Asset Flow Data#

morningstar_data.direct.get_asset_flow(
market_id: str,
data_point_settings: DataFrame,
investments: List[str] | str | Dict[str, Any] | None = None,
dry_run: bool | None = False,
) → DataFrame | DryRunResults#

Get asset flow data for a market of investments or specific investments within a market

Parameters:

  • market_id (str) – A numeric code representing a broad market of investments. For example, the code for “US Open-end & ETF ex MM ex FoF” is “5”. Use the get_asset_flow_markets function to retrieve a full list of codes.

  • data_point_settings (DataFrame) – A DataFrame of data points with defined settings. Each row represents a data point. Each column is a configurable setting. This DataFrame can be obtained by retrieving asset flow data points, or by retrieving data point settings.

  • investments (Union, required) –

    Defines the investments to fetch. Input can be:

    • Investment IDs (list, optional): Investment identifiers, in the format of SecId;Universe or just SecId. E.g., [“F00000YOOK;FO”,”FOUSA00CFV;FO”] or [“F00000YOOK”,”FOUSA00CFV”]. Use the investments function to discover identifiers.

    • Investment List ID (str, optional): Saved investment list in Morningstar Direct. Use the get_investment_lists function to discover saved lists.

    • Search Criteria ID (str, optional): Saved search criteria in Morningstar Direct. Use the get_search_criteria function to discover saved search criteria.

    • Search Criteria Condition (dict, optional): Search criteria definition. See details in the Reference section of get_investment_data or use the get_search_criteria_conditions function to discover the definition of a saved search criteria.

  • dry_run (bool, optional) – When True, the query will not be executed. Instead, a DryRunResults object will be returned with details about the query’s impact on daily cell limit usage. When True, the ‘investments’ parameter must be a list containing at least one element.

Returns:

There are two return types:

  • DataFrame: A DataFrame object with asset flow data. DataFrame columns include investmentId and data point names, as provided in data_point_settings.

  • DryRunResults: Returned if dry_run=True is passed

    • estimated_cells_used: Number of cells by this query

    • daily_cells_remaining_before: How many cells are remaining in your daily cell limit before running this query

    • daily_cells_remaining_after: How many cells would be remaining in your daily cell limit after running this query

    • daily_cell_limit: Your total daily cell limit

Examples:

import morningstar_data as md
import pandas

ASSET_FLOW_DATA_POINT_SETTINGS = [
    {
        "datapointId": "TNA0M",
        "datapointName": "Total Net Assets-Market Value(Share Class)",
        "asOfDate": "2021-08-30",
        "alias": "Total Net Assets-Market Value(Share Class)",
        "startDate": None,
        "endDate": None,
        "frequency": None,
    }
]
settings = pandas.DataFrame(ASSET_FLOW_DATA_POINT_SETTINGS)
df = md.direct.get_asset_flow(
    investments=["F000010HRO"], market_id="165", data_point_settings=settings
)
df
Output:

investmentId

Total Net Assets-Market Value(Share Class) - 2021-06-30

F000010HRO

0.00188
Errors:

AccessDeniedError: Raised when the user is not authenticated.

BadRequestError: Raised when the user does not provide a properly formatted request.

ForbiddenError: Raised when the user does not have permission to access the requested resource.

InternalServerError: Raised when the server encounters an unhandled error.

NetworkExceptionError: Raised when the request fails to reach the server due to a network error.

ResourceNotFoundError: Raised when the requested resource does not exist in Direct.

morningstar_data.direct.get_asset_flow_data_points() → DataFrame#
Returns:

Returns all available data points related to asset flows.

DataFrame: A DataFrame object with asset flow data points data. DataFrame columns include:

  • datapointId

  • datapointName

  • asOfDate

  • alias

  • startDate

  • endDate

  • frequency

Examples:

import morningstar_data as md

df = md.direct.get_asset_flow_data_points()
df
Output:

datapointId

datapointName

asOfDate

alias

startDate

endDate

frequency

TNA0M

XXX

2021-09-30

Total Net Assets-Market Value(Share Class)

None

None

None

…
morningstar_data.direct.get_asset_flow_markets() → DataFrame#
Returns:

Returns all investment markets that can be used to retrieve asset flow data. For example, “US Open-end & ETFs ex MM ex FoF”.

DataFrame: A DataFrame object with asset flow markets data. DataFrame columns include:

  • marketId

  • marketName

  • currency

Examples:

import morningstar_data as md

df = md.direct.get_asset_flow_markets()
df
Output:

marketId

marketName

currency

5

US Open-end & ETF ex MM ex FoF

USD

6

US Open-end, ETF, and MM ex FoF

USD

…
Errors:

AccessDeniedError: Raised when the user is not authenticated.

BadRequestError: Raised when the user does not provide a properly formatted request.

ForbiddenError: Raised when the user does not have permission to access the requested resource.

InternalServerError: Raised when the server encounters an unhandled error.

NetworkExceptionError: Raised when the request fails to reach the server due to a network error.

ResourceNotFoundError: Raised when the requested resource does not exist in Direct.