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Testing custom adjustment types

The Market Risk Application supports cube-level, fact-level, and roll-over adjustments for the Sensitivities, PnL, and VaR-ES cubes.

As an example of how to configure a new custom adjustment, this page explains how PNL adjustments have been defined within the mr-application module in the mr-application/src/main/java/com/activeviam/mr/application/signoff/adjustments/ directory.

note

PNL adjustments are scalar adjustments.

Configure Adjustment Execution

All adjustment processes are defined within the AdjustmentExecutionConfig.java class. Other library classes are mentioned where relevant.

Fact-level adjustments

Fact-level adjustments are modifications of the underlying data held in the datastore. They operate directly on datastore rows, using Execution objects containing functional components.

These functional components are defined in the ExecutionFunctionalComponents.java class.

The `Execution` object

Field	Description
appliesOnAdjustments	Determines whether or not the current adjustment applies for rows created by a previous adjustment.
inputParser	A `BiFunction` that takes the request and definition DTOs and matches the required definition inputs to the input provided in the request. The values of the request inputs are parsed using the `IParser` objects matching the type found in the definition.
inputRetriever	A `TriFunction` that takes a datastore row, the store format and a list of store fields and returns an object representing the value to be adjusted.
sourceTagger	A `List` of `TriFunction` objects that take an execution ID and the initial values of the Source and Input type fields and return a map with the new values of those fields.
valueField	The datastore field that holds the values that will be adjusted by the execution.
expectedInputs	The `List` of expected inputs required by the execution.
inputConverter	A `BiFunction` that takes the result of the `inputParser` and the `expectedInputs` and generates the request inputs to be used in the adjustment steps.
steps	A list of `BiFunction` objects that use the results of the `inputRetriever` and `inputConverter` to create the adjusted values that will be written to the datastore.

As PnL adjustments are scalar (i.e. a fact is a single value, as opposed to a vector), the scalar functions are used:

inputParser is parseInput()
inputRetriever is scalarInputRetriever() - given the base tuple, the store format and a list of fields, returns a double value held by the first field in the list
inputConverter is scalarInputConverter() - given a map of values and the expected input, returns a single double-typed value

For the PnL datastore:

valueField parameter is StoreFieldNames.DAILY
expectedInputs parameter only contains StoreFieldNames.DAILY

Add On Execution

appliesOnAdjustments is false
sourceTagger contains:
- userInputSourceTagging() - tags the row as direct user input
steps contains:
- doubleInputReplacer() - replaces the initial value with the request input value

    private final Execution<Double, Double, Double> pnlAddOnExecution = new Execution<>(
            false,
            executionFunctionalComponents.parseInput(),
            executionFunctionalComponents.scalarInputRetriever(),
            List.of(executionFunctionalComponents.userInputSourceTagging()),
            StoreFieldNames.DAILY,
            List.of(StoreFieldNames.DAILY),
            executionFunctionalComponents.scalarInputConverter(),
            List.of(executionFunctionalComponents.doubleInputReplacer())
    );

Scaling Execution

appliesOnAdjustments is true
sourceTagger contains:
- inverseTagging() - tags a row as the inversion of the initial row
- scaleTagging() - tags the row as a scaling of the initial row
steps contains:
- doubleInverter() - the execution replaces the initial value with its inverse (initialValue * -1.0)
- doubleScaler() - the execution replaces the initial value with the scaled value (initialValue * scalingFactor)

    private final Execution<Double, Double, Double> pnlScalingExecution = new Execution<>(
            true,
            executionFunctionalComponents.parseInput(),
            executionFunctionalComponents.scalarInputRetriever(),
            List.of(executionFunctionalComponents.inverseTagging(), executionFunctionalComponents.scaleTagging()),
            StoreFieldNames.DAILY,
            List.of(StoreFieldNames.DAILY),
            executionFunctionalComponents.scalarInputConverter(),
            List.of(executionFunctionalComponents.doubleInverter(), executionFunctionalComponents.doubleScaler())
    );

Override Execution

appliesOnAdjustments is true
sourceTagger contains:
- inverseTagging() - tags a row as the inversion of the initial row
- userInputSourceTagging() - tags the row as direct user input
steps contains:
- doubleInverter() - the execution replaces the initial value with its inverse (initialValue * -1.0)
- doubleInputReplacer() - replaces the initial value with the request input value

    private final Execution<Double, Double, Double> pnlOverrideExecution = new Execution<>(
            true,
            executionFunctionalComponents.parseInput(),
            executionFunctionalComponents.scalarInputRetriever(),
            List.of(executionFunctionalComponents.inverseTagging(), executionFunctionalComponents.userInputSourceTagging()),
            StoreFieldNames.DAILY,
            List.of(StoreFieldNames.DAILY),
            executionFunctionalComponents.scalarInputConverter(),
            List.of(executionFunctionalComponents.doubleInverter(), executionFunctionalComponents.doubleInputReplacer())
    );

Cube-level adjustments

Cube-level adjustments rely on the concept of ’location digest’, a string representation of a location in the cube that is more robust to changes in the cube configuration than the native toString() value of a location object. A digest is a string representation of the form: “dimensionName@hierarchyName=…|dimensionName@hierarchyName=…” in which hierarchies for which the path is “AllMember” are excluded . The location digest is independent of the order in which hierarchies are defined in the cube configuration: the hierarchies digests are sorted alphabetically in the location digest.

Cube-level adjustments work by submitting entries into the SignOffDigestStore store to specify:

the location digest
the measure to override
the value used for the override
the currency in which that value is expressed

The executor used for cube-level adjustments does not take any argument into account.

Roll-over adjustments

Roll-over adjustments operate on the datastore, replacing the rows corresponding to the current as-of date with the approved rows from the input as-of date.

The executors use the rollOver() method with the following arguments:

Argument	Description
inverters	A `Map` of inverter `Function` objects for the datastore fields that should be inverted by the roll-over.
inverseTagging	Tagging `TriFunction` to generate the source and input tags for an inverted datastore row.
rollOverTagging	Tagging `TriFunction` to generate the source and input tags for a rolled-over row.

For the PnL roll-over adjustment, the inverters are as follows:

    private final Map<String, Function<Object, Object>> pnlInverters = Map.of(
            StoreFieldNames.DAILY, executionFunctionalComponents.inPlaceDoubleOrArrayInverter(),
            MONTHLY, executionFunctionalComponents.inPlaceDoubleOrArrayInverter(),
            YEARLY, executionFunctionalComponents.inPlaceDoubleOrArrayInverter(),
            LIFETIME, executionFunctionalComponents.inPlaceDoubleOrArrayInverter()
    );

The inPlaceDoubleOrArrayInverter() function returns input * 1.0 for double inputs and ((IVector) input).scale(-1.0) for IVector inputs.

Include Defined Executions in Executors Map

Now that we have defined our adjustment executions we need to add them to our executors Map which will be accessed by the services defined in the Sign-off API library.

We want to include our executors in the bean with the qualifier SP_QUALIFIER__EXECUTORS Note that our executors are included in both profiles.

    @Bean
    @Qualifier(SP_QUALIFIER__EXECUTORS)
    public Map<String, BiConsumer<AdjustmentRequestDTO, String>> executors() {
        executionFunctionalComponents.setStatusService(statusService);
        Map<String, BiConsumer<AdjustmentRequestDTO, String>> executors = new HashMap<>();
        Boolean onBranch = Boolean.parseBoolean(performOnBranch);
		...
        executors.put(PNL_ADD_ON, execution(onBranch, pnlAddOnExecution));
        executors.put(PNL_OVERRIDE, execution(onBranch, pnlOverrideExecution));
        executors.put(PNL_SCALING, execution(onBranch, pnlScalingExecution));
        executors.put(PNL_ROLL_OVER, rollOver(
                pnlInverters,
                executionFunctionalComponents.inverseTagging(),
                executionFunctionalComponents.rollOverTagging()));
        executors.put(PNL_CUBE_LEVEL, cubeLevelAdjustment());
        ...
        return executors;
    }

Define required dimensions for sign-off adjustments

Navigate to AdjustmentPivotConfig.java

We add SignOff Source Dimension to the appropriate schema in this case PnlSchema.

    @Bean
    @Qualifier("aPnlDimension")
    @Order(75)
    public ICanStartBuildingDimensions.DimensionsAdder signOffPnlDimensionAdder() {
        return AdjustmentPivotConfig::getSignOffSourceDimension;
    }

Add source tagging fields

Navigate to SignOffDatastoreCustomisations

    public static void loadCustomisations(IDatastoreConfigurator configurator) {
	    ...
        addTagging(configurator, PnLDatastoreDescriptionConfig.SCHEMA, StoreNames.PNL_STORE_NAME);
        addTagging(configurator, PnLDatastoreDescriptionConfig.SCALAR_SCHEMA, StoreNames.PNL_STORE_NAME);
        addTaggingAfterField(configurator, PnLFlatDatastoreDescriptionConfig.SCHEMA, StoreNames.PNL_BASE_STORE, StoreFieldNames.INSTRUMENT_SUB_TYPE);
        addTaggingAfterField(configurator, PnLFlatDatastoreDescriptionConfig.SCALAR_SCHEMA, StoreNames.PNL_BASE_STORE, StoreFieldNames.INSTRUMENT_SUB_TYPE);
        addTaggingAfterField(configurator, PnLAggregatedDatastoreDescriptionConfig.SCHEMA, StoreNames.PNL_BASE_STORE, StoreFieldNames.INSTRUMENT_SUB_TYPE);
        addTaggingAfterField(configurator, PnLAggregatedDatastoreDescriptionConfig.SCALAR_SCHEMA, StoreNames.PNL_BASE_STORE, StoreFieldNames.INSTRUMENT_SUB_TYPE);
        ...
    }

Define supported Adjustment

Navigate to SupportedAdjustmentsConfig

Define a SupportedAdjustmentDTO bean.

Add-on

    @Bean
    public SupportedAdjustmentDTO pnlAddOn() {
        return new SupportedAdjustmentDTO(
                ADD_ON_NAME,
                PNL_ADD_ON,
                true,
                PnLCubeConfig.CUBE_NAME,
                Set.of(StoreNames.PNL_STORE_NAME),
                Set.of(
                        new TypedFieldDTO(AS_OF_LEVEL, StoreFieldNames.AS_OF_DATE, LOCAL_DATE),
                        new TypedFieldDTO(TRADE_LEVEL, StoreFieldNames.TRADE_ID, STRING),
                        new TypedFieldDTO(TYPE_LEVEL, StoreFieldNames.TYPE, STRING),
                        new TypedFieldDTO(RISK_FACTOR_LEVEL, StoreFieldNames.RISK_FACTOR, STRING),
                        new TypedFieldDTO(CURRENCY_LEVEL, StoreFieldNames.VALUE_CCY, STRING)
                ),
                Set.of(DTD_PNL_NATIVE),
                Set.of(new TypedFieldDTO(StoreFieldNames.DAILY, DOUBLE))
        );
    }

Scaling

    @Bean
    public SupportedAdjustmentDTO pnlScaling() {
        return new SupportedAdjustmentDTO(
                SCALING_NAME,
                PNL_SCALING,
                true,
                PnlCubeConfig.CUBE_NAME,
                Set.of(StoreNames.PNL_STORE_NAME),
                Set.of(
                        new TypedFieldDTO(AS_OF_LEVEL, StoreFieldNames.AS_OF_DATE, LOCAL_DATE),
                        new TypedFieldDTO(TRADE_LEVEL, StoreFieldNames.TRADE_ID, STRING),
                        new TypedFieldDTO(TYPE_LEVEL, StoreFieldNames.TYPE, STRING),
                        new TypedFieldDTO(RISK_FACTOR_LEVEL, StoreFieldNames.RISK_FACTOR, STRING),
                        new TypedFieldDTO(CURRENCY_LEVEL, StoreFieldNames.VALUE_CCY, STRING)
                ),
                Set.of(DTD_PNL_NATIVE),
                Set.of(new TypedFieldDTO(StoreFieldNames.DAILY, DOUBLE))
        );
    }

Override

    @Bean
    public SupportedAdjustmentDTO pnlOverride() {
        return new SupportedAdjustmentDTO(
                OVERRIDE_NAME,
                PNL_OVERRIDE,
                true,
                PnlCubeConfig.CUBE_NAME,
                Set.of(StoreNames.PNL_STORE_NAME),
                Set.of(
                        new TypedFieldDTO(AS_OF_LEVEL, StoreFieldNames.AS_OF_DATE, LOCAL_DATE),
                        new TypedFieldDTO(TRADE_LEVEL, StoreFieldNames.TRADE_ID, STRING),
                        new TypedFieldDTO(TYPE_LEVEL, StoreFieldNames.TYPE, STRING),
                        new TypedFieldDTO(RISK_FACTOR_LEVEL, StoreFieldNames.RISK_FACTOR, STRING),
                        new TypedFieldDTO(CURRENCY_LEVEL, StoreFieldNames.VALUE_CCY, STRING)
                ),
                Set.of(DTD_PNL_NATIVE),
                Set.of(new TypedFieldDTO(StoreFieldNames.DAILY, DOUBLE))
        );
    }

Roll-over

    @Bean
    public SupportedAdjustmentDTO pnlRollOver() {
        return new SupportedAdjustmentDTO(
                ROLL_OVER_NAME,
                PNL_ROLL_OVER,
                false,
                PnlCubeConfig.CUBE_NAME,
                Set.of(StoreNames.PNL_STORE_NAME),
                Set.of(
                        new TypedFieldDTO(AS_OF_LEVEL, StoreFieldNames.AS_OF_DATE, LOCAL_DATE),
                        new TypedFieldDTO(DESK_LEVEL, StoreFieldNames.DESK, STRING),
                        new TypedFieldDTO(BOOK_LEVEL, StoreFieldNames.BOOK, STRING, true),
                        new TypedFieldDTO(TRADE_LEVEL, StoreFieldNames.TRADE_ID, STRING, true),
                        new TypedFieldDTO(SIGNOFF_STATUS_LEVEL, ADJUSTMENT_LEVEL_STATUS, LEVEL_PATH, true),
                        new TypedFieldDTO(SIGNOFF_TASK_LEVEL, ADJUSTMENT_LEVEL_TASK, LEVEL_PATH, true),
                        new TypedFieldDTO(SIGNOFF_SOURCE_LEVEL, SOURCE_FIELD, LEVEL_PATH, true),
                        new TypedFieldDTO(SIGNOFF_INPUT_TYPE_LEVEL, INPUT_FIELD, LEVEL_PATH, true)
                ),
                null,
                Set.of(new TypedFieldDTO(StoreFieldNames.AS_OF_DATE, LOCAL_DATE))
        );
    }

Cube-level

    @Bean
    public SupportedAdjustmentDTO pnlCubeLevelAdjustment() {
        return new SupportedAdjustmentDTO(
                CUBE_LEVEL_NAME,
                PNL_CUBE_LEVEL,
                false,
                PnlCubeConfig.CUBE_NAME,
                null,
                Set.of(
                        new TypedFieldDTO(AS_OF_LEVEL, StoreFieldNames.AS_OF_DATE, LEVEL_PATH),
                        new TypedFieldDTO(DESK_LEVEL, StoreFieldNames.DESK, LEVEL_PATH),
                        new TypedFieldDTO(BOOK_LEVEL, StoreFieldNames.BOOK, LEVEL_PATH, true),
                        new TypedFieldDTO(TRADE_LEVEL, StoreFieldNames.TRADE_ID, LEVEL_PATH, true),
                        new TypedFieldDTO(SIGNOFF_STATUS_LEVEL, ADJUSTMENT_LEVEL_STATUS, LEVEL_PATH, true),
                        new TypedFieldDTO(SIGNOFF_TASK_LEVEL, ADJUSTMENT_LEVEL_TASK, LEVEL_PATH, true),
                        new TypedFieldDTO(SIGNOFF_SOURCE_LEVEL, SOURCE_FIELD, LEVEL_PATH, true),
                        new TypedFieldDTO(SIGNOFF_INPUT_TYPE_LEVEL, INPUT_FIELD, LEVEL_PATH, true)
                ),
                null,
                Set.of( new TypedFieldDTO(CURRENCY, STRING),
                        new TypedFieldDTO(StoreFieldNames.SENSITIVITY_VALUES, DOUBLE))
        );
    }