API reference - Reference

Databento's reference data service can be accessed programmatically over its HTTP API. To make it easier to integrate the API, we also provide official client libraries that simplify the code you need to write.

Our HTTP API is designed as a collection of RPC-style methods, which can be called using URLs in the form https://hist.databento.com/v0/METHOD_FAMILY.METHOD.

Our client libraries wrap these HTTP RPC-style methods with more idiomatic interfaces in their respective languages.

REFERENCE DATA

Client Libraries

Python

APIs

HTTP

pip install -U databento

209

Basics

Overview

Our reference API has the following structure:

Corporate actions provides point-in-time (PIT) corporate actions events with global coverage.
Adjustment factors provides end-of-day price adjustment factors for capital events, spanning multiple currencies for the same event.
Security master provides up-to-date (latest) or point-in-time (PIT) static information on global market securities.

Authentication

Databento uses API keys to authenticate requests. You can view and manage your keys on the API keys page of your portal.

Each API key is a 32-character string starting with db-. By default, our library uses the environment variable DATABENTO_API_KEY as your API key. However, if you pass an API key to the Historical constructor through the key parameter, then this value will be used instead.

Related: Securing your API keys.

Example usage
PythonHTTP
  
import databento as db # Establish connection and authenticate client = db.Reference("$YOUR_API_KEY") # Authenticated request print(client.security_master.get_last(symbols="AAPL")) 

Symbology

The reference datasets support querying by raw_symbol and additionally symbology types listed in the below table (depending on the endpoint).

The raw_symbol symbology type uses a standardized convention for the symbols parameter for requests. This convention is referred to as the "Nasdaq Integrated Platform Suffix" and is used consistently across our datasets, regardless of the suffix originally used by the exchange.

Standardization. This approach aligns with our symbology standardization for US equity datasets. For a detailed explanation, see our symbology standards documentation.
Local Codes. The local_code field retains the original symbol from the primary exchange. For example, symbols from NYSE-owned feeds such as NYSE Texas use the "CMS Suffix".

For additional context on symbol conventions, including the four most common suffix patterns used for US equities, refer to Nasdaq's guide on ticker symbol conventions.

The symbology related fields included with every data record are as follows:

Field	Description
`nasdaq_symbol`	Nasdaq Integrated Platform Suffix convention symbol (standardized `raw_symbol`).
`local_code`	Local code, typically the original symbol and unique at the listing level (but there are exceptions to this rule). Either an alpha string, or a number.
`isin`	International Securities Identification Number (ISIN) - ISO 6166.
`us_code`	US domestic Committee on Uniform Securities Identification Procedure (CUSIP) code.
`bbg_comp_id`	Bloomberg composite global ID.
`bbg_ticker`	Bloomberg composite ticker.
`figi`	Financial Instrument Global Identifier (FIGI), formerly Bloomberg Global Identifier (BBGID). Listing level identifier.
`figi_ticker`	Bloomberg exchange level ticker.

Info
Note a symbol may match multiple securities across a time range if the symbol has been recycled.

Tip
When identifying a listing on a specific venue, we suggest using the nasdaq_symbol field, as it aligns with the normalized raw_symbol for instruments. Once you have the ISIN for the security, using the isin symbology type will provide the most accurate results when querying for related records.

Dates and times

Our Python client library has several functions with timestamp arguments. These arguments will have type pandas.Timestamp | datetime.date | str | int and support a variety of formats.

It's recommended to use pandas.Timestamp, which fully supports timezones and nanosecond-precision. If a datetime.date is used, the time is set to midnight UTC. If an int is provided, the value is interpreted as UNIX nanoseconds.

The client library also handles several string-based timestamp formats based on ISO 8601.

yyyy-mm-dd, e.g. "2022-02-28" (midnight UTC)
yyyy-mm-ddTHH:MM, e.g. "2022-02-28T23:50"
yyyy-mm-ddTHH:MM:SS, e.g. "2022-02-28T23:50:59"
yyyy-mm-ddTHH:MM:SS.NNNNNNNNN, e.g. "2022-02-28T23:50:59.123456789"

Timezone specification is also supported.

yyyy-mm-ddTHH:MMZ
yyyy-mm-ddTHH:MM±hh
yyyy-mm-ddTHH:MM±hhmm
yyyy-mm-ddTHH:MM±hh:mm

Bare dates

Some parameters require a bare date, without a time. These arguments have type datetime.date | str and must either be a datetime.date object, or a string in yyyy-mm-dd format, e.g. "2022-02-28".

Errors

Our reference API uses HTTP response codes to indicate the success or failure of an API request. The client library provides exceptions that wrap these response codes.

2xx indicates success.
4xx indicates an error on the client side. Represented as a BentoClientError.
5xx indicates an error with Databento's servers. Represented as a BentoServerError.

The full list of the response codes and associated causes is as follows:

Code	Message	Cause
200	OK	Successful request.
400	Bad Request	Invalid request. Usually due to a missing, malformed or unsupported parameter.
401	Unauthorized	Invalid username or API key.
403	Forbidden	The API key has insufficient permissions to perform the request.
404	Not Found	A resource is not found, or a requested symbol does not exist.
422	Unprocessable Entity	The request is well formed, but we cannot or will not process the contained instructions.
429	Too Many Requests	API rate limit exceeded.
500	Internal Server Error	Unexpected condition encountered in our system.
503	Service Unavailable	Data gateway is offline or overloaded.
504	Gateway Timeout	Data gateway is available but other parts of our system are offline or overloaded.

API method
PythonHTTP
  
class databento.BentoError(Exception) class databento.BentoHttpError(databento.BentoError) class databento.BentoClientError(databento.BentoHttpError) class databento.BentoServerError(databento.BentoHttpError) 

Example usage
PythonHTTP
  
import databento as db client = db.Reference("INVALID_API_KEY") try: print(client.security_master.get_last("TEST")) except db.BentoClientError as e: print(e) 

Example response
PythonHTTP
  
400 auth_invalid_username_in_basic_auth Invalid username in Basic auth ('INVALID_API_KEY'). documentation: https://databento.com/docs 

Rate limits

Our reference API allows each IP address up to:

100 concurrent connections.
100 corporate actions requests per second.
100 adjustment factors requests per second.
100 security master requests per second.

When a request exceeds a rate limit, a BentoClientError exception is raised with a 429 error code.

Retry-After

The Retry-After response header indicates how long the user should wait before retrying.

If you find that your application has been rate-limited, you can retry after waiting for the time specified in the Retry-After header.

If you are using Python, you may use the time.sleep function as seen below to wait for the time specified in the Retry-After header. e.g. time.sleep(int(response.headers("Retry-After", 1)))

This code snippet works best for our current APIs with their rate limits. Future APIs may have different rate limits, and might require a different default time delay.

Client

Reference

To access Databento's reference API, first create an instance of the Reference client. The entire API is exposed through instance methods of the client.

Note that the API key can be passed as a parameter, which is not recommended for production applications. Instead, you can leave out this parameter to pass your API key via the DATABENTO_API_KEY environment variable:

Currently, only the 'bo1' gateway is supported for historical data.

Parameters

key

optional | str

32-character API key. Found on your API keys page. If None then DATABENTO_API_KEY environment variable is used.

gateway

optional | databento.HistoricalGateway or str

Site of historical gateway to connect to. Currently only 'bo1' is supported. If None then will connect to the default historical gateway.

API method
PythonHTTP
  
class Reference( key: str | None = None, gateway: HistoricalGateway | str = 'bo1', ) 

Example usage
PythonHTTP
  
import databento as db # Pass as parameter client = db.Reference("$YOUR_API_KEY") # Or, pass as `DATABENTO_API_KEY` environment variable client = db.Reference() 

Corporate actions

Reference.corporate_actions.get_range

Make a streaming request for corporate actions time series data from Databento.

Each record represents a unique corporate actions event at the listing level.

The specified index will be used to filter the time range and order the records. It will also be set as the index of the resulting data frame.

See also
Corporate actions dataset guide for further details.

Parameters

start

required | pd.Timestamp, datetime, date, str, or int

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

optional | pd.Timestamp, datetime, date, str, or int

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.

index

optional | str, default 'event_date'

The index column to use for filtering the start and end time range and for record ordering. Use any of 'event_date', 'ex_date' or 'ts_record'. Records with a NULL value in the specified index column will be excluded.

symbols

optional | list or str

The symbols to filter for. Takes up to 2,000 symbols per request. If more than 1 symbol is specified, the data is merged and sorted by time. If 'ALL_SYMBOLS' or None then will select all symbols by default.

stype_in

optional | SType or str, default 'raw_symbol'

The symbology type of input symbols. Use any of 'raw_symbol', 'nasdaq_symbol', 'isin', 'us_code', 'bbg_comp_id', 'bbg_comp_ticker', 'figi', 'figi_ticker'.

events

optional | list or str

The event types to filter for. Takes any number of events per request. If not specified then will select all event types by default. See EVENT enum.

countries

optional | list or str

The listing countries to filter for. Takes any number of country codes per request as two letter ISO 3166-1 alpha-2 strings. If not specified then will select all countries by default. See CNTRY enum.

exchanges

optional | list or str

The (listing) exchanges to filter for. Takes any number of exchanges per request. If not specified then will select all exchanges by default. See EXCHANGE enum.

security_types

optional | list or str

The security types to filter for. If not specified then will select all security types by default. See SECTYPE enum.

flatten

optional | bool, default True

If nested JSON objects within the date_info, rate_info, and event_info fields should be flattened into separate columns in the resulting data frame.

pit

optional | bool, default False

Determines whether to retain all historical records or only the latest records. If True, all historical records for each event_unique_id will be retained, preserving the complete point-in-time history. If False (default), the data frame will include only the most recent record for each event_unique_id based on the ts_record timestamp.

Returns

pd.Dataframe

The data converted into a DataFrame.

See also
Corporate actions field descriptions for further details.

API method
PythonHTTP
  
Reference.corporate_actions.get_range( start: pd.Timestamp | datetime | date | str | int, end: pd.Timestamp | datetime | date | str | int | None = None, index: str = "event_date", symbols: Iterable[str] | str | None = None, stype_in: SType | str = "raw_symbol", events: Iterable[str] | str | None = None, countries: list[str] | str | None = None, security_types: list[str] | str | None = None, flatten: bool = True, pit: bool = False, ) -> pd.DataFrame 

Example usage
PythonHTTP
  
import databento as db client = db.Reference("$YOUR_API_KEY") df_raw = client.corporate_actions.get_range( symbols=["AAPL"], stype_in="raw_symbol", start="2024-01", events=["SHOCH"], countries=["US"], ) print(df_raw.head()) 

Example response
PythonHTTP
  
 event_unique_id ts_record event_id listing_id listing_group_id security_id issuer_id event_action event event_subtype event_date_label event_created_date effective_date ex_date record_date record_date_id related_event related_event_id global_status listing_status listing_source listing_date delisting_date issuer_name security_type security_description primary_exchange exchange operating_mic symbol nasdaq_symbol local_code isin us_code bbg_comp_id bbg_comp_ticker figi figi_ticker listing_country register_country trading_currency multi_currency segment_mic_name segment_mic mand_volu_flag rd_priority lot_size par_value par_value_currency payment_date duebills_redemption_date from_date to_date registration_date start_date end_date open_date close_date start_subscription_date end_subscription_date option_election_date withdrawal_rights_from_date withdrawal_rights_to_date notification_date financial_year_end_date exp_completion_date payment_type option_id serial_id default_option_flag rate_currency ratio_old ratio_new fraction outturn_style outturn_security_type outturn_security_id outturn_isin outturn_us_code outturn_local_code outturn_bbg_comp_id outturn_bbg_comp_ticker outturn_figi outturn_figi_ticker min_offer_qty max_offer_qty min_qualify_qty max_qualify_qty min_accept_qty max_accept_qty tender_strike_price tender_price_step option_expiry_time option_expiry_tz withdrawal_rights_flag withdrawal_rights_expiry_time withdrawal_rights_expiry_tz expiry_time expiry_tz ts_created old_outstanding_date new_outstanding_date old_shares_outstanding new_shares_outstanding event_date 2024-01-02 U-40685533904-135825 2024-01-12 13:12:12+00:00 E-5533904-SHOCH L-135825 LG-35825 S-33449 I-30017 I SHOCH NaN effective_date 2024-01-12 2024-01-02 None None NaN SHOCH NaN A L M 1980-12-12 None Apple Inc EQS Ordinary Shares USNASD USNASD XNAS AAPL AAPL AAPL US0378331005 37833100 BBG000B9XRY4 AAPL US BBG000B9Y5X2 AAPL UW US US USD False Global Select XNGS M NaN 100 0.00001 USD None None None None None None None None None None None None NaN None None None None NaN NaN NaN False NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaT NaN NaN NaT NaN NaT NaN 2024-07-24 06:32:10.375440+00:00 2023-10-20T00:00:00 2024-01-02T00:00:00 15552752000 15461896000 2024-01-19 U-40685575281-135825 2024-02-05 03:37:49+00:00 E-5575281-SHOCH L-135825 LG-35825 S-33449 I-30017 I SHOCH NaN effective_date 2024-02-03 2024-01-19 None None NaN SHOCH NaN A L M 1980-12-12 None Apple Inc EQS Ordinary Shares USNASD USNASD XNAS AAPL AAPL AAPL US0378331005 37833100 BBG000B9XRY4 AAPL US BBG000B9Y5X2 AAPL UW US US USD False Global Select XNGS M NaN 100 0.00001 USD None None None None None None None None None None None None NaN None None None None NaN NaN NaN False NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaT NaN NaN NaT NaN NaT NaN 2024-07-24 22:44:12.284177+00:00 2024-01-02T00:00:00 2024-01-19T00:00:00 15461896000 15441881000 2024-04-19 U-40685752069-135825 2024-05-06 13:21:00+00:00 E-5752069-SHOCH L-135825 LG-35825 S-33449 I-30017 I SHOCH NaN effective_date 2024-05-06 2024-04-19 None None NaN SHOCH NaN A L M 1980-12-12 None Apple Inc EQS Ordinary Shares USNASD USNASD XNAS AAPL AAPL AAPL US0378331005 37833100 BBG000B9XRY4 AAPL US BBG000B9Y5X2 AAPL UW US US USD False Global Select XNGS M NaN 100 0.00001 USD None None None None None None None None None None None None NaN None None None None NaN NaN NaN False NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaT NaN NaN NaT NaN NaT NaN 2024-07-25 01:52:32.148864+00:00 2024-01-19T00:00:00 2024-04-19T00:00:00 15441881000 15334082000 2024-07-19 U-40685933868-135825 2024-08-05 03:41:52+00:00 E-5933868-SHOCH L-135825 LG-35825 S-33449 I-30017 I SHOCH NaN effective_date 2024-08-03 2024-07-19 None None NaN SHOCH NaN A L M 1980-12-12 None Apple Inc EQS Ordinary Shares USNASD USNASD XNAS AAPL AAPL AAPL US0378331005 37833100 BBG000B9XRY4 AAPL US BBG000B9Y5X2 AAPL UW US US USD False Global Select XNGS M NaN 100 0.00001 USD None None None None None None None None None None None None NaN None None None None NaN NaN NaN False NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaT NaN NaN NaT NaN NaT NaN 2024-08-05 06:00:56.552900+00:00 2024-04-19T00:00:00 2024-07-19T00:00:00 15334082000 15204137000 

Adjustment factors

Reference.adjustment_factors.get_range

Make a streaming request for adjustment factors time series data from Databento.

Each record represents a unique corporate actions event with an adjustment factor at the listing level.

The ex_date column will be used to filter the time range and order the records. It will also be set as the index of the resulting data frame.

See also
Adjustment factors dataset guide for further details.

Parameters

start

required | pd.Timestamp, datetime, date, str, or int

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

optional | pd.Timestamp, datetime, date, str, or int

symbols

optional | list or str

stype_in

optional | SType or str, default 'raw_symbol'

The symbology type of input symbols. Use any of 'raw_symbol', 'nasdaq_symbol', 'isin', 'us_code'.

countries

optional | list or str

security_types

optional | list or str

The security types to filter for. If not specified then will select all security types by default. See SECTYPE enum.

Returns

pd.Dataframe

The data converted into a DataFrame.

See also
Adjustment factors field descriptions for further details.

API method
PythonHTTP
  
Reference.adjustment_factors.get_range( start: pd.Timestamp | datetime | date | str | int, end: pd.Timestamp | datetime | date | str | int | None = None, symbols: Iterable[str] | str | None = None, stype_in: SType | str = "raw_symbol", countries: list[str] | str | None = None, security_types: list[str] | str | None = None, ) -> pd.DataFrame 

Example usage
PythonHTTP
  
import databento as db client = db.Reference("$YOUR_API_KEY") df_raw = client.adjustment_factors.get_range( symbols=["MSFT"], stype_in="raw_symbol", start="2009", end="2010", countries=["US"], ) print(df_raw.head()) 

Example response
PythonHTTP
  
 security_id event_id event issuer_name security_type primary_exchange exchange operating_mic symbol nasdaq_symbol local_code local_code_resulting isin isin_resulting us_code status factor close currency sentiment reason gross_dividend dividend_currency frequency option detail ts_created ex_date 2009-02-17 S-39827 E-336073-DIV DIV Microsoft Corporation EQS USNASD NaN XNAS MSFT MSFT MSFT NaN US5949181045 NaN 594918104 A 0.993190 19.09 USD 0.968570 17 0.13 USD FNL 1 Dividend (cash) of USD0.130000000/Share 2024-08-23 10:04:48.075003+00:00 2009-05-19 S-39827 E-353927-DIV DIV Microsoft Corporation EQS USNASD NaN XNAS MSFT MSFT MSFT NaN US5949181045 NaN 594918104 A 0.993689 20.60 USD 0.992718 17 0.13 USD QTR 1 Dividend (cash) of USD0.130000000/Share 2024-08-23 10:07:09.546929+00:00 2009-08-18 S-39827 E-381906-DIV DIV Microsoft Corporation EQS USNASD NaN XNAS MSFT MSFT MSFT NaN US5949181045 NaN 594918104 A 0.994409 23.25 USD 1.003871 17 0.13 USD QTR 1 Dividend (cash) of USD0.130000000/Share 2024-08-23 10:09:33.205558+00:00 2009-11-17 S-39827 E-400830-DIV DIV Microsoft Corporation EQS USNASD NaN XNAS MSFT MSFT MSFT NaN US5949181045 NaN 594918104 A 0.995599 29.54 USD 0.998646 17 0.13 USD QTR 1 Dividend (cash) of USD0.130000000/Share 2024-08-23 10:11:40.477633+00:00 

Security master

Reference.security_master.get_last

Make a streaming request for the latest security master data from Databento.

Each record represents the a unique listing for a security.

The resulting data frame will be indexed and sorted by ts_effective.

See also
Security master dataset guide for further details.

Parameters

symbols

optional | list or str

stype_in

optional | SType or str, default 'raw_symbol'

The symbology type of input symbols. Use any of 'raw_symbol', 'nasdaq_symbol', 'isin', 'us_code', 'bbg_comp_id', 'bbg_comp_ticker', 'figi', 'figi_ticker'.

countries

optional | list or str

security_types

optional | list or str

The security types to filter for. If not specified then will select all security types by default. See SECTYPE enum.

Returns

pandas.Dataframe

The data converted into a DataFrame.

See also
Security master field descriptions for further details.

API method
PythonHTTP
  
Reference.security_master.get_last( symbols: Iterable[str] | str | None = None, stype_in: SType | str = "raw_symbol", countries: list[str] | str | None = None, security_types: list[str] | str | None = None, ) -> pd.DataFrame 

Example usage
PythonHTTP
  
import databento as db client = db.Reference("$YOUR_API_KEY") df_raw = client.security_master.get_last( symbols=["AAPL"], countries=["US"], ) print(df_raw.head()) 

Example response
PythonHTTP
  
 ts_record listing_id listing_group_id security_id issuer_id listing_status listing_source listing_created_date listing_date delisting_date issuer_name security_type security_description primary_exchange exchange operating_mic symbol nasdaq_symbol local_code isin us_code bbg_comp_id bbg_comp_ticker figi figi_ticker fisn lei sic cik gics naics cic cfi incorporation_country listing_country register_country trading_currency multi_currency segment_mic_name segment_mic structure lot_size par_value par_value_currency voting vote_per_sec shares_outstanding shares_outstanding_date ts_created ts_effective 2024-07-19 00:00:00+00:00 2024-08-05 02:41:52+00:00 L-135825 LG-35825 S-33449 I-30017 L M 2001-05-05 1980-12-12 None Apple Inc EQS Ordinary Shares USNASD USNASD XNAS AAPL AAPL AAPL US0378331005 37833100 BBG000B9XRY4 AAPL US BBG000B9Y5X2 AAPL UW NaN HWUPKR0MPOU8FGXBT394 3571 320193 NaN 334111 US31 ESVUFR US US US USD False Global Select XNGS NaN 100 0 USD V 1 15204137000 2024-07-19 2024-11-11 22:41:29.670132+00:00 

Reference.security_master.get_range

Make a streaming request for security master point-in-time (PIT) time series data from Databento.

Each record represents the status of a unique listing for a security with an effective date.

The specified index will be used to filter the time range and order the records. It will also be set as the index of the resulting data frame.

See also
Security master dataset guide for further details.

Parameters

start

required | pd.Timestamp, datetime, date, str, or int

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

optional | pd.Timestamp, datetime, date, str, or int

index

optional | str, default 'ts_effective'

The index column to use for filtering the start and end time range and recording ordering. Use any of 'ts_effective' or 'ts_record'.

symbols

optional | list or str

stype_in

optional | SType or str, default 'raw_symbol'

The symbology type of input symbols. Use any of 'raw_symbol', 'nasdaq_symbol', 'isin', 'us_code', 'bbg_comp_id', 'bbg_comp_ticker', 'figi', 'figi_ticker'.

countries

optional | list or str

security_types

optional | list or str

The security types to filter for. If not specified then will select all security types by default. See SECTYPE enum.

Returns

pd.Dataframe

The data converted into a DataFrame.

See also
Security master field descriptions for further details.

API method
PythonHTTP
  
Reference.security_master.get_range( start: pd.Timestamp | datetime | date | str | int, end: pd.Timestamp | datetime | date | str | int | None = None, index: str = "ts_effective", symbols: Iterable[str] | str | None = None, stype_in: SType | str = "raw_symbol", countries: list[str] | str | None = None, security_types: list[str] | str | None = None, ) -> pd.DataFrame 

Example usage
PythonHTTP
  
import databento as db client = db.Reference("$YOUR_API_KEY") df_raw = client.security_master.get_range( symbols=["AAPL"], countries=["US"], start="2005-01-01", ) print(df_raw.head(10)) 

Example response
PythonHTTP
  
 ts_record listing_id listing_group_id security_id issuer_id listing_status listing_source listing_created_date listing_date delisting_date issuer_name security_type security_description primary_exchange exchange operating_mic symbol nasdaq_symbol local_code isin us_code bbg_comp_id bbg_comp_ticker figi figi_ticker fisn lei sic cik gics naics cic cfi incorporation_country listing_country register_country trading_currency multi_currency segment_mic_name segment_mic structure lot_size par_value par_value_currency voting vote_per_sec shares_outstanding shares_outstanding_date ts_created ts_effective 2004-11-19 00:00:00+00:00 2004-12-06 15:09:30+00:00 L-135825 LG-35825 S-33449 I-30017 L M 2001-05-05 1980-12-12 None Apple Computer Inc. EQS Ordinary Shares USNASD USNASD XNAS AAPL AAPL AAPL US0378331005 37833100 BBG000B9XRY4 AAPL US BBG000B9Y5X2 AAPL UW NaN HWUPKR0MPOU8FGXBT394 3571 320193 NaN 334111 US31 ESVUFR US US US USD False Global Select XNGS NaN 100 0 USD V 1 402057856 2004-11-19 2024-11-11 22:41:24.780487+00:00 2005-01-25 00:00:00+00:00 2005-02-03 16:00:32+00:00 L-135825 LG-35825 S-33449 I-30017 L M 2001-05-05 1980-12-12 None Apple Computer Inc. EQS Ordinary Shares USNASD USNASD XNAS AAPL AAPL AAPL US0378331005 37833100 BBG000B9XRY4 AAPL US BBG000B9Y5X2 AAPL UW NaN HWUPKR0MPOU8FGXBT394 3571 320193 NaN 334111 US31 ESVUFR US US US USD False Global Select XNGS NaN 100 0 USD V 1 408585970 2005-01-25 2024-11-11 22:41:24.780487+00:00 2005-02-28 00:00:00+00:00 2005-03-01 15:28:04+00:00 L-135825 LG-35825 S-33449 I-30017 L M 2001-05-05 1980-12-12 None Apple Computer Inc. EQS Ordinary Shares USNASD USNASD XNAS AAPL AAPL AAPL US0378331005 37833100 BBG000B9XRY4 AAPL US BBG000B9Y5X2 AAPL UW NaN HWUPKR0MPOU8FGXBT394 3571 320193 NaN 334111 US31 ESVUFR US US US USD False Global Select XNGS NaN 100 0 USD V 1 817171940 2005-02-28 2024-11-11 22:41:24.780487+00:00 2005-04-22 00:00:00+00:00 2005-05-06 14:06:41+00:00 L-135825 LG-35825 S-33449 I-30017 L M 2001-05-05 1980-12-12 None Apple Computer Inc. EQS Ordinary Shares USNASD USNASD XNAS AAPL AAPL AAPL US0378331005 37833100 BBG000B9XRY4 AAPL US BBG000B9Y5X2 AAPL UW NaN HWUPKR0MPOU8FGXBT394 3571 320193 NaN 334111 US31 ESVUFR US US US USD False Global Select XNGS NaN 100 0 USD V 1 823933916 2005-04-22 2024-11-11 22:41:24.780487+00:00 2005-07-22 00:00:00+00:00 2005-08-04 14:39:03+00:00 L-135825 LG-35825 S-33449 I-30017 L M 2001-05-05 1980-12-12 None Apple Computer Inc. EQS Ordinary Shares USNASD USNASD XNAS AAPL AAPL AAPL US0378331005 37833100 BBG000B9XRY4 AAPL US BBG000B9Y5X2 AAPL UW NaN HWUPKR0MPOU8FGXBT394 3571 320193 NaN 334111 US31 ESVUFR US US US USD False Global Select XNGS NaN 100 0 USD V 1 829829296 2005-07-22 2024-11-11 22:41:24.780487+00:00 2005-11-18 00:00:00+00:00 2005-12-02 15:25:18+00:00 L-135825 LG-35825 S-33449 I-30017 L M 2001-05-05 1980-12-12 None Apple Computer Inc. EQS Ordinary Shares USNASD USNASD XNAS AAPL AAPL AAPL US0378331005 37833100 BBG000B9XRY4 AAPL US BBG000B9Y5X2 AAPL UW NaN HWUPKR0MPOU8FGXBT394 3571 320193 NaN 334111 US31 ESVUFR US US US USD False Global Select XNGS NaN 100 0 USD V 1 842767948 2005-11-18 2024-11-11 22:41:24.780487+00:00 2006-01-25 00:00:00+00:00 2006-02-07 15:32:26+00:00 L-135825 LG-35825 S-33449 I-30017 L M 2001-05-05 1980-12-12 None Apple Computer Inc. EQS Ordinary Shares USNASD USNASD XNAS AAPL AAPL AAPL US0378331005 37833100 BBG000B9XRY4 AAPL US BBG000B9Y5X2 AAPL UW NaN HWUPKR0MPOU8FGXBT394 3571 320193 NaN 334111 US31 ESVUFR US US US USD False Global Select XNGS NaN 100 0 USD V 1 848612359 2006-01-25 2024-11-11 22:41:24.780487+00:00 2006-04-26 00:00:00+00:00 2006-05-09 14:36:54+00:00 L-135825 LG-35825 S-33449 I-30017 L M 2001-05-05 1980-12-12 None Apple Computer Inc. EQS Ordinary Shares USNASD USNASD XNAS AAPL AAPL AAPL US0378331005 37833100 BBG000B9XRY4 AAPL US BBG000B9Y5X2 AAPL UW NaN HWUPKR0MPOU8FGXBT394 3571 320193 NaN 334111 US31 ESVUFR US US US USD False Global Select XNGS NaN 100 0 USD V 1 850508144 2006-04-26 2024-11-11 22:41:24.780487+00:00 2006-12-13 00:00:00+00:00 2007-01-02 15:42:39+00:00 L-135825 LG-35825 S-33449 I-30017 L M 2001-05-05 1980-12-12 None Apple Computer Inc. EQS Ordinary Shares USNASD USNASD XNAS AAPL AAPL AAPL US0378331005 37833100 BBG000B9XRY4 AAPL US BBG000B9Y5X2 AAPL UW NaN HWUPKR0MPOU8FGXBT394 3571 320193 NaN 334111 US31 ESVUFR US US US USD False Global Select XNGS NaN 100 0 USD V 1 859273757 2006-12-13 2024-11-11 22:41:24.780487+00:00 2007-01-11 00:00:00+00:00 2007-01-11 00:00:00+00:00 L-135825 LG-35825 S-33449 I-30017 L M 2001-05-05 1980-12-12 None Apple Inc EQS Ordinary Shares USNASD USNASD XNAS AAPL AAPL AAPL US0378331005 37833100 BBG000B9XRY4 AAPL US BBG000B9Y5X2 AAPL UW NaN HWUPKR0MPOU8FGXBT394 3571 320193 NaN 334111 US31 ESVUFR US US US USD False Global Select XNGS NaN 100 0 USD V 1 859273757 2006-12-13 2024-11-11 22:41:29.670132+00:00 