Quotes

Quotes

In order to perform a valuation, LUSID must have a set of quotes available in the scope. If current Quotes have not been provided then LUSID chooses the most recently added quotes. The documentation for Upserting Quotes can be found here.

name	FIGI	Price	Price Code
Apple	BBG0013T5HY0	50	Stock
MICROSOFT CORP	BBG007F5XJZ0	62	Stock
UBER Tech	BBG00NW4HSM1	24	Stock
Gvmnt 2020	BBG00UF9KSP9	99.73	Bond

mapping["quotes"] = {
        "required": {
            "quote_id.quote_series_id.instrument_id_type": "$Figi",
            "quote_id.effective_at": "$2019-11-20T00:00:00Z",
            "quote_id.quote_series_id.provider": "$DataProvider",
            "quote_id.quote_series_id.field": "$mid",
            "quote_id.quote_series_id.quote_type": "$Price",
            "quote_id.quote_series_id.instrument_id": "FIGI",
            "metric_value.unit": "Currency",
            "metric_value.value": "__adjusted_quote"
        },
        "quote_scalar":{
            "price": "Price",
            "type": "Price Code",
            "type_code": "Bond",
            "scale_factor": 0.01
        }
    }

This set of quotes includes bonds, who's prices are quoted as value of a bond, instead of share/par. The utility function scale_quote_of_type() takes the quote_scalar mapping configuration and uses it to adjust the prices for bonds by a provided scale factor.

# Load data
df_quotes = pd.read_excel("data/initial-data-load/Fund Accounting Detailed Positions Report with SEG1.xlsx")

# Add "__adjusted_quotes" column to dataframe containing updated prices, and update the required fields in mapping
df_adjusted_quotes, mapping = lpt.cocoon.cocoon.scale_quote_of_type(copy.deepcopy(df_quotes), mapping)

result = lpt.load_from_data_frame(
    api_factory=api_factory,
    scope=scope,
    data_frame=df_adjusted_quotes,
    mapping_required=mapping["quotes"]["required"],
    mapping_optional={},
    file_type="quotes"
)