Before you run

Before you specify what you want to calculate make sure the measure/risk metric you are interested in is present in the dataset:

print( ds.measures )

What you get there is a dict. The keys are measures' name. For example SensitivitySpot or FX DeltaCharge Low. The items indicate if there is any restriction on aggregation method, where scalar is a special method explained below. If None, you are free to use any of the availiable:

print( ul.aggregation_ops() )

Note that any numeric column automatically considered a measure (eg SensitivitySpot). These measures can be aggregated any way you want for a given level, say Desk. In other words, for a given Desk you can find the mean, max, min or sum (etc etc) of SensitivitySpot. On the other hand, you can not find mean, max, min (etc etc) of FX DeltaCharge Low. This wouldn't make sence since FX DeltaCharge Low is simply a single number defined by the regulation. Hence we call such aggregation method scalar and we restrict you to use only this method.

Also, make sure that columns which appear in measures, grouby and filters are present as well.


Now we are good to form the request which we need. Say, we want to understand the DRC capital charge and all intermediate results, for every combination of Desk - BucketBCBS.

# What do we want to calculate?
request = dict(
        ["DRC nonSec GrossJTD", "sum"],
        ["DRC nonSec GrossJTD Scaled", "sum"],
        ["DRC nonSec CapitalCharge", "scalar"],
        ["DRC nonSec NetLongJTD", "scalar"],
        ["DRC nonSec NetShortJTD", "scalar"],
        ["DRC nonSec NetLongJTD Weighted", "scalar"],
        ["DRC nonSec NetAbsShortJTD Weighted", "scalar"],
        ["DRC nonSec HBR", "scalar"],
    groupby=["Desk", "BucketBCBS"],
        "jurisdiction": "BCBS",
        "apply_fx_curv_div": "true",
        "drc_offset": "true",

Note: if you don't care about level of aggregation and just want a total say DRC Capital Charge for your portfolio, just provide an extra column to your portfolio, name it "Total"(for exmaple) and set all values in the columns as "Total". Then do groupby=["Total"]. Above request has two optional parameters which we haven't talked about yet: hide_zeros and calc_params. hide_zeros simply removes rows from the result where each measure is 0, and calc_params allows you to override default parameters such as jurisdiction, reporting_ccy, girr_delta_rho_diff_curve_base (and many many others. We will talk about in analysis chapter) etc.

Valide that you've formed a legitimate request (ie no compulsory field is missing, datatypes are correct etc):

aggrequest = ul.AggRequest(request)

Finally, to execute

result = ds.compute(request)
print("Type: ", type(result))

Notice the returned object is a polars DataFrame. You can then do whatever you want with it. Print (to set how many columns you want to print make sure to use polars config), or any on the I/O: save to csv, parquet, database etc.