PINE LIBRARY
NodialTreesLows2: ML Random Forest / Pivot Lows (Part 2 of 2)
Title: `Library: ML Random Forest / Pivot Lows (Part 2 of 2)`
Description:
This library contains the second half (Trees 6-11) of the Random Forest Classifier designed to validate Pivot Lows (Long setups).
It is a direct extension of NodialTreesL1 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<float> f)`
- `tree_7(array<float> f)`
- `tree_8(array<float> f)`
- `tree_9(array<float> f)`
- `tree_10(array<float> f)`
- `tree_11(array<float> f)`
### ⚠️ Implementation Guide
To use this library, you must combine it with Part 1.
Please refer to the NodialTreesLows1 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.
Description:
This library contains the second half (Trees 6-11) of the Random Forest Classifier designed to validate Pivot Lows (Long setups).
It is a direct extension of NodialTreesL1 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<float> f)`
- `tree_7(array<float> f)`
- `tree_8(array<float> f)`
- `tree_9(array<float> f)`
- `tree_10(array<float> f)`
- `tree_11(array<float> f)`
### ⚠️ Implementation Guide
To use this library, you must combine it with Part 1.
Please refer to the NodialTreesLows1 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.
Bibliothèque Pine
Dans l'esprit TradingView, l'auteur a publié ce code Pine sous forme de bibliothèque open source afin que d'autres programmeurs Pine de notre communauté puissent le réutiliser. Bravo à l'auteur! Vous pouvez utiliser cette bibliothèque à titre privé ou dans d'autres publications open source, mais la réutilisation de ce code dans des publications est régie par nos Règles.
Clause de non-responsabilité
Les informations et publications ne sont pas destinées à être, et ne constituent pas, des conseils ou recommandations financiers, d'investissement, de trading ou autres fournis ou approuvés par TradingView. Pour en savoir plus, consultez les Conditions d'utilisation.
Bibliothèque Pine
Dans l'esprit TradingView, l'auteur a publié ce code Pine sous forme de bibliothèque open source afin que d'autres programmeurs Pine de notre communauté puissent le réutiliser. Bravo à l'auteur! Vous pouvez utiliser cette bibliothèque à titre privé ou dans d'autres publications open source, mais la réutilisation de ce code dans des publications est régie par nos Règles.
Clause de non-responsabilité
Les informations et publications ne sont pas destinées à être, et ne constituent pas, des conseils ou recommandations financiers, d'investissement, de trading ou autres fournis ou approuvés par TradingView. Pour en savoir plus, consultez les Conditions d'utilisation.