--- file_format: mystnb --- # 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. ```{literalinclude} webapi_configuration.py ``` ## Adding Multiple Filters Applying multiple filters to the dataset as two separate operations. ```{code-cell} ipython3 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 ``` ## Filtering on a Single Trade Condition Filtering that a trade condition is present in the ``COND`` field of the ``US_COMP_SAMPLE`` database. ```{code-cell} ipython3 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 ``` ## 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 {meth}`~onetick.py.Source.character_present`. ```{code-cell} ipython3 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 ``` ## 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 {meth}`~onetick.py.Source.character_present` with parameter `discard_on_match=True`. ```{code-cell} ipython3 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 ``` ## Filtering on Specific Time Ranges Applying a filter on the Data Source based on specific time periods per day, using {meth}`~onetick.py.Source.time_filter`. ```{code-cell} ipython3 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 ``` ## Filtering on Excluding Specific Time Ranges Applying a filter on the Data Source based on excluding specific time periods per day. Using {meth}`~onetick.py.Source.time_filter` with parameter `discard_on_match=True`. ```{code-cell} ipython3 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 ``` ## Relative Time Filtering on the last 5 minutes Trades Retrieving Trades for the last 5 minutes, using relative syntax for start and end in {func}`otp.run `. ```python 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 ``` {.dataframe} | | 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 {func}`otp.run `. ```python 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 ``` {.dataframe} | | 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