otp.RefDB

class RefDB(name=None, kind='archive', db_properties=None, db_location=None, write=True, clean_up=True, destroy_access=False)

Bases: onetick.py.db.db.DB

Creates reference database object.

Parameters

• name (str) – Database name

• clean_up (bool, optional) – Flag that controls temporary database cleanup

• db_properties (dict, optional) – Properties of database to add to locator

• db_location (dict, optional) – Location of database to add to locator. Reference database must have a single location, pointing to a continuous archive database.

• write (bool, optional) – Flag that controls access to write to database

• destroy_access (bool, optional) – Flag that controls access to destroy to database

Examples

>>> properties = {'symbology': 'TICKER'}
>>> location = {'archive_duration': 'continuous'}
>>> ref_db = otp.RefDB('REF_DATA_MYDB', db_properties=properties, db_location=location)
>>> session.use(ref_db)
>>> data = 'A||20100102000000|20100103000000|B||20100103000000|20100104000000|'
>>> out, err = ref_db.put([otp.RefDB.SymbolNameHistory(data, 'TICKER')])
>>> b'Total ticks 8' in err and b'Total symbols 6' in err
True
>>> properties = {'ref_data_db': ref_db.name, 'symbology': 'TICKER'}
>>> db = otp.DB('MYDB', db_properties=properties)
>>> session.use(db)
>>> data = otp.Ticks(X=['hello'], start=otp.dt(2010, 1, 2), end=otp.dt(2010, 1, 3))
>>> data = otp.run(data.write(db.name, 'A', 'MSG', date=otp.dt(2010, 1, 2)))
>>> data = otp.Ticks(X=['world!'], start=otp.dt(2010, 1, 3), end=otp.dt(2010, 1, 4))
>>> data = otp.run(data.write(db.name, 'B', 'MSG', date=otp.dt(2010, 1, 3)))
>>> data = otp.DataSource(db.name, tick_type='MSG')
>>> s_dt, e_dt, symbol_date = otp.dt(2010, 1, 1), otp.dt(2010, 1, 4), otp.dt(2010, 1, 2)
>>> otp.run(data, symbols='A', start=s_dt, end=e_dt, symbol_date=symbol_date)
        Time       X
0 2010-01-02   hello
1 2010-01-03  world!

put(self, src, tickdb_symbology=None, delta_mode=False, full_integrity_check=False, load_by_sections=True)

Loads data in database with reference_data_loader.exe. If db properties contain SUPPORT_DELTAS=YES, delta_mode set to True, and proper delta file is used then data is loaded in incremental mode (in other words, replace or modification mode). If the above conditions are not met, reference database content is entirely rewritten with the new data.

Parameters

• src (str, list of str or otp.RefDB.Section) – Path to data file, or list of data per section in specified format

• tickdb_symbology (list of str, optional) – All symbologies for which the reference data needs to be generated

• delta_mode (bool, default is False) – If set to True loader will perform incremental load. Cannot be used if tickdb_symbology is specified

• full_integrity_check (bool, default is False) – If set to True loader checks all mappings to symbologies with symbol name history section and gives warning if mapped securities do not have symbol name history

• load_by_sections (bool, default is True) – If set to True loader will perform input data file splitting by data types and symbologies to load each part separately instead loading the entire file at once

class Section(name, data, attrs=None)

Bases: object

Specification of a reference database section. Section content can be specified as a string or source. The format of string and output columns of source must correspond with the section documentation.

Parameters

• name (str) – Section name

• data (str or otp.Source) – Content of the section

• attrs (dict, optional) – Attributes of the section

Examples

Data provided as a string:

>>> data = 'SYM1|20100101093000|20100101110000' + os.linesep
>>> data += 'SYM2|20100101110000|20100103140000'
>>> section = otp.RefDB.Section('SECTION_NAME', data, {'ATTR1': 'VAL1', 'ATTR2': 'VAL2'})
>>> print(section)
<SECTION_NAME ATTR1="VAL1" ATTR2="VAL2">
SYM1|20100101093000|20100101110000
SYM2|20100101110000|20100103140000
</SECTION_NAME>

Data provided as a otp.Source:

>>> data = dict()
>>> data['SYMBOL_NAME'] = ['SYM1', 'SYM2']
>>> data['START_DATETIME'] = [otp.dt(2010, 1, 1, 9, 30, tz='EST5EDT'), otp.dt(2010, 1, 1, 11, tz='EST5EDT')]
>>> data['END_DATETIME'] = [otp.dt(2010, 1, 1, 11, tz='EST5EDT'), otp.dt(2010, 1, 3, 14, tz='EST5EDT')]
>>> ticks = otp.Ticks(**data, offset=[0] * 2, db='LOCAL')
>>> section = otp.RefDB.Section('SECTION_NAME', ticks, {'ATTR1': 'VAL1', 'ATTR2': 'VAL2'})
>>> print(section) 
<SECTION_NAME ATTR1="VAL1" ATTR2="VAL2" OTQ_QUERY=...>
</SECTION_NAME>

where OTQ_QUERY is path to otp.Source, dumped to disk as temporary .otq file.

class SymbolNameHistory(data, symbology)

Bases: onetick.py.db.db.RefDB.Section

Describes symbol changes for the same security. The continuity can be expressed in terms of any symbol type and can be specified on the security level or the security+exchange level (more explicit).

Examples

>>> data = 'CORE_A||20100101093000|20100101110000|CORE_B||20100101110000|20100103140000|'
>>> section = otp.RefDB.SymbolNameHistory(data, symbology='CORE')
>>> print(section)
<SYMBOL_NAME_HISTORY SYMBOLOGY="CORE">
CORE_A||20100101093000|20100101110000|CORE_B||20100101110000|20100103140000|
</SYMBOL_NAME_HISTORY>

Equivalent otp.Source:

>>> data = dict()
>>> data['SYMBOL_NAME'] = ['CORE_A'] * 2
>>> data['SYMBOL_NAME_IN_HISTORY'] = ['CORE_A', 'CORE_B']
>>> data['SYMBOL_START_DATETIME'] = [otp.dt(2010, 1, 2, tz='EST5EDT')] * 2
>>> data['SYMBOL_END_DATETIME'] = [otp.dt(2010, 1, 5, tz='EST5EDT')] * 2
>>> data['START_DATETIME'] = [otp.dt(2010, 1, 2, tz='EST5EDT'), otp.dt(2010, 1, 3, tz='EST5EDT')]
>>> data['END_DATETIME'] = [otp.dt(2010, 1, 3, tz='EST5EDT'), otp.dt(2010, 1, 4, tz='EST5EDT')]
>>> ticks = otp.Ticks(**data, offset=[0] * 2, db='LOCAL')
>>> section = otp.RefDB.SymbolNameHistory(ticks, symbology='CORE')
>>> print(section) 
<SYMBOL_NAME_HISTORY SYMBOLOGY="CORE" OTQ_QUERY=...>
</SYMBOL_NAME_HISTORY>

Parameters

• data (Union[str, otp.Source]) –

• symbology (str) –

class SymbologyMapping(data, source_symbology, dest_symbology)

Bases: onetick.py.db.db.RefDB.Section

Describes a history of mapping of symbols of one symbology to the symbols of another symbology.

Examples

>>> data = 'A||20100101093000|20100101110000|CORE_A|' + os.linesep
>>> data += 'B||20100101110000|20100103140000|CORE_B|'
>>> section = otp.RefDB.SymbologyMapping(data, source_symbology='TICKER', dest_symbology='CORE')
>>> print(section)
<SYMBOLOGY_MAPPING SOURCE_SYMBOLOGY="TICKER" DEST_SYMBOLOGY="CORE">
A||20100101093000|20100101110000|CORE_A|
B||20100101110000|20100103140000|CORE_B|
</SYMBOLOGY_MAPPING>

Equivalent otp.Source:

>>> data = dict()
>>> data['SYMBOL_NAME'] = ['A', 'B']
>>> data['MAPPED_SYMBOL_NAME'] = ['CORE_A', 'CORE_B']
>>> data['START_DATETIME'] = [otp.dt(2010, 1, 2, tz='EST5EDT'), otp.dt(2010, 1, 3, tz='EST5EDT')]
>>> data['END_DATETIME'] = [otp.dt(2010, 1, 3, tz='EST5EDT'), otp.dt(2010, 1, 4, tz='EST5EDT')]
>>> ticks = otp.Ticks(**data, offset=[0] * 2, db='LOCAL')
>>> section = otp.RefDB.SymbologyMapping(ticks, source_symbology='TICKER', dest_symbology='CORE')
>>> print(section) 
<SYMBOLOGY_MAPPING SOURCE_SYMBOLOGY="TICKER" DEST_SYMBOLOGY="CORE" OTQ_QUERY=...>
</SYMBOLOGY_MAPPING>

Parameters

• data (Union[str, otp.Source]) –

• source_symbology (str) –

• dest_symbology (str) –

class CorpActions(data, symbology)

Bases: onetick.py.db.db.RefDB.Section

Describes corporate actions. Used by OneTick to compute prices adjusted for various types of corporate actions. Supports both built-in and custom (user-defined) types of corporate actions.

Examples

>>> data = 'CORE_C||20100103180000|0.25|0.0|SPLIT'
>>> section = otp.RefDB.CorpActions(data, symbology='CORE')
>>> print(section)
<CORP_ACTIONS SYMBOLOGY="CORE">
CORE_C||20100103180000|0.25|0.0|SPLIT
</CORP_ACTIONS>

Equivalent otp.Source:

>>> data = dict()
>>> data['SYMBOL_NAME'] = ['CORE_C']
>>> data['EFFECTIVE_DATETIME'] = [otp.dt(2010, 1, 3, 18, tz='EST5EDT')]
>>> data['MULTIPLICATIVE_ADJUSTMENT'] = [0.25]
>>> data['ADDITIVE_ADJUSTMENT'] = [0.0]
>>> data['ADJUSTMENT_TYPE_NAME'] = ['SPLIT']
>>> ticks = otp.Ticks(**data, offset=[0], db='LOCAL')
>>> section = otp.RefDB.CorpActions(ticks, symbology='CORE')
>>> print(section) 
<CORP_ACTIONS SYMBOLOGY="CORE" OTQ_QUERY=...>
</CORP_ACTIONS>

Parameters

• data (Union[str, otp.Source]) –

• symbology (str) –

class ContinuousContracts(data, symbology)

Bases: onetick.py.db.db.RefDB.Section

Describes continuous contracts. Continuity is expressed in terms of stitched history of real contracts and rollover adjustments in between them and can be specified on the continuous contract level or continuous contract+exchange level (more explicit).

Examples

>>> data = 'CC||CORE_A||20100101093000|20100101110000|0.5|0|CORE_B||20100101110000|20100103140000'
>>> section = otp.RefDB.ContinuousContracts(data, symbology='CORE')
>>> print(section)
<CONTINUOUS_CONTRACTS SYMBOLOGY="CORE">
CC||CORE_A||20100101093000|20100101110000|0.5|0|CORE_B||20100101110000|20100103140000
</CONTINUOUS_CONTRACTS>

Equivalent otp.Source:

>>> data = dict()
>>> data['CONTINUOUS_CONTRACT_NAME'] = ['CC'] * 2
>>> data['SYMBOL_NAME'] = ['CORE_A', 'CORE_B']
>>> data['START_DATETIME'] = [otp.dt(2010, 1, 2, tz='EST5EDT'), otp.dt(2010, 1, 3, tz='EST5EDT')]
>>> data['END_DATETIME'] = [otp.dt(2010, 1, 3, tz='EST5EDT'), otp.dt(2010, 1, 4, tz='EST5EDT')]
>>> data['MULTIPLICATIVE_ADJUSTMENT'] = [0.5, None]
>>> data['ADDITIVE_ADJUSTMENT'] = [3, None]
>>> ticks = otp.Ticks(**data, offset=[0] * 2, db='LOCAL')
>>> section = otp.RefDB.ContinuousContracts(ticks, symbology='CORE')
>>> print(section) 
<CONTINUOUS_CONTRACTS SYMBOLOGY="CORE" OTQ_QUERY=...>
</CONTINUOUS_CONTRACTS>

Parameters

• data (Union[str, otp.Source]) –

• symbology (str) –

class SymbolCurrency(data, symbology)

Bases: onetick.py.db.db.RefDB.Section

Specifies symbols’ currencies in 3-letter ISO codes for currencies. These are used for currency conversion (e.g., when calculating portfolio price for a list of securities with different currencies).

Examples

>>> data = 'CORE_A||20100101093000|20100101110000|USD|1.0' + os.linesep
>>> data += 'CORE_B||20100101110000|20100103140000|RUB|1.8'
>>> section = otp.RefDB.SymbolCurrency(data, symbology='CORE')
>>> print(section)
<SYMBOL_CURRENCY SYMBOLOGY="CORE">
CORE_A||20100101093000|20100101110000|USD|1.0
CORE_B||20100101110000|20100103140000|RUB|1.8
</SYMBOL_CURRENCY>

Equivalent otp.Source:

>>> data = dict()
>>> data['SYMBOL_NAME'] = ['CORE_A', 'CORE_B',]
>>> data['CURRENCY'] = ['USD', 'RUB']
>>> data['MULTIPLIER'] = [1., 1.8]
>>> data['START_DATETIME'] = [otp.dt(2010, 1, 1, 9, 30, tz='EST5EDT'), otp.dt(2010, 1, 1, 11, tz='EST5EDT')]
>>> data['END_DATETIME'] = [otp.dt(2010, 1, 1, 11, tz='EST5EDT'), otp.dt(2010, 1, 3, 14, tz='EST5EDT')]
>>> ticks = otp.Ticks(**data, offset=[0] * 2, db='LOCAL')
>>> section = otp.RefDB.SymbolCurrency(ticks, symbology='CORE')
>>> print(section) 
<SYMBOL_CURRENCY SYMBOLOGY="CORE" OTQ_QUERY=...>
</SYMBOL_CURRENCY>

Parameters

• data (Union[str, otp.Source]) –

• symbology (str) –

class Calendar(data)

Bases: onetick.py.db.db.RefDB.Section

Specifies a named calendar. Needed to analyze tick data during specific market time intervals (i.e., during normal trading hours). Can either be used directly in queries as described below, or referred to from the SYMBOL_CALENDAR and EXCH_CALENDAR sections.

Examples

>>> data = 'CAL1|20100101093000|20100101110000|Regular|R|0.0.12345|093000|160000|GMT|1|DESCRIPTION1'
>>> data += os.linesep
>>> data += 'CAL2|20100101110000|20100103140000|Holiday|F|0.0.12345|094000|170000|GMT|0|DESCRIPTION2'
>>> section = otp.RefDB.Calendar(data)
>>> print(section)
<CALENDAR >
CAL1|20100101093000|20100101110000|Regular|R|0.0.12345|093000|160000|GMT|1|DESCRIPTION1
CAL2|20100101110000|20100103140000|Holiday|F|0.0.12345|094000|170000|GMT|0|DESCRIPTION2
</CALENDAR>

Equivalent otp.Source:

>>> data = dict()
>>> data['CALENDAR_NAME'] = ['CAL1', 'CAL2']
>>> data['START_DATETIME'] = [otp.dt(2010, 1, 1, 9, 30, tz='EST5EDT'), otp.dt(2010, 1, 1, 11, tz='EST5EDT')]
>>> data['END_DATETIME'] = [otp.dt(2010, 1, 1, 11, tz='EST5EDT'), otp.dt(2010, 1, 3, 14, tz='EST5EDT')]
>>> data['SESSION_NAME'] = ['Regular', 'Holiday']
>>> data['SESSION_FLAGS'] = ['R', 'H']
>>> data['DAY_PATTERN'] = ['0.0.12345', '0.0.12345']
>>> data['START_HHMMSS'] = ['093000', '094000']
>>> data['END_HHMMSS'] = ['160000', '170000']
>>> data['TIMEZONE'] = ['GMT', 'GMT']
>>> data['PRIORITY'] = [1, 0]
>>> data['DESCRIPTION'] = ['DESCRIPTION1', 'DESCRIPTION2']
>>> ticks = otp.Ticks(**data, offset=[0] * 2, db='LOCAL')
>>> section = otp.RefDB.Calendar(ticks)
>>> print(section) 
<CALENDAR  OTQ_QUERY=...>
</CALENDAR>

Parameters

data (Union[str, otp.Source]) –

class SymbolCalendar(data, symbology)

Bases: onetick.py.db.db.RefDB.Section

Specifies a calendar for a symbol. Needed to analyze tick data during specific market time intervals (i.e., during normal trading hours). Can either be specified directly or refer to a named calendar by its name (see the CALENDAR section).

Examples

Symbol calendar section, referring to named calendar section:

>>> data = 'CORE_A|20100101093000|20100101110000|CAL1' + os.linesep
>>> data += 'CORE_B|20100101110000|20100103140000|CAL2'
>>> section = otp.RefDB.SymbolCalendar(data, symbology='CORE')
>>> print(section)
<SYMBOL_CALENDAR SYMBOLOGY="CORE">
CORE_A|20100101093000|20100101110000|CAL1
CORE_B|20100101110000|20100103140000|CAL2
</SYMBOL_CALENDAR>

Equivalent otp.Source:

>>> data = dict()
>>> data['SYMBOL_NAME'] = ['CORE_A', 'CORE_B']
>>> data['START_DATETIME'] = [otp.dt(2010, 1, 1, 9, 30, tz='EST5EDT'), otp.dt(2010, 1, 1, 11, tz='EST5EDT')]
>>> data['END_DATETIME'] = [otp.dt(2010, 1, 1, 11, tz='EST5EDT'), otp.dt(2010, 1, 3, 14, tz='EST5EDT')]
>>> data['CALENDAR_NAME'] = ['CAL1', 'CAL2']
>>> ticks = otp.Ticks(**data, offset=[0] * 2, db='LOCAL')
>>> section = otp.RefDB.SymbolCalendar(ticks, symbology='CORE')
>>> print(section) 
<SYMBOL_CALENDAR SYMBOLOGY="CORE" OTQ_QUERY=...>
</SYMBOL_CALENDAR>

Symbol calendar section without using named calendar section:

>>> data = 'CORE_A|20100101093000|20100101110000|Regular|R|0.0.12345|093000|160000|EST5EDT|1|' + os.linesep
>>> data += 'CORE_B|20100101110000|20100103140000|Regular|F|0.0.12345|093000|160000|EST5EDT|1|'
>>> section = otp.RefDB.SymbolCalendar(data, symbology='CORE')
>>> print(section)
<SYMBOL_CALENDAR SYMBOLOGY="CORE">
CORE_A|20100101093000|20100101110000|Regular|R|0.0.12345|093000|160000|EST5EDT|1|
CORE_B|20100101110000|20100103140000|Regular|F|0.0.12345|093000|160000|EST5EDT|1|
</SYMBOL_CALENDAR>

Equivalent otp.Source:

>>> data = dict()
>>> data['SYMBOL_NAME'] = ['CORE_A', 'CORE_B']
>>> data['START_DATETIME'] = [otp.dt(2010, 1, 1, 9, 30, tz='EST5EDT'), otp.dt(2010, 1, 1, 11, tz='EST5EDT')]
>>> data['END_DATETIME'] = [otp.dt(2010, 1, 1, 11, tz='EST5EDT'), otp.dt(2010, 1, 3, 14, tz='EST5EDT')]
>>> data['SESSION_NAME'] = ['Regular', 'Regular']
>>> data['SESSION_FLAGS'] = ['R', 'F']
>>> data['DAY_PATTERN'] = ['0.0.12345', '0.0.12345']
>>> data['START_HHMMSS'] = ['093000', '160000']
>>> data['END_HHMMSS'] = ['CAL1', 'CAL2']
>>> data['TIMEZONE'] = ['EST5EDT', 'EST5EDT']
>>> data['PRIORITY'] = [1, 1]
>>> data['DESCRIPTION'] = ['', '']
>>> ticks = otp.Ticks(**data, offset=[0] * 2, db='LOCAL')
>>> section = otp.RefDB.SymbolCalendar(ticks, symbology='CORE')
>>> print(section) 
<SYMBOL_CALENDAR SYMBOLOGY="CORE" OTQ_QUERY=...>
</SYMBOL_CALENDAR>

Parameters

• data (Union[str, otp.Source]) –

• symbology (str) –

class SectionStr(name, data, attrs=None)

Bases: onetick.py.db.db.RefDB.Section

Specification of a reference database section that can be specified only as a string. Section content still can be provided as a otp.Source, but the otp.Source is executed and result data is used as string in section. It’s up to user to provide otp.Source with correct number and order of columns.

Examples

Data provided as a string returns the same result as otp.RefDB.Section.

Data provided as a otp.Source:

>>> data = dict()
>>> data['SYMBOL_NAME'] = ['SYM1', 'SYM2']
>>> data['START_DATETIME'] = [otp.dt(2010, 1, 1, 9, 30, tz='EST5EDT'), otp.dt(2010, 1, 1, 11, tz='EST5EDT')]
>>> data['END_DATETIME'] = [otp.dt(2010, 1, 1, 11, tz='EST5EDT'), otp.dt(2010, 1, 3, 14, tz='EST5EDT')]
>>> ticks = otp.Ticks(**data, offset=[0] * 2, db='LOCAL')
>>> ticks = ticks.table(SYMBOL_NAME=otp.string[128], START_DATETIME=otp.msectime, END_DATETIME=otp.msectime)
>>> section = otp.RefDB.SectionStr('SECTION_NAME', ticks, {'ATTR1': 'VAL1', 'ATTR2': 'VAL2'})
>>> print(section) 
<SECTION_NAME ATTR1="VAL1" ATTR2="VAL2">
SYM1|20100101093000|20100101110000
SYM2|20100101110000|20100103140000
</SECTION_NAME>

where OTQ_QUERY is path to otp.Source, dumped to disk as temporary .otq file.

Parameters

• name (str) –

• data (Union[str, otp.Source]) –

• attrs (Optional[dict]) –

class PrimaryExchange(data, symbology)

Bases: onetick.py.db.db.RefDB.SectionStr

Specifies symbols’ primary exchanges. Used to extract and analyze tick data for a security on its primary exchange, without having to explicitly specify the name of the primary exchange.

Examples

>>> data = 'A||19991118000000|99999999000000|N|'
>>> data += os.linesep
>>> data += 'AA||19991118000000|99999999000000|N|AA.N'
>>> section = otp.RefDB.PrimaryExchange(data, symbology='TICKER')
>>> print(section)
<PRIMARY_EXCHANGE SYMBOLOGY="TICKER">
A||19991118000000|99999999000000|N|
AA||19991118000000|99999999000000|N|AA.N
</PRIMARY_EXCHANGE>

Equivalent query should return the same data values in the same order. Column names does not matter.

Parameters

• data (Union[str, otp.Source]) –

• symbology (str) –

class ExchCalendar(data, symbology)

Bases: onetick.py.db.db.RefDB.SectionStr

Specifies symbols’ primary exchanges. Used to extract and analyze tick data for a security on its primary exchange, without having to explicitly specify the name of the primary exchange.

Examples

>>> data = 'NYSE||19600101000000|20501231235959|Regular|R|0.0.12345|093000|160000|EST5EDT|'
>>> data += os.linesep
>>> data += 'NYSE||19600101000000|20501231235959|Half-day|RL|12/31|093000|130000|EST5EDT|'
>>> data += os.linesep
>>> data += 'NYSE||19600101000000|20501231235959|Holiday|H|01/01|000000|240000|EST5EDT|'
>>> section = otp.RefDB.ExchCalendar(data, symbology='MIC')
>>> print(section)
<EXCH_CALENDAR SYMBOLOGY="MIC">
NYSE||19600101000000|20501231235959|Regular|R|0.0.12345|093000|160000|EST5EDT|
NYSE||19600101000000|20501231235959|Half-day|RL|12/31|093000|130000|EST5EDT|
NYSE||19600101000000|20501231235959|Holiday|H|01/01|000000|240000|EST5EDT|
</EXCH_CALENDAR>

If a CALENDAR section is used:

>>> data = 'LSE||19600101000000|20501231235959|WNY'
>>> section = otp.RefDB.ExchCalendar(data, symbology='MIC')
>>> print(section)
<EXCH_CALENDAR SYMBOLOGY="MIC">
LSE||19600101000000|20501231235959|WNY
</EXCH_CALENDAR>

Equivalent query should return the same data values in the same order. Column names does not matter.

Parameters

• data (Union[str, otp.Source]) –

• symbology (str) –

class SymbolExchange(data, symbology, exchange_symbology)

Bases: onetick.py.db.db.RefDB.SectionStr

Specifies the exchange where a security is traded. Needs to be provided for the symbologies where the symbol name is unique across all exchanges.

Examples

>>> data = 'IBM.N|19980825000000|20501231235959|NYSE||'
>>> section = otp.RefDB.SymbolExchange(data, symbology='RIC', exchange_symbology='MIC')
>>> print(section)
<SYMBOL_EXCHANGE SYMBOLOGY="RIC" EXCHANGE_SYMBOLOGY="MIC">
IBM.N|19980825000000|20501231235959|NYSE||
</SYMBOL_EXCHANGE>

Equivalent query should return the same data values in the same order. Column names does not matter.

Parameters

• data (Union[str, otp.Source]) –

• symbology (str) –

• exchange_symbology (str) –