otp.inspection.DB#
- class DB(name, description='', context=utils.default)#
Bases:
objectAn object of available databases that the
otp.databases()function returns. It helps to make initial analysis on the database level: available tick types, dates with data, symbols, tick schema, etc.- access_info(deep_scan=False, username=None)#
Get access info for this database and
username.All dates are returned in GMT timezone.
- Parameters
deep_scan – If False (default) then the access fields are returned from the configuration of the database (basically the same fields as specified in the locator) and the dictionary is returned. If True then access fields are returned for each available remote host and time interval and the pandas.DataFrame object is returned.
username – Can be used to specify the user for which the query will be executed. By default the query is executed for the current user.
- Return type
Examples
By default access fields from the basic configuration of the database are returned:
>>> some_db = otp.databases()['SOME_DB'] >>> some_db.access_info() {'DB_NAME': 'SOME_DB', 'READ_ACCESS': 1, 'WRITE_ACCESS': 1, 'MIN_AGE_SET': 0, 'MIN_AGE_MSEC': 0, 'MAX_AGE_SET': 0, 'MAX_AGE_MSEC': 0, 'MIN_START_DATE_SET': 0, 'MIN_START_DATE_MSEC': Timestamp('1970-01-01 00:00:00'), 'MAX_END_DATE_SET': 0, 'MAX_END_DATE_MSEC': Timestamp('1970-01-01 00:00:00'), 'MIN_AGE_DB_DAYS': 0, 'MIN_AGE_DB_DAYS_SET': 0, 'MAX_AGE_DB_DAYS': 0, 'MAX_AGE_DB_DAYS_SET': 0, 'CEP_ACCESS': 1, 'DESTROY_ACCESS': 0, 'MEMDB_ACCESS': 1}
Set parameter
deep_scanto True to return access fields from each available host and time interval:>>> some_db.access_info(deep_scan=True) DB_NAME READ_ACCESS WRITE_ACCESS MIN_AGE_SET MIN_AGE_MSEC MAX_AGE_SET MAX_AGE_MSEC MIN_START_DATE_SET MIN_START_DATE_MSEC MAX_END_DATE_SET MAX_END_DATE_MSEC MIN_AGE_DB_DAYS MIN_AGE_DB_DAYS_SET MAX_AGE_DB_DAYS MAX_AGE_DB_DAYS_SET CEP_ACCESS DESTROY_ACCESS MEMDB_ACCESS SERVER_ADDRESS INTERVAL_START INTERVAL_END 0 SOME_DB 1 1 0 0 0 0 0 1970-01-01 0 1970-01-01 0 0 0 0 1 0 1 ... 2002-12-30 2100-01-01
See also
ACCESS_INFO OneTick event processor
- show_config(config_type='locator_entry')#
Shows the specified configuration for a database.
- Parameters
config_type (str) –
If ‘locator_entry’ is specified, a string representing db’s locator entry along with VDB_FLAG (this flag equals 1 when the database is virtual and 0 otherwise) will be returned.
If ‘db_time_intervals’ is specified, then time intervals configured in the locator file will be propagated including additional information, such as LOCATION, ARCHIVE_DURATION, DAY_BOUNDARY_TZ, DAY_BOUNDARY_OFFSET, ALTERNATIVE_LOCATIONS, etc.
- Return type
Examples
some_db = otp.databases()['SOME_DB'] print(some_db.show_config()['LOCATOR_STRING'])
<DB ARCHIVE_COMPRESSION_TYPE="NATIVE_PLUS_ZSTD" ID="SOME_DB" SYMBOLOGY="BZX" TICK_TIMESTAMP_TYPE="NANOS" > <LOCATIONS > <LOCATION ACCESS_METHOD="file" DAY_BOUNDARY_TZ="EST5EDT" END_TIME="21000101000000" LOCATION="..." START_TIME="20021230000000" /> </LOCATIONS> <RAW_DATA /> </DB>>>> some_db = otp.databases()['SOME_DB'] >>> some_db.show_config(config_type='db_time_intervals') {'START_DATE': 1041206400000, 'END_DATE': 4102444800000, 'GROWABLE_ARCHIVE_FLAG': 0, 'ARCHIVE_DURATION': 0, 'LOCATION': '...', 'DAY_BOUNDARY_TZ': 'EST5EDT', 'DAY_BOUNDARY_OFFSET': 0, 'ALTERNATIVE_LOCATIONS': ''}
See also
DB/SHOW_CONFIG OneTick event processor
- property min_acl_start_date: Optional[datetime.date]#
Minimum start date set in ACL for current user. Returns None if not set.
- property max_acl_end_date: Optional[datetime.date]#
Maximum end date set in ACL for current user. Returns None if not set.
- dates(respect_acl=False, check_index_file=utils.adaptive)#
Returns list of dates in GMT timezone for which data is available.
- Parameters
respect_acl (bool) – If True then only the dates that current user has access to will be returned
check_index_file (bool) – If True, then file index will be searched for to determine if a database is loaded for a date. This check may be expensive, in terms of time it takes, when the file resides on NFS or on object storage, such as S3. If this parameter is set to False, then only the database directory for a date will be searched. This will increase performance, but may also return the days that are configured but where there is actually no data. By default this option is set to False if it is supported by API and the server, otherwise it is set to True.
- Returns
Returns
Nonewhen there is no data in the database- Return type
datetime.dateorNone
Examples
>>> some_db = otp.databases()['SOME_DB'] >>> some_db.dates() [datetime.date(2003, 12, 1)]
- property last_date#
The latest date on which db has data and the current user has access to.
- Returns
Returns
Nonewhen there is no data in the database- Return type
datetime.dateorNone
Examples
>>> some_db = otp.databases()['SOME_DB'] >>> some_db.last_date datetime.date(2003, 12, 1)
- tick_types(date=None, timezone=None)#
Returns list of tick types for the
date.- Parameters
date (
otp.dt,datetime.datetime, optional) – Date for the tick types look up.Nonemeans thelast_datetimezone (str, optional) – Timezone for the look up.
Nonemeans the default timezone.
- Returns
List with string values of available tick types.
- Return type
Examples
>>> us_comp_db = otp.databases()['US_COMP'] >>> us_comp_db.tick_types(date=otp.dt(2022, 3, 1)) ['QTE', 'TRD']
- schema(date=None, tick_type=None, timezone=None, check_index_file=utils.adaptive)#
Gets the schema of the database.
- Parameters
date (
otp.dt,datetime.datetime, optional) – Date for the schema.Nonemeans thelast_datetick_type (str, optional) – Specifies a tick type for schema.
Nonemeans use the one available tick type, if there are multiple tick types then it raises theException. It uses thetick_types()method.timezone (str, optional) – Allows to specify a timezone for searching tick types.
check_index_file (bool) – If True, then file index will be searched for to determine if a database is loaded for a date. This check may be expensive, in terms of time it takes, when the file resides on NFS or on object storage, such as S3. If this parameter is set to False, then only the database directory for a date will be searched. This will increase performance, but may also return the days that are configured but where there is actually no data. By default this option is set to False if it is supported by API and the server, otherwise it is set to True.
- Returns
Dict where keys are field names and values are
onetick.pytypes. It’s compatible with theonetick.py.Source.schemamethods.- Return type
Examples
>>> us_comp_db = otp.databases()['US_COMP'] >>> us_comp_db.schema(tick_type='TRD', date=otp.dt(2022, 3, 1)) {'PRICE': <class 'float'>, 'SIZE': <class 'int'>}
- symbols(date=None, timezone=None, tick_type=None, pattern='.*')#
Finds a list of available symbols in the database
- Parameters
date (
otp.dt,datetime.datetime, optional) – Date for the symbols look up.Nonemeans thelast_datetick_type (str, optional) – Tick type for symbols.
Nonemeans union across all tick types.timezone (str, optional) – Timezone for the lookup.
Nonemeans the default timezone.pattern (str) – Regular expression to select symbols.
- Return type
Examples
>>> us_comp_db = otp.databases()['US_COMP'] >>> us_comp_db.symbols(date=otp.dt(2022, 3, 1), tick_type='TRD', pattern='^AAP.*') ['AAP', 'AAPL']
- show_archive_stats(start=utils.adaptive, end=utils.adaptive, date=None, timezone='GMT')#
This method shows various stats about the queried symbol, as well as an archive as a whole for each day within the queried interval.
Accelerator databases are not supported. Memory databases will be ignored even within their life hours.
Archive stats returned:
COMPRESSION_TYPE - archive compression type. In older archives native compression flag is not stored, so for example for gzip compression this field may say “GZIP or NATIVE_PLUS_GZIP”. The meta_data_upgrader.exe tool can be used to determine and inject that information in such cases in order to get a more precise result in this field.
TIME_RANGE_VALIDITY - whether lowest and highest loaded timestamps (see below) are known. Like native compression flag, this information is missing in older archives and can be added using meta_data_upgrader.exe tool.
LOWEST_LOADED_DATETIME - the lowest loaded timestamp for the queried interval (across all symbols)
HIGHEST_LOADED_DATETIME - the highest loaded timestamp for the queried interval (across all symbols)
TOTAL_TICKS - the number of ticks for the queried interval (across all symbols). Also missing in older archives and can be added using meta_data_upgrader.exe. If not available, -1 will be returned.
SYMBOL_DATA_SIZE - the size of the symbol in archive in bytes. This information is also missing in older archives, however the other options, it cannot later be added. In such cases -1 will be returned.
TOTAL_SYMBOLS - the number of symbols for the queried interval
TOTAL_SIZE - archive size in bytes for the queried interval (including the garbage potentially accumulated during appends).
Note
Fields LOWEST_LOADED_DATETIME and HIGHEST_LOADED_DATETIME are returned in GMT timezone, so the default value of parameter
timezoneis GMT too.Examples
Show stats for a particular date for a database SOME_DB:
db = otp.databases()['SOME_DB'] stats = db.show_archive_stats(date=otp.dt(2003, 12, 1)) print(stats)
Time COMPRESSION_TYPE TIME_RANGE_VALIDITY LOWEST_LOADED_DATETIME HIGHEST_LOADED_DATETIME... 0 2003-12-01 05:00:00 NATIVE_PLUS_ZSTD VALID 2003-12-01 05:00:00 2003-12-01 05:00:00.002...
See also
SHOW_ARCHIVE_STATS OneTick event processor
- Return type
pandas.core.frame.DataFrame
- ref_data(ref_data_type, symbol_date=None, start=utils.adaptive, end=utils.adaptive, date=None, timezone='GMT', symbol='')#
Shows reference data for the specified security and reference data type.
It can be used to view corporation actions, symbol name changes, primary exchange info and symbology mapping for a securities, as well as the list of symbologies, names of custom adjustment types for corporate actions present in a reference database as well as names of continuous contracts in database symbology.
- Parameters
ref_data_type (str) –
Type of reference data to be queried. Possible values are:
corp_actions
symbol_name_history
primary_exchange
symbol_calendar
symbol_currency
symbology_mapping
symbology_list
custom_adjustment_type_list
all_calendars
all_continuous_contract_names
symbol_date – This parameter must be specified for some reference data types to be queried.
symbol (str) – Symbol name for the query (may be useful for some
ref_data_type).
- Return type
pandas.core.frame.DataFrame
Examples
Show calendars for a database TRAIN_A_PRL_TRD in the given range:
>>> db = otp.databases()['TRAIN_A_PRL_TRD'] >>> db.ref_data('all_calendars', ... start=otp.dt(2018, 2, 1), ... end=otp.dt(2018, 2, 9), ... symbol_date=otp.dt(2018, 2, 1)) Time END_DATETIME CALENDAR_NAME SESSION_NAME SESSION_FLAGS DAY_PATTERN START_HHMMSS END_HHMMSS TIMEZONE PRIORITY DESCRIPTION 0 2018-02-01 00:00:00 2018-02-06 23:59:59 FRED Regular R 0.0.12345 93000 160000 EST5EDT 0 1 2018-02-06 23:59:59 2018-02-07 23:59:59 FRED Holiday H 0.0.12345 93000 160000 EST5EDT 1 2 2018-02-07 23:59:59 2050-12-31 23:59:59 FRED Regular F 0.0.12345 93000 160000 EST5EDT 0
Set symbol name with
symbolparameter:>>> db = otp.databases()['US_COMP_SAMPLE'] >>> db.ref_data(ref_data_type='corp_actions', ... start=otp.dt(2025, 1, 2), ... end=otp.dt(2025, 7, 2), ... symbol_date=otp.dt(2025, 7, 1), ... symbol='WMT', ... timezone='America/New_York') Time MULTIPLICATIVE_ADJUSTMENT ADDITIVE_ADJUSTMENT ADJUSTMENT_TYPE 0 2025-03-21 1.000000 0.235 CASH_DIVIDEND 1 2025-03-21 0.997261 0.000 MULTI_ADJ_CASH 2 2025-05-09 1.000000 0.235 CASH_DIVIDEND 3 2025-05-09 0.997588 0.000 MULTI_ADJ_CASH
See also
REF_DATA OneTick event processor