NodialTreesHighs1: ML Random Forest / Pivot Highs (Part 1 of 2)
Description:
This library contains the first half (Trees 0-5) of a Random Forest Classifier designed to validate Pivot Highs (Short setups).
Due to Pine Script size constraints, the model is split into two libraries. You must use this library in conjunction with NodialTreesH2 to run the full ensemble.
### 🧩 System Architecture
- Model: Random Forest (12 Trees total).
- This Library: Contains `tree_0` to `tree_5`.
- Logic: Each tree analyzes a feature array and outputs a probability score.
- Target: Validating Swing Highs / Resistance Rejections.
### 📊 Input Requirements
The methods expect an `array<float>` of size 27 containing market features (Price Action, Momentum, Volatility, Volume, Structure). The exact order of features is critical for the model's accuracy.
### 🛠️ Integration Example
Since this is a modular library, you need to import both parts and average their results to get the final prediction.
### 📋 Feature Mapping (Array Indexing)
To get accurate predictions, the input array must contain exactly 27 floats in this specific order:
0. Timeframe (in seconds)
1. RSI (Raw Value)
2. MACD Histogram
3. Relative Volume
4. EMA Distance (%)
5. EMA Slope
6. ATR Ratio
7. ADX
8. Buying/Selling Pressure
9. Wick Ratio
10-16. Divergences & Pattern Flags (Boolean 0.0/1.0)
17-22. Proprietary Momentum Metrics ("Onion" Structure)
23-26. Derived Volatility/Volume Features
*Note: For the advanced proprietary metrics (Indices 17-26), users must implement their own calculations or use compatible indicators.*
//version=6
indicator("My ML Short Strategy", overlay=true)
// Import BOTH libraries
import YourUsername/NodialTreesH1/1 as rf_part1
import YourUsername/NodialTreesH2/1 as rf_part2
// ... (Calculate your 27 features and fill the array) ...
// var features = array.from(timeframe, rsi, macd, ...)
// Calculate Ensemble Probability (Average of 12 Trees)
float vote_sum = 0.0
// Trees from Part 1
vote_sum += rf_part1.tree_0(features)
vote_sum += rf_part1.tree_1(features)
vote_sum += rf_part1.tree_2(features)
vote_sum += rf_part1.tree_3(features)
vote_sum += rf_part1.tree_4(features)
vote_sum += rf_part1.tree_5(features)
// Trees from Part 2 (Trees 6-11)
vote_sum += rf_part2.tree_6(features)
vote_sum += rf_part2.tree_7(features)
vote_sum += rf_part2.tree_8(features)
vote_sum += rf_part2.tree_9(features)
vote_sum += rf_part2.tree_10(features)
vote_sum += rf_part2.tree_11(features)
// Final Probability (0.0 to 1.0)
float final_prob = vote_sum / 12.0
if final_prob > 0.60
label.new(bar_index, high, "Valid Short", color=color.red)
Pine Bibliothek
Ganz im Sinne von TradingView hat dieser Autor seinen/ihren Pine Code als Open-Source-Bibliothek veröffentlicht. Auf diese Weise können nun auch andere Pine-Programmierer aus unserer Community den Code verwenden. Vielen Dank an den Autor! Sie können diese Bibliothek privat oder in anderen Open-Source-Veröffentlichungen verwenden. Die Nutzung dieses Codes in einer Veröffentlichung wird in unseren Hausregeln reguliert.
Haftungsausschluss
Pine Bibliothek
Ganz im Sinne von TradingView hat dieser Autor seinen/ihren Pine Code als Open-Source-Bibliothek veröffentlicht. Auf diese Weise können nun auch andere Pine-Programmierer aus unserer Community den Code verwenden. Vielen Dank an den Autor! Sie können diese Bibliothek privat oder in anderen Open-Source-Veröffentlichungen verwenden. Die Nutzung dieses Codes in einer Veröffentlichung wird in unseren Hausregeln reguliert.