Superalgos - Data Mining - How to Build an Indicator

Data Mining - How to Build an Indicator - Video Series

Summary: This video series goes through the entire process of building an indicator, step by step.

This video series focuses on the “how to” aspect of building an indicator. It does not go through definitions nor does it explain the underlying concepts. Therefore, it is highly recommended to read the data mines page as well as the pages about indicator bots before attempting to build an indicator.

Note: Reading the whole documentation on indicators and plotters is certainly desirable and will save you time in the long run.

Important: The video series on how to build an indicator was produced for the Beta 5 version (inbetween we are at Beta12) of the system; however, the functionality still works similarly. That said, there may be minor differences that are yet to be documented. Please drop by the group and ask any questions you may have, particularly if something is not working as expected, or as instructed in the videos. The group is linked in the footer.

Step 1 - Visual Definitions

Setting up the and process definitions, status and data dependencies, execution events, and dataset definitions for the main product.

Step 2 - Code & Record Definition

Reviewing common coding practice and configuring record properties and their formulas.

Step 3 - Testing & Troubleshooting

Setting up a data mining task to run the bot instance, checking the dataset files for errors, and troubleshooting common issues with the code and the process definitions.

Step 4 - Plotters

How to create the device that will interpret the dataset generated by the indicator and render a graphical representation over the charts.

Step 5 - Trading Bot Dependencies

Before you may use the new indicator on a trading system, you must create the corresponding dependencies on the Low frequency trading bot.

To call the properties of a new indicator from within the definitions of a trading system, you first need to let the Low Frequency trading bot that your data mine exists. To do this, you need to add the corresponding data dependencies at the trading bot’s process definitions.

Important: Because the Low Frequency trading bot is a plugin, the modifications you make to the hierarchy do not persist after closing the frontend app. The workaround is to take a backup of the hierarchy after making the modifications, and replace the plugin file with the backup. Should you decide to contribute your data mine to the project, we will incorporate the dependencies in the official release.

Data Mining — TOC

You just read page 23 in the topic.

1. Data Mining - Introduction

2. Data Mining - Workflows

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

11. Data Mining - Deployments

12. Data Mining - Products

13. Data Mining - Plotting

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

21. Data Mining - Plotters

22. Data Mining - Plotters - Shapes

23. Data Mining - How to Build an Indicator - Video Series

Superalgos is an open-source project run and governed by a decentralized community of contributors. There is no legal entity behind the project. Our body of work lives on the Internet.