Aggregation
Components in this category add information and value to strategies.
Group By
Group By components set strategy properties using an aggregation method applied to properties from the source components. The Properties tab of this component allows you to define the aggregation operations.
So that you can use the results of a list of elements, the Group output rows by setting is available in this component. The properties that can be used to group the results are properties listed in the Strategy Properties tab; that is, properties of DatapxStrategyResult and properties available to the strategy depending on its applicability in the context of the proposition hierarchy. For example, selecting grouping by . pyName allows you to obtain the list of results for each proposition name.
 In the Aggregators section, select strategy properties in the
Property column, the method for setting the property value based on an expression (SUM,
COUNT, FIRST, MIN, MAX, AVERAGE, TRUE IF ANY, TRUE IF NONE, TRUE IF ALL or STDEV) and type
the expression in the Source column.
 The properties that can be used in the Property column are properties listed in the Strategy Properties tab of the strategy.
 The properties that can be used in the Source fields are properties of DatapxStrategyResult and properties available to the strategy depending on its applicability in the context of the proposition hierarchy.
 Properties that are not mapped in the component are automatically copied. In the
For remaining properties setting, define how to handle the
remaining properties by selecting one of the options from the dropdown. When using the
options that copy with the highest/lowest value, specify which property in the SR class
corresponding to the level of the strategy in the proposition hierarchy provides the
value.
 First: copy with first value.
 None: empty.
 With highest: copy with highest value.
 With lowest: copy with lowest value.
 Decision strategies can store the predictor values and outputs of predictive and
adaptive models in decision results. Adaptive models use this information for learning.
You can also use this information to monitor predictive models. To control which model
results are propagated, you can associate each strategy result with one or more of these
model results if the corresponding models are ran as part of a decision strategy.
 Include model results for – This is the default setting when adding a new Group by component in a decision strategy. When adaptive models are run, propagate only the results from the model with an associated first, lowest, or highest property value, for example, highest performance.
 Include all model results in group – When models are ran as part of a decision strategy, propagate each model result in the group. For example, in a champion challenger scenario, you can select this setting when the Group by component selects the adaptive model with the highest value of the pyPerformance property, because all adaptive models might then learn from each response. This is the default setting for already existing Group by components (when included in decision strategies in product versions earlier than 8.2).
Iteration
Iteration components perform cumulative calculations based on the settings defined in the Parameters tab.

Iteration components operate in two modes:
 Without source components, you can define the properties, number of iterations and early stop conditions. The order of the properties is taken into account when performing the calculation. Depending on the setting used to control how to return the results, the component returns only the final calculation, or final calculation and intermediate results.
 With source components, the number of iterations equals the number of results in the source component. The result of running the iteration component contains the final calculation and no intermediate results. If the value of the arguments is set through source components, the order of the components in the Source tab is important because it is directly related to the order of arguments considered to perform the calculation.
 The settings you can use to define the iteration calculation consist of iteration
settings, early stop conditions and results options:
 Iteration settings: select the property for the set value action, define the initial value for the set value action, define the progression value for the set action., and define the maximum number of iterations in terms of results.
 Early stop conditions allow you to define conditions that apply before the maximum number of iterations. The conditions are expressed by the value of a property, the difference between the current and the previous value, or a combination of the two.
 In the Return option, select if the component returns the last final calculation, or final and intermediate calculations.
Financial Calculation
Financial calculation components perform financial calculations using the following functions:
 Net present value calculates the net present value of an investment.
 Internal rate of return calculates the internal rate of return for a series of cash flows.
 Modified internal rate of return calculates the modified internal rate of return for a series of periodic cash flows.
The Properties tab of this component allows you to define the calculation and select properties that provide the arguments for each financial function. The arguments that can be selected in the Target and Payments dropdown lists are strategy properties of type Decimal, Double or Integer.
If the value of the arguments is set through source components, the order of the components in the Source tab is important because it is directly related to the order of arguments considered by the function to perform the financial calculation.
Typically, the Payments argument should be a list of values and not a single value. So that you can use a list of values to provide the Payments argument, use a data import component to set properties that can be used by this component.