Ternary operators allow you to build if/then/else conditional logic within your transforms. Please use the
NOTE: Ternary operators have been superseded by the
In the following, if the
test expression evaluates to
true_expression is executed. Otherwise, the
false_expression is executed.
(test_expression) ? (true_expression) : (false_expression)
All of these expressions can be constants (strings, integers, or any other supported literal value) or sophisticated elements of logical, although the test expression must evaluate to a Boolean value.
Your output looks like the following:
You have a set of stock prices that you want to analyze. Based on a set of rules, you want to determine any buy, sell, or hold action to take.
You can perform evaluations of this data using ternary operators to determine if you want to take action.
NOTE: In a larger dataset, you might maintain your buy, sell, and hold evaluations for each stock in a separate dataset that you join to the source dataset before performing comparisons between column values. See Join Panel.
To assist in evaluation, you might first want to create columns that contain the cost (
Basis) and the current value (
CurrentValue) for each stock:
Now, you can build some rules based on the spread between
The most important action is determining if it is time to sell. The following rule writes a
sell notification if the current value is $1000 or more than the cost. Otherwise, no value is written to the action column.
But what about buying more? The following transform is an edit to the previous one. In this new version, the sell test is performed, and if writes a
buy action if the
CurrentPrice is within 10% of the
This second evaluation is performed after the first one, as it replaces the else clause, which did nothing in the previous version. In the Recipe panel, click the previous transform and edit it, replacing it with the new version:
If neither test evaluates to
true, the written action is
You might want to format some of your columns using dollar formatting, as in the following:
NOTE: The following formatting inserts a dollar sign ($) in front of the value, which changes the data type to String.
After moving your columns, your dataset should look like the following, if you completed the number formatting steps:
|GOOG||10||705.25||$ 674.50||$ 7,052.50||$ 6,745.00||buy|
|FB||100||84.00||$ 101.13||$ 8,400.00||$ 10,112.50||sell|
|AAPL||50||125.25||$ 97.38||$ 6,262.50||$ 4,868.75||hold|
|MSFT||100||38.88||$ 45.25||$ 3,887.50||$ 4,525.00||hold|