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

Parameter
Type
Required
Description
start
pd.Timestamp, datetime, date, str, or int
required
The inclusive start of the request range based on ex_date. 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 based on ex_date. Takes pd.Timestamp, Python datetime, Python date, ISO 8601 string, or UNIX timestamp in nanoseconds. Assumes UTC as timezone unless otherwise specified. If None, then will return all data available after start.
symbols
list or str
optional
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
SType or str, default 'raw_symbol'
optional
The symbology type of input symbols. Use any of 'raw_symbol', 'nasdaq_symbol', 'isin', 'us_code', 'security_id'.
countries
list or str
optional
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.
security_types
list or str
optional
The security types to filter for. If not specified then will select all security types by default. See SECTYPE enum.
allocate_isins
bool, default True
optional
Whether the request should allocate any new ISINs for plans that are ISIN-limited. The request will drop any rows that would create new allocations.

Returns

pd.Dataframe

The data converted into a DataFrame.

See also

Adjustment factors field descriptions for further details.

API method 
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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,
    allocate_isins: bool = True,
) -> pd.DataFrame
Example usage 
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import databento as db

client = db.Reference("$YOUR_API_KEY")

df_raw = client.adjustment_factors.get_range(
    symbols=["MSFT"],
    stype_in="raw_symbol",
    start="2019",
    end="2020",
    countries=["US"],
)

print(df_raw.head())
Example response 
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           security_id       event_id event            issuer_name security_type  ... dividend_currency frequency option                              detail                       ts_created
ex_date                                                                           ...
2019-02-20     S-39827  E-1443840-DIV   DIV  Microsoft Corporation           EQS  ...               USD       INT      1  INT Dividend (cash) of USD0.46/EQS 2024-09-23 00:25:46.589794+00:00
2019-05-15     S-39827  E-1550148-DIV   DIV  Microsoft Corporation           EQS  ...               USD       FNL      1  FNL Dividend (cash) of USD0.46/EQS 2024-09-23 00:28:54.651339+00:00
2019-08-14     S-39827  E-1617896-DIV   DIV  Microsoft Corporation           EQS  ...               USD       INT      1  INT Dividend (cash) of USD0.46/EQS 2024-09-23 00:32:47.230682+00:00
2019-11-20     S-39827  E-1669990-DIV   DIV  Microsoft Corporation           EQS  ...               USD       INT      1  INT Dividend (cash) of USD0.51/EQS 2024-09-23 00:36:33.357489+00:00

[4 rows x 27 columns]