Source: https://databento.com/docs/api-historical/metadata


## Metadata



### Historical.metadata.list\_publishers

List all [publisher ID](https://databento.com/docs/faqs/venues-and-publishers.md) mappings.

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

#### API method

```python
Historical.metadata.list_publishers() -> list[dict[str, int | str]]
```

#### 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. |

#### Example usage

```python
import databento as db

client = db.Historical("$YOUR_API_KEY")

publishers = client.metadata.list_publishers()
print(publishers)
```
#### Example response

```plaintext
[{'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](https://databento.com/docs/api-historical/basics/datasets.md) 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.

#### API method

```python
Historical.metadata.list_datasets(
    start_date: date | str | None = None,
    end_date: date | str | None = None,
) -> list[str]
```

#### Parameters

| Parameter | Type | Required | Description |
| `start_date` | date or str | optional | The inclusive UTC start date of the request range as a [Python date](https://docs.python.org/3/library/datetime.html#date-objects) or [ISO 8601](https://www.iso.org/iso-8601-date-and-time-format.html) 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](https://docs.python.org/3/library/datetime.html#date-objects) or [ISO 8601](https://www.iso.org/iso-8601-date-and-time-format.html) date string. If `None` then last date available. |

#### Returns

`list[str]`

A list of available dataset IDs.

#### Example usage

```python
import databento as db

client = db.Historical("$YOUR_API_KEY")

datasets = client.metadata.list_datasets()
print(datasets)
```
#### Example response

```json
[
    "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.

#### API method

```python
Historical.metadata.list_schemas(
    dataset: Dataset | str,
) -> list[str]
```

#### Parameters

| Parameter | Type | Required | Description |
| `dataset` | Dataset or str | required | The [dataset](https://databento.com/docs/api-historical/basics/datasets.md) code (string identifier). Must be one of the values from [list\_datasets](https://databento.com/docs/api-historical/metadata/metadata-list-datasets.md). |

#### Returns

`list[str]`

A list of available data schemas.

#### Example usage

```python
import databento as db

client = db.Historical("$YOUR_API_KEY")

schemas = client.metadata.list_schemas(dataset="GLBX.MDP3")
print(schemas)
```
#### Example response

```json
[
    "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.

#### API method

```python
Historical.metadata.list_fields(
    schema: Schema | str,
    encoding: Encoding | str,
    dataset: Dataset | str | None = None,
) -> list[dict[str, str]]
```

#### Parameters

| Parameter | Type | Required | Description |
| `schema` | Schema or str | required | The data record [schema](https://databento.com/docs/schemas/whats-a-schema.md). Must be one of the values from [list\_schemas](https://databento.com/docs/api-historical/metadata/metadata-list-schemas.md). |
| `encoding` | Encoding or str | required | The data [encoding](https://databento.com/docs/api-historical/basics/encodings.md). Must be one of 'dbn', 'csv', or 'json'. 'dbn' is recommended. |
| `dataset` | Dataset or str | optional | The [dataset](https://databento.com/docs/api-historical/basics/datasets.md) 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. |

#### Example usage

```python
import databento as db

client = db.Historical("$YOUR_API_KEY")

fields = client.metadata.list_fields(schema="trades", encoding="dbn")
print(fields)
```
#### Example response

```json
[
    {
        "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.

#### API method

```python
Historical.metadata.list_unit_prices(
    dataset: Dataset | str,
) -> list[dict[str, Any]]
```

#### Parameters

| Parameter | Type | Required | Description |
| `dataset` | Dataset or str | required | The [dataset](https://databento.com/docs/api-historical/basics/datasets.md) code (string identifier). Must be one of the values from [list\_datasets](https://databento.com/docs/api-historical/metadata/metadata-list-datasets.md). |

#### 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. |

#### Example usage

```python
import databento as db

client = db.Historical("$YOUR_API_KEY")

unit_prices = client.metadata.list_unit_prices(dataset="OPRA.PILLAR")
print(unit_prices)
```
#### Example response

```json
[
    {
        "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](https://databento.com/docs/api-historical/basics/datasets.md) condition from Databento.

Use this method to discover data availability and quality.

#### API method

```python
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]]
```

#### Parameters

| Parameter | Type | Required | Description |
| `dataset` | Dataset or str | required | The [dataset](https://databento.com/docs/api-historical/basics/datasets.md) code (string identifier). Must be one of the values from [list\_datasets](https://databento.com/docs/api-historical/metadata/metadata-list-datasets.md). |
| `start_date` | date or str | optional | The inclusive UTC start date of the request range as a [Python date](https://docs.python.org/3/library/datetime.html#date-objects) or [ISO 8601](https://www.iso.org/iso-8601-date-and-time-format.html) 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](https://docs.python.org/3/library/datetime.html#date-objects) or [ISO 8601](https://www.iso.org/iso-8601-date-and-time-format.html) 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

#### Example usage

```python
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

```json
[
    {
        "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](https://databento.com/docs/api-historical/basics/datasets.md) 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](https://databento.com/docs/api-historical/timeseries/timeseries-get-range.md) and [batch.submit\_job](https://databento.com/docs/api-historical/batch/batch-submit-job.md) 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.

#### API method

```python
Historical.metadata.get_dataset_range(
    dataset: Dataset | str,
) -> dict[str, str | dict[str, dict[str, str]]]
```

#### Parameters

| Parameter | Type | Required | Description |
| `dataset` | Dataset or str | required | The [dataset](https://databento.com/docs/api-historical/basics/datasets.md) code (string identifier). Must be one of the values from [list\_datasets](https://databento.com/docs/api-historical/metadata/metadata-list-datasets.md). |

#### Returns

`dict[str, str | dict[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](https://www.iso.org/iso-8601-date-and-time-format.html). |
| `end` | str | The exclusive end of the available range as an [ISO 8601 timestamp](https://www.iso.org/iso-8601-date-and-time-format.html). |
| `schema` | dict[str, dict[str, str]] | A mapping of schema names to per-schema `start` and `end` timestamps. |

#### Example usage

```python
import databento as db

client = db.Historical("$YOUR_API_KEY")

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

print(available_range)
```
#### Example response

```json
{
    "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.

#### API method

```python
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
```

#### Parameters

| Parameter | Type | Required | Description |
| `dataset` | Dataset or str | required | The [dataset](https://databento.com/docs/api-historical/basics/datasets.md) code (string identifier). Must be one of the values from [list\_datasets](https://databento.com/docs/api-historical/metadata/metadata-list-datasets.md). |
| `start` | pd.Timestamp, datetime, date, str, or int | required | The inclusive start of the request range. Takes [pd.Timestamp](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html), [Python datetime](https://docs.python.org/3/library/datetime.html#datetime-objects), [Python date](https://docs.python.org/3/library/datetime.html#date-objects), [ISO 8601](https://www.iso.org/iso-8601-date-and-time-format.html) string, or [UNIX timestamp](https://en.wikipedia.org/wiki/Unix_time) 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](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html), [Python datetime](https://docs.python.org/3/library/datetime.html#datetime-objects), [Python date](https://docs.python.org/3/library/datetime.html#date-objects), [ISO 8601](https://www.iso.org/iso-8601-date-and-time-format.html) string, or [UNIX timestamp](https://en.wikipedia.org/wiki/Unix_time) in nanoseconds. Assumes UTC as timezone unless otherwise specified. Defaults to the [forward filled](https://databento.com/docs/conventions/common-fields-enums-types.md#forward-filling-end-parameters) 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](https://databento.com/docs/schemas/whats-a-schema.md). Must be one of the values from [list\_schemas](https://databento.com/docs/api-historical/metadata/metadata-list-schemas.md). |
| `stype_in` | SType or str, default 'raw\_symbol' | optional | The [symbology type](https://databento.com/docs/api-historical/basics/symbology.md) 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.

#### Example usage

```python
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

```plaintext
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](https://databento.com/docs/faqs/usage-pricing-and-data-credits.md) for more details.

#### API method

```python
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
```

#### Parameters

| Parameter | Type | Required | Description |
| `dataset` | Dataset or str | required | The [dataset](https://databento.com/docs/api-historical/basics/datasets.md) code (string identifier). Must be one of the values from [list\_datasets](https://databento.com/docs/api-historical/metadata/metadata-list-datasets.md). |
| `start` | pd.Timestamp, datetime, date, str, or int | required | The inclusive start of the request range. Takes [pd.Timestamp](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html), [Python datetime](https://docs.python.org/3/library/datetime.html#datetime-objects), [Python date](https://docs.python.org/3/library/datetime.html#date-objects), [ISO 8601](https://www.iso.org/iso-8601-date-and-time-format.html) string, or [UNIX timestamp](https://en.wikipedia.org/wiki/Unix_time) 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](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html), [Python datetime](https://docs.python.org/3/library/datetime.html#datetime-objects), [Python date](https://docs.python.org/3/library/datetime.html#date-objects), [ISO 8601](https://www.iso.org/iso-8601-date-and-time-format.html) string, or [UNIX timestamp](https://en.wikipedia.org/wiki/Unix_time) in nanoseconds. Assumes UTC as timezone unless otherwise specified. Defaults to the [forward filled](https://databento.com/docs/conventions/common-fields-enums-types.md#forward-filling-end-parameters) 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](https://databento.com/docs/schemas/whats-a-schema.md). Must be one of the values from [list\_schemas](https://databento.com/docs/api-historical/metadata/metadata-list-schemas.md). |
| `stype_in` | SType or str, default 'raw_symbol' | optional | The [symbology type](https://databento.com/docs/api-historical/basics/symbology.md) 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.

#### Example usage

```python
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

```plaintext
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](https://databento.com/docs/faqs/usage-pricing-and-data-credits.md) for more details.

#### API method

```python
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
```

#### Parameters

| Parameter | Type | Required | Description |
| `dataset` | Dataset or str | required | The [dataset](https://databento.com/docs/api-historical/basics/datasets.md) code (string identifier). Must be one of the values from [list\_datasets](https://databento.com/docs/api-historical/metadata/metadata-list-datasets.md). |
| `start` | pd.Timestamp, datetime, date, str, or int | required | The inclusive start of the request range. Takes [pd.Timestamp](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html), [Python datetime](https://docs.python.org/3/library/datetime.html#datetime-objects), [Python date](https://docs.python.org/3/library/datetime.html#date-objects), [ISO 8601](https://www.iso.org/iso-8601-date-and-time-format.html) string, or [UNIX timestamp](https://en.wikipedia.org/wiki/Unix_time) 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](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html), [Python datetime](https://docs.python.org/3/library/datetime.html#datetime-objects), [Python date](https://docs.python.org/3/library/datetime.html#date-objects), [ISO 8601](https://www.iso.org/iso-8601-date-and-time-format.html) string, or [UNIX timestamp](https://en.wikipedia.org/wiki/Unix_time) in nanoseconds. Assumes UTC as timezone unless otherwise specified. Defaults to the [forward filled](https://databento.com/docs/conventions/common-fields-enums-types.md#forward-filling-end-parameters) 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](https://databento.com/docs/schemas/whats-a-schema.md). Must be one of the values from [list\_schemas](https://databento.com/docs/api-historical/metadata/metadata-list-schemas.md). |
| `stype_in` | SType or str, default 'raw_symbol' | optional | The [symbology type](https://databento.com/docs/api-historical/basics/symbology.md) 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.

#### Example usage

```python
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

```plaintext
2.587353944778
```