OPEN-SOURCE SCRIPT

Machine Learning Gaussian Mixture Model | AlphaNatt

290
Machine Learning Gaussian Mixture Model | AlphaNatt

A revolutionary oscillator that uses Gaussian Mixture Models (GMM) with unsupervised machine learning to identify market regimes and automatically adapt momentum calculations - bringing statistical pattern recognition techniques to trading.

"Markets don't follow a single distribution - they're a mixture of different regimes. This oscillator identifies which regime we're in and adapts accordingly."


━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

🤖 THE MACHINE LEARNING

Gaussian Mixture Models (GMM):
Unlike K-means clustering which assigns hard boundaries, GMM uses probabilistic clustering:
  • Models data as coming from multiple Gaussian distributions
  • Each market regime is a different Gaussian component
  • Provides probability of belonging to each regime
  • More sophisticated than simple clustering


Expectation-Maximization Algorithm:
The indicator continuously learns and adapts using the E-M algorithm:
  • E-step: Calculate probability of current market belonging to each regime
  • M-step: Update regime parameters based on new data
  • Continuous learning without repainting
  • Adapts to changing market conditions


━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

🎯 THREE MARKET REGIMES

The GMM identifies three distinct market states:

Regime 1 - Low Volatility:
  • Quiet, ranging markets
  • Uses RSI-based momentum calculation
  • Reduces false signals in choppy conditions
  • Background: Pink tint


Regime 2 - Normal Market:
  • Standard trending conditions
  • Uses Rate of Change momentum
  • Balanced sensitivity
  • Background: Gray tint


Regime 3 - High Volatility:
  • Strong trends or volatility events
  • Uses Z-score based momentum
  • Captures extreme moves
  • Background: Cyan tint


━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

💡 KEY INNOVATIONS

1. Probabilistic Regime Detection:
Instead of binary regime assignment, provides probabilities:
  • 30% Regime 1, 60% Regime 2, 10% Regime 3
  • Smooth transitions between regimes
  • No sudden indicator jumps


2. Weighted Momentum Calculation:
  • Combines three different momentum formulas
  • Weights based on regime probabilities
  • Automatically adapts to market conditions


3. Confidence Indicator:
  • Shows how certain the model is (white line)
  • High confidence = strong regime identification
  • Low confidence = transitional market state
  • Line transparency changes with confidence


━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

⚙️ PARAMETER OPTIMIZATION

Training Period (50-500):
  • 50-100: Quick adaptation to recent conditions
  • 100: Balanced (default)
  • 200-500: Stable regime identification


Number of Components (2-5):
  • 2: Simple bull/bear regimes
  • 3: Low/Normal/High volatility (default)
  • 4-5: More granular regime detection


Learning Rate (0.1-1.0):
  • 0.1-0.3: Slow, stable learning
  • 0.3: Balanced (default)
  • 0.5-1.0: Fast adaptation


━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

📊 TRADING STRATEGIES

Visual Signals:
  • Cyan gradient: Bullish momentum
  • Magenta gradient: Bearish momentum
  • Background color: Current regime
  • Confidence line: Model certainty


1. Regime-Based Trading:
  • Regime 1 (pink): Expect mean reversion
  • Regime 2 (gray): Standard trend following
  • Regime 3 (cyan): Strong momentum trades


2. Confidence-Filtered Signals:
  • Only trade when confidence > 70%
  • High confidence = clearer market state
  • Avoid transitions (low confidence)


3. Adaptive Position Sizing:
  • Regime 1: Smaller positions (choppy)
  • Regime 2: Normal positions
  • Regime 3: Larger positions (trending)


━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

🚀 ADVANTAGES OVER OTHER ML INDICATORS

vs K-Means Clustering:
  • Soft clustering (probabilities) vs hard boundaries
  • Captures uncertainty and transitions
  • More mathematically robust


vs KNN (K-Nearest Neighbors):
  • Unsupervised learning (no historical labels needed)
  • Continuous adaptation
  • Lower computational complexity


vs Neural Networks:
  • Interpretable (know what each regime means)
  • No overfitting issues
  • Works with limited data


━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

📈 PERFORMANCE CHARACTERISTICS

Best Market Conditions:
  • Markets with clear regime shifts
  • Volatile to trending transitions
  • Multi-timeframe analysis
  • Cryptocurrency markets (high regime variation)


Key Strengths:
  • Automatically adapts to market changes
  • No manual parameter adjustment needed
  • Smooth transitions between regimes
  • Probabilistic confidence measure


━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

🔬 TECHNICAL BACKGROUND

Gaussian Mixture Models are used extensively in:
  • Speech recognition (Google Assistant)
  • Computer vision (facial recognition)
  • Astronomy (galaxy classification)
  • Genomics (gene expression analysis)
  • Finance (risk modeling at investment banks)


The E-M algorithm was developed at Stanford in 1977 and is one of the most important algorithms in unsupervised machine learning.

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

💡 PRO TIPS

  1. Watch regime transitions: Best opportunities often occur when regimes change
  2. Combine with volume: High volume + regime change = strong signal
  3. Use confidence filter: Avoid low confidence periods
  4. Multi-timeframe: Compare regimes across timeframes
  5. Adjust position size: Scale based on identified regime


━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

⚠️ IMPORTANT NOTES

  • Machine learning adapts but doesn't predict the future
  • Best used with other confirmation indicators
  • Allow time for model to learn (100+ bars)
  • Not financial advice - educational purposes
  • Backtest thoroughly on your instruments


━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

🏆 CONCLUSION

The GMM Momentum Oscillator brings institutional-grade machine learning to retail trading. By identifying market regimes probabilistically and adapting momentum calculations accordingly, it provides:

  • Automatic adaptation to market conditions
  • Clear regime identification with confidence levels
  • Smooth, professional signal generation
  • True unsupervised machine learning


This isn't just another indicator with "ML" in the name - it's a genuine implementation of Gaussian Mixture Models with the Expectation-Maximization algorithm, the same technology used in:
  • Google's speech recognition
  • Tesla's computer vision
  • NASA's data analysis
  • Wall Street risk models


"Let the machine learn the market regimes. Trade with statistical confidence."


━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Developed by AlphaNatt | Machine Learning Trading Systems

Version: 1.0
Algorithm: Gaussian Mixture Model with E-M
Classification: Unsupervised Learning Oscillator
Not financial advice. Always DYOR.

Wyłączenie odpowiedzialności

Informacje i publikacje przygotowane przez TradingView lub jego użytkowników, prezentowane na tej stronie, nie stanowią rekomendacji ani porad handlowych, inwestycyjnych i finansowych i nie powinny być w ten sposób traktowane ani wykorzystywane. Więcej informacji na ten temat znajdziesz w naszym Regulaminie.