NodialTreesHighs2: ML Random Forest / Pivot Highs (Part 2 of 2)Title: `Library: ML Random Forest / Pivot Highs (Part 2 of 2)`
Description:
This library contains the second half (Trees 6-11) of the Random Forest Classifier designed to validate Pivot Highs (Short setups).
It is a direct extension of NodialTreesH1 and cannot be used alone. Due to Pine Script's compilation limits on complexity and file size, the 12-tree ensemble model has been split into two separate libraries.
### 🧩 Library Contents
This module exports the following methods representing the specific decision paths of the trained AI model:
- `tree_6(array f)`
- `tree_7(array f)`
- `tree_8(array f)`
- `tree_9(array f)`
- `tree_10(array f)`
- `tree_11(array f)`
### ⚠️ Implementation Guide
To use this library, you must combine it with Part 1.
Please refer to the NodialTreesH1 library description for:
1. The full Integration Code Example (how to average the votes).
2. The exact Input Feature List (the 27 required metrics).
3. Detailed explanation of the Machine Learning logic.
How to finish the integration:
Import this library alongside Part 1 and add the results of `tree_6` through `tree_11` to your voting sum, as shown in the Part 1 documentation.
Biblioteka Pine Script®
