# Data Retrieval - Tick This section contains 6 examples for Data Retrieval - Tick using the `onetick-py`.
\\\\ Each example is a self-contained script that can be run against the OneTick Cloud sample databases. ```default # 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__' ``` ## Trades Retrieve Exchange trades from the `US_COMP_SAMPLE` database for `CSCO`, across the specified time range. ```ipython3 import onetick.py as otp data = otp.DataSource(db='US_COMP_SAMPLE', tick_type='TRD') # Return first 100 Rows data = data.limit(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 ``` ```myst-ansi Time EXCHANGE COND STOP_STOCK SOURCE TRF TTE \ 0 2024-01-03 09:30:00.065443591 Z @ I N 0 1 2024-01-03 09:30:00.111130049 Z @ I N 0 2 2024-01-03 09:30:00.127459523 V @ N 0 3 2024-01-03 09:30:00.128498068 Z @F I N 1 4 2024-01-03 09:30:00.135190071 Q @FTI N 1 .. ... ... ... ... ... .. .. 95 2024-01-03 09:30:00.897416820 Q @F I N 1 96 2024-01-03 09:30:00.897444128 Q @F I N 1 97 2024-01-03 09:30:00.897474806 Q @F I N 1 98 2024-01-03 09:30:00.897476583 Q @F I N 1 99 2024-01-03 09:30:00.897670983 D @ N Q 0 TICKER PRICE DELETED_TIME TICK_STATUS SIZE CORR SEQ_NUM \ 0 CSCO 50.0200 1969-12-31 19:00:00 0 2 0 169103 1 CSCO 50.1600 1969-12-31 19:00:00 0 3 0 169148 2 CSCO 50.1700 1969-12-31 19:00:00 0 100 0 169181 3 CSCO 50.1700 1969-12-31 19:00:00 0 5 0 169182 4 CSCO 50.1300 1969-12-31 19:00:00 0 46 0 169193 .. ... ... ... ... ... ... ... 95 CSCO 50.0800 1969-12-31 19:00:00 0 51 0 171857 96 CSCO 50.0800 1969-12-31 19:00:00 0 29 0 171858 97 CSCO 50.0800 1969-12-31 19:00:00 0 12 0 171859 98 CSCO 50.0800 1969-12-31 19:00:00 0 2 0 171860 99 CSCO 50.0797 1969-12-31 19:00:00 0 100 0 171862 TRADE_ID PARTICIPANT_TIME TRF_TIME \ 0 42 2024-01-03 09:30:00.065250000 1969-12-31 19:00:00.000000000 1 43 2024-01-03 09:30:00.110938000 1969-12-31 19:00:00.000000000 2 13 2024-01-03 09:30:00.065044730 1969-12-31 19:00:00.000000000 3 44 2024-01-03 09:30:00.128306000 1969-12-31 19:00:00.000000000 4 349 2024-01-03 09:30:00.135173836 1969-12-31 19:00:00.000000000 .. ... ... ... 95 365 2024-01-03 09:30:00.897398456 1969-12-31 19:00:00.000000000 96 366 2024-01-03 09:30:00.897428791 1969-12-31 19:00:00.000000000 97 367 2024-01-03 09:30:00.897456284 1969-12-31 19:00:00.000000000 98 368 2024-01-03 09:30:00.897456284 1969-12-31 19:00:00.000000000 99 100 2024-01-03 09:30:00.896374950 2024-01-03 09:30:00.897637536 OMDSEQ 0 0 1 0 2 0 3 0 4 0 .. ... 95 3 96 4 97 5 98 6 99 7 [100 rows x 18 columns] ``` ## Quotes Retrieve Exchange quotes from the `US_COMP_SAMPLE` database for `CSCO`, across the specified time range. ```ipython3 import onetick.py as otp data = otp.DataSource(db='US_COMP_SAMPLE', tick_type='QTE') # Return first 100 Rows data = data.limit(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 ``` ```myst-ansi Time EXCHANGE CORR DELETED_TIME \ 0 2024-01-03 09:30:00.001830617 Z 1969-12-31 19:00:00 1 2024-01-03 09:30:00.002078349 K 1969-12-31 19:00:00 2 2024-01-03 09:30:00.002215206 Q 1969-12-31 19:00:00 3 2024-01-03 09:30:00.002341904 K 1969-12-31 19:00:00 4 2024-01-03 09:30:00.002524728 Z 1969-12-31 19:00:00 .. ... ... ... ... 95 2024-01-03 09:30:00.546456694 K 1969-12-31 19:00:00 96 2024-01-03 09:30:00.546476188 Z 1969-12-31 19:00:00 97 2024-01-03 09:30:00.546535306 K 1969-12-31 19:00:00 98 2024-01-03 09:30:00.546541854 U 1969-12-31 19:00:00 99 2024-01-03 09:30:00.546550892 K 1969-12-31 19:00:00 TICK_STATUS COND NBBO_IND FINRA_BBO_IND FINRA_ADF_MPID_IND SOURCE ... \ 0 0 R 4 N ... 1 0 R 0 N ... 2 0 R 0 N ... 3 0 R 0 N ... 4 0 R 4 N ... .. ... ... ... ... ... ... ... 95 0 R 0 N ... 96 0 R 0 N ... 97 0 R 0 N ... 98 0 R 0 N ... 99 0 R 0 N ... TICKER BID_PRICE BID_SIZE ASK_PRICE ASK_SIZE SEQ_NUM \ 0 CSCO 50.00 9 50.18 2 760290 1 CSCO 49.80 1 50.37 1 760345 2 CSCO 50.00 6 50.20 7 760370 3 CSCO 49.80 1 50.37 1 760434 4 CSCO 50.00 9 50.18 3 760445 .. ... ... ... ... ... ... 95 CSCO 50.00 2 50.17 2 777868 96 CSCO 50.01 2 50.16 1 777869 97 CSCO 50.00 2 50.17 2 777873 98 CSCO 50.00 2 50.17 2 777874 99 CSCO 50.00 2 50.17 2 777875 PARTICIPANT_TIME FINRA_ADF_TIME SECURITY_STATUS_IND \ 0 2024-01-03 09:30:00.001336000 1969-12-31 19:00:00 1 2024-01-03 09:30:00.001881000 1969-12-31 19:00:00 2 2024-01-03 09:30:00.002199928 1969-12-31 19:00:00 3 2024-01-03 09:30:00.002147000 1969-12-31 19:00:00 4 2024-01-03 09:30:00.002344000 1969-12-31 19:00:00 .. ... ... ... 95 2024-01-03 09:30:00.546282000 1969-12-31 19:00:00 96 2024-01-03 09:30:00.546298000 1969-12-31 19:00:00 97 2024-01-03 09:30:00.546361000 1969-12-31 19:00:00 98 2024-01-03 09:30:00.546367116 1969-12-31 19:00:00 99 2024-01-03 09:30:00.546374000 1969-12-31 19:00:00 OMDSEQ 0 0 1 0 2 1 3 2 4 3 .. ... 95 14 96 15 97 16 98 17 99 18 [100 rows x 26 columns] ``` ## NBBO Retrieve NBBO quotes from the `US_COMP_SAMPLE` database for `CSCO`, across the specified time range. ```ipython3 import onetick.py as otp data = otp.DataSource(db='US_COMP_SAMPLE', tick_type='NBBO') # Return first 100 Rows data = data.limit(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 ``` ```myst-ansi Time BID_PRICE BID_SIZE BID_SIZE_TOTAL \ 0 2024-01-03 09:30:00.001830617 50.00 9 14 1 2024-01-03 09:30:00.002215206 50.00 9 15 2 2024-01-03 09:30:00.002524728 50.00 9 15 3 2024-01-03 09:30:00.003295288 50.00 10 16 4 2024-01-03 09:30:00.010072539 50.00 10 16 .. ... ... ... ... 95 2024-01-03 09:30:00.652918680 50.06 2 4 96 2024-01-03 09:30:00.652981633 50.06 2 2 97 2024-01-03 09:30:00.652986724 50.05 2 4 98 2024-01-03 09:30:00.653005094 50.05 2 6 99 2024-01-03 09:30:00.653037674 50.05 2 6 BID_EXCHANGE ASK_PRICE ASK_SIZE ASK_SIZE_TOTAL ASK_EXCHANGE \ 0 Z 50.18 2 2 Z 1 Z 50.18 2 2 Z 2 Z 50.18 3 3 Z 3 Z 50.18 3 3 Z 4 Z 50.18 4 4 Z .. ... ... ... ... ... 95 U 50.09 2 3 U 96 U 50.09 2 3 U 97 Z 50.09 2 3 U 98 Z 50.09 2 3 U 99 Z 50.09 3 5 Z IS_PRE_OPEN OMDSEQ 0 0 0 1 0 0 2 0 1 3 0 0 4 0 0 .. ... ... 95 0 2 96 0 3 97 0 4 98 0 0 99 0 1 [100 rows x 11 columns] ``` ## Indicative Prices Retrieve Indicative Auction Prices from the `LSE_SAMPLE` database for `VOD`, across the specified time range. ```ipython3 import onetick.py as otp data = otp.DataSource(db='LSE_SAMPLE', tick_type='IND') # Return first 100 Rows data = data.limit(100) result = otp.run(data, start=otp.dt(2024, 1, 3), end=otp.dt(2024, 1, 4), timezone='Europe/London', symbols='VOD') result ``` ```myst-ansi Time EXCH_TIME PRICE SIZE \ 0 2024-01-03 07:50:00.075 2024-01-03 07:50:00.074938266 69.00 6000 1 2024-01-03 07:50:00.076 2024-01-03 07:50:00.075324726 69.00 6880 2 2024-01-03 07:50:00.085 2024-01-03 07:50:00.085221086 69.00 7370 3 2024-01-03 07:50:00.088 2024-01-03 07:50:00.087546226 69.00 8870 4 2024-01-03 07:50:00.090 2024-01-03 07:50:00.089537586 68.28 9505 .. ... ... ... ... 95 2024-01-03 07:55:00.003 2024-01-03 07:55:00.003081449 69.00 116438 96 2024-01-03 07:55:00.534 2024-01-03 07:55:00.533325177 68.35 148938 97 2024-01-03 07:55:00.644 2024-01-03 07:55:00.643580237 68.43 148938 98 2024-01-03 07:55:00.644 2024-01-03 07:55:00.643616077 68.35 148938 99 2024-01-03 07:55:05.134 2024-01-03 07:55:05.133979316 68.35 156237 IMB_SIDE IMB_VOLUME AUCTION_TYPE OMDSEQ 0 B 2900 O 28 1 B 2020 O 5 2 B 1530 O 8 3 B 30 O 1 4 B 1945 O 8 .. ... ... ... ... 95 B 4774 O 5 96 B 29604 O 0 97 B 1256 O 1 98 B 29604 O 2 99 B 22305 O 1 [100 rows x 8 columns] ``` ## Market Phases Retrieve Exchange market phase changes from the `LSE_SAMPLE` database for `VOD`, across the specified time range. ```ipython3 import onetick.py as otp data = otp.DataSource(db='LSE_SAMPLE', tick_type='MKT') result = otp.run(data, start=otp.dt(2024, 1, 3), end=otp.dt(2024, 1, 4), timezone='Europe/London', symbols='VOD') result ``` ```myst-ansi Time MKT_PHASE QUOTE_BOOK_STATUS OFF_BOOK_STATUS \ 0 2024-01-03 07:15:00.037 T 1 2024-01-03 07:50:00.067 a T 2 2024-01-03 08:00:06.226 T T 3 2024-01-03 16:30:00.107 d T 4 2024-01-03 16:35:07.331 u T 5 2024-01-03 16:40:00.025 b T 6 2024-01-03 17:15:00.080 x T 7 2024-01-03 17:30:00.041 c T 8 2024-01-03 17:30:00.041 c c OMD_STATUS OMDSEQ 0 R 77 1 O 134 2 T 26 3 C 91 4 t 0 5 p 34 6 p 106 7 c 34 8 c 0 ``` ## Short Interest Query short interest data for `AAPL` over the last 7 days.
\\\\ Returns all available short interest metrics for the specified date range. ```python import onetick.py as otp data = otp.DataSource(db='US_SHORT_INT', tick_type='DAY') result = otp.run(data, symbols='AAPL', start=otp.now() - otp.Day(7), end=otp.now(), timezone='America/New_York') # Display the result result ``` | | Time | SHORT_VOLUME | SHORT_EXEMPT_VOLUME | VOLUME | TRF | OMDSEQ | |----|---------------------|----------------|-----------------------|-------------|-------|----------| | 0 | 2026-07-27 20:15:00 | 7.12266e+06 | 36753 | 1.76945e+07 | BQN | 30 | | 1 | 2026-07-28 20:15:00 | 9.60599e+06 | 42284 | 1.90899e+07 | BQN | 32 | | 2 | 2026-07-29 20:15:00 | 8.47753e+06 | 43470 | 1.76618e+07 | BQN | 30 | | 3 | 2026-07-30 20:15:00 | 9.69986e+06 | 39078 | 2.02463e+07 | BQN | 29 | | 4 | 2026-07-31 20:15:00 | 2.36486e+07 | 822016 | 4.49851e+07 | BQN | 31 |