Metadata

Historical.metadata.list_publishers

List all publisher ID mappings.

Use this method to list the details of publishers, including their dataset and venue mappings.

Returns

list[dict[str, int | str]]

A list of publisher details objects.

Field
Type
Description
publisher_id
int
The publisher ID assigned by Databento.
dataset
str
The dataset ID for the publisher.
venue
str
The venue for the publisher.
description
str
The publisher description.
API method 
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Historical.metadata.list_publishers() -> list[dict[str, int | str]]
Example usage 
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import databento as db

client = db.Historical("$YOUR_API_KEY")

publishers = client.metadata.list_publishers()
print(publishers)
Example response 
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[{'dataset': 'GLBX.MDP3',
  'description': 'CME Globex MDP 3.0',
  'publisher_id': 1,
  'venue': 'GLBX'},
 {'dataset': 'XNAS.ITCH',
  'description': 'Nasdaq TotalView-ITCH',
  'publisher_id': 2,
  'venue': 'XNAS'},
 {'dataset': 'XBOS.ITCH',
  'description': 'Nasdaq Texas TotalView-ITCH',
  'publisher_id': 3,
  'venue': 'XBOS'},
 {'dataset': 'XPSX.ITCH',
  'description': 'Nasdaq PSX TotalView-ITCH',
  'publisher_id': 4,
  'venue': 'XPSX'},
 {'dataset': 'BATS.PITCH',
  'description': 'Cboe BZX Depth',
  'publisher_id': 5,
  'venue': 'BATS'},
 {'dataset': 'BATY.PITCH',
  'description': 'Cboe BYX Depth',
  'publisher_id': 6,
  'venue': 'BATY'},
 {'dataset': 'EDGA.PITCH',
  'description': 'Cboe EDGA Depth',
  'publisher_id': 7,
  'venue': 'EDGA'},
 {'dataset': 'EDGX.PITCH',
  'description': 'Cboe EDGX Depth',
  'publisher_id': 8,
  'venue': 'EDGX'},
 {'dataset': 'XNYS.PILLAR',
  'description': 'NYSE Integrated',
  'publisher_id': 9,
  'venue': 'XNYS'},
 {'dataset': 'XCIS.PILLAR',
  'description': 'NYSE National Integrated',
  'publisher_id': 10,
  'venue': 'XCIS'},
 {'dataset': 'XASE.PILLAR',
  'description': 'NYSE American Integrated',
  'publisher_id': 11,
  'venue': 'XASE'},
 {'dataset': 'XCHI.PILLAR',
  'description': 'NYSE Texas Integrated',
  'publisher_id': 12,
  'venue': 'XCHI'},
 {'dataset': 'XCIS.BBO',
  'description': 'NYSE National BBO',
  'publisher_id': 13,
  'venue': 'XCIS'},
 {'dataset': 'XCIS.TRADES',
  'description': 'NYSE National Trades',
  'publisher_id': 14,
  'venue': 'XCIS'},
 {'dataset': 'MEMX.MEMOIR',
  'description': 'MEMX MEMOIR Depth',
  'publisher_id': 15,
  'venue': 'MEMX'},
 {'dataset': 'EPRL.DOM',
  'description': 'MIAX Pearl Depth',
  'publisher_id': 16,
  'venue': 'EPRL'},
 {'dataset': 'XNAS.NLS',
  'description': 'FINRA/Nasdaq TRF Carteret',
  'publisher_id': 17,
  'venue': 'FINN'},
 {'dataset': 'XNAS.NLS',
  'description': 'FINRA/Nasdaq TRF Chicago',
  'publisher_id': 18,
  'venue': 'FINC'},
 {'dataset': 'XNYS.TRADES',
  'description': 'FINRA/NYSE TRF',
  'publisher_id': 19,
  'venue': 'FINY'},
 {'dataset': 'OPRA.PILLAR',
  'description': 'OPRA - NYSE American Options',
  'publisher_id': 20,
  'venue': 'AMXO'},
 {'dataset': 'OPRA.PILLAR',
  'description': 'OPRA - BOX Options',
  'publisher_id': 21,
  'venue': 'XBOX'},
 {'dataset': 'OPRA.PILLAR',
  'description': 'OPRA - Cboe Options',
  'publisher_id': 22,
  'venue': 'XCBO'},
 {'dataset': 'OPRA.PILLAR',
  'description': 'OPRA - MIAX Emerald',
  'publisher_id': 23,
  'venue': 'EMLD'},
 {'dataset': 'OPRA.PILLAR',
  'description': 'OPRA - Cboe EDGX Options',
  'publisher_id': 24,
  'venue': 'EDGO'},
 {'dataset': 'OPRA.PILLAR',
  'description': 'OPRA - Nasdaq GEMX',
  'publisher_id': 25,
  'venue': 'GMNI'},
 {'dataset': 'OPRA.PILLAR',
  'description': 'OPRA - Nasdaq ISE',
  'publisher_id': 26,
  'venue': 'XISX'},
 {'dataset': 'OPRA.PILLAR',
  'description': 'OPRA - Nasdaq MRX',
  'publisher_id': 27,
  'venue': 'MCRY'},
 {'dataset': 'OPRA.PILLAR',
  'description': 'OPRA - MIAX Options',
  'publisher_id': 28,
  'venue': 'XMIO'},
 {'dataset': 'OPRA.PILLAR',
  'description': 'OPRA - NYSE Arca Options',
  'publisher_id': 29,
  'venue': 'ARCO'},
 {'dataset': 'OPRA.PILLAR',
  'description': 'OPRA - Options Price Reporting Authority',
  'publisher_id': 30,
  'venue': 'OPRA'},
 {'dataset': 'OPRA.PILLAR',
  'description': 'OPRA - MIAX Pearl',
  'publisher_id': 31,
  'venue': 'MPRL'},
 {'dataset': 'OPRA.PILLAR',
  'description': 'OPRA - Nasdaq Options',
  'publisher_id': 32,
  'venue': 'XNDQ'},
 {'dataset': 'OPRA.PILLAR',
  'description': 'OPRA - Nasdaq Texas Options',
  'publisher_id': 33,
  'venue': 'XBXO'},
 {'dataset': 'OPRA.PILLAR',
  'description': 'OPRA - Cboe C2 Options',
  'publisher_id': 34,
  'venue': 'C2OX'},
 {'dataset': 'OPRA.PILLAR',
  'description': 'OPRA - Nasdaq PHLX',
  'publisher_id': 35,
  'venue': 'XPHL'},
 {'dataset': 'OPRA.PILLAR',
  'description': 'OPRA - Cboe BZX Options',
  'publisher_id': 36,
  'venue': 'BATO'},
 {'dataset': 'OPRA.PILLAR',
  'description': 'OPRA - MEMX Options',
  'publisher_id': 37,
  'venue': 'MXOP'},
 {'dataset': 'IEXG.TOPS',
  'description': 'IEX TOPS',
  'publisher_id': 38,
  'venue': 'IEXG'},
 {'dataset': 'DBEQ.BASIC',
  'description': 'DBEQ Basic - NYSE Texas',
  'publisher_id': 39,
  'venue': 'XCHI'},
 {'dataset': 'DBEQ.BASIC',
  'description': 'DBEQ Basic - NYSE National',
  'publisher_id': 40,
  'venue': 'XCIS'},
 {'dataset': 'DBEQ.BASIC',
  'description': 'DBEQ Basic - IEX',
  'publisher_id': 41,
  'venue': 'IEXG'},
 {'dataset': 'DBEQ.BASIC',
  'description': 'DBEQ Basic - MIAX Pearl',
  'publisher_id': 42,
  'venue': 'EPRL'},
 {'dataset': 'ARCX.PILLAR',
  'description': 'NYSE Arca Integrated',
  'publisher_id': 43,
  'venue': 'ARCX'},
 {'dataset': 'XNYS.BBO',
  'description': 'NYSE BBO',
  'publisher_id': 44,
  'venue': 'XNYS'},
 {'dataset': 'XNYS.TRADES',
  'description': 'NYSE Trades',
  'publisher_id': 45,
  'venue': 'XNYS'},
 {'dataset': 'XNAS.QBBO',
  'description': 'Nasdaq QBBO',
  'publisher_id': 46,
  'venue': 'XNAS'},
 {'dataset': 'XNAS.NLS',
  'description': 'Nasdaq Trades',
  'publisher_id': 47,
  'venue': 'XNAS'},
 {'dataset': 'EQUS.PLUS',
  'description': 'Databento US Equities Plus - NYSE Texas',
  'publisher_id': 48,
  'venue': 'XCHI'},
 {'dataset': 'EQUS.PLUS',
  'description': 'Databento US Equities Plus - NYSE National',
  'publisher_id': 49,
  'venue': 'XCIS'},
 {'dataset': 'EQUS.PLUS',
  'description': 'Databento US Equities Plus - IEX',
  'publisher_id': 50,
  'venue': 'IEXG'},
 {'dataset': 'EQUS.PLUS',
  'description': 'Databento US Equities Plus - MIAX Pearl',
  'publisher_id': 51,
  'venue': 'EPRL'},
 {'dataset': 'EQUS.PLUS',
  'description': 'Databento US Equities Plus - Nasdaq',
  'publisher_id': 52,
  'venue': 'XNAS'},
 {'dataset': 'EQUS.PLUS',
  'description': 'Databento US Equities Plus - NYSE',
  'publisher_id': 53,
  'venue': 'XNYS'},
 {'dataset': 'EQUS.PLUS',
  'description': 'Databento US Equities Plus - FINRA/Nasdaq TRF Carteret',
  'publisher_id': 54,
  'venue': 'FINN'},
 {'dataset': 'EQUS.PLUS',
  'description': 'Databento US Equities Plus - FINRA/NYSE TRF',
  'publisher_id': 55,
  'venue': 'FINY'},
 {'dataset': 'EQUS.PLUS',
  'description': 'Databento US Equities Plus - FINRA/Nasdaq TRF Chicago',
  'publisher_id': 56,
  'venue': 'FINC'},
 {'dataset': 'IFEU.IMPACT',
  'description': 'ICE Europe Commodities',
  'publisher_id': 57,
  'venue': 'IFEU'},
 {'dataset': 'NDEX.IMPACT',
  'description': 'ICE Endex',
  'publisher_id': 58,
  'venue': 'NDEX'},
 {'dataset': 'DBEQ.BASIC',
  'description': 'Databento US Equities Basic - Consolidated',
  'publisher_id': 59,
  'venue': 'DBEQ'},
 {'dataset': 'EQUS.PLUS',
  'description': 'EQUS Plus - Consolidated',
  'publisher_id': 60,
  'venue': 'EQUS'},
 {'dataset': 'OPRA.PILLAR',
  'description': 'OPRA - MIAX Sapphire',
  'publisher_id': 61,
  'venue': 'SPHR'},
 {'dataset': 'EQUS.ALL',
  'description': 'Databento US Equities (All Feeds) - NYSE Texas',
  'publisher_id': 62,
  'venue': 'XCHI'},
 {'dataset': 'EQUS.ALL',
  'description': 'Databento US Equities (All Feeds) - NYSE National',
  'publisher_id': 63,
  'venue': 'XCIS'},
 {'dataset': 'EQUS.ALL',
  'description': 'Databento US Equities (All Feeds) - IEX',
  'publisher_id': 64,
  'venue': 'IEXG'},
 {'dataset': 'EQUS.ALL',
  'description': 'Databento US Equities (All Feeds) - MIAX Pearl',
  'publisher_id': 65,
  'venue': 'EPRL'},
 {'dataset': 'EQUS.ALL',
  'description': 'Databento US Equities (All Feeds) - Nasdaq',
  'publisher_id': 66,
  'venue': 'XNAS'},
 {'dataset': 'EQUS.ALL',
  'description': 'Databento US Equities (All Feeds) - NYSE',
  'publisher_id': 67,
  'venue': 'XNYS'},
 {'dataset': 'EQUS.ALL',
  'description': 'Databento US Equities (All Feeds) - FINRA/Nasdaq TRF Carteret',
  'publisher_id': 68,
  'venue': 'FINN'},
 {'dataset': 'EQUS.ALL',
  'description': 'Databento US Equities (All Feeds) - FINRA/NYSE TRF',
  'publisher_id': 69,
  'venue': 'FINY'},
 {'dataset': 'EQUS.ALL',
  'description': 'Databento US Equities (All Feeds) - FINRA/Nasdaq TRF Chicago',
  'publisher_id': 70,
  'venue': 'FINC'},
 {'dataset': 'EQUS.ALL',
  'description': 'Databento US Equities (All Feeds) - Cboe BZX',
  'publisher_id': 71,
  'venue': 'BATS'},
 {'dataset': 'EQUS.ALL',
  'description': 'Databento US Equities (All Feeds) - Cboe BYX',
  'publisher_id': 72,
  'venue': 'BATY'},
 {'dataset': 'EQUS.ALL',
  'description': 'Databento US Equities (All Feeds) - Cboe EDGA',
  'publisher_id': 73,
  'venue': 'EDGA'},
 {'dataset': 'EQUS.ALL',
  'description': 'Databento US Equities (All Feeds) - Cboe EDGX',
  'publisher_id': 74,
  'venue': 'EDGX'},
 {'dataset': 'EQUS.ALL',
  'description': 'Databento US Equities (All Feeds) - Nasdaq Texas',
  'publisher_id': 75,
  'venue': 'XBOS'},
 {'dataset': 'EQUS.ALL',
  'description': 'Databento US Equities (All Feeds) - Nasdaq PSX',
  'publisher_id': 76,
  'venue': 'XPSX'},
 {'dataset': 'EQUS.ALL',
  'description': 'Databento US Equities (All Feeds) - MEMX',
  'publisher_id': 77,
  'venue': 'MEMX'},
 {'dataset': 'EQUS.ALL',
  'description': 'Databento US Equities (All Feeds) - NYSE American',
  'publisher_id': 78,
  'venue': 'XASE'},
 {'dataset': 'EQUS.ALL',
  'description': 'Databento US Equities (All Feeds) - NYSE Arca',
  'publisher_id': 79,
  'venue': 'ARCX'},
 {'dataset': 'EQUS.ALL',
  'description': 'Databento US Equities (All Feeds) - Long-Term Stock Exchange',
  'publisher_id': 80,
  'venue': 'LTSE'},
 {'dataset': 'XNAS.BASIC',
  'description': 'Nasdaq Basic - Nasdaq',
  'publisher_id': 81,
  'venue': 'XNAS'},
 {'dataset': 'XNAS.BASIC',
  'description': 'Nasdaq Basic - FINRA/Nasdaq TRF Carteret',
  'publisher_id': 82,
  'venue': 'FINN'},
 {'dataset': 'XNAS.BASIC',
  'description': 'Nasdaq Basic - FINRA/Nasdaq TRF Chicago',
  'publisher_id': 83,
  'venue': 'FINC'},
 {'dataset': 'IFEU.IMPACT',
  'description': 'ICE Europe - Off-Market Trades',
  'publisher_id': 84,
  'venue': 'XOFF'},
 {'dataset': 'NDEX.IMPACT',
  'description': 'ICE Endex - Off-Market Trades',
  'publisher_id': 85,
  'venue': 'XOFF'},
 {'dataset': 'XNAS.NLS',
  'description': 'Nasdaq NLS - Nasdaq Texas',
  'publisher_id': 86,
  'venue': 'XBOS'},
 {'dataset': 'XNAS.NLS',
  'description': 'Nasdaq NLS - Nasdaq PSX',
  'publisher_id': 87,
  'venue': 'XPSX'},
 {'dataset': 'XNAS.BASIC',
  'description': 'Nasdaq Basic - Nasdaq Texas',
  'publisher_id': 88,
  'venue': 'XBOS'},
 {'dataset': 'XNAS.BASIC',
  'description': 'Nasdaq Basic - Nasdaq PSX',
  'publisher_id': 89,
  'venue': 'XPSX'},
 {'dataset': 'EQUS.SUMMARY',
  'description': 'Databento Equities Summary',
  'publisher_id': 90,
  'venue': 'EQUS'},
 {'dataset': 'XCIS.TRADESBBO',
  'description': 'NYSE National Trades and BBO',
  'publisher_id': 91,
  'venue': 'XCIS'},
 {'dataset': 'XNYS.TRADESBBO',
  'description': 'NYSE Trades and BBO',
  'publisher_id': 92,
  'venue': 'XNYS'},
 {'dataset': 'XNAS.BASIC',
  'description': 'Nasdaq Basic - Consolidated',
  'publisher_id': 93,
  'venue': 'EQUS'},
 {'dataset': 'EQUS.ALL',
  'description': 'Databento US Equities (All Feeds) - Consolidated',
  'publisher_id': 94,
  'venue': 'EQUS'},
 {'dataset': 'EQUS.MINI',
  'description': 'Databento US Equities Mini',
  'publisher_id': 95,
  'venue': 'EQUS'},
 {'dataset': 'XNYS.TRADES',
  'description': 'NYSE Trades - Consolidated',
  'publisher_id': 96,
  'venue': 'EQUS'},
 {'dataset': 'IFUS.IMPACT',
  'description': 'ICE Futures US',
  'publisher_id': 97,
  'venue': 'IFUS'},
 {'dataset': 'IFUS.IMPACT',
  'description': 'ICE Futures US - Off-Market Trades',
  'publisher_id': 98,
  'venue': 'XOFF'},
 {'dataset': 'IFLL.IMPACT',
  'description': 'ICE Europe Financials',
  'publisher_id': 99,
  'venue': 'IFLL'},
 {'dataset': 'IFLL.IMPACT',
  'description': 'ICE Europe Financials - Off-Market Trades',
  'publisher_id': 100,
  'venue': 'XOFF'},
 {'dataset': 'XEUR.EOBI',
  'description': 'Eurex EOBI',
  'publisher_id': 101,
  'venue': 'XEUR'},
 {'dataset': 'XEEE.EOBI',
  'description': 'European Energy Exchange EOBI',
  'publisher_id': 102,
  'venue': 'XEEE'},
 {'dataset': 'XEUR.EOBI',
  'description': 'Eurex EOBI - Off-Market Trades',
  'publisher_id': 103,
  'venue': 'XOFF'},
 {'dataset': 'XEEE.EOBI',
  'description': 'European Energy Exchange EOBI - Off-Market Trades',
  'publisher_id': 104,
  'venue': 'XOFF'},
 {'dataset': 'XCBF.PITCH',
  'description': 'Cboe Futures Exchange (CFE)',
  'publisher_id': 105,
  'venue': 'XCBF'},
 {'dataset': 'XCBF.PITCH',
  'description': 'Cboe Futures Exchange (CFE) - Off-Market Trades',
  'publisher_id': 106,
  'venue': 'XOFF'},
 {'dataset': 'OCEA.MEMOIR',
  'description': 'Blue Ocean ATS MEMOIR',
  'publisher_id': 107,
  'venue': 'OCEA'},
 {'dataset': 'OPRA.PILLAR',
  'description': 'OPRA - MEMX MX2 Options',
  'publisher_id': 108,
  'venue': 'MXTO'},
 {'dataset': 'OPRA.PILLAR',
  'description': 'OPRA - IEX Options LLC',
  'publisher_id': 109,
  'venue': 'IEXO'},
 {'dataset': 'MAIN.CGIF',
  'description': 'Cboe Global Indices Feed',
  'publisher_id': 110,
  'venue': 'MAIN'},
 {'dataset': 'EQUS.SIP',
  'description': 'US Equities SIP - NYSE American',
  'publisher_id': 111,
  'venue': 'XASE'},
 {'dataset': 'EQUS.SIP',
  'description': 'US Equities SIP - Nasdaq Texas',
  'publisher_id': 112,
  'venue': 'XBOS'},
 {'dataset': 'EQUS.SIP',
  'description': 'US Equities SIP - NYSE National',
  'publisher_id': 113,
  'venue': 'XCIS'},
 {'dataset': 'EQUS.SIP',
  'description': 'US Equities SIP - Texas Stock Exchange',
  'publisher_id': 114,
  'venue': 'TXSE'},
 {'dataset': 'EQUS.SIP',
  'description': 'US Equities SIP - 24X National Exchange',
  'publisher_id': 115,
  'venue': '24EQ'},
 {'dataset': 'EQUS.SIP',
  'description': 'US Equities SIP - MIAX Pearl',
  'publisher_id': 116,
  'venue': 'EPRL'},
 {'dataset': 'EQUS.SIP',
  'description': 'US Equities SIP - Nasdaq ISE',
  'publisher_id': 117,
  'venue': 'XISX'},
 {'dataset': 'EQUS.SIP',
  'description': 'US Equities SIP - Cboe EDGA',
  'publisher_id': 118,
  'venue': 'EDGA'},
 {'dataset': 'EQUS.SIP',
  'description': 'US Equities SIP - Cboe EDGX',
  'publisher_id': 119,
  'venue': 'EDGX'},
 {'dataset': 'EQUS.SIP',
  'description': 'US Equities SIP - Long-Term Stock Exchange',
  'publisher_id': 120,
  'venue': 'LTSE'},
 {'dataset': 'EQUS.SIP',
  'description': 'US Equities SIP - NYSE Texas',
  'publisher_id': 121,
  'venue': 'XCHI'},
 {'dataset': 'EQUS.SIP',
  'description': 'US Equities SIP - NYSE',
  'publisher_id': 122,
  'venue': 'XNYS'},
 {'dataset': 'EQUS.SIP',
  'description': 'US Equities SIP - NYSE Arca',
  'publisher_id': 123,
  'venue': 'ARCX'},
 {'dataset': 'EQUS.SIP',
  'description': 'US Equities SIP - Nasdaq',
  'publisher_id': 124,
  'venue': 'XNAS'},
 {'dataset': 'EQUS.SIP',
  'description': 'US Equities SIP - MEMX',
  'publisher_id': 125,
  'venue': 'MEMX'},
 {'dataset': 'EQUS.SIP',
  'description': 'US Equities SIP - IEX',
  'publisher_id': 126,
  'venue': 'IEXG'},
 {'dataset': 'EQUS.SIP',
  'description': 'US Equities SIP - Nasdaq PSX',
  'publisher_id': 127,
  'venue': 'XPSX'},
 {'dataset': 'EQUS.SIP',
  'description': 'US Equities SIP - Cboe BYX',
  'publisher_id': 128,
  'venue': 'BATY'},
 {'dataset': 'EQUS.SIP',
  'description': 'US Equities SIP - Cboe BZX',
  'publisher_id': 129,
  'venue': 'BATS'},
 {'dataset': 'EQUS.SIP',
  'description': 'US Equities SIP - FINRA/Nasdaq TRF Carteret',
  'publisher_id': 130,
  'venue': 'FINN'},
 {'dataset': 'EQUS.SIP',
  'description': 'US Equities SIP - FINRA/NYSE TRF',
  'publisher_id': 131,
  'venue': 'FINY'},
 {'dataset': 'EQUS.SIP',
  'description': 'US Equities SIP - FINRA/Nasdaq TRF Chicago',
  'publisher_id': 132,
  'venue': 'FINC'},
 {'dataset': 'EQUS.SIP',
  'description': 'US Equities SIP - FINRA Alternative Display Facility',
  'publisher_id': 133,
  'venue': 'XADF'},
 {'dataset': 'EQUS.SIP',
  'description': 'US Equities SIP - CTA',
  'publisher_id': 134,
  'venue': 'CTA'},
 {'dataset': 'EQUS.SIP',
  'description': 'US Equities SIP - UTP',
  'publisher_id': 135,
  'venue': 'UTP'},
 {'dataset': 'MSCI.CGIF',
  'description': 'CGIF - Morgan Stanley Capital International',
  'publisher_id': 136,
  'venue': 'MSCI'},
 {'dataset': 'FTSE.CGIF',
  'description': 'CGIF - FTSE Russell',
  'publisher_id': 137,
  'venue': 'FTSE'},
 {'dataset': 'INAV.CGIF',
  'description': 'CGIF - Intraday Net Asset Values',
  'publisher_id': 138,
  'venue': 'INAV'},
 {'dataset': 'MSTAR.CGIF',
  'description': 'CGIF - Morning Star',
  'publisher_id': 139,
  'venue': 'MSTAR'},
 {'dataset': 'CCCY.CGIF',
  'description': 'CGIF - Crypto Currency',
  'publisher_id': 140,
  'venue': 'CCCY'},
 {'dataset': 'CGI.CGIF',
  'description': 'CGIF - CBOE Global Indices',
  'publisher_id': 141,
  'venue': 'CGI'},
 {'dataset': 'MAIN.CGIF',
  'description': 'CGIF - Definitions',
  'publisher_id': 142,
  'venue': 'CDEF'},
 {'dataset': 'XTKS.FLEX',
  'description': 'JPX - Tokyo Stock Exchange',
  'publisher_id': 143,
  'venue': 'XTKS'},
 {'dataset': 'XTKT.ITCH',
  'description': 'JPX - Tokyo Commodity Exchange',
  'publisher_id': 144,
  'venue': 'XTKT'},
 {'dataset': 'XOSE.ITCH',
  'description': 'JPX - Osaka Exchange',
  'publisher_id': 145,
  'venue': 'XOSE'},
 {'dataset': 'DBIX.CORE',
  'description': 'CGIF - Main Feed',
  'publisher_id': 146,
  'venue': 'MAIN'},
 {'dataset': 'DBIX.CORE',
  'description': 'CGIF - Morgan Stanley Capital International',
  'publisher_id': 147,
  'venue': 'MSCI'},
 {'dataset': 'DBIX.CORE',
  'description': 'CGIF - FTSE Russell',
  'publisher_id': 148,
  'venue': 'FTSE'},
 {'dataset': 'DBIX.CORE',
  'description': 'CGIF - Intraday Net Asset Values',
  'publisher_id': 149,
  'venue': 'INAV'},
 {'dataset': 'DBIX.CORE',
  'description': 'CGIF - Morning Star',
  'publisher_id': 150,
  'venue': 'MSTAR'},
 {'dataset': 'DBIX.CORE',
  'description': 'CGIF - Crypto Currency',
  'publisher_id': 151,
  'venue': 'CCCY'},
 {'dataset': 'DBIX.CORE',
  'description': 'CGIF - CBOE Global Indices',
  'publisher_id': 152,
  'venue': 'CGI'},
 {'dataset': 'DBIX.CORE',
  'description': 'CGIF - Definitions',
  'publisher_id': 153,
  'venue': 'CDEF'}
 ]

Historical.metadata.list_datasets

List all valid dataset IDs on Databento.

Use this method to list the available dataset IDs (string identifiers), so you can use other methods which take the dataset parameter.

Parameters

Parameter
Type
Required
Description
start_date
date or str
optional
The inclusive UTC start date of the request range as a Python date or ISO 8601 date string. If None then first date available.
end_date
date or str
optional
The exclusive UTC end date of the request range as a Python date or ISO 8601 date string. If None then last date available.

Returns

list[str]

A list of available dataset IDs.

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Historical.metadata.list_datasets(
    start_date: date | str | None = None,
    end_date: date | str | None = None,
) -> list[str]
Example usage 
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import databento as db

client = db.Historical("$YOUR_API_KEY")

datasets = client.metadata.list_datasets()
print(datasets)
Example response 
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[
    "ARCX.PILLAR",
    "BATS.PITCH",
    "BATY.PITCH",
    "DBEQ.BASIC",
    "EDGA.PITCH",
    "EDGX.PITCH",
    "EPRL.DOM",
    "EQUS.MINI",
    "EQUS.SUMMARY",
    "GLBX.MDP3",
    "IEXG.TOPS",
    "IFEU.IMPACT",
    "IFLL.IMPACT",
    "IFUS.IMPACT",
    "MEMX.MEMOIR",
    "NDEX.IMPACT",
    "OCEA.MEMOIR",
    "OPRA.PILLAR",
    "XASE.PILLAR",
    "XBOS.ITCH",
    "XCBF.PITCH",
    "XCHI.PILLAR",
    "XCIS.TRADESBBO",
    "XEEE.EOBI",
    "XEUR.EOBI",
    "XNAS.BASIC",
    "XNAS.ITCH",
    "XNYS.PILLAR",
    "XPSX.ITCH"
]

Historical.metadata.list_schemas

List all available schemas for a dataset.

Parameters

Parameter
Type
Required
Description
dataset
Dataset or str
required
The dataset code (string identifier). Must be one of the values from list_datasets.

Returns

list[str]

A list of available data schemas.

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Historical.metadata.list_schemas(
    dataset: Dataset | str,
) -> list[str]
Example usage 
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import databento as db

client = db.Historical("$YOUR_API_KEY")

schemas = client.metadata.list_schemas(dataset="GLBX.MDP3")
print(schemas)
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[
    "mbo",
    "mbp-1",
    "mbp-10",
    "tbbo",
    "trades",
    "bbo-1s",
    "bbo-1m",
    "ohlcv-1s",
    "ohlcv-1m",
    "ohlcv-1h",
    "ohlcv-1d",
    "definition",
    "statistics",
    "status"
]

Historical.metadata.list_fields

List all fields for a particular schema and encoding.

Parameters

Parameter
Type
Required
Description
schema
Schema or str
required
The data record schema. Must be one of the values from list_schemas.
encoding
Encoding or str
required
The data encoding. Must be one of 'dbn', 'csv', or 'json'. 'dbn' is recommended.
dataset
Dataset or str
optional
The dataset used to determine which fields are relevant. If None then the fields for the latest DBN encoding version are returned, which may differ from a specific dataset's schema.

Returns

list[dict[str, str]]

A list of field details objects.

Field
Type
Description
name
str
The field name.
type
str
The field data type.
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Historical.metadata.list_fields(
    schema: Schema | str,
    encoding: Encoding | str,
    dataset: Dataset | str | None = None,
) -> list[dict[str, str]]
Example usage 
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import databento as db

client = db.Historical("$YOUR_API_KEY")

fields = client.metadata.list_fields(schema="trades", encoding="dbn")
print(fields)
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[
    {
        "name": "length",
        "type": "uint8_t"
    },
    {
        "name": "rtype",
        "type": "uint8_t"
    },
    {
        "name": "publisher_id",
        "type": "uint16_t"
    },
    {
        "name": "instrument_id",
        "type": "uint32_t"
    },
    {
        "name": "ts_event",
        "type": "uint64_t"
    },
    {
        "name": "price",
        "type": "int64_t"
    },
    {
        "name": "size",
        "type": "uint32_t"
    },
    {
        "name": "action",
        "type": "char"
    },
    {
        "name": "side",
        "type": "char"
    },
    {
        "name": "flags",
        "type": "uint8_t"
    },
    {
        "name": "depth",
        "type": "uint8_t"
    },
    {
        "name": "ts_recv",
        "type": "uint64_t"
    },
    {
        "name": "ts_in_delta",
        "type": "int32_t"
    },
    {
        "name": "sequence",
        "type": "uint32_t"
    }
]

Historical.metadata.list_unit_prices

List unit prices for each feed mode and data schema in US dollars per gigabyte.

Parameters

Parameter
Type
Required
Description
dataset
Dataset or str
required
The dataset code (string identifier). Must be one of the values from list_datasets.

Returns

list[dict[str, Any]]

A list of maps of feed mode to schema to unit price.

Field
Type
Description
mode
str
The feed mode. Will be one of "historical", "historical-streaming", or "live".
unit_prices
dict[str | float]
A mapping of schemas to unit prices in US dollars per gigabyte.
API method 
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Historical.metadata.list_unit_prices(
    dataset: Dataset | str,
) -> list[dict[str, Any]]
Example usage 
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import databento as db

client = db.Historical("$YOUR_API_KEY")

unit_prices = client.metadata.list_unit_prices(dataset="OPRA.PILLAR")
print(unit_prices)
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[
    {
        "mode": "historical",
        "unit_prices": {
            "cmbp-1": 0.16,
            "cbbo-1s": 2.0,
            "cbbo-1m": 2.0,
            "tcbbo": 210.0,
            "trades": 280.0,
            "ohlcv-1s": 280.0,
            "ohlcv-1m": 280.0,
            "ohlcv-1h": 600.0,
            "ohlcv-1d": 600.0,
            "statistics": 11.0,
            "status": 5.0,
            "definition": 5.0
        }
    },
    {
        "mode": "historical-streaming",
        "unit_prices": {
            "cmbp-1": 0.16,
            "cbbo-1s": 2.0,
            "cbbo-1m": 2.0,
            "tcbbo": 210.0,
            "trades": 280.0,
            "ohlcv-1s": 280.0,
            "ohlcv-1m": 280.0,
            "ohlcv-1h": 600.0,
            "ohlcv-1d": 600.0,
            "statistics": 11.0,
            "status": 5.0,
            "definition": 5.0
        }
    },
    {
        "mode": "live",
        "unit_prices": {
            "cmbp-1": 0.2,
            "cbbo-1s": 2.4,
            "cbbo-1m": 2.4,
            "tcbbo": 252.0,
            "trades": 336.0,
            "ohlcv-1s": 336.0,
            "ohlcv-1m": 336.0,
            "ohlcv-1h": 720.0,
            "ohlcv-1d": 720.0,
            "statistics": 13.2,
            "status": 6.0,
            "definition": 6.0
        }
    }
]

Historical.metadata.get_dataset_condition

Get the dataset condition from Databento.

Use this method to discover data availability and quality.

Parameters

Parameter
Type
Required
Description
dataset
Dataset or str
required
The dataset code (string identifier). Must be one of the values from list_datasets.
start_date
date or str
optional
The inclusive UTC start date of the request range as a Python date or ISO 8601 date string. If None then first date available.
end_date
date or str
optional
The inclusive UTC end date of the request range as a Python date or ISO 8601 date string. If None then last date available.

Returns

list[dict[str, str | None]]

A list of conditions per date.

Field
Type
Description
date
str
The day of the described data, as an ISO 8601 date string.
condition
str
The condition code describing the quality and availability of the data on the given day. Possible values are listed below.
last_modified_date
str or None
The date when any schema in the dataset on the given day was last generated or modified, as an ISO 8601 date string. Will be None when condition is 'missing'.

Possible values for condition:

  • available: the data is available with no known issues
  • degraded: the data is available, but there may be missing data or other correctness issues
  • pending: the data is not yet available, but may be available soon
  • missing: the data is not available
API method 
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Historical.metadata.get_dataset_condition(
    dataset: Dataset | str,
    start_date: date | str | None = None,
    end_date: date | str | None = None,
) -> list[dict[str, str | None]]
Example usage 
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import databento as db

client = db.Historical("$YOUR_API_KEY")

conditions = client.metadata.get_dataset_condition(
    dataset="GLBX.MDP3",
    start_date="2022-06-06",
    end_date="2022-06-10",
)
print(conditions)
Example response 
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[
    {
        "date": "2022-06-06",
        "condition": "available",
        "last_modified_date": "2024-05-18"
    },
    {
        "date": "2022-06-07",
        "condition": "available",
        "last_modified_date": "2024-05-21"
    },
    {
        "date": "2022-06-08",
        "condition": "available",
        "last_modified_date": "2024-05-21"
    },
    {
        "date": "2022-06-09",
        "condition": "available",
        "last_modified_date": "2024-05-21"
    },
    {
        "date": "2022-06-10",
        "condition": "available",
        "last_modified_date": "2024-05-22"
    }
]

Historical.metadata.get_dataset_range

Get the available range for the dataset given the user's entitlements.

Use this method to discover data availability. The start and end values in the response can be used with the timeseries.get_range and batch.submit_job endpoints.

This endpoint will return the start and end timestamps over the entire dataset as well as the per-schema start and end timestamps under the schema key. In some cases, a schema's availability is a subset of the entire dataset availability.

Parameters

Parameter
Type
Required
Description
dataset
Dataset or str
required
The dataset code (string identifier). Must be one of the values from list_datasets.

Returns

dict[str, str | dict[str, str]]

The available range for the dataset.

Field
Type
Description
start
str
The inclusive start of the available range as an ISO 8601 timestamp.
end
str
The exclusive end of the available range as an ISO 8601 timestamp.
schema
dict[str, str]
A mapping of schema names to per-schema start and end timestamps.
API method 
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Historical.metadata.get_dataset_range(
    dataset: Dataset | str,
) -> dict[str, str | dict[str, str]]
Example usage 
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import databento as db

client = db.Historical("$YOUR_API_KEY")

available_range = client.metadata.get_dataset_range(dataset="XNAS.BASIC")

print(available_range)
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{
    "start":"2018-05-01T00:00:00.000000000Z",
    "end":"2025-01-30T00:00:00.000000000Z",
    "schema": {
        "mbo": {
            "start":"2018-05-01T00:00:00.000000000Z",
            "end":"2025-01-30T00:00:00.000000000Z"
        },
        "mbp-1": {
            "start":"2018-05-01T00:00:00.000000000Z",
            "end":"2025-01-30T00:00:00.000000000Z"
        },
        "mbp-10": {
            "start":"2018-05-01T00:00:00.000000000Z",
            "end":"2025-01-30T00:00:00.000000000Z"
        },
        "bbo-1s": {
            "start":"2018-05-01T00:00:00.000000000Z",
            "end":"2025-01-30T00:00:00.000000000Z"
        },
        "bbo-1m": {
            "start":"2018-05-01T00:00:00.000000000Z",
            "end":"2025-01-30T00:00:00.000000000Z"
        },
        "tbbo": {
            "start":"2018-05-01T00:00:00.000000000Z",
            "end":"2025-01-30T00:00:00.000000000Z"
        },
        "trades": {
            "start":"2018-05-01T00:00:00.000000000Z",
            "end":"2025-01-30T00:00:00.000000000Z"
        },
        "ohlcv-1s": {
            "start":"2018-05-01T00:00:00.000000000Z",
            "end":"2025-01-30T00:00:00.000000000Z"
        },
        "ohlcv-1m": {
            "start":"2018-05-01T00:00:00.000000000Z",
            "end":"2025-01-30T00:00:00.000000000Z"
        },
        "ohlcv-1h": {
            "start":"2018-05-01T00:00:00.000000000Z",
            "end":"2025-01-30T00:00:00.000000000Z"
        },
        "ohlcv-1d": {
            "start":"2018-05-01T00:00:00.000000000Z",
            "end":"2025-01-30T00:00:00.000000000Z"
        },
        "definition": {
            "start":"2018-05-01T00:00:00.000000000Z",
            "end":"2025-01-30T00:00:00.000000000Z"
        },
        "statistics": {
            "start":"2018-05-01T00:00:00.000000000Z",
            "end":"2025-01-30T00:00:00.000000000Z"
        },
        "status": {
            "start":"2018-05-01T00:00:00.000000000Z",
            "end":"2025-01-30T00:00:00.000000000Z"
        },
        "imbalance": {
            "start":"2018-05-01T00:00:00.000000000Z",
            "end":"2025-01-30T00:00:00.000000000Z"
        }
    }
}

Historical.metadata.get_record_count

Get the record count of the time series data query.

This method may not be accurate for time ranges that are not discrete multiples of 10 minutes, potentially over-reporting the number of records in such cases. The definition schema is only accurate for discrete multiples of 24 hours.

Parameters

Parameter
Type
Required
Description
dataset
Dataset or str
required
The dataset code (string identifier). Must be one of the values from list_datasets.
start
pd.Timestamp, datetime, date, str, or int
required
The inclusive start of the request range. Takes pd.Timestamp, Python datetime, Python date, ISO 8601 string, or UNIX timestamp in nanoseconds. Assumes UTC as timezone unless otherwise specified.
end
pd.Timestamp, datetime, date, str, or int
optional
The exclusive end of the request range. Takes pd.Timestamp, Python datetime, Python date, ISO 8601 string, or UNIX timestamp in nanoseconds. Assumes UTC as timezone unless otherwise specified. Defaults to the forward filled value of start based on the resolution provided.
symbols
Iterable[str | int] or str or int
optional
The product symbols to filter for. Takes up to 2,000 symbols per request. If 'ALL_SYMBOLS' or None then will select all symbols.
schema
Schema or str, default 'trades'
optional
The data record schema. Must be one of the values from list_schemas.
stype_in
SType or str, default 'raw_symbol'
optional
The symbology type of input symbols. Must be one of 'raw_symbol', 'instrument_id', 'parent', or 'continuous'.
limit
int
optional
The maximum number of records to return. If None then no limit.

Returns

int

The record count.

API method 
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Historical.metadata.get_record_count(
    dataset: Dataset | str,
    start: pd.Timestamp | datetime | date | str | int,
    end: pd.Timestamp | datetime | date | str | int | None = None,
    symbols: Iterable[str | int] | str | int | None = None,
    schema: Schema | str = "trades",
    stype_in: SType | str = "raw_symbol",
    limit: int | None = None,
) -> int
Example usage 
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import databento as db

client = db.Historical("$YOUR_API_KEY")

count = client.metadata.get_record_count(
    dataset="GLBX.MDP3",
    symbols=["ESM2"],
    schema="mbo",
    start="2022-01-06",
)
print(count)
Example response 
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1329107

Historical.metadata.get_billable_size

Get the billable uncompressed raw binary size for historical streaming or batched files.

This method may not be accurate for time ranges that are not discrete multiples of 10 minutes, potentially over-reporting the size in such cases. The definition schema is only accurate for discrete multiples of 24 hours.

Info

The amount billed will be based on the actual amount of bytes sent; see our pricing documentation for more details.

Parameters

Parameter
Type
Required
Description
dataset
Dataset or str
required
The dataset code (string identifier). Must be one of the values from list_datasets.
start
pd.Timestamp, datetime, date, str, or int
required
The inclusive start of the request range. Takes pd.Timestamp, Python datetime, Python date, ISO 8601 string, or UNIX timestamp in nanoseconds. Assumes UTC as timezone unless otherwise specified.
end
pd.Timestamp, datetime, date, str, or int
optional
The exclusive end of the request range. Takes pd.Timestamp, Python datetime, Python date, ISO 8601 string, or UNIX timestamp in nanoseconds. Assumes UTC as timezone unless otherwise specified. Defaults to the forward filled value of start based on the resolution provided.
symbols
Iterable[str | int] or str or int
optional
The product symbols to filter for. Takes up to 2,000 symbols per request. If 'ALL_SYMBOLS' or None then will select all symbols.
schema
Schema or str, default 'trades'
optional
The data record schema. Must be one of the values from list_schemas.
stype_in
SType or str, default 'raw_symbol'
optional
The symbology type of input symbols. Must be one of 'raw_symbol', 'instrument_id', 'parent', or 'continuous'.
limit
int
optional
The maximum number of records to return. If None then no limit.

Returns

int

The size in number of bytes used for billing.

API method 
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Historical.metadata.get_billable_size(
    dataset: Dataset | str,
    start: pd.Timestamp | datetime | date | str | int,
    end: pd.Timestamp | datetime | date | str | int | None = None,
    symbols: Iterable[str | int] | str | int | None = None,
    schema: Schema | str = "trades",
    stype_in: Stype | str = "raw_symbol",
    limit: int | None = None,
) -> int
Example usage 
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import databento as db

client = db.Historical("$YOUR_API_KEY")

size = client.metadata.get_billable_size(
    dataset="GLBX.MDP3",
    symbols=["ESM2"],
    schema="trades",
    start="2022-06-06T00:00:00",
    end="2022-06-10T12:10:00",
)
print(size)
Example response 
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99219648

Historical.metadata.get_cost

Get the cost in US dollars for a historical streaming or batch download request. This cost respects any discounts provided by flat rate plans.

This method may not be accurate for time ranges that are not discrete multiples of 10 minutes, potentially over-reporting the cost in such cases. The definition schema is only accurate for discrete multiples of 24 hours.

Info

The amount billed will be based on the actual amount of bytes sent; see our pricing documentation for more details.

Parameters

Parameter
Type
Required
Description
dataset
Dataset or str
required
The dataset code (string identifier). Must be one of the values from list_datasets.
start
pd.Timestamp, datetime, date, str, or int
required
The inclusive start of the request range. Takes pd.Timestamp, Python datetime, Python date, ISO 8601 string, or UNIX timestamp in nanoseconds. Assumes UTC as timezone unless otherwise specified.
end
pd.Timestamp, datetime, date, str, or int
optional
The exclusive end of the request range. Takes pd.Timestamp, Python datetime, Python date, ISO 8601 string, or UNIX timestamp in nanoseconds. Assumes UTC as timezone unless otherwise specified. Defaults to the forward filled value of start based on the resolution provided.
symbols
Iterable[str | int] or str or int
optional
The product symbols to filter for. Takes up to 2,000 symbols per request. If 'ALL_SYMBOLS' or None then will select all symbols.
schema
Schema or str, default 'trades'
optional
The data record schema. Must be one of the values from list_schemas.
stype_in
SType or str, default 'raw_symbol'
optional
The symbology type of input symbols. Must be one of 'raw_symbol', 'instrument_id', 'parent', or 'continuous'.
limit
int
optional
The maximum number of records to return. If None then no limit.

Returns

float

The cost in US dollars.

API method 
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Historical.metadata.get_cost(
    dataset: Dataset | str,
    start: pd.Timestamp | datetime | date | str | int,
    end: pd.Timestamp | datetime | date | str | int | None = None,
    symbols: Iterable[str | int] | str | int | None = None,
    schema: Schema | str = "trades",
    stype_in: SType | str = "raw_symbol",
    limit: int | None = None,
) -> float
Example usage 
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import databento as db

client = db.Historical("$YOUR_API_KEY")

cost = client.metadata.get_cost(
    dataset="GLBX.MDP3",
    symbols=["ESM2"],
    schema="trades",
    start="2022-06-06T00:00:00",
    end="2022-06-10T12:10:00",
)
print(cost)
Example response 
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2.587353944778