34:59.152378746 50.02 300893 100 N CSCO 0 512 1969-12-31 19:00:00.000000000 1
781 2024-01-03 09:34:59.263442478 @ I 0 1969-12-31 19:00:00 D 0 2024-01-03 09:34:59.263054575 50.01 300948 21 N CSCO 0 1372 Q 2024-01-03 09:34:59.263418818 0
782 2024-01-03 09:34:59.263934938 @F I 0 1969-12-31 19:00:00 K 1 2024-01-03 09:34:59.263744000 50.01 300949 1 N CSCO 0 367 1969-12-31 19:00:00.000000000 1
783 2024-01-03 09:34:59.263938903 @F I 0 1969-12-31 19:00:00 K 2 2024-01-03 09:34:59.263744000 50.01 300950 99 N CSCO 0 368 1969-12-31 19:00:00.000000000 1
784 2024-01-03 09:34:59.547741776 @ I 0 1969-12-31 19:00:00 D 0 2024-01-03 09:34:59.524000000 50.02 300986 1 N CSCO 0 1373 Q 2024-01-03 09:34:59.547719733 0

785 rows × 18 columns

Filtering on Excluding Specific Time Ranges#

Applying a filter on the Data Source based on excluding specific time periods per day.
Using time_filter() with parameter discard_on_match=True.

import onetick.py as otp

data = otp.DataSource(db='US_COMP_SAMPLE', tick_type='TRD')
data = data.time_filter(start_time='09:30:00', end_time='09:33:00', discard_on_match=True)
data = data[:1000]
result = otp.run(data,
                 start=otp.dt(2024, 1, 3, 9, 30),
                 end=otp.dt(2024, 1, 3, 9, 40),
                 timezone='America/New_York',
                 symbols='CSCO')
result
Time COND CORR DELETED_TIME EXCHANGE OMDSEQ PARTICIPANT_TIME PRICE SEQ_NUM SIZE SOURCE STOP_STOCK TICKER TICK_STATUS TRADE_ID TRF TRF_TIME TTE
0 2024-01-03 09:33:00.084705868 @ I 0 1969-12-31 19:00:00 Z 0 2024-01-03 09:33:00.084511000 50.05 262862 1 N CSCO 0 305 1969-12-31 19:00:00 0
1 2024-01-03 09:33:00.230210508 @ I 0 1969-12-31 19:00:00 Z 0 2024-01-03 09:33:00.230021000 50.05 262931 2 N CSCO 0 306 1969-12-31 19:00:00 0
2 2024-01-03 09:33:00.320146067 @ I 0 1969-12-31 19:00:00 Z 0 2024-01-03 09:33:00.319957000 50.05 262947 1 N CSCO 0 307 1969-12-31 19:00:00 0
3 2024-01-03 09:33:00.441710628 @ I 0 1969-12-31 19:00:00 Z 0 2024-01-03 09:33:00.441521000 50.05 262984 1 N CSCO 0 308 1969-12-31 19:00:00 0
4 2024-01-03 09:33:00.444156451 @F I 0 1969-12-31 19:00:00 Q 0 2024-01-03 09:33:00.444135893 50.05 262986 2 N CSCO 0 1142 1969-12-31 19:00:00 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
995 2024-01-03 09:35:19.333430053 @F I 0 1969-12-31 19:00:00 K 57 2024-01-03 09:35:19.333225000 50.00 309118 50 N CSCO 0 378 1969-12-31 19:00:00 1
996 2024-01-03 09:35:19.333435686 @F I 0 1969-12-31 19:00:00 K 58 2024-01-03 09:35:19.333225000 50.00 309119 25 N CSCO 0 379 1969-12-31 19:00:00 1
997 2024-01-03 09:35:19.333440538 @ I 0 1969-12-31 19:00:00 Z 59 2024-01-03 09:35:19.333213000 50.00 309120 16 N CSCO 0 427 1969-12-31 19:00:00 0
998 2024-01-03 09:35:19.333444799 @ I 0 1969-12-31 19:00:00 Z 60 2024-01-03 09:35:19.333250000 50.00 309121 9 N CSCO 0 428 1969-12-31 19:00:00 0
999 2024-01-03 09:35:19.333448520 @ 0 1969-12-31 19:00:00 U 61 2024-01-03 09:35:19.333227544 50.00 309122 398 N CSCO 0 319 1969-12-31 19:00:00 0

1000 rows × 18 columns

Relative Time Filtering on the last 5 minutes Trades#

Retrieving Trades for the last 5 minutes, using relative syntax for start and end in otp.run.

import onetick.py as otp

data = otp.DataSource(db='US_COMP', tick_type='TRD')
data = data[['PRICE', 'SIZE', 'COND']]
data = data.limit(1000)
result = otp.run(data,
                 start=otp.now() - otp.Minute(5),
                 end=otp.now(),
                 timezone='America/New_York',
                 symbols='CSCO')
result

Time

PRICE

SIZE

COND

0

2026-08-04 09:38:09.337531279

120.1068

1

@ I

1

2026-08-04 09:38:09.509870550

120.1200

14

@ I

2

2026-08-04 09:38:09.572110788

120.1500

10

@ I

3

2026-08-04 09:38:09.572151798

120.1500

398

@F

4

2026-08-04 09:38:09.572172621

120.1500

1

@ I

…

…

…

…

…

995

2026-08-04 09:39:06.025000447

120.3100

20

@F I

996

2026-08-04 09:39:06.281626529

120.2871

1

@ I

997

2026-08-04 09:39:06.311070704

120.3138

1

@ I

998

2026-08-04 09:39:06.475337961

120.3050

100

@

999

2026-08-04 09:39:06.475912328

120.3050

17

@ I

1000 rows x 4 columns

Relative Time Filtering on Trades from Today#

Retrieving Trades from Today until Now, using relative syntax for start and end in otp.run.

import onetick.py as otp

data = otp.DataSource(db='US_COMP', tick_type='TRD')
data = data[['PRICE', 'SIZE', 'COND']]
data = data.limit(1000)
result = otp.run(data,
                 # get the current date == start of day
                 start=otp.now().dt.date(),
                 end=otp.now(),
                 timezone='America/New_York',
                 symbols='CSCO')
result

Time

PRICE

SIZE

COND

0

2026-08-04 04:00:00.062628594

115.8700

10

@ TI

1

2026-08-04 04:00:00.517801239

115.8700

28

@ TI

2

2026-08-04 04:00:01.572798398

116.2988

1

@ TI

3

2026-08-04 04:00:01.961360151

115.4412

1

@ TI

4

2026-08-04 04:00:02.804289238

115.8300

5

@ TI

…

…

…

…

…

995

2026-08-04 07:34:13.936435065

117.7400

18

@FTI

996

2026-08-04 07:34:13.936615045

117.7400

2

@FTI

997

2026-08-04 07:34:13.941759948

117.8000

37

@FTI

998

2026-08-04 07:34:13.941765195

117.8000

31

@FTI

999

2026-08-04 07:34:13.952417322

117.7800

34

@ TI

1000 rows x 4 columns