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Data Mining - Data Dependencies
Summary: Indicator Bots have data dependencies. This means that they receive their input as Datasets created by other Bots.
Data Dependency Tree
You can think about data dependencies as a type of relationship between bots. Bots producing raw data (Sensor Bot and API Data Fetcher Bot) don't have them, since their job is to extract data from an external source. At the same time, they are also the root of the data dependancy tree of relationships. This tree is very similar to the execution tree but instead of describing how bots run, it describes how bots send and receive Data Products.
Defining Data Dependencies
The definition of data dependencias is held at the Process Definition level. Each process might have its own set of data dependencies. This means that a single bot may have many varying dependencies depending on the number of Process Definitions it contains.
Note: In addtion to this, it is possible for a single Bot's process to have more than one Data Dependency (from the same Bot or from different Bots). That is particulary useful to aggregate data from many datasets into another one. The dependent datasets could belong to Bots at the same Data Mine or at bots at other Data Mines.
The first declared dependency is called the Main Dependency and has a special meaning. It is used by the process of the Bot to iterate. What this means is that the process will loop over the Dataset of the Main Dependency to produce the output Dataset. All the other data dependencies will be used each iteration to extract additional data.
Tip: As with bot execution, the dependant datasets can be physically located on the same machine, or at multiple different machines. However, when a Bot has a Data Dependency of a dataset produced by a Bot defined at a different Data Mine, both Data Mines must be present at the Workspace where the Bot is going to be launched.
The Candles Data Mine is the most referenced Data Mine because it defines the Candles-Volumes Indicator Bot, that produces the most used information for trading systems: Candles.
Data Mining — TOC
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3. Data Mining - Dataset Types
4. Data Mining - Datasets Type Conversions
5. Data Mining - Indicator Frameworks
6. Data Mining - Data Mine Hierarchy
7. Data Mining - Managing Execution
8. Data Mining - Data Dependencies
9. Data Mining - Status Reports
10. Data Mining - Status Dependencies
14. Data Mining - Fetching Data From APIs
15. Data Mining - Consuming Data From Strategies
16. Data Mining - Indicator Bots
17. Data Mining - Indicator Bots - Process Definition
18. Data Mining - Indicator Bots - Product Definition
19. Data Mining - Indicator Bots - Data Mining Workflow
20. Data Mining - Indicator Bots - Coding the Indicator's Logic